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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Inoue, Taiki Ogata, Jun Iida, Makoto Ogawa, Tetsuji Waseda University Department of Communications and Computer Engineering Japan Japan Research Center for Advanced Science and Technology The University of Tokyo Japan

ISBN: (纸本)9798350345346

To achieve robust wind turbine anomaly detection, we attempted to incorporate metric learning into the process of learning discriminative feature representations. In the context of anomaly detection based on inlier modeling, where anomalies are identified as inputs deviating from the normal state distribution, the key to building a high-performance and robust system, even with limited training data, lies in eliminating the influence of environmental differences from the inputs and obtaining a compact normal state distribution. To address this, we designed a deep neural network-based feature extractor to mitigate the impact of environmental changes. Additionally, we integrated metric learning into its learning process to obtain a more compact distribution by embedding inputs with similar properties close to one another. We validated the effectiveness of the developed network as a feature extractor for constructing a normal model with limited data through experimental comparisons using vibration data collected from sensors. Specifically, we utilized MobileNet as a discriminative feature extractor to identify 37 wind turbines. By introducing metric learning based on t-SNE into its training process, we achieved a notable 50 % improvement in the AUC. Remarkably, this significant improvement was accomplished using only a small amount of data, approximately 13 minutes, obtained from the monitored turbine. © 2023 IEEE.

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Energy Theft Detection Using the Wasserstein Distance on Residuals

Energy Theft Detection Using the Wasserstein Distance on Res...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Altamimi, Emran Al-Ali, Abdulaziz Malluhi, Qutaibah M. Al-Ali, Abdulla K. Qatar University Department of Computer Science and Engineering Doha Qatar Qatar University KINDI Center for Computing Research Doha Qatar

ISBN: (纸本)9781665490719

Detection of electricity theft improves the sustainability of the smart grid, helps electrical utilities mitigate their financial risks, and improves the overall management of resources. In this work, we utilize an LSTM neural network to forecast a given day's energy consumption and construct residuals. The residuals are then compared to previous residuals from normal days using the Wasserstein distance. If the Wasserstein distance for the residuals of a day exceeds a threshold, the day is highlighted to indicate suspected energy theft. Our framework can be built upon existing forecasting models with minimal computational overhead to calculate the Wasserstein distance. The framework is also highly explainable, which reduces the cost of false positives significantly. Our framework was evaluated using a public dataset and was able to detect six attack models of energy theft and faulty meters, with a false positive rate of 9% and an average F1 score of 0.91. © 2023 IEEE.

关键词： Energy utilization

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Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, Mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Modified Integrated Regulator Strategy for High Density Interleaved DC-DC Converter with Enhancement Conversion Ratio and Losses Mitigation 50

Modified Integrated Regulator Strategy for High Density Inte...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Karimi, Mohsen Farsijani, Mohammad Jamil, Mohsin Iranian Research Institute for Electrical Engineering Academic Center for Education Culture and Research Tehran Iran Department of Electrical and Computer Engineering and Applied Science Memorial University of Newfoundland Newfoundland St John’sNL Canada Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL United States

ISBN: (纸本)9781665464543

The high power DC-DC converters are widely used in industrial and renewable application due to variable dc source. For this purpose, the interleaved dual cascade DCDC converter (IDCC) is the most advantageous due to its and low ripple with high boosting factor and high potential efficiency. To optimize IDCC performance, an integrated regulator ensures equal current sharing between levels, with voltage cascading to boost the output voltage. The integrated regulator technique (IRT) is designed to control all regulators at each level to maintain an optimal switching period, thereby reducing switching dissipation and decreasing ripple. Its ability to handle high voltages conversion with a consistent low ripple output and modular structure enhance performance. It has demonstrated a 1.5% current ripple and up to 94% efficiency in high-power density applications. Simulation and experimental results of a 2 kW prototype validate the IRT's performance. © 2024 IEEE.

关键词： DC-DC converters

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Object-Attribute-Relation Representation Based Video Semantic Communication

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2446-2461页

作者： Du, Qiyuan Duan, Yiping Yang, Qianqian Tao, Xiaoming Debbah, Merouane Tsinghua University Department of Electronic Engineering Beijing China Beijing China State Key Laboratory of Space Network and Communications Beijing China Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Xinjiang University School of Computer Science and Technology Xinjiang China Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding (JSCC) that depends on end-to-end training. These methods often lack an interpretable semantic representation and struggle with adaptability to various downstream tasks. In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission. We utilize OAR sequences for both low bit-rate representation and generative video reconstruction. Additionally, we incorporate OAR into the image JSCC model to prioritize communication resources for areas more critical to downstream tasks. Our experiments on traffic surveillance video datasets assess the effectiveness of our approach in terms of video transmission performance. The empirical findings demonstrate that our OAR-based video coding method not only outperforms H.265 coding at lower bit-rates but also synergizes with JSCC to deliver robust and efficient video transmission. © 1983-2012 IEEE.

关键词： Video streaming

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A Real Time Food Auto Traceable Authentication System

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International Journal of Network Security 2023年第4期25卷 630-639页

作者： Wu, Chia-Chun Ling, Chung-Huei Tsaur, Shyh-Chang Hwang, Min-Shiang Department of Industrial Engineering and Management National Quemoy University Kinmen County 892 Taiwan Department of Computer Science and Information Engineering Asia University Taiwan Fintech and Blockchain Research Center Asia University Taichung41354 Taiwan

The current traceability system certifies traceability ac-curacy by on-site examination or sampling inspection to promote food safety. However, such an afterward test and trace model no longer guarantees food safety. The food safety scandals that happened in the past few years called into question the effectiveness of the entire examination system. An instantaneous tracking and tracing system will significantly lower the usage of substandard products and low-price substitutes at the marketing stage and pre-vent dishonest conduct in traceability-related businesses. This study designed a new tracking system incorporating logistic codes and tracking codes to enhance the current agricultural produce’s security mechanism and the food traceability certification system. This mechanism adopts the virtual correlation model to reduce certification costs. It provides a complete and sound real-time tracking sys-tem for society and the industry to safeguard consumer rights and promote the competitiveness of qualified busi-nesses. © (2023), All Rights Reserved.

关键词： Radio frequency identification (RFID)

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Decentralized Edge-Based Detection of Label Flipping Attacks in Federated Learning 17th

Decentralized Edge-Based Detection of Label Flipping Attack...

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17th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2024 and the 15th International Conference on EUropean Transnational Education, ICEUTE 2024

作者： AlOtaibi, Nourah Felemban, Muhamad Mahmood, Sajjad Information and Computer Science Department KFUPM Dhahran Saudi Arabia Dammam Saudi Arabia Computer Engineering Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

ISBN: (纸本)9783031750151

Federated learning (FL) enhances data privacy by enabling decentralized model training without sharing local data. However, FL is vulnerable to Label-Flipping-Attacks (LFA), where malicious clients flip data labels. Traditionally, cloud-centric centralized defenses against LFA pose security and efficiency challenges. We propose a novel edge-based decentralized LFA detection method using edge servers for efficient detection. The proposed method considers the overall and class-wise accuracy in identifying suspicious clients. Initially, we adopt a strict zero-tolerance approach by excluding the entire update from detected malicious clients. We then experiment with four aggregation techniques-subtracting, masking, clipping, and reweighting–to handle the malicious parts of updates by focusing on the final layer neurons corresponding to specific classes. Experiments using three datasets demonstrate the effectiveness, robustness, and efficiency of our method, showing improved model performance and reduced latency under adversarial conditions. This approach improves the security and reliability of FL systems while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

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