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Soft computing Technique for automatic Detection, Classification and Location of Transmission Line Faults

Soft Computing Technique for Automatic Detection, Classifica...

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international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Mongjam Monika Devi Amrita Ganguly Mridusmita Sharma Dept. of Electrical Engineering Assam Engineering College Guwahati India

Proper monitoring and control of power system includes detection of Transmission lines faults. Faults in transmission line is the main factor in power interruption. To ensure a reliable, high quality, uninterrupted power supply to consumers, a smart, efficient, accurate system that can detect, classify, and locate faults is important. Artificial Neural Network (ANN) is one of the best choice for it. However, acquiring real fault data is difficult due to high data processing needs, security issues etc. Therefore, in this paper a transmission line is modelled using MATLAB/Simulink and dataset to train the neural network is generated by simulating the Simulink model for various fault types and different fault location. Three phase RMS voltages and currents measured on the sending end bus is input to the neural network to train it. Later the trained ANN model is tested by applying random unknown set of data. The simulation results exhibit good performance, ANN can effectively detect, classify and locate the faults.

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Collaborative Trolley Transportation System with Autonomous ...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Bingyi Xia Hao Luan Ziqi Zhao Xuheng Gao Peijia Xie Anxing Xiao Jiankun Wang Max Q.-H. Meng Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China School of Computing National University of Singapore Singapore Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering Department of Electrical and Computer Engineering Chinese University of Hong Kong in Hong Kong University of Alberta Canada

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video 1 1 Video demonstration: https://***/efnPERm0Rco.)

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automatic Generation of Test Cases of Multi-Agent Systems Based on Model Checking 16

Automatic Generation of Test Cases of Multi-Agent Systems Ba...

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16th IEEE international conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Tang, Weijian Wang, Keming Southwest Jiaotong University School of Information Science and Technology Chengdu610031 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu610031 China National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Chengdu610031 China

ISBN: (纸本)9781665405539

The reliability of a train control system depends on the consistency between the system under the test cases and the design specification. It is vital to ensure consistency by verifying that the functions of the train control system meet the design specifications. This paper introduces a novel and formal approach of test cases generation that guarantees the reliability of a train control system. The proposed methodology is effectively applied to a reactive, concurrent and complex case study of the train control system, namely the level transition, and uses multi-agent systems to describe each system component as an intelligent agent. We use the model check tool, NuSMV, that supports formal specifications, modelling the communication among agents. Furthermore, this paper combines computation tree logic with modified condition/decision coverage to design the trap properties. Based on the model checking technology, the test sequence is automatically generated by verifying the trap properties. Lastly, a set of functional test cases that satisfy the transition coverage are obtained by revising the test sequence. The research results show that the method in this paper can be used to generate test cases that are executed to verify the consistency between the system under testing and the design specification. © 2021 IEEE.

关键词： Multi agent systems

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud

Watermarking and Encryption for Electronic Medical Records (...

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Cyber Resilience (ICCR), international conference on

作者： Haider Rasheed Abdulshaheed Yaseen Saadi Abbas Jamal F. Tawfeq Karamath Ateeq Saif. E. A. Alnawayseh Taher M. Ghazal Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Al-Nahrain University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing “big EMR data” from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article.

关键词： Cloud computing Hospitals Watermarking Big Data Mobile handsets Robustness Safety

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automatic Brain Tumor Detection Using Feature Selection and Machine Learning from MRI Images 6th

Automatic Brain Tumor Detection Using Feature Selection and ...

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6th international conference on electrical, control and Computer engineering, InECCE 2021

作者： Shafi, A.S.M. Hasan, Md. Mahmudul Molla, M. M. Imran Alam, Mohammad Khurshed Islam, Md. Tarequl Department of Computer Science and Engineering Khwaja Yunus Ali University Sirajgonj6751 Bangladesh Faculty of Electrical and Electronic Engineering Technology University Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9789811686894

A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Exploring the Influence of Kesterites as Hole Transport Layers in Cs2TiBr6-Based Eco-Friendly Solar Cells

Exploring the Influence of Kesterites as Hole Transport Laye...

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Power, control and computing Technologies (ICPC2T), conference on

作者： Raja Agarwala Safra Binte Sabbir Mehedi Hasan Tanim Prince Kumar Department of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Electrical Engineering National Institute of Technology Silchar India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Lead-free, eco-friendly perovskites have gained significant attention for their non-toxicity, addressing environmental concerns while enhancing the sustainability of photovoltaic (PV) technologies and maintaining high power conversion efficiency (PCE). Through detailed numerical simulations this study investigates the impact of kesterite materials as hole transport layers (HTLs) in perovskite solar cells (PSCs), utilizing Caesium Titanium Bromide (Cs 2 TiBr 6 ) as the perovskite absorber layer (PAL) and Titanium Dioxide $\left(\mathrm{TiO}_{2}\right)$ as the electron transport layer (ETL). The objective is to enhance performance, and stability and reach a PCE greater than $\mathbf{2 5 \%}$. Various kesterite compounds, including copper zinc tin selenide (CZTSe), copper manganese tin sulphide (CMTS), copper iron tin sulphide (CFTS), copper nickel tin sulphide (CNTS), copper zinc tin sulphide (CZTS), and copper barium tin sulphide (CBTS), were analyzed to evaluate their effectiveness as HTLs. Using the SCAPS-1D tool, key factors such as band alignment, optimal layer thickness, and current-voltage (J-V) characteristics, along with quantum efficiency (QE), were evaluated to identify the most effective HTL. Additional simulations investigated the impact of series resistance ($\mathrm{R}_{\mathrm{s}}$), shunt resistance (Rsh) and temperature on device performance. The findings demonstrated that CZTSe outperformed other kesterites as HTL due to its superior absorption coefficient, favorable band alignment, and higher charge carrier formation and extraction capabilities. The configuration resulted in an open-circuit voltage ($V_{O C}$) of 0.78 V, a short-circuit current leading to an overall PCE of $\mathbf{2 5. 3 6 \%}$. These findings highlight the potential of CZTSe as a promising HTL for high-PCE, leadfree PSCs, advancing sustainable solar technology.

关键词： Resistance Absorption Photovoltaic cells Tin Lead Numerical simulation Perovskites Copper Power conversion Zinc

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3D Voxel Reconstruction Based on Shape Layer 14th

3D Voxel Reconstruction Based on Shape Layer

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14th international conference on Genetic and Evolutionary computing, ICGEC 2021

作者： Tang, Linlin Shi, Shuaijie Qin, Shiyu Huang, Xin Fan, Yijie Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (纸本)9789811684296

3D reconstruction is a basic and important task in computer vision. The overall reconstruction quality evaluation of voxel based 3D reconstruc-tion method is not high currently. This paper mainly aims at the problem that the overall reconstruction quality evaluation of voxel based 3D reconstruction methods is not high. According to the good conversion relationship between shape layer and voxels, a new shape layer based 3D voxel recon-struction network is proposed to transform the prediction problem of 3D voxels into the prediction problem of 2D depth map. Experiments show that it is superior to other voxel based 3D reconstruction methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality control

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Modifying Crystal Symmetries via Shear Distortion

Modifying Crystal Symmetries via Shear Distortion

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Quantum computing and engineering (QCE), IEEE international conference on

作者： Sarah Edwards Qixuan Lin Morgan Sherer Elliott Rosenberg Jiun-Haw Chu Arthur Barnard Physics Department University of Washington Seattle Washington Electrical and Computer Engineering Department University of Washington Seattle Washington Material Science Department University of Washington Seattle Washington

Strain is a powerful tool for studying how electric and magnetic phenomena arise from crystal symmetries and creating flexible (no pun intended) solid-state devices for a wide range of practical applications, including efficient spin-based memory storage and tunable quantum dots. The ability to finely tune symmetries within crystal lattices means a large degree of control over the optical transitions of the lattice, magnetic ordering, and spin domain population. While many methods for applying lateral strain and pressure have been well documented, the ability to reliably apply small-scale shear strain in physics research applications is uncommon. The goal for this project is to fill that vacuum by building and benchmarking a device which applies precise shear strain to a single crystal and interfaces directly with Quantum Design's PPMS (Physical Property Measurement System). For the first experiment run in this cell, an investigation of the strain-switchable antifer-romagnetic ordering of FeTe has been selected. This material displays a spontaneous monoclinic structural distortion as it enters a double-stripe antiferromagnetic phase, which may couple to applied shear strain and serve as both proof of concept of our device and an interesting investigation into the Eg elastoresistivity of FeTe in its own right. This poster discusses design considerations, construction details, and anticipated and preliminary measurements of FeTe.

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Prediction of Workpiece Film Thickness via Multi-Region Segmented Model of Painting Process Parameters

Prediction of Workpiece Film Thickness via Multi-Region Segm...

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IEEE international conference on Industrial engineering and engineering Management

作者： Jhan-Yu Liao Shang-Chih Lin Shun-Feng Su Yennun Huang Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

This study proposes a method to solve the problem of predicting the coating film thickness of the workpiece, which is called the multi-region segmentation model. First, four types of sensing data contribute 125 features, including Clean, Oven, Painting, and Environment. Data preprocessing covers several aspects, such as missing values, outliers, and scales. Then, the key features are extracted and given to the machine learning algorithm to build a model, verify, and test. In addition, the multi-region segmentation model is the main idea and aims to reduce the model from falling into the trap of overfitting when modeling. At the same time, the classifier guides the test data to a more suitable regressor. The experimental results show that the multi-region segmentation model based on the Pearson correlation coefficient has obtained a relatively ideal performance. The RMSE is 15.3724. This is superior to the results submitted for IMBD Competition 2022 and the official standards. In future research work, we will devote ourselves to strengthening the strategy of data pre-processing, and it is expected to improve the model's error.

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Imbalance Classification Based on Deep Learning and Fuzzy Support Vector Machine 16th

Imbalance Classification Based on Deep Learning and Fuzzy Su...

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16th international conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2021

作者： Wang, Kefan An, Jing Ma, Xianghua Ma, Chao Bao, Hanqiu School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201418 China Department of Control Science and Engineering Tongji University Shanghai201804 China

ISBN: (纸本)9789811912528

Imbalanced data is widespread in the fields of medical diagnosis, information security and industrial production. Traditional classification methods can handle balanced data very well. However, when dealing with imbalanced classification, it will favor majority classes, which results in low classification performance. This paper proposes an imbalanced classification method based on deep feature representation, named DL-FSVM. DL-FSVM extracts feature information in the input space using a deep neural network (DNN) to ensure similarity within class and improve the separation between different classes. After obtaining the feature representation, oversampling is performed in this embedding space based on the center distance to enhance the balance of the data distribution. Fuzzy Support Vector Machine (FSVM) is used as the final classifier. Assigning higher misclassification costs to minority class samples through cost-sensitive learning. Experiments were performed on six real-world datasets. The experimental results show that DL-FSVM achieves promising classification performance in three evaluation metrics: G-means, F1-score and AUC. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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