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Thematische Bibliographien / Feature Identification and Location Experiment / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Feature Identification and Location Experiment“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Almajalid, Rania, Juan Shan, Yaodong Du, and Ming Zhang. "Identification of Knee Cartilage Changing Pattern." Applied Sciences 9, no. 17 (2019): 3469. http://dx.doi.org/10.3390/app9173469.

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This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changin

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Zou, Li, and Haowen Cheng. "Research on Wind Turbine Blade Surface Damage Identification Based on Improved Convolution Neural Network." Applied Sciences 12, no. 18 (2022): 9338. http://dx.doi.org/10.3390/app12189338.

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Wind turbine blades are easily affected by the working environment and often show damage features such as cracks and surface shedding. An improved convolution neural network, ED Net, is proposed to identify their damage features. An EAC block based on the improved asymmetric convolution is introduced which strengthens the feature extraction during convolution. A DPCI_SC block, which is improved based on the attention module, is embedded to enhance the ability to obtain spatial location information of the damage. GELU is used as the activation function. The loss function is smoothed and labeled

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Ma, Haomiao, Yingfeng Xu, and Jianye Wang. "A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy." Mathematical Problems in Engineering 2022 (September 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/9642550.

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Rolling bearings are the key components for the safe operation of mechanical equipment. It plays an irreplaceable role in the normal operation of mechanical equipment. Higher load makes higher failure rate of rolling bearing. Accurate identification of the fault location is an important step in the diagnosis of the rolling bearing fault. In recent years, the entropy features of rolling bearing vibration signals are usually extracted to identify fault. In this paper, a double feature extraction method based on slope entropy (SlE) and fuzzy entropy (FE) is proposed to recognize the fault state o

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Liu, Qiong, JiZhuang Hui, Li Luo, and YanPu Yang. "Target Identification and Location Algorithm Based on SURF-BRISK Operator." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (2016): 1655016. http://dx.doi.org/10.1142/s0218001416550168.

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Accurate and fast target image recognition is an important function of applications such as remote sensing imaging and medical imaging. However, an operator such as speeded up robust feature (SURF) cannot be accurately matched in the recognition process of a target image. This led us to propose the use of a method capable of matching identification, i.e. binary robust invariant scalable keypoints (BRISK) operators, in combination with SURF operators. The proposed algorithm combines the accuracy of SURF operators and the rapidity of BRISK operators to obtain a quick and accurate way of matching

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Pathmakumar, Thejus, M. A. Viraj J. Muthugala, S. M. Bhagya P. Samarakoon, Braulio Félix Gómez, and Mohan Rajesh Elara. "A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms." Sensors 22, no. 14 (2022): 5317. http://dx.doi.org/10.3390/s22145317.

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Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combinin

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Boal, Jaime, Álvaro Sánchez-Miralles, and Manuel Alvar. "Matching monocular lightweight features using N-gram techniques for topological location identification." Robotica 33, no. 9 (2014): 1835–49. http://dx.doi.org/10.1017/s0263574714001076.

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SUMMARYIn SLAM (simultaneous localization and mapping), the topological paradigm provides a more natural and compact solution that scales better with the size of the environment. Computer vision has always been regarded as the ideal sensor technology for topological feature extraction and description and several methods have been proposed in the literature, but they are either time-consuming, require plenty of different sensors, or are very sensitive to perceptual aliasing, all of which limit their application scope.This paper presents a fast-to-compute collection of features extracted from mo

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Homberg, Nicolas, Mariana Galvão Ferrarini, Christine Gaspin, and Marie-France Sagot. "MicroRNA Target Identification: Revisiting Accessibility and Seed Anchoring." Genes 14, no. 3 (2023): 664. http://dx.doi.org/10.3390/genes14030664.

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By pairing to messenger RNAs (mRNAs for short), microRNAs (miRNAs) regulate gene expression in animals and plants. Accurately identifying which mRNAs interact with a given miRNA and the precise location of the interaction sites is crucial to reaching a more complete view of the regulatory network of an organism. Only a few experimental approaches, however, allow the identification of both within a single experiment. Computational predictions of miRNA–mRNA interactions thus remain generally the first step used, despite their drawback of a high rate of false-positive predictions. The major compu

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Zeng, Jiang, Guang-Zhong Cao, Ye-Ping Peng, and Su-Dan Huang. "A Weld Joint Type Identification Method for Visual Sensor Based on Image Features and SVM." Sensors 20, no. 2 (2020): 471. http://dx.doi.org/10.3390/s20020471.

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In the field of welding robotics, visual sensors, which are mainly composed of a camera and a laser, have proven to be promising devices because of their high precision, good stability, and high safety factor. In real welding environments, there are various kinds of weld joints due to the diversity of the workpieces. The location algorithms for different weld joint types are different, and the welding parameters applied in welding are also different. It is very inefficient to manually change the image processing algorithm and welding parameters according to the weld joint type before each weld

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Cui, Lei, Qing Zhu, and Jing Kang Zhang. "Algorithm Study of Edge Location and Feature Measurement of Building Surface Crack Images." Applied Mechanics and Materials 380-384 (August 2013): 1045–49. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1045.

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Old buildings surface cracks are more obvious with the accumulation of its disrepair, which will impact aesthetics, durability and even threaten the safety of persons and properties if feature values surpass the security thresholds. In this paper, firstly, the algorithm of the improved Canny operator gray threshold edge detection and location, based on traditional differential operators and sub-pixel positioning algorithms, achieve coarse location, furthermore, the sub-pixel location algorithm of Gaussian curve fitting based on neighbor gradient features realize sub-pixel precision positioning

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Li, Zhaoyang, Ping Xu, Jie Xing, and Chengxing Yang. "SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map." Sensors 22, no. 6 (2022): 2358. http://dx.doi.org/10.3390/s22062358.

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Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based on specific damage index needs the designer to have rich experience and background knowledge, and the designed damage index is hard to apply to different structures. In this paper, a U-shaped efficient structural strain damage identification network SDFormer (structural damage transformer) based on self-attention feature is proposed. SDFormer rega

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Han, Dongying, Shimin Wei, Peiming Shi, Ying Zhang, Kai Gao, and Nengyuan Tian. "Damage Identification of a Derrick Steel Structure Based on the HHT Marginal Spectrum Amplitude Curvature Difference." Shock and Vibration 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1062949.

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For the damage identification of derrick steel structures, traditional methods often require high-order vibration information of structures to identify damage accurately. However, the high-order vibration information of structures is difficult to acquire. Based on the technology of signal feature extraction, only using the low-order vibration information, taking the right front leg as an example, we analyzed the selection of HHT marginal spectrum amplitude and the calculation process of its curvature in practical application, designed the damage conditions of a derrick steel structure, used th

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Algabri, Redhwan, and Mun-Taek Choi. "Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots." Sensors 22, no. 21 (2022): 8422. http://dx.doi.org/10.3390/s22218422.

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It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and

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Zhang, Jing, Ming Zhong, and Bo Huang. "A Monocular Vision System Based on the Equidistance Pseudo Binocular Feature Recognition." Applied Mechanics and Materials 226-228 (November 2012): 1987–92. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1987.

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Monocular vision system for medical robot must have high accuracy. In traditional monocular vision system, localization algorithm is realized by image space transformation, which leads to a trivial calculation process and a complex camera calibration method, therefore, this paper presents a new target characteristics identification method. Through the establishment of an equidistance pseudo binocular image acquisition way, searching the image and space coordinate transformation relation, avoid the complicated space matrix conversion and improve the position precision. Using Pointer method for

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Kanchana, S., and G. Balakrishnan. "Palm-Print Pattern Matching Based on Features Using Rabin-Karp for Person Identification." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/382697.

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Palm-print based individual identification is regarded as an effectual method for identifying persons with high confidence. Palm-print with larger inner surface of hand contains many features such as principle lines, ridges, minutiae points, singular points, and textures. Feature based pattern matching has faced the challenge that the spatial positional variations occur between the training and test samples. To perform effective palm-print features matching, Rabin-Karp Palm-Print Pattern Matching (RPPM) method is proposed in this paper. With the objective of improving the accuracy of pattern m

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Hu, Chang Jie, and Hong Li Xu. "Face Image Segmentation Technology Research." Advanced Materials Research 846-847 (November 2013): 1339–42. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1339.

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Face contains the very rich information, which is a typical biological feature .It has a wide application prospect in personal identification, intelligent video surveillance and human-computer interaction. Face detection is to determine the number, the location, size and other information of all the faces among the color images that have been input. Firstly, skin color model is established, and then we use the skin color model to convert color image to gray image, and then we can denoise gray image, at last use the Fisher criterion to obtain the dynamic threshold segmentation of the face image

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Zou, Li, Haowen Cheng, and Qianhui Sun. "Surface Damage Identification of Wind Turbine Blade Based on Improved Lightweight Asymmetric Convolutional Neural Network." Applied Sciences 13, no. 10 (2023): 6330. http://dx.doi.org/10.3390/app13106330.

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Wind turbine blades are readily damaged by the workplace environment and frequently experience flaws such as surface peeling and cracking. To address the problems of cumbersome operation, high cost, and harsh application conditions with traditional damage identification methods, and to cater to the wide application of mobile terminal devices such as unmanned aerial vehicles, a novel lightweight asymmetric convolution neural network is proposed. The network introduces a lightweight asymmetric convolution module based on the improved asymmetric convolution, which applies depthwise separable conv

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Zhang, Qiang, and Dongdong Wei. "DASANet: A 3D Object Detector with Density-and-Sparsity Feature Aggregation." Remote Sensing 15, no. 18 (2023): 4587. http://dx.doi.org/10.3390/rs15184587.

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In the field of autonomous driving and robotics, 3D object detection is a difficult, but important task. To improve the accuracy of detection, LiDAR, which collects the 3D point cloud of a scene, is updated constantly. But the density of the collected 3D points is low, and its distribution is unbalanced in the scene, which influences the accuracy of 3D object detectors in regards to object location and identification. Although corresponding high-resolution scene images from cameras can be used as supplemental information, poor fusion strategies can result in decreased accuracy compared with th

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Li, Xinyue. "Smoke Detection and Recognition Based on Flame Feature Extraction." Theoretical and Natural Science 52, no. 1 (2024): 163–70. http://dx.doi.org/10.54254/2753-8818/52/2024ch0127.

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The sensor-based fire monitoring scheme requires the installation of temperature and smoke detectors near the monitoring target. Meanwhile, the pictorial fire detection technology is capable of capturing the location and development degree of the fire instantly, enabling rapid detection. Aiming at the issue that the traditional fire monitoring system is prone to failure in large-space indoor and open outdoor environments, a digital image fire monitoring algorithm based on the characteristics of fire flames and smoke is proposed. In the early stage of a fire, smoke is frequently accompanied by

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R. Maheshwari, Dr. D. Banumathy, Dr. P. Thiyagarajan, and P. Rajeshwaran. "Kernel Block Based Morphing Identification and Elimination In Server." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 281–90. http://dx.doi.org/10.32628/ijsrset24113130.

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The primary stage of duplicated region identification for investigating duplicate and insert image forgeries does the image block match. Because of evolution of highly sophisticated tools, numerous image modifications have been made. When images are difficult to duplicate and transfer, forgeries of those parts may happen. The images are examined closely in the area where they were forged. By using the suggested Gaussian RBF kernel PCA, the segment of the image that is copied and pasted will be known. One of the most significant issues in this step is the high processing time required to locate

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Ren, Yong. "Traffic Signal Indicator Classification Based on Color-Shape Feature." Advanced Materials Research 765-767 (September 2013): 2797–800. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2797.

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Traffic signal lights are generally composed by digital indicator and direction indicator. Its identification and classification is to analysis image content on the base of the semaphores characteristics, to identify the semaphores goals and classify them, including pre-processing, positioning, segmentation and classification. Based on image preprocessing, global threshold processing is used in the HSI color space to find the location of traffic signal lights; then to get each indicator through the projection method dividing; finally to distinguish digital processing and direction indicator by

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Ngeljaratan, Luna, and Mohamed A. Moustafa. "Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring." Applied Sciences 11, no. 23 (2021): 11086. http://dx.doi.org/10.3390/app112311086.

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This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identi

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Li, Tianwen, and Gongquan Li. "Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm." Sensors 24, no. 21 (2024): 6783. http://dx.doi.org/10.3390/s24216783.

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The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamic

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Zhang, Xiang, Renwen Chen, and Qinbang Zhou. "Damage Identification Using Wavelet Packet Transform and Neural Network Ensembles." International Journal of Structural Stability and Dynamics 18, no. 12 (2018): 1850148. http://dx.doi.org/10.1142/s0219455418501481.

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This study presents a damage identification method that combines wavelet packet transforms (WPTs) with neural network ensembles (NNEs). The WPT is used to extract damage features, which are taken as the input vectors in the NNEs used for damage identification. An experiment was performed on a helicopter rotor blades structure to verify the proposed method. First, the vibration responses collected by different sensors are decomposed using the WPT. Second, the relative band energy of each decomposed frequency band is calculated and fused as the damage feature vectors. Third, two types of the NNE

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Wu, Wen, Sergio Cantero-Chinchilla, Wang-ji Yan, Manuel Chiachio Ruano, Rasa Remenyte-Prescott, and Dimitrios Chronopoulos. "Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme." Sensors 23, no. 8 (2023): 4160. http://dx.doi.org/10.3390/s23084160.

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In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three-dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to pr

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Harezlak, Katarzyna, Michal Blasiak, and Pawel Kasprowski. "Biometric Identification Based on Eye Movement Dynamic Features." Sensors 21, no. 18 (2021): 6020. http://dx.doi.org/10.3390/s21186020.

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The paper presents studies on biometric identification methods based on the eye movement signal. New signal features were investigated for this purpose. They included its representation in the frequency domain and the largest Lyapunov exponent, which characterizes the dynamics of the eye movement signal seen as a nonlinear time series. These features, along with the velocities and accelerations used in the previously conducted works, were determined for 100-ms eye movement segments. 24 participants took part in the experiment, composed of two sessions. The users’ task was to observe a point ap

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Jeyapal, Akilandeswari, Jothi Ganesan, Sabeenian Royappan Savarimuthu, et al. "A Comparative Study of Feature Detection Techniques for Navigation of Visually Impaired Person in an Indoor Environment." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 21–26. http://dx.doi.org/10.1166/jctn.2020.8623.

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A development of automatic location identification and tracking system for visually impaired/ challenged person is a very challenging task in an indoor environment. In this paper, the comprehensive study of different feature detection and matching techniques namely, Minimum Eigenvalue (MinEigen) algorithm, Harris–Stephens (Harris) algorithm, Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Binary Robust Invariant Scalable Keypoints (BRISK) and Maximally Stable Extremal Regions (MSER) is presented. These algorithms are employed to detect and match the features o

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Vidakovic, Vesna, and Suncica Zdravkovic. "Color influences identification of the moving objects more than shape." Psihologija 42, no. 1 (2009): 79–93. http://dx.doi.org/10.2298/psi0901079v.

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When people track moving objects, they concentrate on different characteristics. Recent results show that people more often concentrate on spatiotemporal than featural properties of the objects. In other words, location and direction of motion seem to be more informative properties than the stable featural characteristics. This finding contradicts some of our knowledge about cognitive system. Current research was done in attempt to specify the effect of featural characteristics, especially color and shape. In Experiment 1, subjects were asked to track four mobile targets presented with another

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Zuo, Heng, and Huiyong Guo. "Structural Nonlinear Damage Identification Method Based on the Kullback–Leibler Distance of Time Domain Model Residuals." Remote Sensing 15, no. 4 (2023): 1135. http://dx.doi.org/10.3390/rs15041135.

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Under external load excitation, damage such as breathing cracks and bolt loosening will cause structural time domain acceleration to have nonlinear features. To solve the problem of time domain nonlinear damage identification, a damage identification method based on the Kullback–Leibler (KL) distance of time domain model residuals is proposed in this paper. First, an autoregressive (AR) model order was selected using the autocorrelation function (ACF) and Akaike information criterion (AIC). Then, an AR model was obtained based on the structural acceleration response time series, and the AR mod

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Zhao, Yijiang, Xiao Tang, Zhuhua Liao, Yizhi Liu, Min Liu, and Jian Lin. "Multi-Type Features Embedded Deep Learning Framework for Residential Building Prediction." ISPRS International Journal of Geo-Information 12, no. 9 (2023): 356. http://dx.doi.org/10.3390/ijgi12090356.

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Building type prediction is a critical task for urban planning and population estimation. The growing availability of multi-source data presents rich semantic information for building type prediction. However, existing residential building prediction methods have problems with feature extraction and fusion from multi-type data and multi-level interactions between features. To overcome these limitations, we propose a deep learning approach that takes both the internal and external characteristics of buildings into consideration for residential building prediction. The internal features are the

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Chen, Lei, Zuowei Yang, Fengyun Huang, Yiwei Dai, Rui Liu, and Jiajia Li. "Transmission Tower Re-Identification Algorithm Based on Machine Vision." Applied Sciences 14, no. 2 (2024): 539. http://dx.doi.org/10.3390/app14020539.

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Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a temp

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Cao, Jinfeng, Mingzhong Gao, Jihong Guo, et al. "Automatic Identification of Sunken Oil in Homogeneous Information Perturbed Environment through Fusion Image Enhancement with Convolutional Neural Network." Sustainability 16, no. 15 (2024): 6665. http://dx.doi.org/10.3390/su16156665.

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With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a hi

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Jin Su Kim, Gyu Ho Choi, and Sung Bum Pan. "A Study on ECG-based Biometrics using Open Source Hardware." Research Briefs on Information and Communication Technology Evolution 3 (November 15, 2017): 165–71. http://dx.doi.org/10.56801/rebicte.v3i.56.

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Biometric technology uses unique physical features as bioinformation. Electrocardiogram(ECG) isa signal recording potential changes related to heartbeats in the form of voltage, and each individualhas unique ECG signals in terms of heart location and size thereof. Therefore, they are used to diagnosediseases and examine personal identity. The ECG device can be made compact in comparisonwith other biosignal measurement instruments and the signals measured in locations easily accessible.This paper aims to implement a personal identification system based on ECG signals. Noiseremoval and signal sp

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Zhang, Haiyan, Huiqi Li, Guodong Sun, and Feng Yang. "MDA-DETR:Enhancing Offending Animal Detection with Multi-Channel Attention and Multi-Scale Feature Aggregation." Animals 15, no. 2 (2025): 259. https://doi.org/10.3390/ani15020259.

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Conflicts between humans and animals in agricultural and settlement areas have recently increased, resulting in significant resource loss and risks to human and animal lives. This growing issue presents a global challenge. This paper addresses the detection and identification of offending animals, particularly in obscured or blurry nighttime images. This article introduces Multi-Channel Coordinated Attention and Multi-Dimension Feature Aggregation (MDA-DETR). It integrates multi-scale features for enhanced detection accuracy, employing a Multi-Channel Coordinated Attention (MCCA) mechanism to

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Zhang, Huanqiang, Lei Yu, Jian Shu, et al. "Research on sensing node location technology based on AOA and fingerprint recognition." Journal of Physics: Conference Series 2310, no. 1 (2022): 012062. http://dx.doi.org/10.1088/1742-6596/2310/1/012062.

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Abstract In order to meet the needs of intelligent operation and maintenance of distribution networks, a large number of wireless sensors are deployed inside and outside the power grid for digital sensing and immediate control of the grid. However, once these wireless sensors fail, they will generate erroneous data, which affects the assessment of grid security and brings great hindrance to the automation of power system operation and maintenance. Therefore, we propose an AOA and fingerprint recognition-based sensing node location method to achieve accurate positioning of wireless sensing node

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Borandag, Emin, Akin Ozcift, Deniz Kilinc, and Fatih Yucalar. "Majority vote feature selection algorithm in software fault prediction." Computer Science and Information Systems 16, no. 2 (2019): 515–39. http://dx.doi.org/10.2298/csis180312039b.

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Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to dete

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Sun, Yajie, Xiaowen Zhou, Qian Long, and Mingchun Jian. "CNN-LSTM-Based Damage Localization of Plate Structure." Materials 18, no. 9 (2025): 2081. https://doi.org/10.3390/ma18092081.

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To address the challenges of feature extraction from time-domain signals and imprecise damage localization in conventional plate structure damage identification methods, this study proposes an innovative damage localization approach integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The one-dimensional signal data extracted from the aluminum plate is converted into a two-dimensional grayscale image, leveraging the advantages of CNN to accurately extract the information features of the damaged image on the aluminum plate. These extracted features are su

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Liang, Jinyu, Weiwei Cai, Zhuonong Xu, Guoxiong Zhou, Johnny Li, and Zuofu Xiang. "A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds." Animals 13, no. 10 (2023): 1660. http://dx.doi.org/10.3390/ani13101660.

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In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLA

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Feng, Xiangrui, Caiyong Wei, Xiaojing Xue, Qian Zhang, and Xiangnan Liu. "RST-DeepLabv3+: Multi-Scale Attention for Tailings Pond Identification with DeepLab." Remote Sensing 17, no. 3 (2025): 411. https://doi.org/10.3390/rs17030411.

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Tailing ponds are used to store tailings or industrial waste discharged after beneficiation. Identifying these ponds in advance can help prevent pollution incidents and reduce their harmful impacts on ecosystems. Tailing ponds are traditionally identified via manual inspection, which is time-consuming and labor-intensive. Therefore, tailing pond identification based on computer vision is of practical significance for environmental protection and safety. In the context of identifying tailings ponds in remote sensing, a significant challenge arises due to high-resolution images, which capture ex

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Shah, Bhumi Ankit, and Dipak P. Vakharia. "Testing for detection of crack in rotor using vibration analysis: an experimental approach." International Journal of Quality & Reliability Management 36, no. 6 (2019): 999–1013. http://dx.doi.org/10.1108/ijqrm-06-2017-0107.

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Purpose Many incidents of rotor failures are reported due to the development and propagation of the crack. Condition monitoring is adopted for the identification of symptoms of the crack at very early stage in the rotating machinery. Identification requires a reliable and accurate vibration analysis technique for achieving the objective of the study. The purpose of this paper is to detect the crack in the rotating machinery by measuring vibration parameters at different measurement locations. Design/methodology/approach Two different types of cracks were simulated in these experiments. Experim

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Cabezas, Javier, Roberto Yubero, Beatriz Visitación, et al. "Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection." Entropy 24, no. 3 (2022): 336. http://dx.doi.org/10.3390/e24030336.

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In this paper, a method to classify behavioural patterns of cattle on farms is presented. Animals were equipped with low-cost 3-D accelerometers and GPS sensors, embedded in a commercial device attached to the neck. Accelerometer signals were sampled at 10 Hz, and data from each axis was independently processed to extract 108 features in the time and frequency domains. A total of 238 activity patterns, corresponding to four different classes (grazing, ruminating, laying and steady standing), with duration ranging from few seconds to several minutes, were recorded on video and matched to accele

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Zhang, Huangchuang, Ge Li, and Meilin Pu. "BIM Data Model Based on Multi-Scale Grids in Civil Engineering Buildings." Remote Sensing 16, no. 4 (2024): 690. http://dx.doi.org/10.3390/rs16040690.

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The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly

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Xu, Haoyuan, Chaochen Wang, Yuming Bo, et al. "An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments." Remote Sensing 14, no. 19 (2022): 5055. http://dx.doi.org/10.3390/rs14195055.

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In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual

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Łowczowski, Krzysztof, and Bartosz Olejnik. "Monitoring, Detection and Locating of Transient Earth Fault Using Zero-Sequence Current and Cable Screen Earthing Current in Medium Voltage Cable and Mixed Feeders." Energies 15, no. 3 (2022): 1066. http://dx.doi.org/10.3390/en15031066.

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This paper presents the final development of an expert system utilizing a measurement of cable screen earthing transient current. The developed system allows for identification and location of earth fault in underground cable and mixed lines (underground cable and overhead line) and monitoring of an earthing system and cable screen connections. The unique feature of the developed earth fault locating system is the possibility of identification of line type and branch of the MV underground cable or mixed feeder under earth fault conditions. As a result, the time to remove failure can be greatly

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Yang, Chaojie, Guoen Xia, Liying Zheng, Xianquan Zhang, and Chunqiang Yu. "Customer Churn Prediction Based on Coordinate Attention Mechanism with CNN-BiLSTM." Electronics 14, no. 10 (2025): 1916. https://doi.org/10.3390/electronics14101916.

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Due to increased competition in the marketplace, companies in all industries are facing the problem of customer attrition. In order to expand their market share and increase profits, companies have shifted from the concept of ‘acquiring new customers’ to ‘retaining old customers’. In this study, we design a deep learning model based on multi-network feature extraction and an attention mechanism, convolutional neural network–bidirectional long and short-term memory network–fully connected layer–coordinate attention (CNN-BiLSTM-FC-CoAttention), and apply it to customer churn risk assessment. In

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Dowalla, Krzysztof, Piotr Bilski, Robert Łukaszewski, Augustyn Wójcik, and Ryszard Kowalik. "A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring." Energies 16, no. 1 (2022): 171. http://dx.doi.org/10.3390/en16010171.

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Series arc faults cause the majority of household fires involving electrical failures or malfunctions. Low-fault current amplitude is the reason for the difficulties faced in implementing effective arc detection systems. The paper presents a novel arc detection and faulty line identification method. It can be easily used in the low-voltage Alternate Current (AC) household network for arc detection in the Non-Intrusive Load Monitoring (NILM). Unlike existing methods, the proposed approach exploits both current and voltage signal time domain analysis. Experiments have been conducted with up to s

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Li, Hao, Gen Liu, Haoqi Wang, et al. "Mechanical movement data acquisition method based on the multilayer neural networks and machine vision in a digital twin environment." Digital Twin 1 (October 14, 2021): 6. http://dx.doi.org/10.12688/digitaltwin.17441.1.

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Background: Digital twin requires virtual reality mapping and optimization iteration between physical devices and virtual models. The mechanical movement data collection of physical equipment is essential for the implementation of accurate virtual and physical synchronization in a digital twin environment. However, the traditional approach relying on PLC (programmable logic control) fails to collect various mechanical motion state data. Additionally, few investigations have used machine visions for the virtual and physical synchronization of equipment. Thus, this paper presents a mechanical mo

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Lu, Guan, Tianyu Zhu, and Yiming Xu. "Low Velocity Impact Energy Monitoring and Recognition of Composite Laminates with Variable Thickness Based on Optical Fiber Sensor Network." Applied Sciences 11, no. 2 (2021): 584. http://dx.doi.org/10.3390/app11020584.

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At present, most of the research on low velocity impact of composite laminates focuses on load location and damage assessment. To provide further early warnings about structural impact damage, impact energy can be monitored and identified. For high strength composite laminates with variable thickness, in order to further accurately evaluate the impact energy, it is necessary to adopt more suitable dynamic load signal analysis and impact energy identification methods. Therefore, a new low velocity impact monitoring and identification method for composite plates with variable thickness is propos

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Lu, Guan, Tianyu Zhu, and Yiming Xu. "Low Velocity Impact Energy Monitoring and Recognition of Composite Laminates with Variable Thickness Based on Optical Fiber Sensor Network." Applied Sciences 11, no. 2 (2021): 584. http://dx.doi.org/10.3390/app11020584.

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At present, most of the research on low velocity impact of composite laminates focuses on load location and damage assessment. To provide further early warnings about structural impact damage, impact energy can be monitored and identified. For high strength composite laminates with variable thickness, in order to further accurately evaluate the impact energy, it is necessary to adopt more suitable dynamic load signal analysis and impact energy identification methods. Therefore, a new low velocity impact monitoring and identification method for composite plates with variable thickness is propos

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Ai, Yi, Yuanji Wang, Weijun Pan, and Dingjie Wu. "A Deep Learning Framework Based on Multisensor Fusion Information to Identify the Airplane Wake Vortex." Journal of Sensors 2021 (November 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/4819254.

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Along with the rapid improvement of the aviation industry, flight density also increases with the increase of flight demand, which directly leads to the increasingly prominent influence of wake vortex on flight safety and aviation control. In this paper, we propose a new joint framework—a deep learning framework—based on multisensor fusion information to address the detection and identification of wake vortices in the near-Earth phase. By setting multiple Doppler lidar in near-Earth flight areas at different airports, a large number of accurate wind field data are captured for wake vortex dete

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Huang, Jing, Xiao Li, Bin Wu, Xinyu Wu, and Peng Li. "Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced Prototypical Network." Remote Sensing 14, no. 23 (2022): 6101. http://dx.doi.org/10.3390/rs14236101.

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In recent years, radar emitter signal identification has been greatly developed via the utilization of deep learning and has achieved significant improvements in identification accuracy. However, with the continuous emergence of complex regime radars and the increasing complexity of the electromagnetic environment, some new kinds of radar emitter signals collected are not in sufficient quantities to satisfy the demand of deep learning. As a result, in this paper, we adopted the prototypical network (PN) belonging to metric-based meta-learning to realize few-shot radar emitter signal recognitio

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