Search results for: fault detection and classification

Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

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Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Bouchemha Amel, Chachoui Takieddine, H. Maalem

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Automatic detection of targets in a modern communication system RADAR is based primarily on the concept of adaptive CFAR detector. To have an effective detection, we must minimize the influence of disturbances due to the clutter. The detection algorithm adapts the CFAR detection threshold which is proportional to the average power of the clutter, maintaining a constant probability of false alarm. In this article, we analyze the performance of two variants of adaptive algorithms CA-CFAR and OS-CFAR and we compare the thresholds of these detectors in the marine environment (no-Gaussian) with a Weibull distribution.

Keywords: CFAR, threshold, clutter, distribution, Weibull, detection

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Authors: Shokoufeh Abrisham, Seyed Mahmoud Hossieni, Elham Pishbin

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In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures.

Keywords: advanced cardiac life support, fault tree analysis, Bayesian belief networks, numan performance, healthcare systems

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Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj K. Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

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Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

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Authors: Z. Abdollahi Biron, P. Pisu

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Connected vehicles are one of the promising technologies for future Intelligent Transportation Systems (ITS). A connected vehicle system is essentially a set of vehicles communicating through a network to exchange their information with each other and the infrastructure. Although this interconnection of the vehicles can be potentially beneficial in creating an efficient, sustainable, and green transportation system, a set of safety and reliability challenges come out with this technology. The first challenge arises from the information loss due to unreliable communication network which affects the control/management system of the individual vehicles and the overall system. Such scenario may lead to degraded or even unsafe operation which could be potentially catastrophic. Secondly, faulty sensors and actuators can affect the individual vehicle’s safe operation and in turn will create a potentially unsafe node in the vehicular network. Further, sending that faulty sensor information to other vehicles and failure in actuators may significantly affect the safe operation of the overall vehicular network. Therefore, it is of utmost importance to take these issues into consideration while designing the control/management algorithms of the individual vehicles as a part of connected vehicle system. In this paper, we consider a connected vehicle system under Co-operative Adaptive Cruise Control (CACC) and propose a fault diagnosis scheme that deals with these aforementioned challenges. Specifically, the conventional CACC algorithm is modified by adding a Kalman filter-based estimation algorithm to suppress the effect of lost information under unreliable network. Further, a sliding mode observer-based algorithm is used to improve the sensor reliability under faults. The effectiveness of the overall diagnostic scheme is verified via simulation studies.

Keywords: fault diagnostics, communication network, connected vehicles, packet drop out, platoon

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Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

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Authors: Rashid Mahmood, Muhammad Junaid Sarwar

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Mobile ad hoc networks (MANETs) use has been well-known from the last few years in the many applications, like mission critical applications. In the (MANETS) prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in (MANETs). The authentication and encryption is considered the first solution of the MANETs problem where as now these are not sufficient as MANET use is increasing. In this paper we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in MANET and aim to comparing in some important fields.

Keywords: MANET, IDS, intrusions, signature, detection, prevention

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Authors: Khunsa Fatima, Umar K. Khattak, Allah Bakhsh Kausar

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Satellite images interpretation and analysis assist geologists by providing valuable information about geology and minerals of an area to be surveyed. A test site in Fatejang of district Attock has been studied using Landsat ETM+ and ASTER satellite images for lithological mapping. Five different supervised image classification techniques namely maximum likelihood, parallelepiped, minimum distance to mean, mahalanobis distance and spectral angle mapper have been performed on both satellite data images to find out the suitable classification technique for lithological mapping in the study area. Results of these five image classification techniques were compared with the geological map produced by Geological Survey of Pakistan. The result of maximum likelihood classification technique applied on ASTER satellite image has the highest correlation of 0.66 with the geological map. Field observations and XRD spectra of field samples also verified the results. A lithological map was then prepared based on the maximum likelihood classification of ASTER satellite image.

Keywords: ASTER, Landsat-ETM+, satellite, image classification

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Authors: Aymen Arfaoui, Abdelkader Soumaya, Ali Kadri, Noureddine Ben Ayed

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The major contractional events characterized by strike-slip faulting, folding, and thrusting occurred in the Eocene, Late Miocene, and Quaternary along with the NE Tunisian domain between Bou Kornine-Ressas- Msella and Cap Bon Peninsula. During the Plio-Quaternary, the Grombalia and Mornag grabens show a maximum of collapse in parallelism with the NNW-SSE SHmax direction and developed as 3rd order extensive regions within a regional compressional regime. Using available tectonic and geophysical data supplemented by new fault-kinematic observations, we show that Cenozoic deformations are dominated by first order N-S faults reactivation, this sinistral wrench system is responsible for the formation of strike-slip duplexes, thrusts, folds, and grabens. Based on our new structural interpretation, the major faults of N-S Axis, Bou Kornine-Ressas-Messella (MRB), and Hammamet-Korbous (HK) form an N-S first order restraining stepover within a left-lateral strike-slip duplex. The N-S master MRB fault is dominated by contractional imbricate fans, while the parallel HK fault is characterized by a trailing of extensional imbricate fans. The Eocene and Miocene compression phases in the study area caused sinistral strike-slip reactivation of pre-existing N-S faults, reverse reactivation of NE-SW trending faults, and normal-oblique reactivation of NW-SE faults, creating a NE-SW to N-S trending system of east-verging folds and overlaps. Seismic tomography images reveal a key role for the lithospheric subvertical tear or STEP fault (Slab Transfer Edge Propagator) evidenced below this region on the development of the MRB and the HK relay zone. The presence of extensive syntectonic Pliocene sequences above this crustal scale fault may be the result of a recent lithospheric vertical motion of this STEP fault due to the rollback and lateral migration of the Calabrian slab eastward.

Keywords: Tunisia, strike-slip fault, contractional duplex, tectonic stress, restraining stepover, STEP fault

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Authors: Partha Sarathi Nayek, Abhiparna Dasgupta, Maheshreddy Gade

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Earth retaining structure plays a vital role in stabilizing unstable road cuts and slopes in the mountainous region. The retaining structures located in seismically active regions like the Himalayas may experience moderate to severe earthquakes. An earthquake produces two kinds of ground motion: permanent quasi-static displacement (fault rapture) on the fault rupture plane and transient vibration, traveling a long distance. There has been extensive research work to understand the dynamic behavior of retaining structures subjected to transient ground motions. However, understanding the effect caused by fault rapture phenomena on retaining structures is limited. The presence of shallow crustal active faults and natural slopes in the Himalayan region further highlights the need to study the response of retaining structures subjected to fault rupture phenomena. In this paper, an attempt has been made to understand the dynamic response of the cantilever retaining wall subjected to surface fault rupture. For this purpose, a 2D finite element model consists of a retaining wall, backfill and foundation have been developed using Abaqus 6.14 software. The backfill and foundation material are modeled as per the Mohr-Coulomb failure criterion, and the wall is modeled as linear elastic. In this present study, the interaction between backfill and wall is modeled as ‘surface-surface contact.’ The entire simulation process is divided into three steps, i.e., the initial step, gravity load step, fault rupture step. The interaction property between wall and soil and fixed boundary condition to all the boundary elements are applied in the initial step. In the next step, gravity load is applied, and the boundary elements are allowed to move in the vertical direction to incorporate the settlement of soil due to the gravity load. In the final step, surface fault rupture has been applied to the wall-backfill system. For this purpose, the foundation is divided into two blocks, namely, the hanging wall block and the footwall block. A finite fault rupture displacement is applied to the hanging wall part while the footwall bottom boundary is kept as fixed. Initially, a numerical analysis is performed considering the reverse fault mechanism with a dip angle of 45°. The simulated result is presented in terms of contour maps of permanent displacements of the wall-backfill system. These maps highlighted that surface fault rupture can induce permanent displacement in both horizontal and vertical directions, which can significantly influence the dynamic behavior of the wall-backfill system. Further, the influence of fault mechanism, dip angle, and surface fault rupture position is also investigated in this work.

Keywords: surface fault rupture, retaining wall, dynamic response, finite element analysis

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Authors: Michal Krecichwost, Zauzanna Miodonska, Pawel Badura

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The diagnosis of sigmatism is mostly based on the observation of articulatory organs. It is, however, not always possible to precisely observe the vocal apparatus, in particular in the oral cavity of the patient. Speech processing can allow to objectify the therapy and simplify the verification of its progress. In the described study the methodology for classification of incorrectly pronounced phoneme [s] is proposed. The recordings come from adults. They were registered with the speech recorder at the sampling rate of 44.1 kHz and the resolution of 16 bit. The database of pathological and normative speech has been collected for the study including reference assessments provided by the speech therapy experts. Ten adult subjects were asked to simulate a certain type of stigmatism under the speech therapy expert supervision. In the recordings, the analyzed phone [s] was surrounded by vowels, viz: ASA, ESE, ISI, SPA, USU, YSY. Thirteen MFCC (mel-frequency cepstral coefficients) and RMS (root mean square) values are calculated within each frame being a part of the analyzed phoneme. Additionally, 3 fricative formants along with corresponding amplitudes are determined for the entire segment. In order to aggregate the information within the segment, the average value of each MFCC coefficient is calculated. All features of other types are aggregated by means of their 75th percentile. The proposed method of features aggregation reduces the size of the feature vector used in the classification. Binary SVM (support vector machine) classifier is employed at the phoneme recognition stage. The first group consists of pathological phones, while the other of the normative ones. The proposed feature vector yields classification sensitivity and specificity measures above 90% level in case of individual logo phones. The employment of a fricative formants-based information improves the sole-MFCC classification results average of 5 percentage points. The study shows that the employment of specific parameters for the selected phones improves the efficiency of pathology detection referred to the traditional methods of speech signal parameterization.

Keywords: computer-aided pronunciation evaluation, sibilants, sigmatism diagnosis, speech processing

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

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Authors: McClain Thiel

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Object detection and localization has historically required two or more sensors due to the loss of information from 3D to 2D space, however, most surveillance systems currently in use in the real world only have one sensor per location. Generally, this consists of a single low-resolution camera positioned above the area under observation (mall, jewelry store, traffic camera). This is not sufficient for robust 3D tracking for applications such as security or more recent relevance, contract tracing. This paper proposes a lightweight system for 3D person tracking that requires no additional hardware, based on compressed object detection convolutional-nets, facial landmark detection, and projective geometry. This approach involves classifying the target into a demographic category and then making assumptions about the relative locations of facial landmarks from the demographic information, and from there using simple projective geometry and known constants to find the target's location in 3D space. Preliminary testing, although severely lacking, suggests reasonable success in 3D tracking under ideal conditions.

Keywords: monocular distancing, computer vision, facial analysis, 3D localization

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Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: Sulaiman Al amro, Ali Alkhalifah

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The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses.

Keywords: computer viruses, virus detection, signature-based, behaviour-based, heuristic-based

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: Akshay A. Pandya, B. R. Parekh

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This paper presents how earth fault damage in the transformer can be detected by Sweep Frequency Response Analysis (SFRA). The test methods used by the authors for presenting the results are described. The power transformer of rating 10 KVA, 11000 V/440 V, 3-phase, 50 Hz, Dyn11 has been specially developed in-house for carrying out SFRA testing by practically simulated various transformer damages on it. Earth fault has been practically simulated on HV “U” phase winding and LV “W” phase winding separately. The result of these simulated faults are presented and discussed. The motivation of this presented work is to extend the guideline approach; there are ideas to organize database containing collected measurement results. Since the SFRA interpretation is based on experience, such databases are thought to be of great importance when interpreting SFRA response. The evaluation of the SFRA responses against guidelines and experience have to be performed and conclusions regarding usefulness of each simulation has been drawn and at last overall conclusion has also been drawn.

Keywords: earth fault damage, power transformer, practical simulation, SFRA traces, transformer damages

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Kaewmart Pongakkasira

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This study investigated how different levels of pixelation affect face detection in natural scenes. Eye movements and reaction times, while observers searched for faces in natural scenes rendered in different ranges of pixels, were recorded. Detection performance for coarse visual detail at lower pixel size (3 x 3) was better than with very blurred detail carried by higher pixel size (9 x 9). The result is consistent with the notion that face detection relies on gross detail information of face-shape template, containing crude shape structure and features. In contrast, detection was impaired when face shape and features are obscured. However, it was considered that the degradation of scenic information might also contribute to the effect. In the next experiment, a more direct measurement of the effect of pixelation on face detection, only the embedded face photographs, but not the scene background, will be filtered.

Keywords: eye movements, face detection, face-shape information, pixelation

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Authors: M. Ranjeeth, S. Anuradha

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Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as P_f Vs P_d for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment.

Keywords: spectrum sensing, energy detection, fading channels, probability of detection, probability of false alarm

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Madushani Rodrigo, Banuka Athuraliya

Abstract:

In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

Abstract:

Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

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Authors: N. Zerzouri, N. Benalia, N. Bensiali

Abstract:

During recent years wind turbine technology has undergone rapid developments. Growth in size and the optimization of wind turbines has enabled wind energy to become increasingly competitive with conventional energy sources. As a result today’s wind turbines participate actively in the power production of several countries around the world. These developments raise a number of challenges to be dealt with now and in the future. The penetration of wind energy in the grid raises questions about the compatibility of the wind turbine power production with the grid. In particular, the contribution to grid stability, power quality and behavior during fault situations plays therefore as important a role as the reliability. In the present work, we addressed two fault situations that have shown their influence on the generator and the behavior of the wind over the defects which are briefly discussed based on simulation results.

Keywords: doubly fed induction generator (DFIG), wind energy, grid fault, electrical engineering

Procedia PDF Downloads 455

Authors: John A. Morales, Guillermo Guidi, B. M. Keune

Abstract:

Electric Power supply is a crucial topic for economic and social development. Power outages statistics show that discharges atmospherics are imperative phenomena to produce those outages. In this context, it is necessary to correctly detect when overhead line insulators are faulted. In this paper, an algorithm to detect if a lightning stroke generates or not permanent fault on insulator strings is proposed. On top of that, lightning stroke simulations developed by using the Alternative Transients Program, are used. Based on these insights, a novel approach is designed that depends on mother functions analysis corresponding to the given variance-covariance matrix. Signals registered at the insulator string are projected on corresponding axes by the means of Principal Component Analysis. By exploiting these new axes, it is possible to determine a flashover characteristic zone useful to a good insulation design. The proposed methodology for flashover detection extends the existing approaches for the analysis and study of lightning performance on transmission lines.

Keywords: mother function, outages, lightning, sensitivity analysis

Procedia PDF Downloads 573

Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

Abstract:

It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

Procedia PDF Downloads 68