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1
Tan, Lei, Peng Li, Aimin Miao, and Yong Chen. "Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding." Measurement and Control 52, no. 5-6 (April 16, 2019): 387–98. http://dx.doi.org/10.1177/0020294019838580.
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This study aims to solve the problem involving the high false alarm rate experienced during the detection process when using the traditional multivariate statistical process monitoring method. In addition, the existing model cannot be updated according to the actual situation. This article proposes a novel adaptive neighborhood preserving embedding algorithm as well as an online fault-detection approach based on adaptive neighborhood preserving embedding. This approach combines the approximate linear dependence condition with neighborhood preserving embedding. According to the newly proposed update strategy, the algorithm can achieve an adaptive update model that realizes the online fault detection of processes. The effectiveness and feasibility of the proposed approach are verified by experiments of the Tennessee Eastman process. Theoretical analysis and application experiment of Tennessee Eastman process demonstrate that in this article proposed fault-detection method based on adaptive neighborhood preserving embedding can effectively reduce the false alarm rate and improve the fault-detection performance.
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Kobayashi, Takahisa, and Donald L. Simon. "Hybrid Kalman Filter Approach for Aircraft Engine In-Flight Diagnostics: Sensor Fault Detection Case." Journal of Engineering for Gas Turbines and Power 129, no. 3 (November 17, 2006): 746–54. http://dx.doi.org/10.1115/1.2718572.
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In this paper, a diagnostic system based on a uniquely structured Kalman filter is developed for its application to in-flight fault detection of aircraft engine sensors. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the diagnostic system to be updated, through a relatively simple process, to the health condition of degraded engines. Through this health baseline update, the diagnostic effectiveness of the in-flight sensor fault detection system is maintained as the health of the engine degrades over time. The performance of the sensor fault detection system is evaluated in a simulation environment at several operating conditions during the cruise phase of flight.
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Fang, Liang, Yun Zhou, Yunzhong Jiang, Yilin Pei, and Weijian Yi. "Vibration-Based Damage Detection of a Steel-Concrete Composite Slab Using Non-Model-Based and Model-Based Methods." Advances in Civil Engineering 2020 (September 11, 2020): 1–20. http://dx.doi.org/10.1155/2020/8889277.
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This paper presents vibration-based damage detection (VBDD) for testing a steel-concrete composite bridge deck in a laboratory using both model-based and non-model-based methods. Damage that appears on a composite bridge deck may occur either in the service condition or in the loading condition. To verify the efficiency of the dynamic test methods for assessing different damage scenarios, two defect cases were designed in the service condition by removing the connection bolts along half of a steel girder and replacing the boundary conditions, while three damage cases were introduced in the loading condition by increasing the applied load. A static test and a multiple reference impact test (MRIT) were conducted in each case to obtain the corresponding deflection and modal data. For the non-model-based method, modal flexibility and modal flexibility displacement (MFD) were used to detect the location and extent of the damage. The test results showed that the appearance and location of the damage in defect cases and loading conditions can be successfully identified by the MFD values. A finite element (FE) model was rationally selected to represent the dynamic characteristics of the physical model, while four highly sensitive physical parameters were rationally selected using sensitivity analysis. The model updating technique was used to assess the condition of the whole deck in the service condition, including the boundary conditions, connectors, and slab. Using damage functions, Strand7 software was used to conduct FE analysis coupled with the MATLAB application programming interface to update multiple physical parameters. Of the three different FE models used to simulate the behavior of the composite slab, the calculated MFD of the shell-solid FE model was almost identical to the test results, indicating that the performance of the tested composite structure could be accurately predicted by this type of FE model.
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Guo, Ruijun, Guobin Zhang, Qian Zhang, Lei Zhou, Haicun Yu, Meng Lei, and You Lv. "An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique." Energies 14, no. 16 (August 6, 2021): 4787. http://dx.doi.org/10.3390/en14164787.
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The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.
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Han, Jian Ping, and Yong Peng Luo. "Static and Dynamic Finite Element Model Updating of a Rigid Frame-Continuous Girders Bridge Based on Response Surface Method." Advanced Materials Research 639-640 (January 2013): 992–97. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.992.
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Using the static and dynamic test data simultaneously to update the finite element model can increase the available information for updating. It can overcome the disadvantages of updating based on static or dynamic test data only. In this paper, the response surface method is adopted to update the finite element model of the structure based on the static and dynamic test. Using the reasonable experiment design and regression techniques, a response surface model is formulated to approximate the relationships between the parameters and response values instead of the initial finite element model for further updating. First, a numerical example of a reinforced concrete simply supported beam is used to demonstrate the feasibility of this approach. Then, this approach is applied to update the finite element model of a prestressed reinforced concrete rigid frame-continuous girders bridge based on in-situ static and dynamic test data. Results show that this approach works well and achieve reasonable physical explanations for the updated parameters. The results from the updated model are in good agreement with the results from the in-situ measurement. The updated finite element model can accurately represent mechanical properties of the bridge and it can serve as a benchmark model for further damage detection and condition assessment of the bridge.
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Roy, Prasenjit, and Baher Abdulhai. "GAID: Genetic Adaptive Incident Detection for Freeways." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (January 2003): 96–105. http://dx.doi.org/10.3141/1856-10.
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Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect an incident between the said stations. As input, GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it uses a Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure—that is, updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to off-line tests with real incident data from a number of freeways in California. Results and further comparison with the McMaster algorithm indicate that GAID with a PNN core has a better detection rate and a lower false alarm rate than the PNN alone and the well-established McMaster algorithm. Results also indicate that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions.
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Soni, Mukesh, Ihtiram Raza Khan, Sameer Basir, Raman Chadha, Arnold C. Alguno, and Tapas Bhowmik. "Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment." Computational Intelligence and Neuroscience 2022 (June 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/2455259.
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Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model’s online detection and update. The method’s efficacy is demonstrated by experiments based on real data from power plant sensors.
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Jin, Seung-Seop, and Hyung-Jo Jung. "Vibration-based damage detection using online learning algorithm for output-only structural health monitoring." Structural Health Monitoring 17, no. 4 (July 7, 2017): 727–46. http://dx.doi.org/10.1177/1475921717717310.
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Damage-sensitive features such as natural frequencies are widely used for structural health monitoring; however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde–Buzo–Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, the proposed method is the only successful method to deal with the non-linear dependency among features and detect the structural damage timely.
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Akkar, Hanan A. R., and Suhad Q. Hadad. "Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm." Engineering and Technology Journal 38, no. 3B (December 25, 2020): 184–96. http://dx.doi.org/10.30684/etj.v38i3b.1666.
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Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work () computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and () Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis for lung.
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Akkar, Hanan A. R., and Suhad Q. Hadad. "Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm." Engineering and Technology Journal 38, no. 3B (December 25, 2020): 184–96. http://dx.doi.org/10.30684/etj.v38i3b.1666.
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Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work () computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and () Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis for lung.
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Liao, Jing Bo, Guang Wu Tang, and Fei Pan. "Finite Element Model Updating of Existing T-Girder Bridge by Field Quasi-Static Generalized Influence Line." Applied Mechanics and Materials 226-228 (November 2012): 1609–13. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1609.
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Finite element model updating is the important foundation of structural damage detection, condition assessment for engineering structure. A new method, for finite element model updating based on the quasi-static generalized influence line (QSGIL) residual objection, is presented to update the finite element model of beam-structure in order to improve the quality and precision of finite element analysis. Both of the theory and model experimental study show that the proposal method can efficiently update the finite element model in the previous study [1]. In this paper, the updating techniques are further developed to update the finite element model of the existing T-Girder bridge, the QSGIL of the updating model agrees very well with the field QSGIL of the existing bridge, which illustrates that the proposal methodology is promising in the practical bridge structure and other structures.
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Lee, Sang Kwon, Jiseon Back, Kanghyun An, Sunwon Kim, Changho Lee, and Pungil Kim. "Condition Monitoring of Chain Sprocket Drive System Based on IoT Device and Convolutional Neural Network." Shock and Vibration 2020 (July 25, 2020): 1–17. http://dx.doi.org/10.1155/2020/8826507.
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This paper proposes a condition monitoring method for the early defect detection in a chain sprocket drive (CSD) system and classification of fault types before a catastrophic failure occurs. In the operation of a CSD system, early defect detection is very useful in preventing system failure. In this work, eight fault types associated with the CSD system components, such as the gear tooth, bearings, and drive motor shaft, were arbitrarily damaged and incorporated into the CSD system. To detect the fault signals during the CSD system operation, the vibration was measured using an Internet of Things (IoT) device, which features a wireless MEMS accelerometer, Bluetooth function, Wi-Fi function, and battery. The IoT device was mounted on the gearbox housing. The measured one-dimensional vibration time-series was transformed into time-scale images using continuous wavelet transform (CWT). A convolution neural network (CNN) was employed to extract deep features embedded in the images, which are closely related to fault types. To update the learning parameters of the CNN, the RMSprop learning algorithm was applied, and the CNN was trained using 500 image samples. Multiple-classification performance of the trained network was tested using 100 image samples. Feature maps for different fault types were obtained from the final CNN convolution layer. For the visualization of fault types, t-stochastic neighbor embedding was employed and applied to the feature maps to convert high-dimensional data into two-dimensional data. Two-dimensional features enabled excellent classification of the eight fault types and one normal type.
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Kuo, Chiao-Ling, and Ming-Hua Tsai. "Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares." ISPRS International Journal of Geo-Information 10, no. 6 (June 2, 2021): 377. http://dx.doi.org/10.3390/ijgi10060377.
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The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.
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Reddy, Vundrala Sumedha, Bhawana Agarwal, Zhen Ye, Chuanqi Zhang, Kallol Roy, Amutha Chinnappan, Roger J. Narayan, Seeram Ramakrishna, and Rituparna Ghosh. "Recent Advancement in Biofluid-Based Glucose Sensors Using Invasive, Minimally Invasive, and Non-Invasive Technologies: A Review." Nanomaterials 12, no. 7 (March 25, 2022): 1082. http://dx.doi.org/10.3390/nano12071082.
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Biosensors have potentially revolutionized the biomedical field. Their portability, cost-effectiveness, and ease of operation have made the market for these biosensors to grow rapidly. Diabetes mellitus is the condition of having high glucose content in the body, and it has become one of the very common conditions that is leading to deaths worldwide. Although it still has no cure or prevention, if monitored and treated with appropriate medication, the complications can be hindered and mitigated. Glucose content in the body can be detected using various biological fluids, namely blood, sweat, urine, interstitial fluids, tears, breath, and saliva. In the past decade, there has been an influx of potential biosensor technologies for continuous glucose level estimation. This literature review provides a comprehensive update on the recent advances in the field of biofluid-based sensors for glucose level detection in terms of methods, methodology and materials used.
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Stefani, Stefani, Yanny Trisyani, and Anita Setyawati. "The Knowledge of Nursing Internship Program Students about Early Detection of Sepsis." Open Access Macedonian Journal of Medical Sciences 9, T6 (November 17, 2021): 116–21. http://dx.doi.org/10.3889/oamjms.2021.7602.
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Background: Sepsis is a life-threatening condition due to the failure of the body’s regulation of infection. Knowledge deficit is one of the barriers to early detection and initiation of sepsis care. Nursing internship program students as future nurses need to have sufficient knowledge about early detection of sepsis to support their behavior. Thus, the purpose of this study was to describe the knowledge of nursing internship program students regarding the early detection of sepsis and the demographic factor related to the knowledge. Methods: The study design was a quantitative study. Through the proportionate stratified non-random sampling technique, the researcher involved 143 nursing internship program students of Universitas Padjadjaran. Data collection used a questionnaire based on the Sepsis-3 guidelines to measure nursing internship program students’ knowledge about early detection of sepsis. The data was carried out in July-August 2021. Results: The average knowledge score of the respondents was 70.4 (SD=11.9). More than half of the respondents (56.6%) got a score below the average. Almost all respondents do not know the current definition of sepsis and still use the SIRS definition as clinical criteria for sepsis. However, respondents could identify clinical criteria for sepsis based on qSOFA and analyse sepsis indicators based on case scenarios. Meanwhile, based on its characteristics, the information is a factor that significantly affects the knowledge score (p < 0.05). Conclusion: In conclusion, there is still a gap in the knowledge of the nursing internship program students regarding the update of the Sepsis-3 guidelines. Besides, information is identified as the factor that influences knowledge. Therefore, it suggested that the institution provide further effective educational methods to update students’ knowledge about the early detection of sepsis.
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Gao, Feng, Bo He, and Yingdong He. "Detection of Driving Capability Degradation for Human-Machine Cooperative Driving." Sensors 20, no. 7 (April 1, 2020): 1968. http://dx.doi.org/10.3390/s20071968.
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Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.
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Liang, S. Y., and D. A. Dornfeld. "Tool Wear Detection Using Time Series Analysis of Acoustic Emission." Journal of Engineering for Industry 111, no. 3 (August 1, 1989): 199–205. http://dx.doi.org/10.1115/1.3188750.
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This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.
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Aljanabi, Mohammad, Mohd Arfian Ismail, and Vitaly Mezhuyev. "Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System." Complexity 2020 (May 31, 2020): 1–18. http://dx.doi.org/10.1155/2020/5287684.
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Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.
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Cancelli, Alessandro, Simon Laflamme, Alice Alipour, Sri Sritharan, and Filippo Ubertini. "Vibration-based damage localization and quantification in a pretensioned concrete girder using stochastic subspace identification and particle swarm model updating." Structural Health Monitoring 19, no. 2 (February 28, 2019): 587–605. http://dx.doi.org/10.1177/1475921718820015.
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A popular method to conduct structural health monitoring is the spatio-temporal study of vibration signatures, where vibration properties are extracted from collected vibration responses. In this article, a novel methodology for extracting and analyzing distributed acceleration data for condition assessment of bridge girders is proposed. Three different techniques are fused, enabling robust damage detection, localization, and quantification. First, stochastic subspace identification is used as an output-only method to extract modal properties of the monitored structure. Second, a reduced-order stiffness matrix is reconstructed from the stochastic subspace identification data using the system equivalent reduction expansion process. Third, a particle swarm optimization algorithm is used to update a finite element model of the bridge girder to match the extracted reduced-order stiffness matrix and modal properties. The proposed approach is first verified through numerically simulated data of the girder and then validated using experimental data obtained from a full-scale pretensioned concrete beam that experienced two distinct states of damage. Results show that the method is capable of localizing and quantifying damages along the girder with good accuracy, and that results can be used to create a high-fidelity finite element model of the girder that could be leveraged for condition prognosis and forecasting.
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Yang, Yuexin, and Zhuoxun Chen. "Optimization of Dynamic Obstacle Avoidance Path of Multirotor UAV Based on Ant Colony Algorithm." Wireless Communications and Mobile Computing 2022 (July 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/1299434.
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In this paper, the real-time path avoidance problem of multirotor UAV in the case of sudden obstacles in two-dimensional environment is studied. The principle, model, and application of ant colony algorithm are analyzed. On this basis, the adaptive dynamic window ant colony algorithm is proposed, and the adaptive dynamic window method is designed; the heuristic function of adding obstacle detection factors and the double pheromone update strategy are made to the ant colony algorithm, and the improved ant colony algorithm is used to replan the path within the dynamic window that can be automatically adjusted to achieve the purpose of obstacle avoidance. A real-time simulation experiment of path planning was conducted by constructing an environment map in MATLAB. The simulation results show that with the continuous increase of the number of sudden obstacles, the real-time replacement path of multirotor UAV also gradually increases, and when approaching the obstacles, the replacement path is more dense, indicating that the adaptive window ant colony algorithm can be applied to dynamic path replacement, and the multirotor UAV can realize dynamic obstacle avoidance path optimization under the condition of sudden obstacles in a short time.
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Wei, Baoli, Chengchao Guo, and Miaoyi Deng. "An Innovation of the Markov Probability Model for Predicting the Remaining Service Life of Civil Airport Rigid Pavements." Materials 15, no. 17 (September 2, 2022): 6082. http://dx.doi.org/10.3390/ma15176082.
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In view of the time series update of airport runway health status detection data, the Markov chain of stochastic process theory was adopted. Considering the influence of aircraft traffic load, age, and pavement structure surface-layer thickness on the performance deterioration process of airport runways, the method of survival analysis was used. The parameter model of survival analysis was used to establish the duration function model of the four condition states of the airport runway PCI (pavement condition index). The Markov transition matrix for the performance prediction of airport runways was constructed. In order to evaluate the ability of the Markov transition matrix method to predict the trend of deterioration for PCI of the airport runway under different conditions of aircraft traffic volume and thickness of the runway pavement surface, a data set was constructed with the actual inspection data of the airport runway, and the corresponding samples were selected for analysis. The results showed that a Markov transition matrix for airport runway performance prediction, constructed based on survival analysis theory, can combine discontinuous inspection data or monitoring data with Weibull function survival curves. The method proposed in this paper can quantitatively predict the remaining service life of airport runways and provide support for cost-effective decisions about airport pavement maintenance and rehabilitation.
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Tagliafico, Alberto Stefano, Bianca Bignotti, Lorenzo Torri, and Federica Rossi. "Sarcopenia: how to measure, when and why." La radiologia medica 127, no. 3 (January 18, 2022): 228–37. http://dx.doi.org/10.1007/s11547-022-01450-3.
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AbstractSarcopenia indicates a loss of skeletal muscle mass, a condition that leads to a decline in physical performance. In 2018, the European Working Group on Sarcopenia in Older People met to update the original definition of sarcopenia: New scientific and clinical insights were introduced to emphasize the importance of muscle strength loss as a prime indicator of probable sarcopenia. In addition, the skeletal muscle is not only the organ related to mobility, but it is recognized as a secondary secretory organ too, with endocrine functions influencing several systems and preserving health. In this perspective, radiology could have a major role in early detection of sarcopenia and guarantee improvement in its treatment in clinical practice. We present here an update of clinical knowledge about sarcopenia and advantages and limitations of radiological evaluation of sarcopenia focusing on major body composition imaging modalities such as dual-energy X-ray absorptiometry, CT, and MRI. In addition, we discuss controversial such as the lack of consensus or standardization, different measurement methods, and diagnostic radiological cutoff points. Sarcopenia evaluation with radiological methods could enhance the role of radiologist in performing studies with relevant impact on medical and social outcome, placing radiology at the pinnacle of quality in evidence-based practice with high-level studies.
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Grosu-Creangă, Ionela-Alina, Antigona Carmen Trofor, Radu Adrian Crișan-Dabija, Daniela Robu-Popa, Cristina Mihaela Ghiciuc, and Elena Cătălina Lupușoru. "Adverse effects induced by second-line antituberculosis drugs: an update based on last WHO treatment recommendations for drug-resistant tuberculosis." Pneumologia 70, no. 3 (October 1, 2022): 117–26. http://dx.doi.org/10.2478/pneum-2022-0029.
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Abstract Introduction: Tuberculosis (TB), a common condition worldwide, is still among the main infectious diseases with high mortality rates, both in adults and infants. Drug-resistant tuberculosis (DR-TB) drugs, revised by the World Health Organization (WHO) in 2018, are a prolonged and complex therapy associated with many adverse drug effects (ADEs). Aim: To systematically review the ADEs of second-line anti-TB drugs reported in multicentric studies published after the latest WHO guidelines, compared with those from clinical trials published before 2018. Material and methods: A PubMed search, using keywords (TB OR DR-TB) AND (adverse effects) AND “second-line anti-TB drugs”, resulted in 56 studies. Only two studies, published after the last update of WHO guidelines in 2018, reported ADEs. Results: A total of 223 participants were included in the two selected studies. The use of multidrug regimens has been associated with an increased incidence of ADEs: 42 ADEs were recorded in 30 patients (26.3%) in the first study, while all patients had at least one ADE that occurred or worsened during treatment; and 19 (17%) had severe ADEs in the second study. However, both studies had a good favourable outcome rate (90% and 79.8%, respectively). Gastrointestinal disturbances, hepatotoxicity, headache and dizziness are the most common ADEs induced by a majority of second-line DR-TB treatments. A special attention should be given in the case of association of drugs determining QT interval (QT) prolongation on electrocardiogram, or psychiatric disorders. Conclusions: Proper strategies about ADE management have to be planned for timely detection of the possible ADEs that can be induced by second-line anti-TB therapy.
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Tripura, Tapas, Basuraj Bhowmik, Vikram Pakrashi, and Budhaditya Hazra. "Real-time damage detection of degrading systems." Structural Health Monitoring 19, no. 3 (August 5, 2019): 810–37. http://dx.doi.org/10.1177/1475921719861801.
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In this article, a robust output-only real-time damage detection technique for multi-degree-of-freedom degrading systems using recursive canonical correlation analysis is presented. It has been observed that the impact of damage to a vibrating system gradually advances with time that sustains until the system degrades up to a considerable extent. Of significant interest is the effect of sudden damage in presence of continuous degradation in real-time, which is studied in the form of a sudden stiffness reduction in a separate floor. The proposed recursive canonical correlation analysis algorithm estimates the iterative update of eigenspace at each instant from the response data, thereby capturing the features of a time varying degrading structure in an online framework. Furthermore, recursive canonical correlation analysis algorithm is shown to reduce the computational cost by updating the eigenspace at each instant of time. This article explores newly developed recursive condition indicators: recursive Mahalanobis distance and recursive Itakura distance that elicit damage information from the eigenspace. In order to model degradation, simulations aimed at successfully capturing the behavior of the process in real-time becomes imperative. A general stochastic formulation of the coupled response-degradation problem accounting for the evolution of degradation is presented in the light of stiffness degradation problems. The evolution of time varying system responses is generated using a newly proposed Ito–Taylor expansion-based stochastic numerical integration formulation. Numerically simulated structural vibrating systems, namely, 2-degree-of-freedom base-isolated and 4-degree-of-freedom linear systems, have been used to check the performance of the recursive canonical correlation analysis method. The spatial damage detectability of the algorithm in real-time is explored through identifying crack location on a beam traversed by a vehicle. Finally, an experimental case study has been carried out to verify the robustness of the proposed algorithm. The identification results for both numerical and experimental cases demonstrate the efficacy of the proposed algorithm in identification of nonlinear and time varying behavior associated with degrading structural systems.
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Daga, Alessandro Paolo, and Luigi Garibaldi. "GA-Adaptive Template Matching for Offline Shape Motion Tracking Based on Edge Detection: IAS Estimation from the SURVISHNO 2019 Challenge Video for Machine Diagnostics Purposes." Algorithms 13, no. 2 (January 29, 2020): 33. http://dx.doi.org/10.3390/a13020033.
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The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8–10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone’s pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers.
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Benzakour, Lamyae, and Guido Bondolfi. "Update of the Potential Treatments for Psychiatric and Neuropsychiatric Symptoms in the Context of the Post-COVID-19 Condition: Still a Lot of Suffering and Many More Things to Learn." Trauma Care 2, no. 2 (March 24, 2022): 131–50. http://dx.doi.org/10.3390/traumacare2020011.
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Background: The World Health Organization (WHO) has defined a post-COVID-19 condition. Some of these symptoms can be categorized as psychiatric long COVID-19 if they appeared in the aftermath of COVID-19, including depression, anxiety, post-traumatic stress disorder, somatic symptoms disorders such as hyperventilation syndrome, fatigue, cognitive and sleep disorders. Psychiatric and neuropsychiatric post-COVID-19 present mental health specialists with difficult challenges because of its complexity and the multiple ways in which it integrates into a singular somatic context. Methods: We conducted a systematic research paradigm from SARS-CoV-2 using LitCOVID and Web of Science to search management strategies and potential treatments for psychiatric post-COVID-19 symptoms. Results: Management strategies must be based on a multidisciplinary approach to promote the global evaluation of psychiatric and physical symptoms, systematic detection and prevention. Selective serotonin reuptake inhibitors appear to be the best choice to treat post-COVID-19 depression and anxiety disorders, and tofisopam could be helpful for anxiety. Cognitive behavioral therapy techniques adjusted to post-COVID-19 fatigue, functional remediation, extracorporeal apheresis, transcutaneous auricular vagus nerve stimulation, monoclonal antibodies, flavonoids, oxytocin or L-carnitine all represent hypothetical therapeutic avenues that remain to be evaluated in clinical trials. Conclusions: Psychiatric and neuropsychiatric post-COVID-19 symptoms occur frequently and are debilitating. Attention should be paid to this condition and studies undertaken to specify the effective treatments.
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Alshamrani, Khalaf, Hassan A. Alshamrani, Fawaz F. Alqahtani, and Bander S. Almutairi. "Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN." Sensors 23, no. 1 (December 26, 2022): 235. http://dx.doi.org/10.3390/s23010235.
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In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.
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Vali, Reza, Ian S. Armstrong, Zvi Bar-Sever, Lorenzo Biassoni, Lise Borgwardt, Justin Brown, Frederick D. Grant, et al. "SNMMI procedure standard/EANM practice guideline on pediatric [99mTc]Tc-DMSA renal cortical scintigraphy: an update." Clinical and Translational Imaging 10, no. 2 (March 4, 2022): 173–84. http://dx.doi.org/10.1007/s40336-022-00484-x.
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AbstractThe Society of Nuclear Medicine and Molecular Imaging (SNMMI), founded in 1954, is an international scientific and professional organization with a purpose to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM), founded in 1985, is a nonprofit professional medical association with a purpose to facilitate international communication among individuals in nuclear medicine pursuing clinical and academic excellence. Members of the SNMMI and EANM are physicians, technologists, and scientists who specialize in the research and practice of nuclear medicine. The SNMMI and EANM will periodically publish new guidelines for nuclear medicine practice to further advance the science of nuclear medicine and improve patient care. Existing standards/guidelines will be reviewed for revision or renewal, as appropriate. Each standard/guideline, representing a policy statement by the SNMMI/EANM, has undergone a thorough review, and represents an expert consensus. The SNMMI and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging requires specific training and skills, as described in each document. These standards/guidelines are educational resources designed to assist practitioners in providing appropriate nuclear medicine care for patients. They are consensus documents, and are not mandatory provisions or requirements of practice. They are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI and the EANM cautions against the use of these standards/guidelines in litigation procedures that call into question the clinical decisions of a practitioner. The ultimate judgment regarding the appropriateness and propriety of any specific procedure or course of action must be made by medical professionals, taking into account the unique context of each case. Thus, there is no implication that action differing from what is detailed in these standards/guidelines, on its own, is below the standard of care. On the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the standards/guidelines when, based on the reasonable judgment of the practitioner, such course of action is warranted based on the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the standards/guidelines. Practicing medicine involves not only the science, but also the art of dealing with the prevention, detection, diagnosis, and treatment of disease. The variety and complexity of human conditions make it impossible for general guidelines to consistently allow for an accurate diagnosis to be reached or a specific treatment response to be predicted. Therefore, it should be recognized that adhering to these standards/guidelines does not ensure a successful outcome. All that should be expected is that a practitioner follows a reasonable course of action based on their level of training, the current landscape of knowledge, the resources at their disposal, and the needs/context of the particular patient being treated. The purpose of this document is to provide nuclear medicine physicians, radiologists, and other clinicians with guidelines for the recommendation, performance and interpretation of 99mTc-dimercaptosuccinic acid renal cortical scintigraphy ([99mTc] Tc-DMSA scintigraphy) in pediatric patients. These recommendations represent the expert opinions of experienced leaders in this field, and these recommendations are not all supported by a high level of evidence. Further studies are required to have evidence-based recommendations for the application of [99mTc] Tc-DMSA renal cortical scintigraphy in pediatrics. This guideline summarizes the views of the SNMMI Renal Cortical Scintigraphy in Children Working Group and the EANM Pediatrics Committee. It reflects recommendations for which the SNMMI and EANM cannot be held responsible. The recommendations should be taken into context of good practice of nuclear medicine and do not substitute for national and international legal or regulatory provisions.
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Sbarufatti, Claudio, Andrea Manes, and Marco Giglio. "On the Integration of Real-Time Diagnosis and Prognosis for Scheduled Maintenance Optimization." Key Engineering Materials 569-570 (July 2013): 1044–51. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.1044.
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Helicopters are very critical aircrafts from the point of view of fatigue loads. A structural damage could grow fast to critical size because of the wide range of manoeuvre loads. As a consequence, scheduled maintenance is a major cost during the operative life of the aircraft. The most advanced methodologies for Structural Health Monitoring and Prognostic Health Management available today are aimed to provide a real-time structural diagnosis, thus maximizing the availability of the helicopter and reducing the maintenance costs. However, on-board diagnostic systems might be gradually introduced in the current maintenance procedures, trying to minimize the risk associated to these newly developed technologies. The work presented inside this paper is about the simulation of the integration of real-time diagnosis and prognosis into a typical scheduled maintenance procedure. A diagnostic unit is capable for anomaly detection and damage quantification (in terms of crack length). It is trained with Finite Element simulated damages and tested with real experimental data. The diagnostic output is then processed in a particle filter algorithm, based on sequential importance sampling technique, aimed at refining the estimation of the structural condition as well as to update the inference on the residual useful life distribution. The coupling of real-time diagnosis with off-line measures (taken during scheduled maintenance stops) is analyzed and applied to a damage tolerant structure, trying to outline the advantages and drawbacks of the proposed approach.
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Patel, Chirag I., Sanjay Garg, Tanish Zaveri, and Asim Banerjee. "Top-Down and Bottom-Up Cues Based Moving Object Detection for Varied Background Video Sequences." Advances in Multimedia 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/879070.
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Moving object detection is a crucial and critical task for any surveillance system. Conventionally, a moving object detection task is performed on the basis of consecutive frame difference or background models which are based on some mathematical aspects or probabilistic approaches. But, these approaches are based on some initial conditions and short amount of time is needed to learn all these models. Also, the bottleneck in all these previous approaches is that they require neat and clean background or need to create a background first by using some approaches and that it is essential to update them regularly to cope with the illuminating changes. In this paper, moving object detection is executed using visual attention where there is no need for background formulation and updates as it is background independent. Many bottom-up approaches and one combination of bottom-up and top-down approaches are proposed in the present paper. The proposed approaches seem more efficient due to inessential requirement of learning background model and due to being independent of previous video frames. Results indicate that the proposed approach works even against slight movements in the background and in various outdoor conditions.
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Kilic, Cagri, Yu Gu, and Jason Gross. "Proprioceptive Slip Detection for Planetary Rovers in Perceptually Degraded Extraterrestrial Environments." Field Robotics 2, no. 1 (March 10, 2022): 1754–78. http://dx.doi.org/10.55417/fr.2022054.
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Slip detection is of fundamental importance for the safety and efficiency of rovers driving on the surface of extraterrestrial bodies. Current planetary rover slip detection systems rely on visual perception on the assumption that sufficient visual features can be acquired in the environment. However, visual-based methods are prone to suffer in perceptually degraded planetary environments with dominant low terrain features such as regolith, glacial terrain, salt evaporites, and poor lighting conditions such as dark caves and permanently shadowed regions. Relying only on visual sensors for slip detection also requires additional computational power and reduces the rover traversal rate. This paper answers the question of how to detect wheel slippage of a planetary rover without depending on visual perception. In this respect, we propose a slip detection system that obtains its information from a proprioceptive localization framework that is capable of providing reliable, continuous, and computationally efficient state estimation over hundreds of meters. This is accomplished by using zero velocity update, zero angular rate update, and non-holonomic constraints as pseudo-measurement updates on an inertial navigation system framework. The proposed method is evaluated on actual hardware and field tested in a planetary-analog environment. The method achieves greater than 92% slip detection accuracy for distances around 150 m using only an inertial measurement unit and wheel encoders.
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Cui, Zhe, Noseong Park, and Tanmoy Chakraborty. "Incremental community discovery via latent network representation and probabilistic inference." Knowledge and Information Systems 62, no. 6 (November 15, 2019): 2281–300. http://dx.doi.org/10.1007/s10115-019-01422-6.
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AbstractMost of the community detection algorithms assume that the complete network structure $$\mathcal {G}=(\mathcal {V},\mathcal {E})$$G=(V,E) is available in advance for analysis. However, in reality this may not be true due to several reasons, such as privacy constraints and restricted access, which result in a partial snapshot of the entire network. In addition, we may be interested in identifying the community information of only a selected subset of nodes (denoted by $$\mathcal {V}_{{\mathrm{T}}} \subseteq \mathcal {V}$$VT⊆V), rather than obtaining the community structure of all the nodes in $$\mathcal {G}$$G. To this end, we propose an incremental community detection method that repeats two stages—(i) network scan and (ii) community update. In the first stage, our method selects an appropriate node in such a way that the discovery of its local neighborhood structure leads to an accurate community detection in the second stage. We propose a novel criterion, called Information Gain, based on existing network embedding algorithms (Deepwalk and node2vec) to scan a node. The proposed community update stage consists of expectation–maximization and Markov Random Field-based denoising strategy. Experiments with 5 diverse networks with known ground-truth community structure show that our algorithm achieves 10.2% higher accuracy on average over state-of-the-art algorithms for both network scan and community update steps.
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Shahidha Banu, S., and N. Maheswari. "Background Modelling using a Q-Tree Based Foreground Segmentation." Scalable Computing: Practice and Experience 21, no. 1 (March 19, 2020): 17–31. http://dx.doi.org/10.12694/scpe.v21i1.1603.
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Background modelling is an empirical part in the procedure of foreground mining of idle and moving objects. The foreground object detection has become a challenging phenomenon due to intermittent objects, intensity variation, image artefact and dynamic background in the video analysis and video surveillance applications. In the video surveillances application, a large amount of data is getting processed by everyday basis. Thus it needs an efficient background modelling technique which could process those larger sets of data which promotes effective foreground detection. In this paper, we presented a renewed background modelling method for foreground segmentation. The main objective of the work is to perform the foreground extraction only inthe intended region of interest using proposed Q-Tree algorithm. At most all the present techniques consider their updates to the pixels of the entire frame which may result in inefficient foreground detection with a quick update to slow moving objects. The proposed method contract these defect by extracting the foreground object by controlling the region of interest (the region only where the background subtraction is to be performed) and thereby reducing the false positive and false negative. The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches. Moreover, we use challenge change detection dataset and the efficiency of our method is analyzed in different environmental conditions (indoor, outdoor) from the CDnet2014 dataset and additional real time videos. The experimental results were satisfactorily verified the strengths and weakness of proposed method against the existing state-of-the-art background modelling methods.
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Ivanovic, Stefan, Ana-Maria Olteanu-Raimond, Sébastien Mustière, and Thomas Devogele. "A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context." ISPRS International Journal of Geo-Information 8, no. 9 (August 30, 2019): 380. http://dx.doi.org/10.3390/ijgi8090380.
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Traces collected by citizens using GNSS (Global Navigation Satellite System) devices during sports activities such as running, hiking or biking are now widely available through different sport-oriented collaborative websites. The traces are collected by citizens for their own purposes and frequently shared with the sports community on the internet. Our research assumption is that crowdsourced GNSS traces may be a valuable source of information to detect updates in authoritative datasets. Despite their availability, the traces present some issues such as poor metadata, attribute incompleteness and heterogeneous positional accuracy. Moreover, certain parts of the traces (GNSS points composing the traces) are results of the displacements made out of the existing paths. In our context (i.e., update authoritative data) these off path GNSS points are considered as noise and should be filtered. Two types of noise are examined in this research: Points representing secondary activities (e.g., having a lunch break) and points representing errors during the acquisition. The first ones we named secondary human behaviour (SHB), whereas we named the second ones outliers. The goal of this paper is to improve the smoothness of traces by detecting and filtering both SHB and outliers. Two methods are proposed. The first one allows for the detection secondary human behaviour by analysing only traces geometry. The second one is a rule-based machine learning method that detects outliers by taking into account the intrinsic characteristics of points composing the traces, as well as the environmental conditions during traces acquisition. The proposed approaches are tested on crowdsourced GNSS traces collected in mountain areas during sports activities.
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Yoshikawa, Fabio Seiti Yamada, Franciane Mouradian Emidio Teixeira, Maria Notomi Sato, and Luanda Mara da Silva Oliveira. "Delivery of microRNAs by Extracellular Vesicles in Viral Infections: Could the News be Packaged?" Cells 8, no. 6 (June 18, 2019): 611. http://dx.doi.org/10.3390/cells8060611.
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Extracellular vesicles (EVs) are released by various cells and recently have attracted attention because they constitute a refined system of cell–cell communication. EVs deliver a diverse array of biomolecules including messenger RNAs (mRNAs), microRNAs (miRNAs), proteins and lipids, and they can be used as potential biomarkers in normal and pathological conditions. The cargo of EVs is a snapshot of the donor cell profile; thus, in viral infections, EVs produced by infected cells could be a central player in disease pathogenesis. In this context, miRNAs incorporated into EVs can affect the immune recognition of viruses and promote or restrict their replication in target cells. In this review, we provide an updated overview of the roles played by EV-delivered miRNAs in viral infections and discuss the potential consequences for the host response. The full understanding of the functions of EVs and miRNAs can turn into useful biomarkers for infection detection and monitoring and/or uncover potential therapeutic targets.
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Khiem, Nguyen Tien. "Damage detection of beam by natural frequencies: General theory and procedure." Vietnam Journal of Mechanics 28, no. 2 (August 1, 2006): 120–32. http://dx.doi.org/10.15625/0866-7136/28/2/5572.
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The frequency equation of single damaged beam has been established for arbitrary boundary conditions that is the main tool for analysis as well as identification of damaged beam by using measured natural frequencies. A procedure for damage detection problem presented in this paper consists of three steps. First, the modelling error is reduced by a model updating procedure, in which the material, geometrical parameters and boundary conditions are updated. Then, measurement data are corrected based on the updated model. Finally, the damage parameters are identified using updated model and corrected measurement data. Theoretical investigation is illustrated by an example.
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Weiss, Matthias, Stephan Staudacher, Duilio Becchio, Christian Keller, and Jürgen Mathes. "Steady-State Fault Detection with Full-Flight Data." Machines 10, no. 2 (February 16, 2022): 140. http://dx.doi.org/10.3390/machines10020140.
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Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency in fault detection. The increased availability of non-mandatory data acquisition hardware in modern airplanes provides so-called full-flight data sampled continuously during flight. These datasets enable the detection of engine faults within one flight by deriving a statistically relevant set of steady-state data points, thus, allowing the application of machine-learning approaches. It is shown that low-pass filtering before steady-state detection significantly increases the success rate in detecting steady-state data points. The application of Principal Component Analysis halves the number of relevant dimensions and provides a coordinate system of principal components retaining most of the variance. Consequently, clusters of data points with and without engine fault can be separated visually and numerically using a One-Class Support Vector Machine. High detection rates are demonstrated for various component faults and even for a minimum instrumentation suite using synthesized datasets derived from full-flight data of commercially operated flights. In addition to the tests conducted with synthesized data, the algorithm is verified based on operational in-flight measurements providing a proof-of-concept. Consequently, the availability of continuously sampled in-flight measurements combined with machine-learning methods allows fault detection within a single flight.
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Reinhardt, Sophia, Joshua Schmidt, Michael Leuschel, Christiane Schüle, and Jörg Schipper. "Update VertiGo – light affection of mobile VNG." Current Directions in Biomedical Engineering 7, no. 1 (August 1, 2021): 140–44. http://dx.doi.org/10.1515/cdbme-2021-1030.
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Abstract Dizziness is one of the most frequent symptoms in outpatient practices. For the differentiation of peripheral or central pathogenesis of vertigo, history taking and clinical examination with the detection of nystagmus is elementary. The aim of this study was to investigate the effect of lighting for the detection of horizontal vestibular nystagmus while utilizing a conventional webcam. In the proof-of-concept study, caloric induced vestibular nystagmus was recorded with a conventional video-nystagmography and mobile webcam in addition to an external light source. The analysis of recorded data was performed with a self-developed software using computer vision techniques. The self-designed algorithm detected the existence of nystagmus and its direction in several cases. The experimental webcam-based vestibular nystagmography could be enhanced by improving lighting conditions. Currently, a clinical application for this technique is not approved. Further software improvements are necessary to increase its accuracy.
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Kang, Daiwen, Kenneth E. Pickering, Dale J. Allen, Kristen M. Foley, David C. Wong, Rohit Mathur, and Shawn J. Roselle. "Simulating lightning NO production in CMAQv5.2: evolution of scientific updates." Geoscientific Model Development 12, no. 7 (July 18, 2019): 3071–83. http://dx.doi.org/10.5194/gmd-12-3071-2019.
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Abstract. This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available (i.e., air quality forecasts and climate studies that assume similar climate conditions), we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40 % during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations.
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Kalinicheva, Svetlana, Alexander Fedorov, and Mikhail Zhelezniak. "Mapping Mountain Permafrost Landscapes in Siberia Using Landsat Thermal Imagery." Geosciences 9, no. 1 (December 20, 2018): 4. http://dx.doi.org/10.3390/geosciences9010004.
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Intensive development of South Yakutia, a mountainous area in the Russian sporadic permafrost zone, must be founded on knowledge about regional permafrost conditions. New permafrost maps for mountainous areas in South Yakutia (the Elkon Mountains and the Olekma-Chara Upland) are presented that provide a more detailed and updated description of permafrost distribution in the area than those that were hitherto available. These maps are based on the previously-developed and tested method of detecting permafrost and unfrozen ground using Landsat-5/TM satellite data with relatively high resolution. The method represents a scheme for permafrost identification based on a set of landscape indicators: terrain elevation, slope angle and exposition, vegetation, snow cover, and land surface temperature (LST). A correlation analysis of satellite data to full-scale field data has been carried out for the two areas under consideration. Indicator properties of LST obtained by Landsat-5/TM Band 6 Infrared have been characterized in detail for detection and regional mapping of permafrost. The effect of landscape factors (landscape cryo-indicators) on ground temperature and condition, frozen or unfrozen reflected in LST intensity, is demonstrated.
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Henderson, Jeffrey, Joan Condell, James Connolly, Daniel Kelly, and Kevin Curran. "Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis." Sensors 21, no. 5 (February 24, 2021): 1576. http://dx.doi.org/10.3390/s21051576.
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Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual’s RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.
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Wang, Hai Tao, You Ming Chen, Cary W. H. Chan, and Jian Ying Qin. "A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings." Applied Mechanics and Materials 90-93 (September 2011): 3061–67. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.3061.
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The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.
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Ramesh, Akshatha, Dhananjay Nikam, Venkat Narayanan Balachandran, Longxiang Guo, Rongyao Wang, Leo Hu, Gurcan Comert, and Yunyi Jia. "Cloud-Based Collaborative Road-Damage Monitoring with Deep Learning and Smartphones." Sustainability 14, no. 14 (July 15, 2022): 8682. http://dx.doi.org/10.3390/su14148682.
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Road damage such as potholes and cracks may reduce ride comfort and traffic safety. This influence can be prevented by regular, proper monitoring and maintenance of roads. Traditional methods and existing methods of surveying are very time-consuming, expensive, require a lot of human effort, and, thus, cannot be conducted frequently. A more efficient and cost-effective process is required to augment profilometer and traditional road-condition recognition systems. In this study, we propose deep-learning methods using smartphone data to devise a cost-effective and ad-hoc approach. Information from sensors on smartphones such as motion sensors and cameras are harnessed to detect road damage using deep-learning algorithms. In order to give heuristic and accurate information about the road damage, we used a cloud-based collaborative approach to fuse all the data and update a map frequently with these road-surface conditions. During the experiment, the deep-learning models achieved good prediction accuracy on our dataset, and the cloud-based fusion approach was able to group and merge the detections from different vehicles.
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Chen, Mengyuan, and Wenchao Hu. "Research on BatSLAM Algorithm for UAV Based on Audio Perceptual Hash Closed-Loop Detection." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (October 11, 2018): 1959002. http://dx.doi.org/10.1142/s021800141959002x.
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This research is aimed at the optimization of a two-dimensional (2D) empirical graph under a certain height and dark conditions for a UAV, using the bionic sonar system to replace the visual sensor’s BatSLAM mode and audio perceptual hash closed-loop detection. The BatSLAM model uses Sum of Absolute Difference (SAD) image processing methods to update the bionic sonar template. This method only judges whether the appearance of the two cochlear images is consistent and does not have geometric processing and feature extraction. Because the cochlear images produce various noises during the acquisition and transmission, there are some differences in cochlear maps obtained at the same position, which can lead to the distortion of the constructed empirical map. In this research, an audio perceptual hash closed-loop detection algorithm is developed to extract features of cochlea. It considers both the appearance and the energy difference between adjacent bands to improve the accuracy of closed-loop detection, thus solving the distortion problem and improving the experience map. The simulation experiment shows that the improved BatSLAM model based on the audio perceptual hash closed-loop detection can improve the 2D experience map for UAV under certain height and dark conditions, through improving the accuracy of the closed-loop detection to solve the distortion problem and thus implementing the optimization of the experience graph.
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Xu, Feng, and Haiwei Wang. "A Discriminative Target Equation-Based Face Recognition Method for Teaching Attendance." Advances in Mathematical Physics 2021 (December 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/9165733.
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In this paper, we use discriminative objective equations to conduct an in-depth study and analysis of face recognition methods in teaching attendance and use the model in actual teaching attendance. It focuses on the design and implementation of the attendance module, which uses wireless network technology to record students’ access to classrooms in real time, and relies on face recognition technology to identify students’ sign-in images to achieve attendance records of students’ independent attendance sign-in. Real-time detection of student attendance is achieved by combining face detection and face recognition technology through regular camera photography and automatic attendance check-in by the server. Based on the recognition results of the attendance check-in image, an attendance mechanism is proposed, and the attendance score of the student for the current course can be calculated using the attendance mechanism, which realizes the automatic management of student attendance. For the face recognition process, the system uses the Ad boost algorithm based on Hear features to achieve face detection, preprocesses the face samples with gray normalization, rotation correction, and size correction, and uses the method based on LBP features to achieve face recognition. Firstly, a combination of histogram equalization and wavelet denoising is chosen to preprocess the training sample images to obtain the face image light invariance description, and then, the initial dictionary is constructed using the dimensionality reduction performance of the PCA method; next, the initial dictionary is updated, and a new dictionary with representation and discrimination capabilities is obtained using the LC-KSVD algorithm that makes improvements in the dictionary update stage. The sparse coefficients of the feature matrix of the test sample image under the new dictionary are calculated, and the class correlation reconstruction is performed on the feature matrix of the test sample image, and the corresponding reconstruction error is solved; finally, the discriminative classification of the test sample image is achieved according to the solved class correlation reconstruction error. The relevant experiments on the face database prove that the algorithm can improve the recognition accuracy to a certain extent and better solve the influence of changing lighting conditions on the face recognition accuracy.
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Panthaplackel, Sheena, Junyi Jessy Li, Milos Gligoric, and Raymond J. Mooney. "Deep Just-In-Time Inconsistency Detection Between Comments and Source Code." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 427–35. http://dx.doi.org/10.1609/aaai.v35i1.16119.
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Natural language comments convey key aspects of source code such as implementation, usage, and pre- and post-conditions. Failure to update comments accordingly when the corresponding code is modified introduces inconsistencies, which is known to lead to confusion and software bugs. In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code, in order to catch potential inconsistencies just-in-time, i.e., before they are committed to a code base. To achieve this, we develop a deep-learning approach that learns to correlate a comment with code changes. By evaluating on a large corpus of comment/code pairs spanning various comment types, we show that our model outperforms multiple baselines by significant margins. For extrinsic evaluation, we show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system which can both detect and resolve inconsistent comments based on code changes.
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Bressan, L., and S. Tinti. "Structure and performance of a real-time algorithm to detect tsunami or tsunami-like alert conditions based on sea-level records analysis." Natural Hazards and Earth System Sciences 11, no. 5 (May 19, 2011): 1499–521. http://dx.doi.org/10.5194/nhess-11-1499-2011.
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Abstract. The goal of this paper is to present an original real-time algorithm devised for detection of tsunami or tsunami-like waves we call TEDA (Tsunami Early Detection Algorithm), and to introduce a methodology to evaluate its performance. TEDA works on the sea level records of a single station and implements two distinct modules running concurrently: one to assess the presence of tsunami waves ("tsunami detection") and the other to identify high-amplitude long waves ("secure detection"). Both detection methods are based on continuously updated time functions depending on a number of parameters that can be varied according to the application. In order to select the most adequate parameter setting for a given station, a methodology to evaluate TEDA performance has been devised, that is based on a number of indicators and that is simple to use. In this paper an example of TEDA application is given by using data from a tide gauge located at the Adak Island in Alaska, USA, that resulted in being quite suitable since it recorded several tsunamis in the last years using the sampling rate of 1 min.
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Auer-Grumbach, Michaela, Jan Senderek, and Sabine Rudnik-Schöneborn. "Hereditary Neuropathies: Update 2017." Neuropediatrics 48, no. 04 (June 8, 2017): 282–93. http://dx.doi.org/10.1055/s-0037-1603518.
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AbstractHereditary neuropathy is an umbrella term for a group of nonsyndromic conditions with a prevalence of approximately 1:2,500. In addition to the most frequent form, Charcot–Marie–Tooth's disease (CMT, or hereditary motor and sensory neuropathy), there are additional entities such as hereditary neuropathy with liability to pressure palsies (HNPP), hereditary motor neuropathies (HMNs), and hereditary sensory and autonomic neuropathies (HSANs). With the exception of HNPP, which is almost always caused by defects of the PMP22 gene, all other forms show genetic heterogeneity with altogether close to 100 genes involved. Mutation detection rates vary considerably, reaching up to 80% in demyelinating CMT (CMT1) but are still as low as 10 to 30% in axonal CMT (CMT2), HMN, and HSAN. Based on current information, analysis of only four genes (PMP22, GJB1, MPZ, MFN2) identifies 80 to 90% of CMT-causing mutations that can be detected in all known disease genes. For the remaining patients, parallel analysis of multiple neuropathy genes using next-generation sequencing is now replacing phenotype-oriented multistep gene-by-gene sequencing. Such approaches tend to generate a wealth of genetic information that requires comprehensive evaluation of the pathogenic relevance of identified variants. In this review, we present current classification systems, specific phenotypic clues, and genetic testing algorithms in the different subgroups of hereditary neuropathies.
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Liao, Shao Wen, and Yong Chen. "An Improved AdaBoost Algorithm." Advanced Materials Research 1049-1050 (October 2014): 1703–6. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1703.
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In view of the higher mistaken-detection rate problem of the human face detection in complex conditions, we put forward an improved algorithm. This article Proposes one kind of the method Which unifies the level of difference between the threshold and the feature value of the weak classifier with the weak classifier's overall error rate. Compared to the method which only based on the overall classification error rate to update the weights, this method can achieve higher detection rate while reduces the mistaken-detection rate. This article redefines the training error which is caused When we train the weak classifier, and Proposes MCE-AdaBoost algorithm. The new definition of training error will pay more attention to the error Which erroneously estimates the face Sample as non-face sample; this much more conforms to face detection of this special target detection issue. The experimental results show that MCE-AdaBoost algorithm can effectively improve the detection Performance of the final classifier.
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Zonta, Daniele, Matteo Pozzi, and Paolo Zanon. "Bayesian Approach to Condition Monitoring of PRC Bridges." Key Engineering Materials 347 (September 2007): 227–32. http://dx.doi.org/10.4028/www.scientific.net/kem.347.227.
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This paper presents a damage detection procedure based on Bayesian analysis of data recorded by permanent monitoring systems as applied to condition assessment of Precast Reinforced Concrete (PRC) bridges. The concept is to assume a set of possible condition states of the structure, including an intact condition and various combinations of damage, such as failure of strands, cover spalling and cracking. Based on these states, a set of potential time response scenarios is evaluated first, each described by a vector of random parameters and by a theoretical model. Given the prior distribution of this vector, the method assigns posterior probability to each scenario as well as updated probability distributions to each parameter. The effectiveness of this method is illustrated as applied to a short span PRC bridge, which is currently in the design phase and will be instrumented with a number of fiber-optic long gauge-length strain sensors. A Finite Element Model is used to simulate the instantaneous and time-dependent behavior of the structure, while Monte Carlo simulations are performed to numerically evaluate the evidence functions necessary for implementation of the method. The ability of the method to recognize damage is discussed.