检索结果-内蒙古大学图书馆

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

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing data processing Vectors Space exploration Resource management Speech processing Mutual information

来源：评论

学校读者我要写书评

暂无评论

Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

引用

arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater Management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

引用

Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： P S Thanigaivelu A. Sairam P. Vijayan A. Suresh Kumar N. Mohankumar T. R. GaneshBabu Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Management Studies Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage.

关键词： Ethics Urban areas Water quality Prediction algorithms Real-time systems Stormwater Floods

来源：评论

学校读者我要写书评

暂无评论

Design and fabrication of library management bot

引用

AIP Conference Proceedings 2025年第1期3204卷

作者： T. Vignesh Senthil Kumar S. Arul Murugan D. Helan Jemima A. Dheeraj R. Saran Sarvesh Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India. Department of Artificial Intelligence & Data Science Erode Sengunthar Engineering College Erode India.

The primary goal of the paper is to design and construct a Library Bot that assists people to easily get through the pathway, the books, etc. These robots navigate the people based on their needs, and help them in shelf management, finding their books, assist them. It also finds and does the stocking of books. It comes with an inbuilt user interface that ensures better communication with the users or readers. It has a touchscreen to help the users effectively. Speech recognition is done to assist the users by interacting effectively with them. The navigation system is also incorporated to guide the users to their respective needs. It also further analyses the requirements of the end users and helps them identify the books based on their interests.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Diagnostic Tools for Detecting Autism Spectrum Disorder: A Review

Diagnostic Tools for Detecting Autism Spectrum Disorder: A R...

引用

Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Khosro Rezaee Mohammad R. Khosravi Hossein Ghayoumi Zadeh Mohammad Kazem Moghimi Ghassan Samara Hani Attar Sattam Almatarneh Deprtement of Biomedical Enginering Meybod University Meybod Iran Weifang University of Science and Technology Weifang Shandong China Department of Electrical Engineering Vali-E-Asr University of Rafsanjan Rafsanjan Iran Department of Communication Engineering University of Sistan and Baluchestan Zahedan Iran Department of Computer Science Zerqa University Zerqa Jordan Department of Energy Engineering Faculty of Enineering Zarqa University Zerqa Jordan Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zerqa Jordan

The autism spectrum disorder (ASD) is a developmental disability caused by abnormalities in the brain. Different methods have been used to diagnose ASD in the past, each with its own advantages and disadvantages. Prior research has focused mainly on algorithms and processing methods, rather than identification tools, to improve the automated diagnosis of ASD. This article identifies and describes diagnostic tools in the ASD literature, including facial features, EEG recordings, speech signals, and neuroimaging, in order to assist researchers interested in developing statistical, computational, and sound clinical approaches to ASD data mining. Accordingly, based on the responses obtained, this review study intends to assess several aspects of the key automatic diagnostic tools for autism spectrum disorders, including diagnostic accuracy, privacy protection, uncertainty, cost, efficiency, absence of methodological interference, and prospective clinical and therapeutic conditions.

关键词： Neuroimaging Autism Privacy Costs Uncertainty Brain modeling Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Clustering Interval and Triangular Granular data: Modeling, Execution, and Assessment

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

来源：评论

学校读者我要写书评

暂无评论

Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting

arXiv

引用

arXiv 2022年

作者： Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Hadid, Abdenour Center for Data Sciences International Institute of Information Technology Bangalore India Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Abu Dhabi United Arab Emirates

Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error, combining the explainability, scalability, and "white-box-like" prediction behavior of both models. Notably, the PARNN model provides uncertainty quantification through prediction intervals, setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. By effectively capturing the complexities present in time series data, it outperforms existing methods in terms of accuracy and reliability. The ability to quantify uncertainty through prediction intervals further enhances the model’s usefulness in decision-making processes. © 2022, CC BY.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Self-Replicating Imperative Node Evaluator for Improved Network Intrusion Detection 2

Self-Replicating Imperative Node Evaluator for Improved Netw...

引用

2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Babitha, B.S. Banchhor, Chitrakant O. Bhuvaneshwari, K.S. Khurana, Saniya Senthilkumar, K.K. Marwaha, Shuthanshu School of Mangement - Ug Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350368949

Cyber-attacks occur when someone attempts to gain unauthorized access to another person's or company's computer network. Network resources are considered compromised when several activities are made that potentially jeopardize their availability, anonymity, or reliability. Any time a system that detects intrusions notices questionable activity on an organization or system, for example, a potential breach of security policy, it notifies a management station. It is hard to efficiently identify threats in real-time due to the massive amount of data sent via cyber networks. The focus of this study is on developing an AI network for the purpose of detecting cyber threats. Checking user behavior for anomalies is an excellent way to spot fraud. When it comes to cyber defense, one of the biggest mysteries is how systems can detect previously unseen vulnerabilities and take action to mitigate or eradicate them. In order to stop cyber assaults, new cyber security measures are needed to identify malicious nodes before they interact. This study introduces an auto-triggering mode architecture and a replicating itself code for an aggressive node evaluator. As soon as it detects an intrusion, it may fix the network immediately, securing it and reducing the number of false alerts. The proposed model has a 98% success rate in both intruder identification and self-replication. Though progress has been made, there is still room for enhancement in reducing the number of false alarms. © 2024 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Distributed energy trading management for renewable prosumers with HVAC and energy storage

arXiv

引用

arXiv 2021年

作者： Yang, Qing Wang, Hao Shenzhen University Guangdong Province Shenzhen China Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia

Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users' privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users' private information locally for solving the primal subproblem hence preserves the users' privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users' peak load and reduce the overall energy cost of the system by 23% on average. © 2021, CC BY.

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

A Fog Cluster-based Interpretable AI Framework for Human Activity Recognition and Personalized Healthcare Monitoring

A Fog Cluster-based Interpretable AI Framework for Human Act...

引用

Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： K.S. Bhuvaneshwari Mukul Mishra V Janakiraman Shreya Ghosal Saritha Srinivasmurthy Raghotham Shilpa M. Katikar Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The innovations made possible by the Internet of Things have been of enormous use to the healthcare sector. These developments have made it feasible for a multitude of applications to collect huge amounts of information from users for evaluation in the cloud. On the other hand, immediate medical care need very low latency as well as which is something that traditional cloud computing is not able to supply. Fog computing is a solution that has emerged as a result of this issue. It allows for the processing of data that is nearer to its origin, which in turn reduces latency and the amount of bandwidth that is used. In the paper, a novel system is proposed that takes use of fog clusters. Fog clusters are groupings of fog nodes that collaborate with one another to accelerate and distribute the processing of tasks. According to the findings, fog clusters significantly cut down on latency and energy usage, which demonstrates the usefulness of fog technology in Internet of Things (IoT) systems that have stringent latency requirements. This study was carried out with the assistance of a simulation of an individual with a COPD monitoring system.

关键词： Hands Cloud computing Technological innovation Medical services Chronic obstructive pulmonary disease Internet of Things Human activity recognition Low latency communication Monitoring Edge computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：