Journal articles: 'Fail-detection'

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Riedel, Friedbert, and Zeno Stössel. "A fail-safe sensor for flame detection." Sensors and Actuators A: Physical 37-38 (June 1993): 534–39. http://dx.doi.org/10.1016/0924-4247(93)80092-u.

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Futsuhara, Koichi, and Masao Mukaidono. "Fail-safe obstacle detection by ultrasoric pulsed radar." IEEJ Transactions on Industry Applications 110, no. 3 (1990): 218–26. http://dx.doi.org/10.1541/ieejias.110.218.

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Rein, Nick J., Emily A. Freund, Courtney D. Jensen, Cynthia Villalobos, and J. Mark VanNess. "Technological Advancements Fail To Elicit Improvements In CVD Detection." Medicine & Science in Sports & Exercise 52, no. 7S (July 2020): 556. http://dx.doi.org/10.1249/01.mss.0000680268.77461.2c.

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Men, Yu Zhuo, Hai Bo Yu, Hua Wang, Jin Gang Gao, and Xin Pan. "Automobile Frame Side Rail Detection System Based on Machine Vision." Advanced Materials Research 605-607 (December 2012): 1527–30. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1527.

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A machine vision on-line detection system for automobile frame side fail mounting holes is proposed in this article to solve the backward and low-efficiency problems for detection methods of large-size automobile frame side fail mounting holes. Many images captured by CDD camera are processed and analyzed by virtue of algorithms such as image stitching, threshold segmentation, edge detection, feature extraction, etc.. The developed detection system prototype has very high detection accuracy.

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Wang, Liu Min, and Bo Mo. "Power-Fail Detection and Data Storage Design for Control System." Advanced Materials Research 383-390 (November 2011): 4121–24. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4121.

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The purpose of power-fail protection design is to ensure the certainty and integrity of system information. The key point of design includes: the signals of power-fail detection and data treatm- ent; real-time clock circuit design which is synchronous with the system or as a mark of time; using non-volatile memory (such as FRAM) or using battery backup to maintain trade volatile memory (eg RAM) power to ensure the information integrity and non-volatile storage when the power is removed.

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Sabhnani, Maheshkumar, and Gursel Serpen. "Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set." Intelligent Data Analysis 8, no. 4 (October 14, 2004): 403–15. http://dx.doi.org/10.3233/ida-2004-8406.

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Staszewski, W. J., K. Worden, R. Wardle, and G. R. Tomlinson. "Fail-safe sensor distributions for impact detection in composite materials." Smart Materials and Structures 9, no. 3 (June 1, 2000): 298–303. http://dx.doi.org/10.1088/0964-1726/9/3/308.

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SUMMATTA, Chuthong. "The Improvement of a Fail-safe Counter for Low-speed Detection." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 7 (July 2, 2021): 25–30. http://dx.doi.org/10.15199/48.2021.07.05.

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Saadoon, Muntadher, Siti Hafizah Ab Hamid, Hazrina Sofian, Hamza Altarturi, Nur Nasuha, Zati Hakim Azizul, Asmiza Abdul Sani, and Adeleh Asemi. "Experimental Analysis in Hadoop MapReduce: A Closer Look at Fault Detection and Recovery Techniques." Sensors 21, no. 11 (May 31, 2021): 3799. http://dx.doi.org/10.3390/s21113799.

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Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages. This paper analyses the response time under two main conditions: fail-stop and fail-slow, when they manifest with node, service, and the task at runtime. In addition, we focus on the relationship between the time for detecting and recovering faults. The experimental analysis is conducted on a real Hadoop cluster comprising MapReduce, YARN and HDFS frameworks. Our analysis shows that the recovery of a single fault leads to an average of 67.6% response time penalty. Even though the detection and recovery times are well-turned, data locality and resource availability must also be considered to obtain the optimum tolerance time and the lowest penalties.

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Liu, Shuming, Ruonan Li, Kate Smith, and Han Che. "Why conventional detection methods fail in identifying the existence of contamination events." Water Research 93 (April 2016): 222–29. http://dx.doi.org/10.1016/j.watres.2016.02.027.

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Outa, Roberto, Fabio Roberto Chavarette, Vishnu Narayan Mishra, Aparecido C. Gonçalves, Luiz G. P. Roefero, and Thiago C. Moro. "Prognosis and fail detection in a dynamic rotor using artificial immunological system." Engineering Computations 37, no. 9 (April 20, 2020): 3127–45. http://dx.doi.org/10.1108/ec-08-2019-0351.

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Purpose In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM). Design/methodology/approach The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection. Findings The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested. Originality/value This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.

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SUGIMOTO, Noboru, and Tsuyoshi SAITO. "Fail-safe Detection System Based on the Gas-chromatography : Conditions of Normality-confirmation of Hazard Detection System." Proceedings of the JSME annual meeting 2000.3 (2000): 401–2. http://dx.doi.org/10.1299/jsmemecjo.2000.3.0_401.

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S, Manu. "Road Lane Marking Detection with Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 708–18. http://dx.doi.org/10.22214/ijraset.2021.37463.

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Road Lane detection is an important factor for Advanced Driver Assistant System (ADAS). In this paper, we propose a lane detection technology using deep convolutional neural network to extract lane marking features. Many conventional approaches detect the lane using the information of edge, color, intensity and shape. In addition, lane detection can be viewed as an image segmentation problem. However, most methods are sensitive to weather condition and noises; and thus, many traditional lane detection systems fail when the external environment has significant variation.

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Wang, Zhe, Min Guo, Gary A. Baker, Joseph R. Stetter, Lu Lin, Andrew J. Mason, and Xiangqun Zeng. "Methane–oxygen electrochemical coupling in an ionic liquid: a robust sensor for simultaneous quantification." Analyst 139, no. 20 (2014): 5140–47. http://dx.doi.org/10.1039/c4an00839a.

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Xiao, Qinfeng, Jing Wang, Youfang Lin, Wenbo Gongsa, Ganghui Hu, Menggang Li, and Fang Wang. "Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble." Entropy 23, no. 2 (February 6, 2021): 201. http://dx.doi.org/10.3390/e23020201.

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We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.

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Tang, Xue Qian, Qiao Li, Guangshan Lu, Huagang Xiong, and Feng He. "An Application-Level Method of Arbitrary Synchronization Failure Detection in TTEthernet Networks." Journal of Circuits, Systems and Computers 29, no. 07 (August 23, 2019): 2050102. http://dx.doi.org/10.1142/s0218126620501029.

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Precise distributed clock synchronization is important for Time-Triggered Ethernet (TTEthernet) in which fail-arbitrary synchronization failure cannot be treated by existed protocols unless every synchronization node is equipped with a dedicated clock monitoring. A novel method was presented to detect arbitrary synchronization failure by some application-level routines which make distributed decisions mainly by monitoring the protocol control frames (PCFs). An arbitrary failure node can be exactly detected and located by the routine resided on each of the Compression Masters based on the concerned accusation messages sent from Synchronization Masters or Synchronization Clients. The proposed method can make up the weak points on the detection of the arbitrary failure node of the existed fault-tolerant synchronization mechanism and enhance the synchronization node to resist the arbitrary failure for TTEthernet. By our SAL-based model checking, this method had been formally verified to have a fail-arbitrary detection capacity even in a standard integration configuration. Simulations imply that the quality of the whole clock synchronization is effectively improved after the failure node has been isolated.

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Park, You-Jin, Shu-Kai S. Fan, and Chia-Yu Hsu. "A Review on Fault Detection and Process Diagnostics in Industrial Processes." Processes 8, no. 9 (September 9, 2020): 1123. http://dx.doi.org/10.3390/pr8091123.

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The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.

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Frey, Darren, Eric D. Johnson, and Wim De Neys. "Individual differences in conflict detection during reasoning." Quarterly Journal of Experimental Psychology 71, no. 5 (January 1, 2018): 1188–208. http://dx.doi.org/10.1080/17470218.2017.1313283.

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Decades of reasoning and decision-making research have established that human judgment is often biased by intuitive heuristics. Recent “error” or bias detection studies have focused on reasoners’ abilities to detect whether their heuristic answer conflicts with logical or probabilistic principles. A key open question is whether there are individual differences in this bias detection efficiency. Here we present three studies in which co-registration of different error detection measures (confidence, response time and confidence response time) allowed us to assess bias detection sensitivity at the individual participant level in a range of reasoning tasks. The results indicate that although most individuals show robust bias detection, as indexed by increased latencies and decreased confidence, there is a subgroup of reasoners who consistently fail to do so. We discuss theoretical and practical implications for the field.

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Granhag, PÄR Anders, and Leif A. Strömwall. "Effects of preconceptions on deception detection and new answers to why lie-catchers often fail." Psychology, Crime & Law 6, no. 3 (July 2000): 197–218. http://dx.doi.org/10.1080/10683160008409804.

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Zhang, Qiang, Xueying Sun, and Mingmin Liu. "Vision based wafer states detection in front opening unified pod load-port system." MATEC Web of Conferences 336 (2021): 02029. http://dx.doi.org/10.1051/matecconf/202133602029.

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In modern integrated circuit manufacturing processes, wafers are always transported from one procedure to another. To reduce the risk of dust, Front Opening Unified Pod (FOUP) load-port system is always adopted. Misplaced wafers should be detected before transported. Traditional methods always fail to detect wafer states correctly. To improve detection accuracy, this paper proposed a vision based method. Wafer overlap and malposition detection approach based on modified YOLO-V3 algorithm was suggested. Experiment results shows superiority of the proposed approach.

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Kim, Jae Kwon, Jong Sik Lee, and Young Shin Han. "Fault Detection Prediction Using a Deep Belief Network-Based Multi-Classifier in the Semiconductor Manufacturing Process." International Journal of Software Engineering and Knowledge Engineering 29, no. 08 (August 2019): 1125–39. http://dx.doi.org/10.1142/s0218194019400126.

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The semiconductor manufacturing process is very complex, and it is the most important part of the semiconductor industry. In order to test whether or not wafers are functioning normally, a pass/fail test is conducted; however, time and cost needed for this testing increase as the number of chips increases. To address this, a machine learning technique is adopted and a high-performance classifier is needed to determine whether a pass/fail test is accurate or not. In this paper, a deep belief network (DBN)-based multi-classifier is proposed for fault detection prediction in the semiconductor manufacturing process. The proposed method consists of two phases: The first phase is a data pre-processing phase in which features required for semiconductor data sets are extracted and the imbalance problem is solved. The second phase is to configure the multi-DBN using selected features. A DBN classifier is created for each feature and, finally, fault detection prediction is performed. The proposed method showed excellent performance and can be used in the semiconductor manufacturing process efficiently.

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Rudraiah*, Bhavya, and Dr Geetha K. S. "Object Detection and Tracking using Multiple Features Extraction." International Journal of Innovative Technology and Exploring Engineering 10, no. 10 (August 30, 2021): 75–79. http://dx.doi.org/10.35940/ijitee.j9437.08101021.

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In most of the video analysis applications, object detection and tracking play vital role. Most of detection and tracking algorithms fail to predict multiple objects with varying orientation. In this paper, the goal is to identify and track multiple objects using different feature extraction methods like Locality Sensitive Histogram, Histogram of Oriented Gradients and Edges. These features are subjected to train classifier that can detect the object of different orientations. Experimental results and performance evaluation depicts the proposed method which uses LSH performs well with an increased accuracy of 98%. This method can precisely track the object and can be utilized to track under different scale and pose variations.

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Ogunseye, Abiodun, and Olamide Omolara Olusanya. "Design and Simulation of a Microcontroller Based Loudspeaker Protection System Against Amplifier Direct Current (D.C) Offsets." Journal of Communications Technology, Electronics and Computer Science 8 (November 3, 2016): 12. http://dx.doi.org/10.22385/jctecs.v8i0.122.

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A number of failure mechanisms can result in the damage of loudspeakers that are directly connected to an audio power amplifier system. One of such failure modes occurs when the amplifier circuit develops an output d.c voltage, in which case, the loudspeaker coil will be damaged by overheating. D.c offset detection circuits, usually based on simple transistor circuits are normally used to protect the loudspeaker against this failure mode. However, as effective as they are, these circuits can fail in ways that can result in loudspeaker damage. In this work, a microcontroller based circuit that monitors the critical components of a loudspeaker d.c detection circuit, namely the switching transistor and the isolating relay circuit was developed. The hardware of the developed circuit was modelled with Proteus® software and its firmware was written using MikroC® software. The modelled circuit successfully detects the presence of d.c signals and also reports the states of the isolating relay and the switching transistors when these components fail.

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Wu, Di, and David A. Kofke. "Phase-space overlap measures. I. Fail-safe bias detection in free energies calculated by molecular simulation." Journal of Chemical Physics 123, no. 5 (August 2005): 054103. http://dx.doi.org/10.1063/1.1992483.

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Park, C. S., W. Seo, and B. H. Yoon. "Simple channel monitoring method for fail detection of multiple WDM channels using a wavelength selective detector." Electronics Letters 34, no. 17 (1998): 1677. http://dx.doi.org/10.1049/el:19981154.

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Khan, Muhammad Ashfaq, Md Rezaul Karim, and Yangwoo Kim. "A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network." Symmetry 11, no. 4 (April 22, 2019): 583. http://dx.doi.org/10.3390/sym11040583.

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With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.

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Ademi, Neslihan, and Suzana Loshkovska. "Early Detection of Drop Outs in E-Learning Systems." Academic Perspective Procedia 2, no. 3 (November 22, 2019): 1008–15. http://dx.doi.org/10.33793/acperpro.02.03.112.

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After the popularity of Learning Management Systems, Data Mining and Learning Analytics have become emerging topics. Learning Management Systems such as Moodle, provide big amount of data to be used in analyzing students&rsquo; online behavior. This paper represents a method for early detection of drop outs from a Bachelor degree course using data mining methods. Data is collected through Moodle logs. For early detection, event logs till the first exam is taken into consideration. Decision Tree (DT) and Bayesian Network (BN) algorithms are used for the prediction. In the end it is shown that DT algorithm gives a higher over-all accuracy but BN is better for discovering fail cases as it has higher specificity.

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Kamalov, Firuz, and Ho Hon Leung. "Outlier Detection in High Dimensional Data." Journal of Information & Knowledge Management 19, no. 01 (March 2020): 2040013. http://dx.doi.org/10.1142/s0219649220400134.

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High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on dataset of small size with a large number of features. In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. The proposed method is designed to address the challenges of dealing with high-dimensional data by projecting the original data onto a smaller space and using the innate structure of the data to calculate anomaly scores for each data point. Numerical experiments on synthetic and real-life data show that our method performs well on high-dimensional data. In particular, the proposed method outperforms the benchmark methods as measured by [Formula: see text]-score. Our method also produces better-than-average execution times compared with the benchmark methods.

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Lo, Owen, William J. Buchanan, Paul Griffiths, and Richard Macfarlane. "Distance Measurement Methods for Improved Insider Threat Detection." Security and Communication Networks 2018 (2018): 1–18. http://dx.doi.org/10.1155/2018/5906368.

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Insider threats are a considerable problem within cyber security and it is often difficult to detect these threats using signature detection. Increasing machine learning can provide a solution, but these methods often fail to take into account changes of behaviour of users. This work builds on a published method of detecting insider threats and applies Hidden Markov method on a CERT data set (CERT r4.2) and analyses a number of distance vector methods (Damerau–Levenshtein Distance, Cosine Distance, and Jaccard Distance) in order to detect changes of behaviour, which are shown to have success in determining different insider threats.

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Jyoti Borah, Kaustav. "Fault Detection and Isolation for Systems in Aerospace: An Experience Report." Advanced Science, Engineering and Medicine 12, no. 10 (October 1, 2020): 1309–14. http://dx.doi.org/10.1166/asem.2020.2712.

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The paper throws light on the review of detection of fault and isolation for aerospace systems. Developing detection framework for small satellites is critical task due to limited availability of on board sensors and computational budget. In aerospace operations there are many subsystems and or components which can fail anytime. Once the fault has occurred, it can cause uncoverable losses and pollution of the environment and so forth. It is important to detect, isolate and find the remaining usefulness of that defective component and enable a suitable conclusion making before such faults make a great damage. Hence the fault detection is very important aspect for improving the economy as well as the safety of the system. The following research is intended to introduce an overview for fault diagnosis and prognosis techniques.

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Vidyarthi, Deepti, S. P. Choudhary, Subrata Rakshit, and C. R. S. Kumar. "Malware Detection by Static Checking and Dynamic Analysis of Executables." International Journal of Information Security and Privacy 11, no. 3 (July 2017): 29–41. http://dx.doi.org/10.4018/ijisp.2017070103.

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The advanced malware continue to be a challenge in digital world that signature-based detection techniques fail to conquer. The malware use many anti-detection techniques to mutate. Thus no virus scanner can claim complete malware detection even for known malware. Static and dynamic analysis techniques focus upon different kinds of malware such as Evasive or Metamorphic malware. This paper proposes a comprehensive approach that combines static checking and dynamic analysis for malware detection. Static analysis is used to check the specific code characteristics. Dynamic analysis is used to analyze the runtime behavior of malware. The authors propose a framework for the automated analysis of an executable's behavior using text mining. Text mining of dynamic attributes identifies the important features for classifying the executable as benign and malware. The synergistic combination proposed in this paper allows detection of not only known variants of malware but even the obfuscated, packed and unknown malware variants and malware evasive to dynamic analysis.

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Sifferlinger, N., and G. Rath. "Functional Safety and Mean Time to Fail for Underground Mining Proximity Detection Device in No-Go-Zones." IFAC-PapersOnLine 49, no. 20 (2016): 31–36. http://dx.doi.org/10.1016/j.ifacol.2016.10.092.

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Man, Jiarui, and Guozi Sun. "A Residual Learning-Based Network Intrusion Detection System." Security and Communication Networks 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5593435.

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Neural networks have been proved to perform well in network intrusion detection. In order to acquire better features of network traffic, more learning layers are necessarily required. However, according to the results of the previous research, adding layers to the neural networks might fail to improve the classification results. In fact, after the number of layers has reached a certain threshold, performance of the model tends to degrade. In this paper, we propose a network intrusion detection model based on residual learning. After transforming the UNSW-NB15 data set into images, deeper convolutional neural networks with residual blocks are built to learn more critical features. Instead of the cross-entropy loss function, the modified focal loss is calculated to address the class imbalance problem in the training set and identify minor attacks in the testing set. Batch normalization and global average pooling are used to avoid overfitting and enhance the model. Experimental results show that the proposed model can improve attack detection accuracy compared with existing models.

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COUSINS, DAVE L., and FRAN MARLATT. "An Evaluation of a Conductance Method for the Enumeration of Enterobacteriaceae in Milk." Journal of Food Protection 53, no. 7 (July 1, 1990): 568–70. http://dx.doi.org/10.4315/0362-028x-53.7.568.

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A method for the quantitative detection of Enterobacteriaceae in raw milk utilizing automated, conductance monitoring of a selective medium was studied. The levels of pure Enterobacteriaceae cultures as well as Enterobacteriaceae levels in naturally contaminated raw milk were accurately measured using the conductance method when statistically compared to results obtained by an agar plating method. The correlation coefficient (r-value) for naturally contaminated raw milk samples was 0.92. Using a pass/fail limit of 100 CFU/ml, additional raw milk samples were analyzed and 90% of these samples were correctly classified into the correct pass/fail group using the results of the conductance monitoring method. Enterobacteriaceae were detected in raw milk at levels of <10 – 500 CFU/ml in 12 - 6 hours, respectively, using the conductance method.

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Lu, Jiazhong, Fengmao Lv, Zhongliu Zhuo, Xiaosong Zhang, Xiaolei Liu, Teng Hu, and Wei Deng. "Integrating Traffics with Network Device Logs for Anomaly Detection." Security and Communication Networks 2019 (June 13, 2019): 1–10. http://dx.doi.org/10.1155/2019/5695021.

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Advanced cyberattacks are often featured by multiple types, layers, and stages, with the goal of cheating the monitors. Existing anomaly detection systems usually search logs or traffics alone for evidence of attacks but ignore further analysis about attack processes. For instance, the traffic detection methods can only detect the attack flows roughly but fail to reconstruct the attack event process and reveal the current network node status. As a result, they cannot fully model the complex multistage attack. To address these problems, we present Traffic-Log Combined Detection (TLCD), which is a multistage intrusion analysis system. Inspired by multiplatform intrusion detection techniques, we integrate traffics with network device logs through association rules. TLCD correlates log data with traffic characteristics to reflect the attack process and construct a federated detection platform. Specifically, TLCD can discover the process steps of a cyberattack attack, reflect the current network status, and reveal the behaviors of normal users. Our experimental results over different cyberattacks demonstrate that TLCD works well with high accuracy and low false positive rate.

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Fegade, Saurabh, Amey Bhadkamka, Kamlesh Karekar, Jaikishan Jeshnani, and Vinayak Kachare. "Network Intrusion Detection System Using C4.5 Algorithm." Journal of Communications Technology, Electronics and Computer Science 10 (March 1, 2017): 15. http://dx.doi.org/10.22385/jctecs.v10i0.139.

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There is a great concern about the security of computer these days. The number of attacks has increased in a great number in the last few years, intrusion detection is the main source of information assurance. While firewalls can provide some protection, they fail to provide protection fully and they even need to be complemented with an intrusion detection system (IDS). A newer approach for Intrusion detection is data mining techniques.IDS system can be developed using individual algorithms like neural networks, clustering, classification, etc. The result of these systems is good detection rate and low false alarm rate. According to a recent study, cascading of multiple algorithms gives a way better performance than single algorithm. Single algorithm systems have a high alarm rate. Therefore, to solve this problem, a combination of different algorithms are required. In this research paper, we use the hybrid algorithm for developing the intrusion detection system. C4.5 Support Vector Machine (SVM) and Decision Tree combined to achieve high accuracy and diminish the false alarm rate. Intrusions can be classified into types like Normal, DOS, R2L and U2R.Intrusion detection with Decision trees and SVM were tested with benchmark standard NSL- KDD, which is the extended version of KDD Cup 1999 for intrusion detection (ID).

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Shi, Xiaoping, Yuehua Wu, and Calyampudi Radhakrishna Rao. "Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data." Proceedings of the National Academy of Sciences 115, no. 23 (May 21, 2018): 5914–19. http://dx.doi.org/10.1073/pnas.1804649115.

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The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees’ flower visits is illustrated.

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Hu, Guo X., Zhong Yang, Lei Hu, Li Huang, and Jia M. Han. "Small Object Detection with Multiscale Features." International Journal of Digital Multimedia Broadcasting 2018 (September 30, 2018): 1–10. http://dx.doi.org/10.1155/2018/4546896.

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The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. The detection models can get better results for big object. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. In this paper, we can achieve good detection accuracy by extracting the features at different convolution levels of the object and using the multiscale features to detect small objects. For our detection model, we extract the features of the image from their third, fourth, and 5th convolutions, respectively, and then these three scales features are concatenated into a one-dimensional vector. The vector is used to classify objects by classifiers and locate position information of objects by regression of bounding box. Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results.

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Mascadri, Francesco, Roberta Ciccimarra, Maddalena M. Bolognesi, Fabio Stellari, Francesca Ravanetti, and Giorgio Cattoretti. "Background-free Detection of Mouse Antibodies on Mouse Tissue by Anti-isotype Secondary Antibodies." Journal of Histochemistry & Cytochemistry 69, no. 8 (July 20, 2021): 535–41. http://dx.doi.org/10.1369/00221554211033239.

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Immunodetection on mouse routinely processed tissue via antibodies raised in mice faces cross-reactivity of the secondary anti-mouse reagents with endogenous immunoglobulins, which permeate the tissue. Various solutions to this problem have been devised and include endogenous Ig block with anti-mouse Fab fragments or directly conjugated primary antibodies. Mouse isotype-specific antibodies, differently from reagents directed against both heavy and light chains, fail to detect endogenous Ig after fixation and embedding, while providing a clean and specific detection system for mouse antibodies on mouse routinely processed tissue.

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Haynes, Trevor B., Amanda E. Rosenberger, Mark S. Lindberg, Matthew Whitman, and Joel A. Schmutz. "Method- and species-specific detection probabilities of fish occupancy in Arctic lakes: implications for design and management." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 7 (July 2013): 1055–62. http://dx.doi.org/10.1139/cjfas-2012-0527.

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Studies examining species occurrence often fail to account for false absences in field sampling. We investigate detection probabilities of five gear types for six fish species in a sample of lakes on the North Slope, Alaska. We used an occupancy modeling approach to provide estimates of detection probabilities for each method. Variation in gear- and species-specific detection probability was considerable. For example, detection probabilities for the fyke net ranged from 0.82 (SE = 0.05) for least cisco (Coregonus sardinella) to 0.04 (SE = 0.01) for slimy sculpin (Cottus cognatus). Detection probabilities were also affected by site-specific variables such as depth of the lake, year, day of sampling, and lake connection to a stream. With the exception of the dip net and shore minnow traps, each gear type provided the highest detection probability of at least one species. Results suggest that a multimethod approach may be most effective when attempting to sample the entire fish community of Arctic lakes. Detection probability estimates will be useful for designing optimal fish sampling and monitoring protocols in Arctic lakes.

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Safavi, Saeid, Mohammad Amin Safavi, Hossein Hamid, and Saber Fallah. "Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles." Sensors 21, no. 7 (April 5, 2021): 2547. http://dx.doi.org/10.3390/s21072547.

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The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network.

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Xiong, Gang, Shu-Ning Zhang, and Hui-Chang Zhao. "The Impact Mechanism of Fractal Noise on PN Code Detection System." Fluctuation and Noise Letters 13, no. 02 (June 2014): 1450017. http://dx.doi.org/10.1142/s0219477514500175.

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Taking the pseudo-random phase modulated CW radar for example, this paper studies the impact mechanism of a class of non-stationary fractal noise on PN code detection system, especially signal mixing and matching filter. The cross correlation function, power spectrum function and average power of pseudo-random signal and fractal noise are deduced, compared with the impact of white noise on the pseudo code detection system. We analyze the impact mechanism of three kinds of sea clutter model, namely fractal Brownian model (FBM), the multifractal (MF) model and the non-stationary random fractal model (e.g., infinitely divisible cascades, IDC), on the pseudo-random code detection system, and demonstrate the reason why the multi-scale filtering method in wavelet domain and the MF methods fail to eliminate the effect of sea clutter. Based on the natural sea clutter data, we simulate and analyze the influence of white noise and fractal noise comparatively on detection system, which indicates that the effect of fractal noise cannot be inhibited effectively by the traditional correlation detection and MF analysis, and finally we put forward possible solutions.

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Nasser Al-mhiqani, Mohammed, Rabiah Ahmad, Zaheera Zainal Abidin, Warusia Yassin, Aslinda Hassan, and Ameera Natasha Mohammad. "New insider threat detection method based on recurrent neural networks." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1474. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1474-1479.

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<p>Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions.</p>

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Abd Rahman, Fatimah, Amesh Eromal Gomes, Noor Ain Kamsani, Roslina Mohd Sidek, Shaiful Jahari Hashim, Fakhrul Zaman Rokhani, Mohd Suffian Zamali, and Farhan Mohamed Ali. "Random missing tooth error detection in crankshaft function of an engine control unit." Journal of Mechanical Engineering and Sciences 14, no. 2 (June 23, 2020): 6895–905. http://dx.doi.org/10.15282/jmes.14.2.2020.28.0540.

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Automotive industry is migrating to electronic based control which has promoted the evaluation and enhancement of engine control performance using electronic components in the research field. Modern engines are controlled by Engine Control Unit (ECU) where not only functions and control systems are in placed, but also the fail-safe mechanisms must be integrated in the ECU to ensure user safety when an error occurred. This work focuses on developing a reliable crankshaft function in an ECU using field-programmable gate array (FPGA). ABa test is carried out and the number of occurrences at unintended location are being monitored in the system to detect the random missing tooth. The reliability of the function is tested by evaluating response of the crankshaft function in an ECU when an unexpected missing tooth occurs during its operation. The developed system is able to detect the random missing tooth on a crank trigger wheel at an accuracy of 100% when the wheel is on a run at a constant wheel rotational speed. It also flags error message for further processing in other functional units of the ECU as a safe-fail mechanism for the system. Implementation of the random missing tooth detection in the crankshaft function is shown to work in the system which is developed using Verilog code

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Singal, Amit G., Mahendra Nehra, Beverley Adams-Huet, Adam C. Yopp, Jasmin A. Tiro, Jorge A. Marrero, Anna S. Lok, and William M. Lee. "Detection of Hepatocellular Carcinoma at Advanced Stages Among Patients in the HALT-C Trial: Where Did Surveillance Fail?" American Journal of Gastroenterology 108, no. 3 (March 2013): 425–32. http://dx.doi.org/10.1038/ajg.2012.449.

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Scolyer, Richard A., Ling-Xi L. Li, Stanley W. McCarthy, Helen M. Shaw, Jonathan R. Stretch, Raghwa Sharma, and John F. Thompson. "Immunohistochemical stains fail to increase the detection rate of micrometastatic melanoma in completion regional lymph node dissection specimens." Melanoma Research 14, no. 4 (August 2004): 263–68. http://dx.doi.org/10.1097/01.cmr.0000136708.90534.71.

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Chaves, Yury Oliveira, Flávio Ribeiro Pereira, Rebeca de Souza Pinheiro, Diego Rafael Lima Batista, Antônio Alcirley da Silva Balieiro, Marcus Vinicius Guimarães de Lacerda, Paulo Afonso Nogueira, and Monick Lindenmeyer Guimarães. "High Detection Rate of HIV Drug Resistance Mutations among Patients Who Fail Combined Antiretroviral Therapy in Manaus, Brazil." BioMed Research International 2021 (June 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/5567332.

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Virologic failure may occur because of poor treatment adherence and/or viral drug resistance mutations (DRM). In Brazil, the northern region exhibits the worst epidemiological scenarios for the human immunodeficiency virus (HIV). Thus, this study is aimed at investigating the genetic diversity of HIV-1 and DRM in Manaus. The cross-sectional study included people living with HIV on combined antiretroviral therapy and who had experienced virological failure during 2018-2019. Sequencing of the protease/reverse transcriptase (PR/RT) and C2V3 of the viral envelope gp120 (Env) regions was analyzed to determine subtypes/variants of HIV-1, DRMs, and tropism. Ninety-two individuals were analyzed in the study. Approximately 72% of them were male and 74% self-declared as heterosexual. Phylogenetic inference (PR/RT-Env) showed that most sequences were B subtype, followed by BF1 or B C mosaic genomes and few F1 and C sequences. Among the variants of subtype B at PR/RT, 84.3% were pandemic ( B PAN ), and 15.7% were Caribbean ( B CAR ). The DRMs most frequent were M184I/V (82.9%) for nucleoside reverse transcriptase inhibitors (NRTI), K103N/S (63.4%) for nonnucleoside reverse transcriptase inhibitor (NNRTI), and V82A/L/M (7.3%) for protease inhibitors (PI). DRM analysis depicted high levels of resistance for lamivudine and efavirenz in over 82.9% of individuals; although, low (7.7%) cross-resistance to etravirine was observed. A low level of resistance to protease inhibitors was found and included patients that take atazanavir/ritonavir (16.6%) and lopinavir (11.1%), which confirms that these antiretrovirals can be used—for most individuals. The thymidine analog mutations-2 (TAM-2) resistance pathway was higher in B CAR than in B PAN . Similar results from other Brazilian studies regarding HIV drug resistance were observed; however, we underscore a need for additional studies regarding subtype B CAR variants. Molecular epidemiology studies are an important tool for monitoring the prevalence of HIV drug resistance and can influence the public health policies.

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Lepine, Julien, and Vincent Rouillard. "Evaluation of Shock Detection Algorithm for Road Vehicle Vibration Analysis." Vibration 1, no. 2 (October 11, 2018): 220–38. http://dx.doi.org/10.3390/vibration1020016.

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The ability to characterize shocks which occur during road transport is a vital prerequisite for the design of optimized protective packaging, which can assist in reducing cost and waste related to products and good transport. Many methods have been developed to detect shocks buried in road vehicle vibration signals, but none has yet considered the nonstationary nature of vehicle vibration and how, individually, they fail to accurately detect shocks. Using machine learning, several shock detection methods can be combined, and the reliability and accuracy of shock detection can also be improved. This paper presents how these methods can be integrated into four different machine learning algorithms (Decision Tree, k-Nearest Neighbors, Bagged Ensemble, and Support Vector Machine). The Pseudo-Energy Ratio/Fall-Out (PERFO) curve, a novel classification assessment tool, is also introduced to calibrate the algorithms and compare their detection performance. In the context of shock detection, the PERFO curve has an advantage over classical assessment tools, such as the Receiver Operating Characteristic (ROC) curve, as it gives more importance to high-amplitude shocks.

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HOHMEYER, MICHAEL E. "A SURFACE INTERSECTION ALGORITHM BASED ON LOOP DETECTION." International Journal of Computational Geometry & Applications 01, no. 04 (December 1991): 473–90. http://dx.doi.org/10.1142/s021819599100030x.

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A robust and efficient surface intersection algorithm that is implementable in floating point arithmetic, accepts surfaces algebraic or otherwise and which operates without human supervision is critical to boundary representation solid modeling. To the author's knowledge, no such algorithms has been developed. All tolerance-based subdivision algorithms will fail on surfaces with sufficiently small intersections. Algebraic techniques, while promising robustness, are presently too slow to be practical and do not accept non-algebraic surfaces. Algorithms based on loop detection hold promise. They do not require tolerances except those associated with machine associated with machine arithmetic, and can handle any surface for which there is a method to construct bounds on the surface and its Gauss map. Published loop detection algorithms are, however, still too slow and do not deal with singularities. We present a new loop detection criterion and discuss its use in a surface intersection algorithms. The algorithm, like other loop detection based intersection algorithms, subdivides the surfaces into pairs of sub-patches which do not intersect in any closed loops. This paper presents new strategies for subdividing surfaces in a way that causes the algorithms to run quickly even when the intersection curve(s) contain(s) singularities.

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Bosler, P. A., E. L. Roesler, M. A. Taylor, and M. Mundt. "Stride Search: a general algorithm for storm detection in high resolution climate data." Geoscientific Model Development Discussions 8, no. 9 (September 8, 2015): 7727–65. http://dx.doi.org/10.5194/gmdd-8-7727-2015.

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Abstract. This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared. The commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. Stride Search is designed to work at all latitudes, while grid point searches may fail in polar regions. Results from the two algorithms are compared for the application of tropical cyclone detection, and shown to produce similar results for the same set of storm identification criteria. The time required for both algorithms to search the same data set is compared. Stride Search's ability to search extreme latitudes is demonstrated for the case of polar low detection.

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Journal articles on the topic 'Fail-detection'

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