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Partial Discharge pattern recognition in High Voltage Transmission Cable Project

Partial Discharge Pattern Recognition in High Voltage Transm...

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Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Guangxiuyuan Zhu Shengjie Wang Yongsheng Zhang Ling Jiang Qiuyang Li Daping Zhang Electric Power Research Institute of State Grid Qinghai Electric Power Company Xining China Qinghai Key Laboratory of High Altitude Electric Power Research Xining China Qinghai High Altitude Power Engineering Technology Research Center Xining China State Grid Qinghai Electric Power Company Xining China Xining Power Supply Company of State Grid Qinghai Electric Power Company Xining China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

this paper aims to improve the reliability and safety of cables, prolong the service life of cables and reduce the operating cost of power systems. the paper examines in detail how partial discharge (PD) patterns are identified in high-voltage transmission cable projects. this paper tests the performance of several layers of a convolutional neural network (CNN) to increase recognition efficiency and accuracy. During the experiment, unified network parameters are set, including the use of maximum pooling mode, ReLU activation function, and 32 33-size convolution kernels in each layer. By contrasting the recognition accuracy of 1-layer, 2-layer, 3-layer, 4-layer, 6-layer, and 9-layer CNN, the paper’s findings indicate that: (1) CNN’s recognition accuracy peaks at 90.77% when there are two network layers. However, as the number of layers increases, the accuracy of recognition gradually declines. this result reveals that moderately increasing the network depth is helpful to improve the performance in PD pattern recognition of high voltage transmission cables, but too deep network may lead to over-fitting. (2) CNN is stable in the detection of most defect types, while Support Vector machine (SVM) is the best in some specific defects. Back propagation neural network (BPNN) performs well in some cases, but its overall performance fluctuates greatly. these results are influenced by many factors, such as defect characteristics, model parameters and data set size. therefore, in practical application, it is necessary to select a suitable model for defect detection according to specific scenarios and requirements. this paper provides a useful reference for the follow-up research in high voltage transmission cable project.

关键词： Support vector machines Partial discharges Accuracy Transfer learning High-voltage techniques pattern recognition Safety

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Color Harmony in Visual Communication: Brand Color Design Based on Genetic Algorithm

Color Harmony in Visual Communication: Brand Color Design Ba...

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Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Xiaolu Fang Wuhan Huaxia Institute of Technology Wuhan China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Color is important for visual communication, especially in branding to transmit feelings, form a brand identity, and leave meaningful traces in people's minds. thus, it is critical to optimize the brand color design with brand objectives and target audience preferences. the proposed solution for such optimization is to use genetic algorithms, a computational optimization technique inspired by natural selection, to generate harmonious color palettes. Case studies demonstrated the effectiveness of this method with optimized color schemes that exhibit superior harmony and desired brand attributes for customer preferences. the results show an increase in brand recognition and customer engagement. With genetic algorithms, companies can develop color schemes according to psychological rules and obtain psychological responses to create a powerful and unique brand identity.

关键词： Visualization Image color analysis Visual communication Brand management Psychology Color machine learning

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Target recognition Method Based on Incremental learning

Target Recognition Method Based on Incremental Learning

引用

international conference on Applied machine learning (ICAML)

作者： Weize Qin Institute of computing technology Chinese academy of science Beijing China

the traditional batch learning method is only suitable for the solidification recognition model with a fixed number of samples. When the number of new training samples is increasing, the existing and new training samples must be reintegrated, and the recognition model must be retrained to complete the model update. the time cost, computational cost and storage resources of each update are very high, which greatly reduces the efficiency of recognition. In this paper, a new target recognition algorithm based on incremental learning is proposed. By expanding the feature extraction network and classification network, the problem of catastrophic forgetting is effectively suppressed. At the same time, the decoupling small sample incremental learning model based on prototype registration uses meta-learning method in the training process to effectively improve the efficiency of model learning on small sample data. the results are verified on CIFA100 and Tianzhi Cup challenge data sets. After two incremental learning, the recognition accuracy of the algorithm is reduced by only 21 % and 13 %, which is higher than the existing best algorithm by 4.7 % / 19.6 %. the results show that the method based on sample playback and model expansion is compatible, which can improve the accuracy of existing methods.

关键词： Training Solid modeling Costs Target recognition Computational modeling Prototypes machine learning

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Enhanced Speech Emotion and Gender recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

the current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. these features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). the approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. this work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction data models Convolutional neural networks

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Improving Image Compression Performance by Spatial-Channel Context Adaptive Model

Improving Image Compression Performance by Spatial-Channel C...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Hao Wang Huifen Wang Junda Xue Enmin Lu Hanming Wang Zijun Wu Yunlong Song Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

the significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding) to achieve high compression efficiency. Our method is evaluated on the classic OpenImage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. the results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression.

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Bangladesh Crime Reports Analysis and Prediction 7

Bangladesh Crime Reports Analysis and Prediction

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7th international conference on Software Engineering and Computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Pavel Rahman, Md Ifranul Hoque, A. K. M Faysal Ahmed, Md. Iftekhirul, Iftekhirul Alam, Ashraful Hossain, Nahid United International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665414074

the systematic method of detecting crime, evaluating crime patterns, and anticipating crime trends is known as crime analysis and prediction. Crime is inherently unexpected and causes societal disruption. As Bangladesh's population grows, so does the prevalence of crime, which is wreaking havoc on our society in various ways. As a result, analyzing crime data has become crucial for a better understanding of future crime patterns. machine learning and data mining techniques can be quite useful in predicting future crime trends and patterns in this situation. Various machine learning algorithms are utilized in this study to predict future crime patterns in Bangladesh. the crime statistics are gathered from the Bangladesh Police website to analyse and predict dacoity, robbery, murder, women and child repression, kidnapping, burglary, theft, and other crimes in Bangladesh's various regions. Another dataset from ACLED has been used to predict different kinds of events such as battles, explosions, protests, riots, strategic developments, violence against civilians with geolocations of the events. this research might assist Bangladesh police and law enforcement authorities to predict, prevent, and solve future crimes. the performance and success rate of the project are highly satisfactory. All resources of the project can be found at https://***/297yykmu © 2021 IEEE.

关键词： Forecasting

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Intelligent Fault Diagnosis of Gearbox in Rotating machinery Using Adversarial Neural Networks under Variable Speeds

Intelligent Fault Diagnosis of Gearbox in Rotating Machinery...

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Electrical machines and Drives (ICEMD), international conference on

作者： Saba Abhari Mostafa Abedi Javad Hasanpour-Sangelaji Electrical Engineering Shahid Beheshti University Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

this research presents an intelligent fault diagnosis method for gearbox systems in rotating machinery using Generative Adversarial Networks (GANs). Due to variable speeds and the scarcity of reliable data in real-world conditions, accurate fault detection faces significant challenges. While GANs can generate realistic synthetic data, this study leverages adversarial training, aiming to make the diagnostic system resilient to speed variations. By combining adversarial learning with real data, diagnostic models can better recognize various fault patterns and improve detection accuracy. the primary goal of this study is to enhance the precision and reliability of gearbox fault diagnosis under different operational conditions using GANs. With deep learning techniques, the proposed approach can identify various gearbox faults even under limited and fluctuating data conditions. Experimental results demonstrate that this approach improves detection performance and fault classification accuracy compared to traditional methods.

关键词： Fault diagnosis Training Accuracy Fault detection Neural networks Generative adversarial networks data models pattern recognition machinery Synthetic data

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AI and machine learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing

AI and Machine Learning Techniques for Managing Complexity, ...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： P. William Yadaiah Nirsanametla Ahmed Al-Samalek Ahmed Hussain Neeraj Varshney ALN Rao Department of Information Technology Sanjivani College of Engineering Kopargaon SPPU Pune MH India Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Department of Medical Laboratory Technics Al-Zahrawi University College Karbala Iraq Medical Technical College Al-Farahidi University Iraq Department of Computer Engineering and Applications GLA University Mathura Lloyd Institute of Engineering& Technology Greater Noida

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Artificial neural networks (ANNs), fuzzy systems, expert systems, pattern recognition methods, and modern hybrid approaches to artificial intelligence (AI) may all be considered as phases of a progression that began more than 20 years ago. this trend began with the creation of artificial neural networks (ANNs). this research article includes a number of original discoveries and focuses on hybrid "artificial intelligence’ (AI) and multi-strategy machine learning approaches. this new knowledge is presented as well as a discussion of the essential stages that comprise this process. One of the possible uses for agent-based holonic systems has been identified as being the management of complexity, changes, and interruptions in production systems. It is envisaged that more approaches would be incorporated together. In the event that one so chooses, the subject of defect detection might be rethought as one of binary categorization. Both the classification task machine learning technique and the choice of the features that make up the data and are most significant to the process's quality were decided on the basis of the l1-regularized logistic regression. the establishment of a brand-new manufacturing industry that is being referred to as Smart Manufacturing. this was done in order to guarantee the highest possible level of quality throughout the whole of the process. this allowed for optimal efficiency in both areas. Because of this, it is feasible to combine the most relevant facts about the procedure. the suggested strategy is supported by a cutting-edge hybrid feature removal technique and the best classification threshold search algorithm currently available. the outcomes of the tests reveal that flaws can always be precisely detected without fail.

关键词： Production systems Uncertainty machine learning Artificial neural networks Quality control Complexity theory Task analysis

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A Comprehensive data Driven Approach on Crop Yield and Fertilizer Efficiency

A Comprehensive Data Driven Approach on Crop Yield and Ferti...

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Emerging Technology (INCET), international conference for

作者： Kiran Kesarapu Nelluru Sai Kiran Erothi Manju Dhara Reddymasu Rupa Gurpreet Singh Chhabra Dept of Computer Science and Engineering GITAM (Deemed to be University) Visakhapatnam India

India’s economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil’s alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. the system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue.

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Crime Hotspot Detection using Optimized K-means Clustering and machine learning Techniques

Crime Hotspot Detection using Optimized K-means Clustering a...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Janakiramaiah Bonam Lakshmi Ramani Burra G Sai Venkata Naga Sudarshan Susheel Kadiyala Narendra Macharla Sandeep Gali Nagamani Department of CSE PVP Siddhartha Institute of Technology Kanuru Vijayawada AP India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Over the world, crimes are becoming increasingly complicated and technologically advanced. It has become essential to prioritize actions targeting crime categories within every area. Nowadays, people are more concerned and stressed by the unprecedented increase in city crimes and violations. Large numbers of crimes are perpetrated frequently each day. Crime data analysts can aid law enforcement officials in discovering criminals more quickly. Crime analysis is an organized method of categorizing locations into crime hotspots and non-hotspot zones. the dataset utilized is the Kaggle-obtained UCI Crime and Communities dataset. the algorithm can categorize the areas into hotspots and non-hotspot areas by examining crime records and associated variables. the suggested system uses data mining and machine learning techniques, such as optimized K-means clustering and the elbow approach, to explore datasets and analyze crimes committed. three classification algorithms - Decision Tree Algorithm, Support Vector machine, and Random Forest Algorithm are included in the model. they are trained on the dataset used to determine if an area is a hotspot based on the criteria that influence the occurrence of crimes in a region.

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