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Image Quality Intelligent Detection Method on Electric Power Video Surveillance

Image Quality Intelligent Detection Method on Electric Power...

引用

IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Jin Wang Yuanlong Peng Jiangqi Chen Hao Liu Longchuan Yan State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd. Beijing China State Grid Information and Telecommunication Branch Beijing China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

At present, artificial intelligence technology in power has achieved certain application results in the field of equipment defect recognition and *** article proposes a joint self-supervised learning method based on contrastive learning and sorting learning, and applies it to pre-training of power inspection image quality feature extraction models. By effectively utilizing massive unlabeled image samples, the training of feature extraction models is achieved, effectively improving the model’s perception of image quality deterioration and scene generalization ability; Then, based on the pre-training model of joint learning, further supervised fine-tuning is carried out, and the results of general data pre-training, contrastive learning, and joint learning test sets are compared through experiments to verify the effectiveness of joint learning in intelligent detection of power image quality deterioration.

关键词： Image quality Training Learning (artificial intelligence) Inspection Feature extraction Video surveillance Data models

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A Short-term Net Load Forecasting Method Based on Two-stage Feature Selection and LightGBM with Hyperparameter Auto-Tuning 59

A Short-term Net Load Forecasting Method Based on Two-stage ...

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59th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2023

作者： Lei, Shuya Liang, Xiao Wang, Xiaohui Ding, Jingyang Ge, Xinxin Wang, Fei Feng, Jia State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing102209 China North China Electric Power University Department of Electrical Engineering Baoding071003 China State Grid Zhejiang Electric Power Company Ltd Hangzhou310007 China

ISBN: (纸本)9798350396515

Reliable short-term net load forecasting (STNLF) is important for the stable operation of power systems and assists utilities in unit commitment and demand side management. With the increasing penetration of invisible behind-the-meter photovoltaic systems (BTM-PVs) installed close to customers, accurate STNLF is becoming more challenging. The intermittent of PV output is carried into the load due to the coupling of PV output and actual load, which makes the net load complex and difficult to forecast. In addition, the related influencing factors of BTM-PV also have a great impact on load characteristics based on the original features of load demand. To eliminate the redundant features is also one of the difficulties. This paper proposes a machine learning method to improve the accuracy of 24-hourahead net load forecasting, including PV-Load decoupling, feature selection and light gradient boosting machine (LightGBM) with Bayesian optimization. Firstly, the net load is decomposed into PV output and actual load using the data of small amount of observable PV customers. Then a two-step feature selection method is used to select key features for load demand and PV output, respectively, based on the forecasting accuracy of different feature combinations in the validation dataset. The selected features are used to train LightGBM model, and in order to fully maximize the performance of the forecasting model, the parameters of the model are optimized by Bayesian. Compared with other benchmark models, the proposed model has the best forecasting accuracy. The proposed framework is conducted on smart meter dataset in Ausgrid. © 2023 IEEE.

关键词： Feature Selection

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Analysis and optimization of sparse LU factorization of power matrix based on GLU

Analysis and optimization of sparse LU factorization of powe...

引用

12th International Conference on Renewable Power Generation (RPG 2023)

作者： C. Shi L. Lin H. Yang S. Wang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute co.Ltd Beijing People's Republic of China

ISBN: (数字)9781839539497

With the continuous improvement of power grid stability and reliability requirements, improving the efficiency and accuracy of power system simulation has become an important research topic. The sparse LU factorization is one of the key modules of the sparse direct solver, which usually plays a dominant role in the solving time of the sparse linear algebra equation of power system. In order to improve the efficiency of sparse LU factorization, we propose: 1) an adaptive mode partition strategy based on GLU, and select the mode to be used according to the characteristics of the matrix to be decomposed, which improves the computational efficiency of LU factorization; 2) a dynamic Nstream value taking method for the stream pattern, which determines the number of streams to be used based on the dimensions of the sub-matrix, 3) a pipeline model for the continuous layer with the size of 1 in GLU, which improves the parallelism of sparse LU factorization and helps to improve the computing efficiency. The results show that the proposed improvement can achieve better results, and the speed of LU factorization is increased by more than 30% on rajat12 and circuit_2.

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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Enhanced Camouflage Detection Using advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Fast Cross-Modality Knowledge Transfer via a Contextual Autoencoder Transformation

Fast Cross-Modality Knowledge Transfer via a Contextual Auto...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Min Zheng Chunpeng Wu Yue Wang Yantao Jia Weiwei Liu Long Lin Shuai Chen Fei Zhou State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Huawei Technologies Co. Ltd Beijing China

Cross-modality knowledge transfer aims to apply knowledge learned in the source modality to the target modality. It is more challenging than the general knowledge transfer task because of the aggravated modality shift problem due to introducing heterogeneous data. This paper proposes a novel fast cross-modality knowledge transfer method via a contextual autoencoder transformation. In particular, the encoder projects the contextual representations of the source modality into the target modality. Then to bridge the semantic shared among source and target modalities, the decoder exerts an additional constraint to reconstruct the original source modality. We show that this constraint is beneficial for mitigating the shift problem and improves the generalization from heterogeneous modalities. Remarkably, the autoencoder is linear and symmetric, facilitating scalability for large-scale datasets. Experimental results on two widely used benchmarks demonstrate that the proposed method surpasses several state-of-the-arts baselines, validating its effectiveness and efficiency.

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Feature Fusion for Malware Traffic Detection Using Image Visualization and Transfer Learning

Feature Fusion for Malware Traffic Detection Using Image Vis...

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International Conference on Communications and Signal Processing

作者： Sanjeev Kumar Yudhishthira Sapru K Purushottama Rao Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Computer Applications NIT Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Malware threats have become increasingly sophisticated and more complex, making detecting and preventing them difficult. Traditional malware detection methods, such as signature-based detection, are no longer effective against new malware variants. Deep learning, especially convolution neural networks, has evolved significantly as end-to-end malware detection without much requirement of domain expert analysis for feature engineering. This research paper proposes a new image visualization-based malware detection and classification using transfer learning and machine learning classifiers. At first, the network traffic dumps (PCAPs) are transformed into grayscale images. After that, pre-trained CNN models, including VGG16, VGG19, EfficientNetB0, EfficientNetB1, and Xception, are customized to extract the textural features via transfer learning by adding a fully connected layer of different dimensions. Feature fusion is performed by stacking the extracted feature maps from different pre-trained models to form a single feature map. Finally, the fused feature maps are fed to train three machine learning models to obtain the best classifier. The hyperparameter optimization of all classifiers is performed using grid-search and randomized search algorithms. This study performs intensive experiments using 913 public image datasets, and the proposed method obtained 94.71% accuracy, which is an excellent performance even using a smaller dataset. The experiment results reveal that the proposed method is well-suited to deal with live malware traffic in real-world industrial IoT environments.

关键词： Visualization Machine learning algorithms Transfer learning Stacking Neural networks Signal processing algorithms Telecommunication traffic

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Deep Learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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Prediction of Cyber Attacks Utilizing Deep Learning Model using Network/Web Traffic Data

Prediction of Cyber Attacks Utilizing Deep Learning Model us...

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Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： Balaji Kannan M. Sakthivanitha S. Jayashree R. Maruthi Department of Information Technology School of Computing Science VELS Institute of Science Technology and Advanced Studies(VISTAS) Chennai Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Nowadays, cyber-attacks are growing predominantly due to the development of technologies. It will lead to financial losses to a company and the other problems related to attacks. It is very important to predict such attacks from outsiders to safeguard our networking systems to provide effective security. The Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques leverage enormous amounts of data to identify the cyber attacks. These learning approaches are used to identify a broad range of cyber-attacks by analyzing the web traffic or network traffic to identify potential threats such as malware, network intrusions and other types of attacks. The study demonstrates the various deep learning methods to predict the anomalies and other potential threats with more accuracy in real time.

关键词： Deep learning Computational modeling Telecommunication traffic Predictive models Real-time systems Malware Data models

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

Enhancing Stock Price Prediction Through Integration of Astr...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Vishal, S. Mahapatra, Sheila Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to Be University Nitte Institute of Professional Education Karnataka Mangalore India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R2, AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day's stock price would be higher or lower than the previous day's price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability. © 2025 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

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