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Comparative analysis of Partial Discharge pattern Recognition Using Deep Learning and Machine Learning 10

Comparative Analysis of Partial Discharge Pattern Recognitio...

引用

10th international conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Hong, Tae-Yun. Ahn, Hyun-Mo. Jang, Hyun-Jae. Park, Jun-Kyu. Sun, Jong-Ho. Kim, Jin-Gyu. Changwon Korea Republic of Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)9788986510225

Partial Discharge (PD) defect type analysis is important for evaluating insulation performance. A machine learning feature extraction algorithm is presented for AC PD pattern data collected in the laboratory, along with deep learning algorithms for PD pattern images and PD time series data. In addition, data is collected under conditions different from those used for artificial intelligence (AI) training, and algorithm performance is evaluated and compared. © 2024 The Korean Institute of Electrical Engineers (KIEE).

关键词： Adversarial machine learning

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Emergence of Communication Through Artificial Evolution in an Orientation Consensus Task in Swarm robotics 19th

Emergence of Communication Through Artificial Evolution in a...

引用

19th international conference on Artificial intelligence Applications and Innovations (AIAI)

作者： Sendra-Arranz, Rafael Gutierrez, Alvaro Univ Politecn Madrid ETS Ingenieros Telecomunicac Madrid 28040 Spain

ISBN: (纸本)9783031341090;9783031341076;9783031341069

The emergence of communication through evolutionary computation in a swarm of initially non-communicative robots is a highly complex research topic that has vastly captured the attention in the swarm robotics field. In this paper, we empirically study the emergence of communication as a result of an evolutionary algorithm in a swarm of simulated robots with the objective of solving an orientation consensus problem. Specifically, the consensus is reached provided that the heading orientations of the robots point into the same direction. The robots are controlled by Continuous-Time Recurrent Neural Networks whose parameters are evolved using a genetic algorithm. Once evolution is concluded, we assess the performance and scalability of the swarm behavior and the type of communication that emerged. The study is accomplished by means of an statistical analysis of the communication variables produced in a sample of 50 independent simulations. The conducted analysis suggests that the emerged communication is situated, meaning that both the message content and its associated context about the environment are informative and useful in the communication. Very interestingly, the environment context is the only piece of information actually relevant for reaching the consensus. On the contrary, the abstract message content is crucial for drastically reducing the rotation speed of the robots after the orientation consensus is achieved.

关键词： Swarm robotics Orientation Consensus Evolutionary robotics Emergence of Communication

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Yolov9-Powered Bangla License Plate Recognition: A Comparative analysis for Optimized Performance in Localized Contexts

Yolov9-Powered Bangla License Plate Recognition: A Comparati...

引用

2024 international conference on Control and Automation, Electronics, robotics, Internet of Things, and Artificial intelligence, CERIA 2024

作者： Das, Akash Hasan, K. M. Asieb Federation University Australia Dept. of Computer Science BallaratVIC Australia Glichin' Dhaka Bangladesh

ISBN: (纸本)9798331511166

This study presents an advanced approach to Bangla license plate recognition using YOLOv9, the latest iteration in the YOLO (You Only Look Once) object detection series. We employ a comprehensive dataset of Bangladeshi vehicle license plates, encompassing 12 vehicle categories across four major cities. The dataset underwent extensive preprocessing, including merger, format conversion, and augmentation to optimize it for YOLOv9 architecture. We evaluate five YOLOv9 model variants (YOLOv9-T, YOLOv9-S, YOLOv9-M, YOLOv9-C, and YOLOv9-E) on this dataset, analyzing their performance in terms of accuracy, efficiency, and generalization capability. Our results demonstrate a clear correlation between model size and detection accuracy, with YOLOv9-E achieving the highest F1 score of 0.9550. We observe a trade-off between model efficiency and accuracy, with larger models offering superior detection capabilities at the cost of increased computational demands. The study provides insights into the optimal model selection for various real-world scenarios, balancing between high-throughput requirements and accuracy priorities. © 2024 IEEE.

关键词： Convolutional neural networks

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Sentiment analysis for Mental Health Using Boosting, Bagging, and DeBERTa from Social Media Data

Sentiment Analysis for Mental Health Using Boosting, Bagging...

引用

2024 international conference on Control and Automation, Electronics, robotics, Internet of Things, and Artificial intelligence, CERIA 2024

作者： Juarto, Budi School of Computer Science Bina Nusantara University Bandung Indonesia

ISBN: (纸本)9798331511166

Nowadays, social media influences from misinformation to cyberbullying and from influencing anxiety and depression to suicidal tendencies have been quite high. All such conditions require proper and timely identification for suitable intervention. The classes used for this work are 'Anxiety', 'Bipolar', 'Depression', 'Normal', 'Personality Disorder', 'Stress', and 'Suicidal'. The text was cleaned, under-sampling was done to make the class labels balanced, and labels were encoded. The research employs machine learning models such as Random Forest, Gradient Boosting, LightGBM, and DeBERTa, using an 80-20% train-test split and undersampling to balance class labels. The objective of this research is to assess the effectiveness of boosting, bagging, and the DeBERTa model in classifying mental health conditions based on social media data. The study achieved very good performance using the DeBERTa model: an accuracy of 0.8741, precision of 0.8749, a recall of 0.8741, and an F1-score of 0.8740. However, classes such as 'Depression' versus 'Suicidal' were slightly overrated. It is a take-home message from the current study that machine learning might effectively enhance diagnostic-related to mental health-more precisely by the DeBERTa algorithm. This study advances the understanding of classifying mental health conditions with a focus on improving detection between closely related conditions like depression and suicidal tendencies. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on fault diagnosis and analysis of AC relays based on CNN 4

Research on fault diagnosis and analysis of AC relays based ...

引用

4th international conference on Artificial intelligence, robotics, and Communication, ICAIRC 2024

作者： Lin, Jianchun Guo, Yanlong Li, Jiazhi Piao, Yonghuan Xiamen University of Technology School of Mechanical and Automotive Engineering Xiamen China Technologies Innovation Department Xiamen China

ISBN: (纸本)9798331531225

Relay detection is an important step to ensure the reliable operation of relays in electrical systems. This paper proposes a fault diagnosis system for alternating current(AC) relays based on Convolutional Neural Networks(CNN). The system uses a vibration sensor to collect signals from the AC relay, and then transmits the vibration signals to the edge processing unit embedded with CNN. The edge processing unit extracts the time domain features of the AC relay vibration signal, and then classifies the features in the CNN model. Finally, the system outputs the results of abnormal vibration detection. The experimental results show that the system proposed in this paper can effectively identify the abnormal vibration of AC relays, reaching an accuracy rate of 98.3%, which is better than the traditional Support Vector Machines(SVM) or Random Forest(RF) methods. This paper provides a standardized, automated and intelligent solution for the field of abnormal vibration detection of AC relays, which helps to improve product quality and production efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features 6

Plant Leaf Diseases Classification Using SVMs, XGBoost, and ...

引用

6th IEEE international conference on Artificial intelligence in Engineering and Technology, IICAIET 2024

作者： Sami, Abdullah Kamal Al-Dulaimi, Khamael Al-Nahrain University Computer Science Dept. Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

ISBN: (纸本)9798350389692

Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models. © 2024 IEEE.

关键词： Machine learning

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Dielectric Resonator-based MIMO Antenna with pattern Diversity for 5G Millimeter-wave Application 5

Dielectric Resonator-based MIMO Antenna with Pattern Diversi...

引用

5th international conference on Smart Sensors and Application, ICSSA 2024

作者： Sahu, Jayant Kumar Gupta, Anshul Department of Electronics and Communication Engineering National Institute of Technology Raipur India

ISBN: (纸本)9798350354294

This study presents a quad-port, box-shaped multiple-input-multiple-output cylindrical dielectric resonator antenna (CDRA) that exhibits quad-directional pattern diversity for new radio frequency bands named n260 under the 5G millimeter-wave communication. The antenna operates between 36.30 GHz to 40.60 GHz frequency range. The primary structure consists of a CDRA excited with the help of a rectangular aperture and a modified microstrip feed line. This structure is replicated and arranged in a box configuration, resulting in a quad-port MIMO antenna system. The DRA elements are placed so that their adjacent broad-side radiation patterns are 90 degrees apart from each other in an azimuthal direction, providing a spatially decoupled radiation pattern and resulting in enhanced isolation. The proposed MIMO antenna achieves over 30 dB of isolation between antenna ports without additional decoupling structures. This structure is suitable for 5G new radio frequency bands n260 under millimeter-wave communication, forthcoming communication and IoT applications. © 2024 IEEE.

关键词： Millimeter waves

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Adaptive Fuzzy Sliding Mode Control for Coal Silo Cleaning Robots Simulation, and Performance analysis 6

Adaptive Fuzzy Sliding Mode Control for Coal Silo Cleaning R...

引用

6th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2024

作者： Qiang, Qiangqiang Ji, Ningbiao Shanxi Luneng Hequ Electric Coal Development Company Limited Shangyuqu Coal Mine Hequ Shanxi Xinzhou China Shanxi Luneng Hequ Electric Coal Development Company Limited Hequ Shanxi Xinzhou China

ISBN: (纸本)9798331541699

During coal mining and storage, significant accumulation of coal dust and chunks often occurs in coal silos, reducing storage capacity and posing safety hazards. The coal silo environment is characterized by confined spaces, complex obstacles, high dust concentration, and significant variations in temperature and humidity, making the cleaning task highly challenging. This necessitates the development of control strategies that are precise, robust, and capable of adapting to complex environments. This paper presents an Adaptive Fuzzy Sliding Mode Controller (AFSMC) that integrates the robustness of sliding mode control with the smoothness of fuzzy control and employs an adaptive mechanism to adjust control parameters in real time. Specifically, AFSMC increases the control gain when encountering significant resistance from coal chunks to ensure sufficient torque for cleaning, and decreases the gain under lighter loads to minimize energy consumption and reduce mechanical fatigue. Simulation results demonstrate that AFSMC achieves high-precision and energy-efficient trajectory tracking, effectively reduces chattering, and significantly enhances overall system performance. This research provides an efficient and robust control solution for coal silo cleaning robots, meeting the demands of dynamic and complex mining environments and advancing intelligent cleaning technology in the coal industry. © 2024 IEEE.

关键词： Coal industry

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Research on Unsupervised Fault Diagnosis based on Sparse Autoencoder and Locally Linear Embedding 2

Research on Unsupervised Fault Diagnosis based on Sparse Aut...

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2nd international conference on robotics, Artificial intelligence and Intelligent Control, RAIIC 2023

作者： Li, Yichen Liu, Guangye Zeng, Wei Jiang, Lei China Tobacco Hubei Industrial Co. Ltd. Hubei Xinye Reconstituted Tobacco Development Co. Ltd. Wuhan China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350328004

In this paper, we address the shortcomings of traditional unsupervised fault diagnosis methods in handling high-dimensional, nonlinear, and highly complex fault diagnosis problems, and propose an unsupervised fault diagnosis method based on sparse autoencoder and local linear embedding. This method combines the characteristics of sparse autoencoder and manifold learning methods to reduce dimensionality and extract features from high-dimensional data while preserving the local linear relationships within the data. The proposed model was experimentally validated on a three-phase flow equipment dataset, demonstrating the effectiveness of the model. © 2023 IEEE.

关键词： Failure analysis

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Research on Time Series Data Prediction Based on STP-GAN Algorithm 5

Research on Time Series Data Prediction Based on STP-GAN Alg...

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5th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2023

作者： Zhang, Rui Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence AnHui XinHua University Hefei China

ISBN: (纸本)9798350357950

In this paper, we utilize the Sentiment analysis technique in NLP field to provide the opinion influence factors for the algorithmic model by performing sentiment analysis on the text of the research report, introduce the GAN algorithm which is more compatible with the logic of the temporal data operation, and on the basis of which we add the Text Pathway, and put forward the text-assisted Generative Adversarial Network Prediction Model, STP-GAN. The model combines temporal data prediction with sentiment analysis to reduce the error of model training, and introduces the Transformer structure to improve the learning ability of temporal data features. In addition, STP-GAN utilizes BERT for fine-tuning and incorporates a lexicon to increase model adaptability. And on the basis of the proposed STP-GAN, the optimizer RMSPropW, which is more suitable for this research, is proposed, which uses the exponentially weighted moving average as the decay coefficient and utilizes weight decay to improve the training stability and training speed of the prediction model. © 2023 IEEE.

关键词： BERT generative adversarial model optimizer sentiment propensity analysis temporal data

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