检索结果-内蒙古大学图书馆

engineering 2023年第5期24卷 229-238,I0006页

作者： Yuanyuan Man Qinli Yang Junming Shao Guoqing Wang Linlong Bai Yunhong Xue School of Resources and Environment University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering Nanjing Hydraulic Research InstituteNanjing 210029China Xinyang Hydrology and Water Resources Survey Bureau Xinyang 450003China

径流预报对防洪具有重要意义。然而,由于径流过程的复杂性和随机性,对日径流量进行准确预测是困难的,尤其是对峰值径流量的预测。为了解决这一问题,本研究提出了一种用于径流预测的增强型长短期记忆(LSTM)模型,其中引入了新的损失函数... 详细信息

径流预报对防洪具有重要意义。然而,由于径流过程的复杂性和随机性,对日径流量进行准确预测是困难的,尤其是对峰值径流量的预测。为了解决这一问题,本研究提出了一种用于径流预测的增强型长短期记忆(LSTM)模型,其中引入了新的损失函数并集成了特征提取器。设计了峰值误差tanh(peak error tanh,PET)和峰值误差swish(peak error swish,PES)两个损失函数,增强了峰值径流预测的重要性,弱化了正常径流预测的权重。为每个气象站建立由3个LSTM网络组成的特征提取器,目的是提取每个气象站输入数据的时间特征。以中国淮河上游为例,利用增强型LSTM模型对1960—2016年的日径流量进行了预测。结果表明,改进后的LSTM模型表现良好,在验证期内(2005年11月至2016年12月),Nash-Sutcliffe效率(NSE)系数在0.917-0.924之间,优于广泛使用的集总水文模型(Australian Water Balance model(AWBM)、Sacramento、Sim Hyd和Tank模型)和数据驱动模型(人工神经网络(ANN)、支持向量回归(SVR)和门控循环单元(GRU))。以PES为损失函数的增强型LSTM对洪水极端径流的预测效果最好,平均NSE为0.873。此外,海拔较高的气象站降水对径流预测的贡献比最近的气象站更大。该研究为流域日径流预测提供了有效工具,有利于流域防洪和水安全管理。

关键词：长短期记忆损失函数数据驱动模型输入数据日径流预测

来源：评论

学校读者我要写书评

暂无评论

A IoT Edge Advanced VANET Technique for Vehicle Communication and Improve Safety in Hill Station Critical Scenario

A IoT Edge Advanced VANET Technique for Vehicle Communicatio...

引用

International Conference on science Technology engineering and Management (ICONSTEM)

作者： Murali N D. Beulah David Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

The safety of the vehicles is given great importance by advanced intelligent transportation systems. We are here to make sure that swift transit vehicles and infrastructure communicate with one other quickly. The majority of the vehicles need regular monitoring for appropriate warnings and road maps to achieve their ultimate objective in a timely manner in the hill station bad environment, and travel in hairpin bends and risky turning in particular is a duty that is vital for ITS. Here, my study will provide a strategy for preventing accidents by applying a sophisticated early warning system before notifying the car in a certain circumstance. In order to prevent accidents, machine learning is a technique where the system automatically learns and enhances the rapid object detection stage in bobby pin road turning scenarios. Here, my study offers effective unsupervised learning of these characteristics based on the acquired data, which in turn serves as the foundation for the clustering I performed, which successfully builds a mobile ad hoc network. In this work, we suggested an innovative automated discovering vehicle prier that uses the UNetXST technique in real-time to notify warring for vehicle in three-way shining LED light with blinking mode, slowing warring message in display unit, and beep alarm sound. By combining V2I and V2V technologies and sending messages to the central traffic light management system, we highlighted safety and communication. In worst-case scenarios, we employ a v2v technology sensor to detect the vehicle that will cross the junction road, and we investigate two-way merging technology's potential to implement and resolve these problems in transparent object tracking in the turning system. We looked at future direction and hurdles at this time in addition to a case study illustrating a VANET-based scenario of a critical urban road turning, receiving the earliest notice to flee from a traffic collision, and experiencing the perfect EV momen

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

引用

Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Murali N D. Beulah David Department of Computer Science and Engineering SIMATS Saveetha School of Engineering Chennai Tamilnadu India Department of Data Analytics SIMATS Institute of Information Technology Saveetha School of Engineering Chennai Tamilnadu India

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design.

关键词： Machine learning algorithms Urban areas Sociology Neural networks Machine learning Traffic control Internet of Things

来源：评论

学校读者我要写书评

暂无评论

MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

引用

computer Modeling in engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

来源：评论

学校读者我要写书评

暂无评论

Enhancing action discrimination via category-specific frame clustering for weakly-supervised temporal action localization

引用

Frontiers of Information Technology & electronic engineering 2024年第6期25卷 809-823页

作者： Huifen XIA Yongzhao ZHAN Honglin LIU Xiaopeng REN School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agricultural Applications Zhenjiang 212013China Changzhou Vocational Institute of Mechatronic Technology Changzhou 213164China

Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action ***, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.

关键词： Weakly supervised Temporal action localization Single-frame annotation Category-specific Action discrimination

来源：评论

学校读者我要写书评

暂无评论

Digital Twins and Intelligent IoT for a Better World (Panel Discussion Report-I of IEEE AIoTSys2023)

Digital Twins and Intelligent IoT for a Better World (Panel ...

引用

2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Huang, Runhe Zhang, Daqing Yu, Zhiwen Nugent, Chris Jia, Weijia Yang, Kun Zhang, Yan Sekimoto, Yoshihide Guo, Bin Faculty of Computer and Information Sciences Hosei University Tokyo Japan Peking University Ip Paris France Harbin Engineering University Northwestern Polytechnical University China School of Computing Ulster University Ireland BNU-UIC Joint Ai Research Institute Beijing Normal University China School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Informatics University of Oslo Norway Institute of Industrial Science University of Tokyo Japan School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)9798350312270

This position paper provides a summary report of the panel discussion on "Digital Twins and Intelligent IoT for a Better World"at the International Conference on Artificial Intelligence of Things and Systems (AIoTSys'23). The panel featured seven distinguished invited panelists, including four IEEE Fellows from Peking University, Beijing Normal University, University of Essex, and the University of Oslo. Additionally, there was one vice president from Harbin engineering University, one school head from Ulster University, and one center vice director from the University of Tokyo. The panel covered seven topics: (1) The cost-effectiveness of IoTenabled digital twin design, (2) Ubiquitous crowd computing systems, (3) The vision for the fusion of the Intelligent IoT with the Digital Twin, (4) Intelligent networking in relation to Chinese culture and education, (5) data-driven digital twin wireless networks, (6) Digital twins edge networking, and (7) The automatic generation of urban digital twins from local government s open data. The panel generated lively and comprehensive discussions, and this report represents the enriched outcome of our panelists insights, visions, and the interactive discussions with the audience. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Power load prediction of smart grid based on deep learning 3

Power load prediction of smart grid based on deep learning

引用

3rd International Conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT2023

作者： Xiang, Siyu Zhen, Cao Peng, Jian Zhang, Linghao Pu, Zhengguo State Grid Sichuan Electric Power Research Institute Sichuan Chengdu610095 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611700 China College of Computer Science Sichuan University Chengdu610065 China Anhui Jiyuan Software Co. Ltd. Hefei230088 China

Real electricity costs, weather, and historical load data are added as new reference data when analyzing the characteristics of the load change policy to improve the forecasting model. A load forecasting model for smart grid based on convolutional neural network(CNN) is proposed. The model first analyzes the characteristics of the variable model and determines the forecast model inputs using two methods of correlation analysis to understand the impact of actual energy costs, weather and other factors on the changes. Second, since Feedforward neural network is deficient in processing the correlation information between loads, the prediction model developed by the author was studied as CNN. After completing the training, input two sets of real datasets for final data analysis and comparison, and the results show that, LSTM, BP, SVR[UNK]MAPE[UNK]RMSE respectively 0.49%, 0.57%, 0.83%, 1.44% and 106.08 MW, 113.82MW, 169.77 MW, 285.61 MW. It can be seen that the prediction model proposed by the author has the highest accuracy and the best fit with the trend of actual load changes. It is proved that the CNN model has certain advantages when dealing with load forecasting problems related to time series. © 2023 Elsevier B.V.. All rights reserved.

关键词： Feedforward neural networks

来源：评论

学校读者我要写书评

暂无评论

Investigating Wireless Networks with Network Coding Algorithms 5th

Investigating Wireless Networks with Network Coding Algorith...

引用

5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahmed, Zahid Gupta, Anjali Preethi, D. Singh, Akhilendra Pratap Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronic Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Community coding algorithms had been demonstrated to have capability to improve the performance of wireless networks. This paper offers a research, assessment, and implementation of community coding algorithms for Wi-Fi networks. The paper affords an overview of community coding algorithms and discusses the related blessings, drawbacks, and tradeoffs that have to be taken into account while evaluating the performance of these algorithms. Furthermore, simulation outcomes are offered to assess the overall performance of one of a kind network coding algorithms while implemented in a wireless community surroundings. Ultimately, the paper outlines the hardware and software elements required to correctly put into effect and test those algorithms in an operational wireless community. The promising consequences of the experiments imply that these community coding algorithms can be used to efficiently improve the overall performance of Wi-Fi networks within the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Bipartite Concept Factorization for Clustering

Fine-Grained Bipartite Concept Factorization for Clustering

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chong Peng Pengfei Zhang Yongyong Chen Zhao Kang Chenglizhao Chen Qiang Cheng College of Computer Science and Technology Qingdao University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) School of Computer Science and Engineering University of Electronic Science and Technology of China College of Computer Science and Technology China University of Petroleum (East China) Department of Computer Science University of Kentucky

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph, which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning, which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method, and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.

关键词： computer vision Clustering algorithms Partitioning algorithms Bipartite graph Pattern recognition Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Application of Artificial Intelligence Technology in Power System Stability Assessment 3

Application of Artificial Intelligence Technology in Power S...

引用

3rd International Conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT2023

In order to realize power system stability assessment, the author studies a deep Convolutional neural network algorithm for power system operation stability assessment. Combining short-term simulation with neural network prediction to reduce the time required for transient stability analysis can be used in various simulation analysis scenarios. A deep Convolutional neural network algorithm is used to improve the performance of the security system. A deep Convolutional Neural Network (CNN) is used to construct a multi-layer and multi-column neural network to evaluate the stability of electrical networks. The system significantly improves the efficiency of evaluating specific faults, particularly enhancing the specificity of evaluation judgments, and reducing the additional maintenance workload caused by providing stability warnings for the operation status of faultless power grids. The experimental results indicate that, the system has shown certain advantages in terms of sensitivity to the judgment of various types of faults mentioned above, especially improving the sensitivity to other faults with unknown causes, which is 10.8 percentage points higher than the previous system. The smallest difference in the improvement of sensitivity indicators is the sensitivity to ground faults. The previous system reached 95.4%, while this system increased by 3.9 percentage points to 99.3%. It is proved that the deep CNN is applicable to the task of power system operation stability assessment. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolution

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：