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Novel Power Load pattern recognition Algorithm Based on Unbalanced Data Clustering

Novel Power Load Pattern Recognition Algorithm Based on Unba...

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New Energy and Power Engineering (ICNEPE), international conference on

作者： Dongyuan Zhu Yifei Sun Jia Cui Zhen Hu Jingbo Huang Jiahui Zhang School of Electric Power Shenyang Institute of Engineering Shenyang Liaoning China State Grid Yingkou Electric Power Supply Yingkou Liaoning China School of Electrical Engineering Shenyang University of Technology Shenyang Liaoning China

An improved fuzzy C-means clustering algorithm is proposed for the imbalance between different date types in the power load dataset and the uniformity effect of the cluster size faced by the fuzzy C-means clustering algorithm when dealing with the unbalanced dataset. Firstly, in the improved algorithm, the sum of the membership values of all samples belonging to a certain class is taken as the volume of the class, and the objective function of the traditional algorithm is normalized by its inverse ratio to overcome the problem of uniformity. Secondly, through the Lagrange multiplier method, a new membership matrix update formula is obtained. Finally, Dynamic Time Warping (DTV) was selected as the distance measure in order to solve the Euclidean distance cannot measure the shape similarity of time series. Experimental results show that the proposed algorithm can effectively identify the load pattern of holidays from the historical load data set.

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Distance of single-valued neutrosophic set and its application in pattern recognition 3

Distance of single-valued neutrosophic set and its applicati...

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2021 3rd international conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Wang, Tongjuan Department of Software Engineering Shandong Polytechnic College Shandong Jining China

Single-valued neutrosophic set (SVNS) is an extension of fuzzy set, which combines the truth, indeterminacy, and falsity information. The measurement of distance of single-valued neutrosophic set will bring new ideas to pattern recognition. This paper introduces the distance definitions and properties of SVNS, and proposes the improved distance definition of single-valued neutrosophic set based on decision maker attitude towards indeterminacy information and applied them to pattern recognition. By assigning a value to decision-maker’s attitude towards indeterminacy information, this attitude comes from the historical experience and risk preference etc. The distance calculated in this way can often get a matching result closer to the reality. The results show that the improved distance formula of SVNSs is more effective and more practical for pattern recognition. © Journal of Physics: conference Series 2021.

关键词： pattern recognition

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Person recognition using Gait Energy Image, MobileNetV3Small and Machine Learning

Person Recognition using Gait Energy Image, MobileNetV3Small...

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Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Omprakash Suthar Vijay Katkar Krunal Vaghela Department of Computer Engineering Marwadi University Rajkot India Department of Information Technology Marwadi University Rajkot India

In the past few decades, gait as a biometric attribute has garnered a lot of attention for its application in a broad variety of security and privacy-related scenarios, including identity recognition and authentication. The deployment of surveillance cameras allows for the collection of Gait data, which is a non-invasive form of identification that is based on a biometric trait that can be seen from a distance and can be utilized for identifying reasons. It is difficult to create reliable automatic gait identification systems since Gait can be modified by several covariate parameters. Some of these covariate elements include garments, walking tempo, camera view angle. In this work, a method for recognizing people with a high degree of accuracy is demonstrated. This paper makes use of the Gait Energy Image, MobileNetV3Sma11 model, and Machine Learning to develop an approach to recognize person using Gait pattern. The paper includes experimental findings, including precision, recall, f1-score, and classification accuracy, all of which point to the effectiveness of the suggested method.

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Efficient FPGA Implementation of Feedback Perceptron for Hardware Acceleration

Efficient FPGA Implementation of Feedback Perceptron for Har...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Tamador Mohaidat Azeemuddin Syed Mohammed Alqodah Kasem Khalil Department of Electrical and Computer Engineering University of Mississippi Mississippi USA CVEST International Institute of Information Technology Hyderabad India

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Artificial Neural Networks (ANNs) have revolutionized machine learning, mimicking human cognitive abilities to recognize patterns and make decisions. The perceptron, a fundamental unit in ANNs, forms the basis for complex network structures. This paper introduces a novel approach to perceptrons by incorporating a feedback mechanism using a gain factor, replacing conventional learning rates. The proposed method aims to optimize network performance while adapting to hardware constraints. Implementation of MNIST and Heart Attack datasets showcase the superiority of the proposed approach over tra-ditional methods, revealing substantial accuracy improvements across various activation functions (Sign, Step, and Sigmoid) in both single perceptron and multilayer perceptron (MLP) architectures. The proposed method achieves significant accuracy of 96.80% with the MNIST dataset, and 88.52% with the Heart Attack dataset compared to other methods. It has a small overhead in power consumption. The proposed approach is implemented in Verilog HDL on Xilinx Virtex-7 FPGA XC7A35T-IFGG484C. The proposed method demonstrates remarkable accuracy enhancements in pattern recognition tasks, promising advancements in real-world applications.

关键词： Accuracy Power demand Cardiac arrest Machine learning Computer architecture Artificial neural networks pattern recognition

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Vehicle Make and Model recognition Using Structural and pattern Based Feature Descriptors 3

Vehicle Make and Model Recognition Using Structural and Patt...

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3rd international conference on Communication Technologies, ComTech 2021

作者： Jabeen, Sara Jabeen, Aqsa Adnan, Syed M. Rao, Wakeel Ahmad University of Engineering and Technology Computer Science Taxila Pakistan

ISBN: (纸本)9781665419017

Vehicle Make and Model recognition (VMMR) is gaining popularity nowadays due to prevailing security situation, as it plays an important role in surveillance and forensics. The main issue one must tackle is the fact, that proposed VMMR algorithm must be able to distinguish between different makes and models, while classifying different variants of a model as one. In addition, the algorithm must consume very less time. In this paper, VMMR problem is solved using four different feature descriptors including Local Tri-Directional patterns, Histogram of oriented gradient (HOG), Wavelet and Curvelet transformation. In addition, we have deployed two different classifiers in our paper to assess their recognition rate while using same feature vectors. These classifiers are Linear Support Vector Machine and K-nearest neighbor. SVM classifier gave an accuracy of 95.67% while the KNN gave an accuracy of 95.89% on BMMRv1 dataset. Achieved accuracy was compared with various contemporary VMMR solutions. © 2021 IEEE.

关键词： Nearest neighbor search

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Adaptive Scaling for Archival Table Structure recognition 16th

Adaptive Scaling for Archival Table Structure Recognition

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16th iapr international conference on Document Analysis and recognition (ICDAR)

作者： Li, Xiao-Hui Yin, Fei Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9783030865498

Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.

关键词： Archival document Table detection Table structure recognition Adaptive scaling

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3rd international conference on Communication, Devices and Computing, ICCDC 2021

3rd International Conference on Communication, Devices and C...

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3rd international conference on Communication, Devices and Computing, ICCDC 2021

ISBN: (纸本)9789811691539

The proceedings contain 70 papers. The special focus in this conference is on Communication, Devices and Computing. The topics include: Accurate Prediction of Petermann I and II Spot Sizes of First Higher Order Mode for Kerr Type Nonlinear Graded Index Fiber by a Simple Method;Analysis of All-Optical XNOR Gate Using Quantum Dot Semiconductor Optical Amplifier (QDSOA);New COVID-19 Normal: An Experimental Prototype of Smart Face Mask Vending Machine—An Indispensable Kiosk;Arduino and ESP32-CAM-Based Automatic Touchless Attendance System;speech Emotion recognition of Tamil Language: An Implementation with Linear and Nonlinear Feature;radiation-Resilient GaN/InxGa1-xN Multi-junction Solar Cells with Varying in Contents;A Simple but Accurate Mathematical Formalism for Prediction of the Far Field pattern of First Higher Order (LP11) Mode in Kerr Type Nonlinear Step Index Optical Fiber;A Priority Based Routing Protocol to Improve Quality of Service (QoS) in Wireless Body Area Network for COVID and Diabetic Patients;accurate Estimation of Dimensionless Vector and Scalar Propagation Constants for First Higher Order Mode of Kerr Type Nonlinear Graded Index Fiber by Simple Mathematical Formalism;Performance Analysis of Berlekamp–Massey-Based KES Block for 3-Byte RS Decoder;a Simple Method for Accurate Prediction of Splice Loss for First Higher-Order Mode of Step-Index Fiber in Presence of Kerr Nonlinearity;issues of Knowledge Management in Deep Web and Its Graph-Based Analysis;a Generic Approach for Interpolation and Image Fusion to Obtain Pan-Sharpening;fastResponse—A Smart Ambulatory System for Monitoring Accident/Disaster Victims Along with Live Tracking Facility;COVID-19 Data Forecasting Using Soft Computing Technique;Analyze DGS Antenna Structure.

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Islanding Detection in PV-Grid System by Random Forest and Spectrogram Based LBP

Islanding Detection in PV-Grid System by Random Forest and S...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Ritu Singh S. P. Shukla Archana Panda Subhendu Sekhar Sahoo Subhendu Pati Basant K Panigrahi Department of Electrical Engineering Bhilai Institute of Technology Durg India Department of Electronics and Communication Engineering SOA Deemed to be University Bhubaneswar India Department of Electrical Engineering Gandhi Institute for Technology Bhubaneswar India Department of Electrical Engineering ITER SOA University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipment's. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected and the islanding must be observed within two seconds. Basically, this paper presents a novel method for detecting islanding based upon image pattern recognition using random forest (RF) classification technique is proposed. Images are utilized to derive the local binary pattern (LBP) characteristics so as to identify non-islanding as well as islanding events. Hence, the time series signal is obtained and transformed into spectrogram images at the point of common connection. These images are utilized to extract the features using histogram of oriented gradient, which is then utilized as a feature vector input for the RF classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter to derive spectrogram image. The RF classifier's effectiveness is assessed with 5-fold cross-validations. The results from classification demonstrates that detection of islanding using RF classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.25% accuracy and detection time of 182 milliseconds.

关键词： Training Accuracy Time series analysis Feature extraction Vectors Regulation pattern recognition Spectrogram Classification tree analysis Testing

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Machine Learning Based pattern recognition with HOG Features for Detection of Islanding

Machine Learning Based Pattern Recognition with HOG Features...

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international conference on Electrical Power and Energy Systems (ICEPES)

作者： Kumaresh Pal Subhadip Goswami Anshuman Bhuyan Santanu Koley Bidisha Bera Sarmistha Basu Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electrical Engineering National Institute of Technology Arunachal Pradesh India Department of Electrical Engineering Gandhi Institute for Education & Technology Bhubaneswar India Department of Computer Science Engineering Haldia Institute of Technology Haldia India Department of Computer Science and Technology Global Institute of Science and Technology Haldia India Department of Physics Calcutta Institute of Engineering and Management Kolkata India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds.

关键词： Histograms Accuracy Voltage Feature extraction Vectors pattern recognition Classification algorithms

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A Comparative Study of Datasets for Cyber-attacks Detection in Wireless Sensor Networks

A Comparative Study of Datasets for Cyber-attacks Detection ...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Shereen Ismail Diana W. Dawoud Hassan Reza School of Electrical Engineering and Computer Science University of North Dakota ND USA College of Engineering and Information Technology University of Dubai Dubai UAE

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Machine Learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless Sensor Networks (WSNs). ML leverages the power of data analysis and pattern recognition to detect and classify various types of cyber-attacks to enhance the security of WSN s. A well-constructed dataset is one of the key factors that significantly impact the performance and generalization capabilities of any ML classifier trained on it. In this paper, we evaluate the effectiveness of two datasets: WSN-DS and WSN-BFSF which are specialized for Denial-of-Service (DoS) attacks targeting WSNs. We compare the two datasets in terms of their key characteristics, dataset quality, and ML classification performance. Mutual Information (MI) and Recursive Feature Elimination (RFE) are used for feature selection. The dataset quality is measured using statistical information calculation. The ML classification performance is investigated for three supervised ensemble techniques: LightGBM, bagging, and stacking using evaluation metrics including probability of detection, probability of false alarm, probability of misdetection, classification accuracy, model size, and processing time.

关键词： Measurement Wireless sensor networks Time division multiple access Stacking Probability Feature extraction pattern recognition

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