检索结果-内蒙古大学图书馆

How to Operate Electricity Markets Using a Trilevel Optimization Framework

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2025年 72卷 888-906页

作者： Yang, Zhiyuan Li, Zifan Wang, Shuaian Zhen, Lu Shanghai Univ Sch Management Shanghai 200444 Peoples R China Hong Kong Polytech Univ Fac Business Hong Kong Peoples R China

In the context of significant changes in the global energy landscape and the rapid transformation of the power industry, the complexity of electricity markets is growing. There is an urgent need for innovative management strategies and optimization solutions. Against this background, we investigate the optimization problems in electricity markets. We focus on the interactions among distribution network operators, microgrid operators, and prosumers and develop a trilevel optimization framework. This framework models the complex processes of electricity price setting, risk management, and prosumer participation to maximize profitability and operational efficiency. To solve this complex model efficiently, we propose a customized exact algorithm. It is based on the Karush-Kuhn-Tucker conditions and the L-shaped algorithm. Extensive computational experiments prove the effectiveness of the algorithm. We conduct sensitivity analyses on the electricity market operations, and the results provide valuable management insights for electricity markets. Our findings indicate that microgrid operators should prioritize flexible energy storage capacities and adaptable pricing mechanisms to effectively manage peak demand, especially during periods with high renewable generation.

关键词： Microgrids Electricity supply industry Electricity Energy storage Optimization Renewable energy sources Pricing Optimization models computational modeling Complexity theory Energy technology exact optimization microgrid three-layer electricity market trilevel optimization model

来源：评论

学校读者我要写书评

暂无评论

Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2676-2692页

作者： Zhong, Yunshan Huang, You Hu, Jiawei Zhang, Yuxin Ji, Rongrong Xiamen Univ Inst Artificial Intelligence Sch Informat Dept Artificial IntelligenceMinist Educ China Xiamen 361005 Peoples R China Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Com Minist Educ China Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Key Lab Multimedia Trusted Percept & Efficient Com Minist Educ China Xiamen 361005 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

Post-training quantization (PTQ) for vision transformers (ViTs) has received increasing attention from both academic and industrial communities due to its minimal data needs and high time efficiency. However, many current methods fail to account for the complex interactions between quantized weights and activations, resulting in significant quantization errors and suboptimal performance. This paper presents ERQ, an innovative two-step PTQ method specifically crafted to reduce quantization errors arising from activation and weight quantization sequentially. The first step, Activation quantization error reduction (Aqer), first applies Reparameterization Initialization aimed at mitigating initial quantization errors in high-variance activations. Then, it further mitigates the errors by formulating a Ridge Regression problem, which updates the weights maintained at full-precision using a closed-form solution. The second step, Weight quantization error reduction (Wqer), first applies Dual Uniform Quantization to handle weights with numerous outliers, which arise from adjustments made during Reparameterization Initialization, thereby reducing initial weight quantization errors. Then, it employs an iterative approach to further tackle the errors. In each iteration, it adopts Rounding Refinement that uses an empirically derived, efficient proxy to refine the rounding directions of quantized weights, complemented by a Ridge Regression solver to reduce the errors. Comprehensive experimental results demonstrate ERQ's superior performance across various ViTs variants and tasks. For example, ERQ surpasses the state-of-the-art GPTQ by a notable 36.81% in accuracy for W3A4 ViT-S.

关键词： Quantization (signal) computational modeling Transformers Computer vision Training Data models Training data Multimedia computing Heavily-tailed distribution Complexity theory Model compression vision transformers post-training quantization quantization error reduction

来源：评论

学校读者我要写书评

暂无评论

Enhanced Sparse LPV-ARMA Model With Ensemble Basis Functions for Mechatronic Transmission Fault Detection Under Variable Speed Conditions

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第11期12卷 17223-17232页

作者： Chen, Yuejian Li, Zihan Jiang, Yuan Yang, Chunsheng Xia, Min Feng, Ke Univ Manitoba Dept Mech Engn Winnipeg MB R3T 2N2 Canada Tongji Univ Coll Transportat Shanghai 201804 Peoples R China Univ Illinois Urbana & Champaign Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Guangzhou Univ Sch Artificial Intelligence Guangzhou 511370 Peoples R China Western Univ Dept Mech & Mat Engn London ON N6A 3K7 Canada Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China

Fault detection in mechatronic transmissions is particularly challenging due to the nonstationary nature of monitoring signals arising from complex operating conditions, coupled with the high-safety requirements that limit the availability of fault data. Sparse linear parameter varying autoregressive moving average (Spa LPV-ARMA) model is a powerful tool for dealing with nonstationary time series, and good fitting results can be achieved through the basis function expansion, where parameters of the model are associated with additional variables. However, current research on Spa LPV-ARMA model only considers single basis function, overlooking the potential complementarity of multiple basis functions. This article proposes a novel enhanced Spa LPV-ARMA model with ensemble basis for mechatronic transmission fault detection. The proposed model incorporates the concept of ensemble learning by combining models with different basis functions, and a stepwise approach is utilized to select the models to be combined. The rational choice of the combination scale allows the ensemble model to have fewer parameters with higher accuracy. Simulation and experimental studies in mechatronic transmission are conducted, verifying that the proposed ensemble basis Spa LPV-ARMA model exhibits higher modeling accuracy and fault detection performance.

关键词： Autoregressive processes Time series analysis computational modeling Fault detection Vibrations Mechatronics Accuracy Adaptation models Training Velocity control Enhanced sparse linear parameter varying autoregressive moving average (Spa LPV-ARMA) model ensemble basis functions fault detection mechatronic transmissions variable speed conditions

来源：评论

学校读者我要写书评

暂无评论

DT-GA-FLANN: Decision Tree-Based Genetically Optimized FLANN for Missing Value Imputation and Classification

引用

IEEE ACCESS 2025年 13卷 44526-44542页

作者： Jena, Monalisa Dehuri, Satchidananda Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea Fakir Mohan Univ Dept Comp Sci Balasore 756019 Odisha India

In the current landscape, addressing missing values poses a significant challenge when dealing with real-world problems. In this work, a hybrid model, DT-GA-FLANN, is proposed to address the missing value problem and enhance classifier performance. The missing values are imputed using Classification and Regression Trees (CART), ensuring robust data preprocessing. For classification, a genetically optimized Functional Link Artificial Neural Network (FLANN) is used, chosen for its faster convergence and lower computational complexity compared to multi-layer neural networks. However, functional expansion in FLANN can increase architectural complexity, which is effectively mitigated using Genetic Algorithm (GA). GA is selected for its adaptability, parallel processing capabilities, and efficiency in complex optimization tasks. The FLANN parameters are iteratively fine-tuned to maximize performance. The performance of DT-GA-FLANN is validated through extensive experiments on four real-world and two synthetic datasets. It is compared against ten hybrid models, comprising four statistical and six machine learning-based hybrid models. Experimental analysis reveals that DT-GA-FLANN achieves an average improvement in accuracy, precision, recall, and F-measure by 3.7%, 4.3%, 3.1%, and 3.8%, respectively over machine learning models, and by 10.2%, 12.4%, 10.8%, and 11.6%, respectively over statistical models on real-world datasets. In case of synthetic datasets, it further improves accuracy, precision, recall, and F-measure by 4.2%, 5.1%, 4.7%, and 4.3%, respectively over machine learning models, and by 9.1%, 10.8%, 9.4%, and 9.9%, respectively over statistical models. These results confirm DT-GA-FLANN's superior classification performance and generalization ability across diverse datasets. The experimental results show that the genetically optimized FLANN significantly outperforms other hybrid models across multiple performance metrics.

关键词： Decision trees Genetic algorithms Imputation Accuracy Neural networks computational modeling Nearest neighbor methods Computer architecture computational complexity Machine learning Decision tree FLANN genetic algorithm machine learning missing value imputation supervised learning

来源：评论

学校读者我要写书评

暂无评论

Foundation Models Defining a New Era in Vision: A Survey and Outlook

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2245-2264页

作者： Awais, Muhammad Naseer, Muzammal Khan, Salman Anwer, Rao Muhammad Cholakkal, Hisham Shah, Mubarak Yang, Ming-Hsuan Khan, Fahad Shahbaz MBZ Univ AI Abu Dhabi U Arab Emirates Georgia Inst Technol Comp Sci Dept Atlanta GA 30332 USA Khalifa Univ Comp Sci Dept Abu Dhabi U Arab Emirates Khalifa Univ Ctr Secure Cyber Phys Secur Syst Abu Dhabi U Arab Emirates Australian Natl Univ CECS Canberra ACT 0200 Australia Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Calif Merced Merced CA 95344 USA Yonsei Univ Seoul 03722 South Korea Google Res Mountain View CA 94043 USA Linkoping Univ Comp Vis Lab S-58183 Linkoping Sweden

Vision systems that see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locations, ambiguities, and variations in the real-world environment can be better described in human language, naturally governed by grammatical rules and other modalities such as audio and depth. The models learned to bridge the gap between such modalities and large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. These models are referred to as foundation models. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions. In this survey, we provide a comprehensive review of such emerging foundation models, including typical architecture designs to combine different modalities (vision, text, audio, etc.), training objectives (contrastive, generative), pre-training datasets, fine-tuning mechanisms, and the common prompting patterns;textual, visual, and heterogeneous. We discuss the open challenges and research directions for foundation models in computer vision, including difficulties in their evaluations and benchmarking, gaps in their real-world understanding, limitations of contextual understanding, biases, vulnerability to adversarial attacks, and interpretability issues. We review recent developments in this field, covering a wide range of applications of foundation models systematically and comprehensively.

关键词： Adaptation models computational modeling Foundation models Data models Surveys Visualization Reviews Computer vision Computer architecture Context modeling Contrastive learning language and vision large language models masked modeling self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Improved Gale-Shapley-Based Hierarchical Federated Learning for IoT Scenarios

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 9195-9205页

作者： Wu, Xiaohong Li, Pei Gu, Yonggen Tao, Jie Shen, Shigen Yu, Shui Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia

Federated learning (FL) allows multiple devices to train a high-performance global model cooperatively without sharing their private data. One of the key challenges in FL for Internet of Things (IoT) scenarios is that the statistical heterogeneity in local data distribution for IoT devices, which will cause the low quality of local models, degrading the performance of the global model. To address this problem, focusing on improving the quality of local models, we propose a match- based hierarchical FL framework (FedAvg-Match), in which two IoT devices with complementary datasets are formed as a training group to reduce the influence of data heterogeneity. By introducing Earth mover's distance for data distribution, we design an improved Gale-Shapley algorithm with time complexity O(N-2 log N) for IoT device grouping, which can obtain a stable matching. Experimental results show that, compared to the ungrouped FedAvg algorithm, the proposed FedAvg-Match method significantly improves both the accuracy of the global model and the convergence speed of the training process, while also reducing communication costs.

关键词： Internet of Things Federated learning Data models Servers Training Data privacy computational modeling Convergence Performance evaluation Accuracy Data quality federated learning (FL) Gale-Shapley (GS) matching

来源：评论

学校读者我要写书评

暂无评论

Toward Sustainable 6G leveraging Digital Twin and Artificial Intelligence: Framework and Case Study

引用

IEEE COMMUNICATIONS MAGAZINE 2025年

作者： Xu, Dianlei Su, Xiang Tarkoma, Sasu Hui, Pan Univ Helsinki Dept Comp Sci Helsinki Finland Univ Helsinki Dept Agr Sci Helsinki Finland Univ Helsinki Comp Sci Helsinki Finland Univ Helsinki Fac Sci Helsinki Finland Hong Kong Univ Sci & Technol Computat Media & Arts Hong Kong Peoples R China Hong Kong Univ Sci & Technol HKUST DT Syst & Media Lab Hong Kong Peoples R China

The forthcoming 6G networks will adopt higher frequency bands, ultra-dense base station deployments, and advanced technologies, such as massive MIMO and edge intelligence, to achieve enhanced bandwidth, connectivity, and intelligence. However, these advancements will lead to significant increases in energy consumption. In this article, we propose to integrate digital twin and artificial intelligence to systematically solve the problem of energy sustainability in 6G networks. We explore the key challenges of integrating these two technologies and design an intelligent digital twin framework to address them. Our framework adopts a modular design, encompassing modeling, simulation, optimization, and long-term evolution modules. As a case study, we conduct a preliminary implementation of the framework in the 5th Ring Road area of Beijing, China, demonstrating its effectiveness in improving energy efficiency. Experimental results show that our framework significantly reduces the energy consumption of 6G networks while optimizing resource allocation to reduce computing latency.

关键词： 6G mobile communication Optimization Artificial intelligence Urban areas Internet of Things Data models Sustainable development computational modeling Vehicle dynamics Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Co-Designing Transformer Architectures for Distributed Inference With Low Communication

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第4期36卷 717-730页

作者： Du, Jiangsu Wei, Yuanxin Ye, Shengyuan Jiang, Jiazhi Chen, Xu Huang, Dan Lu, Yutong Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Transformer models have shown significant success in a wide range of tasks. However, the massive resources required for its inference prevent deployment on a single device with relatively constrainted resources, thus leaving a high threshold of integrating their advancements. Observing scenarios such as smart home applications on edge devices and cloud deployment on commodity hardware, it is promising to distribute Transformer inference across multiple devices. Unfortunately, due to the tightly-coupled feature of Transformer model, existing model parallelism approaches necessitate frequent communication to resolve data dependencies, making them unacceptable for distributed inference, especially under relatively weak interconnection. In this paper, we propose DeTransformer, a communication-efficient distributed Transformer inference system. The key idea of DeTransformer involves the co-design of Transformer architecture to reduce the communication during distributed inference. In detail, DeTransformer is based on a novel block parallelism approach, which restructures the original Transformer layer with a single block to the decoupled layer with multiple sub-blocks. Thus, it can exploit model parallelism between sub-blocks. Next, DeTransformer contains an adaptive execution approach that strikes a trade-off among communication capability, computing power and memory budget over multiple devices. It incorporates a two-phase planning for execution, namely static planning and runtime planning. The static planning runs offline, containing a profiling procedure and a weight placement strategy before execution. The runtime planning dynamically determines the optimal parallel computing strategy from an expertly crafted search space based on real-time requests. Notably, this execution approach can adapt to heterogeneous devices by distributing workload based on devices' computing capabilities. We conduct experiments for both auto-regressive and auto-encoder tasks of Transforme

关键词： Parallel processing Transformers computational modeling Data models Planning Distributed databases Runtime Hardware Adaptation models Throughput Model parallelism distributed transformer inference deep learning system

来源：评论

学校读者我要写书评

暂无评论

Online Credibility Assessment of Equipment Digital Twin for Discrete Manufacturing

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 14763-14774页

作者： Lu, Han Zhang, Lin Jamal Deen, M. Wang, Kunyu Cheng, Hongbo Yang, Laurence T. Beihang Univ Hangzhou Int Innovat Inst Hangzhou 311115 Zhejiang Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China State Key Lab Intelligent Mfg Syst Technol Beijing 100854 Peoples R China Atlas Inc Hamilton L8P 0A1 ON Canada Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Software Engn Zhengzhou 450001 Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

The equipment digital twins (EDTs) for discrete manufacturing should be calibrated quickly to avoid irreversible physical damage to the equipment caused by biased control commands. Therefore, an online credibility assessment method for EDTs is urgently needed. However, existing assessment approaches consume too much time, and thus could not reveal dynamic faults in time. In this paper, the dynamic relationship between online evolution and actual applications of EDTs is investigated. Then, two steps are proposed to accelerate the assessment process significantly. One involves pre-constructing a performance-deviation-agent (PDA), and the other involves dynamically fitting the application-time-window (ATW) probability distribution. The methodology is applicable to discrete manufacturing processes. The dynamic credibility of EDT evolution process can be updated after every iteration of the model evolution. Sorting manufacturing equipment was used as a case study to demonstrate the effectiveness of this method. The time consumption was reduced by 90% compared with traditional assessment methods in the case. Note to Practitioners-The application of digital twins (DTs) for discrete manufacturing equipment typically aims to enhance the control effectiveness of the physical equipment. To ensure the effectiveness of control signals derived from DT-based simulations, the credibility of the equipment DTs must be known and guaranteed. This paper addresses the quick updating of the credibility of equipment DTs as they evolve according to real-time data collected from the equipment. The proposed methods are currently applicable to individual pieces of equipment with straightforward workflows. By adapting the training method for the error prediction model to scenarios involving multiple pieces of equipment and more complex workflows, the overall control efficacy within a workshop can be significantly improved.

关键词： Manufacturing Sorting Real-time systems Digital twins computational modeling Sensors Radiofrequency identification Artificial intelligence Training Sensitivity analysis Credibility assessment equipment digital twins manufacturing online assessment modeling simulation

来源：评论

学校读者我要写书评

暂无评论

Ultrasound Transducer Design Based on Anti-Matching Layer Structure

引用

IEEE SENSORS JOURNAL 2025年第10期25卷 16726-16735页

作者： Cai, Yiqi Yang, Yue Shi, Chenan Chen, Lin Xu, Lijun Ma, Jianguo Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Shenzhen Sonosray Technol Co Ltd Shenzhen 518000 Peoples R China

The matching layer in ultrasound transducer design plays a crucial role in efficiently transmitting ultrasound waves, thereby significantly improving the performance of the transducers. Extensive research has been conducted on the design of matching layers due to their importance. Conversely, structures capable of suppressing acoustic waves are equally vital for transducer design. However, the mechanisms for acoustic wave suppression have not been thoroughly explored, leading to a lack of theoretical foundation and innovative design concepts for these application-specific transducers. In this article, we introduce a double-layer impedance anti-matching (AM) mechanism that identifies four cases of acoustic suppression. We validate the acoustic suppression performance of the AM structure through theoretical analysis, simulations, and experimental testing. Furthermore, we apply the double-layer AM structure in the design of an array ultrasound transducer and compare its performance with that of traditional transducers, presenting the AM structure as a novel approach to ultrasound transducer design.

关键词： Transducers Ultrasonic imaging Acoustics Impedance Finite element analysis Sensors Load modeling computational modeling Mathematical models Intelligent sensors Acoustic wave suppression anti-matching (AM) ultrasound transducer design

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：