Search results for: Robust fault detection

Authors: Hwan-Jun Choi, Sung-Bok Choi, Hyoung-Kyu Song

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Recently, among the MIMO-OFDM detection techniques, a lot of papers suggested V-BLAST scheme which can achieve high data rate. Therefore, the signal detection of MIMOOFDM system is important issue. In this paper, efficient iterative VBLAST detection technique is proposed in wireless communication system. The proposed scheme adjusts the number of candidate symbol and iterative scheme based on channel state. According to the simulation result, the proposed scheme has better BER performance than conventional schemes and similar BER performance of the QRD-M with iterative scheme. Moreover complexity of proposed scheme has 50.6 % less than complexity of QRD-M detection with iterative scheme. Therefore the proposed detection scheme can be efficiently used in wireless communication.

Keywords: MIMO-OFDM, V-BLAST, QR-decomposition, QRDM, DFE, iterative scheme, channel condition

Procedia PDF Downloads 530

Authors: Pavle Dakić, Dimitrije Kotur, Zoran Stojanović

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This paper presents the results of the study in which the excitation system fault of synchronous generator is simulated. In a case of excitation system fault (loss of field), distance relay is used to prevent further damage. Loss-of-field relay calculates complex impedance using measured voltage and current at the generator terminals. In order to obtain phasors from sampled measured values, discrete Fourier transform is used. All simulations are conducted using Matlab and Simulink software package. The analysis is conducted on the two machine system which supplies equivalent load. While simulating loss of excitation on one generator in different conditions (at idle operation, weakly loaded, and fully loaded), diagrams of active power, reactive power, and measured impedance are analyzed and monitored. Moreover, in the simulations, the effect of generator load on relay tripping time is investigated. In conclusion, the performed tests confirm that the fault in the excitation system can be detected by measuring the impedance.

Keywords: loss-of-excitation, synchronous generator, distance protection, Fourier transformation

Procedia PDF Downloads 331

Authors: Saad Chahba, Rabia Sehab, Ahmad Akrad, Cristina Morel

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Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.

Keywords: electric vehicle drivetrain, multiphase drives, induction machine, control, open circuit (OC) fault diagnosis, artificial neural network

Procedia PDF Downloads 209

Authors: Majed Sanan, Mohammad Rammal, Wassim Rammal

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Today, security is a major concern. Intrusion Detection, Prevention Systems and Honeypot can be used to moderate attacks. Many researchers have proposed to use many IDSs ((Intrusion Detection System) time to time. Some of these IDS’s combine their features of two or more IDSs which are called Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS, if an attacker attacks slowly and in organized way, the attack may go undetected through the IDS, as signatures include factors based on duration of the events but the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. So there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we propose a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed: both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed Hybrid Intrusion Detection System (HIDS) is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment, we ran both the Intrusion Detection System individually first and then together and recorded the data from time to time. From the data we can conclude that the resulting IDS are much better in detecting intrusions from the existing IDSs.

Keywords: security, intrusion detection, intrusion prevention, honeypot, anomaly-based detection, signature-based detection, cloud computing, kfsensor

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Authors: Mounir Bekaik, Messaoud Ramdani

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We present in this work a new technique of stabilization for fault nonlinear systems. The approach we adopt focus on a fuzzy Luenverger observer. The T-S approximation of the nonlinear observer is based on fuzzy C-Means clustering algorithm to find local linear subsystems. The MOESP identification approach was applied to design an empirical model describing the subsystems state variables. The gain of the observer is given by the minimization of the estimation error through Lyapunov-krasovskii functional and LMI approach. We consider a three tank hydraulic system for an illustrative example.

Keywords: nonlinear system, fuzzy, faults, TS, Lyapunov-Krasovskii, observer

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Authors: Nader Khalafian, Mohsen Ghaderi

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Field observations have revealed many examples of structures which were damaged due to ground surface deformation caused by the faulting phenomena. In this paper some efforts were made in order to estimate the length and location of the ground surface where large displacements were created due to the reverse faulting. This research has conducted in two steps; (1) in the first step, a 2D explicit finite element model were developed using ABAQUS software. A subroutine for Mohr-Coulomb failure criterion with strain softening model was developed by the authors in order to properly model the stress strain behavior of the soil in the fault rapture zone. The results of the numerical analysis were verified with the results of available centrifuge experiments. Reasonable coincidence was found between the numerical and experimental data. (2) In the second step, the effects of the fault dip angle (δ), depth of soil layer (H), dilation and friction angle of sand (ψ and φ) and the amount of fault offset (d) on the soil surface displacement and fault rupture path were investigated. An artificial neural network-based model (ANN), as a powerful prediction tool, was developed to generate a general model for predicting faulting characteristics. A properly sized database was created to train and test network. It was found that the length and location of the zone of displaced ground surface can be accurately estimated using the proposed model.

Keywords: reverse faulting, surface deformation, numerical, neural network

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Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

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The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

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Authors: Amina Farooq, Nauman Zafar Butt

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This paper is about methods and tools for counting particles of interest, such as cells. A microfluidic system with interconnected electronics on a flexible substrate, inlet-outlet ports and interface schemes, sensitive and selective detection of cells specificity, and processing of cell counting at polymer interfaces in a microscale biosensor for use in the detection of target biological and non-biological cells. The development of fluidic channels, planar fluidic contact ports, integrated metal electrodes on a flexible substrate for impedance measurements, and a surface modification plasma treatment as an intermediate bonding layer are all part of the fabrication process. Magnetron DC sputtering is used to deposit a double metal layer (Ti/Pt) over the polypropylene film. Using a photoresist layer, specified and etched zones are established. Small fluid volumes, a reduced detection region, and electrical impedance measurements over a range of frequencies for cell counts improve detection sensitivity and specificity. The procedure involves continuous flow of fluid samples that contain particles of interest through the microfluidic channels, counting all types of particles in a portion of the sample using the electrical differential counter to generate a bipolar pulse for each passing cell—calculating the total number of particles of interest originally in the fluid sample by using MATLAB program and signal processing. It's indeed potential to develop a robust and economical kit for cell counting in whole-blood samples using these methods and similar devices.

Keywords: impedance, biochip, cell counting, microfluidics

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: fault detection, health monitoring, unmanned aerial vehicles, vibration analysis

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Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang

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An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Keywords: distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone

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Authors: Carlos Alberto Riveros, Edwin Fabián García, Javier Enrique Rivero

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The measured data obtained from sensors in continuous monitoring of civil structures are mainly used for modal identification and damage detection. Therefore when modal identification analysis is carried out the quality in the identification of the modes will highly influence the damage detection results. It is also widely recognized that the usefulness of the measured data used for modal identification and damage detection is significantly influenced by the number and locations of sensors. The objective of this study is the numerical implementation of two widely known optimum sensor placement methods in beam-like structures

Keywords: optimum sensor placement, structural damage detection, modal identification, beam-like structures.

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Authors: Jie Yang, Jian-Min Meng

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A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme.

Keywords: object detection, GPU, motion estimation, parallel processing

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Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Henry Ogbemudia Omoregbee, Mabel Usunobun Olanipekun

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Debris fault diagnosis based on acoustic signals in rolling element bearing running at low speed and high radial loads are more of low amplitudes, particularly in the case of debris faults whose signals necessitate high sensitivity analyses. As the rollers in the bearing roll over debris trapped in grease used to lubricate the bearings, the envelope signal created by amplitude demodulation carries additional diagnostic information that is not available through ordinary spectrum analysis of the raw signal. The kurtosis value obtained for three different scenarios (debris induced, outer crack induced, and a normal good bearing) couldn't be used to easily identify whether the used bearings were defective or not. It was established in this work that the envelope spectrum analysis detected the fault signature and its harmonics induced in the debris bearings when bandpass filtering of the raw signal with the frequency band specified by kurtogram and spectral kurtosis was made.

Keywords: rolling bearings, rolling element bearing noise, bandpass filtering, harmonics, envelope spectrum analysis, spectral kurtosis

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Authors: Vedant Subhash

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This paper discusses the physics of the detection of thermal neutrons using thin-film coated semiconductor detectors. The thermal neutron detection efficiency as a function of film thickness is calculated for the front and back irradiation detector devices coated with ¹⁰B, ⁶LiF, and pure Li thin films. The detection efficiency for back irradiation devices is 4.15% that is slightly higher than that for front irradiation detectors, 4.0% for ¹⁰B films of thickness 2.4μm. The theoretically calculated thermal neutron detection efficiency using ¹⁰B film thickness of 1.1 μm for the back irradiation device is 3.0367%, which has an offset of 0.0367% from the experimental value of 3.0%. The detection efficiency values are compared and proved consistent with the given calculations.

Keywords: detection efficiency, neutron detection, semiconductor detectors, thermal neutrons

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Authors: Alireza Tavakoli Nia

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In order to understand the role of the injured party's fault in dividing liability, we studied its historical background. In common law, the traditional contributory negligence rule was a complete defense. Then the legislature and judicial procedure modified that rule to one of apportionment. In Islamic law, too, the Action rule was at first used when the injured party was the sole cause, but jurists expanded the scope of this rule, so this rule was used in cases where both the injured party's fault and that of the other party are involved. There are some popular approaches for apportionment of damages. Some common law countries like Britain had chosen ‘the causal potency approach’ and ‘fixed apportionment’. Islamic countries like Iran have chosen both ‘the relative blameworthiness’ and ‘equal apportionment’ approaches. The article concludes that both common law and Islamic law believe in the division of responsibility between a wrongdoer claimant and the defendant. In contrast, in the apportionment of responsibility, Islamic law mostly believes in equal apportionment that is way easier and saves time and money, but common law legal systems have chosen the causal potency approach, which is more complicated than the rival approach but is fairer.

Keywords: contributory negligence, tort law, damage apportionment, common law, Islamic law

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Authors: Yi Xiong, Tao Kong

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Traditional safety, risk and reliability analysis approaches are problem-oriented, which make it great workload when analyzing complicated and huge system, besides, too much repetitive work would to do if the analyzed system composed by many similar components. It is pressing need an object and function oriented approach to maintain high consistency with problem domain. A new approach is proposed to overcome these shortcomings of traditional approaches, the concepts: class, abstract, inheritance, polymorphism and encapsulation are introduced into FTA and establish the professional class library that the abstractions of physical objects in real word, four areas relevant information also be proposed as the establish help guide. The interaction between classes is completed by the inside or external methods that mapping the attributes to base events through fully search the knowledge base, which forms good encapsulation. The object oriented fault tree analysis system that analyze and evaluate the system safety and reliability according to the original appearance of the problem is set up, where could mapped directly from the class and object to the problem domain of the fault tree analysis. All the system failure situations can be analyzed through this bottom-up fault tree construction approach. Under this approach architecture, FTA approach is developed, which avoids the human influence of the analyst on analysis results. It reveals the inherent safety problems of analyzed system itself and provides a new way of thinking and development for safety analysis. So that object oriented technology in the field of safety applications and development, safety theory is conducive to innovation.

Keywords: FTA, knowledge base, object-oriented technology, reliability analysis

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: M. Tehranizadeh, E. Shoushtari Rezvani

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Seismic performance of steel moment-resisting frame structures is investigated considering nonlinear soil-structure interaction (SSI) effects. 10-, 15-, and 20-story planar building frames with aspect ratio of 3 are designed in accordance with current building codes. Inelastic seismic demands of the superstructure are considered using concentrated plasticity model. The raft foundation system is designed for different soil types. Beam-on-nonlinear Winkler foundation (BNWF) is used to represent dynamic impedance of the underlying soil. Two sets of pulse-like as well as no-pulse near-fault earthquakes are used as input ground motions. The results show that the reduction in drift demands due to nonlinear SSI is characterized by a more uniform distribution pattern along the height when compared to the fixed-base and linear SSI condition. It is also concluded that beneficial effects of nonlinear SSI on displacement demands is more significant in case of pulse-like ground motions and performance level of the steel moment-resisting frames can be enhanced.

Keywords: soil-structure interaction, uplifting, soil plasticity, near-fault earthquake, tall building

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Authors: G. Khamooshian

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One of the main points of application of the mechanisms of the series and parallel is the subject of managing them. The control of this mechanism and similar mechanisms is one that has always been the intention of the scholars. On the other hand, modeling the behavior of the system is difficult due to the large number of its parameters, and it leads to complex equations that are difficult to solve and eventually difficult to control. In this paper, a six-linkage robot has been presented that could be used in different areas such as medical robots. Using these robots needs a robust control. In this paper, the system equations are first found, and then the system conversion function is written. A new controller has been designed for this robot which could be used in other parallel robots and could be very useful. Parallel robots are so important in robotics because of their stability, so methods for control of them are important and the robust controller, especially in parallel robots, makes a sense.

Keywords: 3-RRS, 6 linkage, parallel robot, control

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Authors: Raffaele A. Calogero

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RNAseq represents an attractive methodology for the detection of functional genomic variants. RNAseq results obtained from polyA+ RNA selection protocol (POLYA) and from exonic regions capturing protocol (ACCESS) indicate that ACCESS detects 10% more coding SNV/INDELs with respect to POLYA. ACCESS requires less reads for coding SNV detection with respect to POLYA. However, if the analysis aims at identifying SNV/INDELs also in the 5’ and 3’ UTRs, POLYA is definitively the preferred method. No particular advantage comes from ACCESS or POLYA in the detection of fusion transcripts.

Keywords: fusion transcripts, INDEL, RNA-seq, WES, SNV

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Authors: M. Y. Kang, Euntaek Yoon, H. D. Choi

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To calculate the full energy (FE) absorption peak efficiency for arbitrary volume sample, we developed and verified the EXVol (Efficiency calculator for EXtended Voluminous source) code which is based on effective solid angle method. EXVol is possible to describe the source area as a non-uniform three-dimensional (x, y, z) source. And decompose and set it into several sets of volume units. Users can equally divide (x, y, z) coordinate system to calculate the detection efficiency at a specific position of a cylindrical volume source. By determining the detection efficiency for differential volume units, the total radiative absolute distribution and the correction factor of the detection efficiency can be obtained from the nondestructive measurement of the source. In order to check the performance of the EXVol code, Si ingot of 20 cm in diameter and 50 cm in height were used as a source. The detector was moved at the collimation geometry to calculate the detection efficiency at a specific position and compared with the experimental values. In this study, the performance of the EXVol code was extended to obtain the detection efficiency distribution at a specific position in a large volume source.

Keywords: attenuation, EXVol, detection efficiency, volume source

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Yang Zhou, Kangfeng Zheng, Wei Ni, Ren Ping Liu

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Software-defined networking (SDN) provides a solution for scalable network framework with decoupled control and data plane. However, this architecture also induces a particular distributed denial-of-service (DDoS) attack that can affect or even overwhelm the SDN network. DDoS attack detection problem has to date been mostly researched as entropy comparison problem. However, this problem lacks the utilization of SDN, and the results are not accurate. In this paper, we propose a DDoS attack detection method, which interprets DDoS detection as a signature matching problem and is formulated as Earth Mover’s Distance (EMD) model. Considering the feasibility and accuracy, we further propose to define the cost function of EMD to be a generalized Kullback-Leibler divergence. Simulation results show that our proposed method can detect DDoS attacks by comparing EMD values with the ones computed in the case without attacks. Moreover, our method can significantly increase the true positive rate of detection.

Keywords: DDoS detection, EMD, relative entropy, SDN

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Authors: Shengxin Yu

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Earthquakes can cause varying degrees of damage to building and bridge structures. Traditional laminated natural rubber bearings (NRB) exhibit inadequate energy dissipation and restraint, particularly under near-fault ground motions, resulting in excessive displacements in the superstructure. This paper presents a composite natural rubber bearing (NFUD-NRB) incorporating two types of shape memory alloy (SMA) U-shaped dampers (UD). The bearing exhibits adjustable features, predominantly characterized by partial self-centering and multi-level energy dissipation, facilitated by nickel-titanium-based SMA (NiTi-SMA) and iron-based SMA (Fe-SMA) UDs. The hysteresis characteristics of NFUD-NRB can be tailored by manipulating the configuration of NiTi-SMA and Fe-SMA UDs. Firstly, the proposed bearing's geometric configuration and working principle are introduced. The rationality of the modeling strategy for the bearing is validated through existing experimental results. Parameterized numerical simulations are subsequently performed to investigate the partially self-centering behavior of NFUD-NRB. The findings indicate that NFUD-NRB can attain the anticipated nonlinear behavior and deliver adequate energy dissipation. Finally, the impact of NFUD-NRB on improving the seismic resilience of highway bridges is examined using the OpenSees software, with particular emphasis on the seismic performance of NFUD-NRB under near-fault ground motions. System-level analysis reveals that bridge systems equipped with NFUD-NRBs exhibit satisfactory residual deformations and higher energy dissipation than those equipped with traditional NRBs. Moreover, NFUD-NRB markedly mitigates the detrimental impacts of near-fault ground motions on the main structure of bridges.

Keywords: partially self-centering behavior, energy dissipation, natural rubber bearing, shape memory alloy, U-shaped damper, numerical investigation, near-fault ground motion

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Authors: Emhimed Saffor

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In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications.

Keywords: CT images, Matlab, medical images, edge detection

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Authors: Ioannis Binas, Marios Moschakis

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Electrical faults in transmission and distribution networks can have great impact on the electrical equipment used. Fault effects depend on the characteristics of the fault as well as the network itself. It is important to anticipate the network’s behavior during faults when planning a new equipment installation, as well as troubleshooting. Moreover, working backwards, we could be able to estimate the characteristics of the fault when checking the perceived effects. Different transformer winding connections dominantly used in the Greek power transfer and distribution networks and the effects of 1-phase to neutral, phase-to-phase, 2-phases to neutral and 3-phase faults on different locations of the network were simulated in order to present voltage sag characteristics. The study was performed on a generic network with three steps down transformers on two voltage level buses (one 150 kV/20 kV transformer and two 20 kV/0.4 kV). We found that during faults, there are significant changes both on voltage magnitudes and on phase angles. The simulations and short-circuit analysis were performed using the PSCAD simulation package. This paper presents voltage characteristics calculated for the simulated network, with different approaches on the transformer winding connections during symmetrical and asymmetrical faults on various locations.

Keywords: Phase angle shift, power quality, transformer winding connections, voltage sag propagation

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Authors: Victoria Siriaki Jorry, I. S. Mbalawata, Hayong Shin

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The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness.

Keywords: average run length, CUSUM control chart, gradual change detection, likelihood ratio test

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Authors: Susan Diamond

Abstract:

Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

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