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Investing and Pricing with Supply Uncertainty in Electricity Market:A General View Combining Wholesale and Retail Market

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China Communications 2015年第3期12卷 20-34页

作者： LI Xiaobo GAO Li WANG Gongpu GAO Feifei WU Qingwei Beijing University of Posts and Telecommunications School of Digital Media and Design Arts Beijing University of Posts and Telecommunications School of Computer and Information Technology Beijing Jiaotong University Department of Automation Tsinghua UniversityState Key Lab of Intelligent Technologies and SystemsTsinghua National Laboratory for Information Science and Technology(TNList)

Renewable energy,such as wind and solar energy,may vary signifi cantly over time and locations depending on the weather and the climate *** leads to the supply uncertainty in the electricity(power) market with renewable energy integrated to power *** this paper,electricity in the market is classified into two types:stablesupply electricity(SSE) and unstablesupply electricity(USE).We investigate the investment and pricing strategies under the electricity supply uncertainty in wholesale and retail electricity *** particular,our model combines the wholesale and retail market and capture the dominant players,i.e.,consumers,power plant(power operator),and electricity *** derive the market behaviors of these players,we formulate the market decision problems as a multistage Stackelberg *** solving the game model,we obtain the optimal,with closedform,wholesale investment and retail pricing strategy for the *** also obtain the energy supplier's best price mechanism numerically under certain *** fi nd the price of SSE being about 1.4 times higher than that of USE will benefi t energy supplieroptimally,under which power plant's optimal strategy of investing is to purchase USE about 4.5 times much more than SSE.

关键词： electricity supply with uncertainty electricity investment electricity pricing wholesale market retail market Stackelberg game

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of computer science & Technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz

arXiv

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arXiv 2025年

作者： Zhou, Lingyou Dong, Xin Qiu, Kehai Yu, Gang Zhang, Jie Zhang, Jiliang School of Electrical and Electronic Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Ranplan Wireless Network Design Ltd. CambridgeCB23 3UY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom College of Engineering Design and Physical Sciences Brunel University of London UxbridgeUB8 3PH United Kingdom 5G/6G Innovation Centre University of Surrey SurreyGU2 7XH United Kingdom State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang110819 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0.85-5 GHz using Ranplan Professional. We first enhance the original AMPLE model by introducing an additional frequency coefficient to support path loss prediction across multiple carrier frequencies. By using measurement-validated Ranplan Professional simulator, we simulate four cities and validate the simulations for further path loss model characterization. Specifically, we extract the close-in (CI) model parameters from the simulations and compare them with parameters extracted from measurements in other works. Under the ray-based model characterization, we compare the AMPLE model with the 3rd Generation Partnership Project (3GPP) path loss model, the CI model, the alpha-beta-gamma (ABG) model, and those with simulation calibrations. In addition to standard performance metrics, we introduce the prediction-measurement difference error (PMDE) to assess overall prediction alignment with measurement, and mean simulation time per data point to evaluate model complexity. The results show that the AMPLE model outperforms existing models while maintaining similar model complexity. Copyright © 2025, The Authors. All rights reserved.

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Twisting Sliding Mode Control of an Electrostatic MEMS Micromirror for a Laser Scanning System

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 1060-1067页

作者： Hui Chen Zhendong Sun Weijie Sun John Tze Wei Yeow the School of Computer and Information Engineering Henan UniversityKaifeng 475000China the Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190 China the College of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China the Systems Design Engineering University of WaterlooWaterloo N2L3G1Canada IEEE

In this paper,we present a twisting control scheme with proportional-integral-derivative(PID)sliding surface for a two-axis electrostatic torsional micromirror,and the utilization of the proposed scheme in a laser scanning *** experimental results of set-point regulation verify that the proposed scheme provides enhanced transient response and positioning performance as compared to traditional sliding mode *** evaluate the tracking performance of the closed-loop system,triangular waves with different frequencies are used as desired *** the proposed scheme the experimental results verified that the closed-loop controlled micromirror follows the given triangular trajectories precisely.A micromirror-based laser scanning system is developed to obtain *** compared with open-loop control,the experimental results demonstrated that the proposed scheme is able to reduce the distortion of the raster scan,and improve the imaging performance in the presence of cross-coupling effect.

关键词： Electrostatic micromirror imaging microelectromechanical system(MEMS) twisting algorithm

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Fusing R Features and Local Features with Context-Aware Kernels for Action Recognition

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International Journal of computer Vision 2016年第2期118卷 151-171页

作者： Yuan, Chunfeng Wu, Baoxin Li, Xi Hu, Weiming Maybank, Stephen Wang, Fangshi National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Systems Birkbeck College London United Kingdom School of Software Engineering Beijing Jiaotong University Beijing China

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition. © 2015, Springer science+Business Media New York.

关键词： Principal component analysis

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

arXiv

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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

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Resident participating GIS-based tsunami disaster control systems for local communities

Resident participating GIS-based tsunami disaster control sy...

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International Symposium on Communications and information Technologies

作者： Muramoto, Takashi Nakayama, Ken Kobayashi, Yoshitake Maekawa, Mamoru Faculty of Business Hachinohe University Aomori Japan Department of Information Systems Design Graduate School of Information Systems University of Electro-Communications Tokyo Japan Grid Computing and Distributed Systems (GRIDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

ISBN: (纸本)9780780397408

We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the system is resident participation. The GIS layer structure proposed in this paper nicely supports this user participation. The system architecture and the use of the system for tsunami disaster control are discussed. We report ongoing developments in Hachinohe-City, Japan.

关键词： GIS tsunami disaster control internet application system design

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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Managing implicit requirements using semantic case-based reasoning research preview

Managing implicit requirements using semantic case-based rea...

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18th Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2012

作者： Daramola, Olawande Moser, Thomas Sindre, Guttorm Biffl, Stefan Department of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems Vienna University of Technology Austria

ISBN: (纸本)9783642287138

[Context and motivation] Implicit requirements (ImRs) are defined as requirements of a system which are not explicitly expressed during requirements elicitation, often because they are considered so basic that developers should already know them. Many products have been rejected or users made unhappy because implicit requirements were not sufficiently addressed. [Question/Problem] Requirement management tools have not addressed the issue of managing ImRs, also despite the challenges of managing ImRs that exist in practice the issue has not received sufficient attention in the literature. [Principal Idea/results] This planned research will investigate how automated support can be provided for managing ImRs within an organizational context, which is currently lacking in practice. This work proposed an approach that is based on semantic case-based reasoning for managing ImRs. [Contribution] We present the concept of a tool which enables managing of ImRs through the analogy-based requirements reuse of previously known ImRs. This ensures the discovery, structured documentation, proper prioritization, and evolution of ImRs, which improves the overall success of software development processes. © 2012 Springer-Verlag.

关键词： Case based reasoning

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