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European control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Lei Chen, Peng Zhang, Cong Pan, Cheng Long, Tao Xu, Weizhen Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518060 China Guangdong Shenzhen518000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to minimize transmission and computation overhead for mobile terminals. To improve viewport prediction accuracy, we utilize both spatial information through a saliency prediction model and temporal information through a modified LSTM model. Different computations introduced by the neural network models are distributed across the network to keep the computation light on mobile devices. To better adapt to the content dynamics in live streaming, we employ the model-agnostic meta-learning (MAML) method for video saliency prediction. The learned saliency prediction model with optimized initialization via offline meta-training can be fast fine-tuned online using a few samples. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results demonstrate that our approach achieves real-time prediction for live video streaming and surpasses existing methods in prediction accuracy on mobile terminals, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. Compared to baseline methods, MFVP achieves a 4.7–28.7% improvement in accuracy and demonstrates faster adaptability to dynamic content changes, enabling rapid fine-tuning and adjustment. When integrated into a streaming syste

关键词： Deep neural networks

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Image Inpainting and Classification Agent Training Based on Reinforcement Learning and Generative Models with Attention Mechanism

Image Inpainting and Classification Agent Training Based on ...

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2021 International Conference on Microelectronics, ICM 2021

作者： Ukwuoma, Chiagoziem C. Belal Bin Heyat, Md Masadeh, Mahmoud Akhtar, Faijan Zhiguang, Qin Bondzie-Selby, Emmanuel Alshorman, Omar Alkahtani, Fahad University of Electronic Science and Technology of China School of Information and Software Engineering Sichuan Chengdu China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Yarmouk University Hijjawi Faculty for Engineering Computer Engineering Deprtment Irbid21163 Jordan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu China Najran University Faculty of Engineering AlShrouk Trading Company Najran Saudi Arabia Najran University Faculty of Engineering Electrical Engineering Department Najran Saudi Arabia

ISBN: (纸本)9781665408394

What distinguishes the field of artificial intelligence (AI) from others is to develop fully independent agents that learn optimal behavior, change, and evolve solely through the communication of trial and error with the surrounding environment. Reinforcement learning (RL) can be seen in multiple aspects of Machine Learning (ML), provided the environment, reward, actions, the state will be defined. Agent training in previous years is seen to only relate to robotics, games, and self-driving cars. While trying to divert the focus of researchers from the view of self-driving cars, games, robots, etc. Here, we investigated using reinforcement learning in the aspect of task completion. We deployed our architecture in an inpainting task where the agent generates the distorted or missing image content into an eminent fidelity completed the image by using reinforcement learning to influence the generative model utilized. The Generative Adversary Network (GAN) problem of not being steady and challenging to train was overwhelmed by utilizing latent space representation. The dimension is reduced compared to the distorted or corrupted image in training the GAN. Then reinforcement learning was deployed to pick the correct GAN input to get the image's latent space representation that is most suitable for the current input of the missing or distorted image region. In this paper, we also learned that the trained agent enhances the accuracy in a classification task of images with missing data. We successfully examined the classification enhancement on images missing 30%, 50%, and 70%. © 2021 IEEE.

关键词： Reinforcement learning

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Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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Optoelectronics Letters 2021年第5期17卷 271-275页

作者： WU Xu-jie SONG Bin-bin WU Ji-xuan HUANG Wei The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 300387China

A refractive index(RI)sensor based on elliptical core photonic crystal fiber(EC-PCF)has been *** asymmetric elliptical core introduces the polarization-dependent characteristics of the fiber core *** performances of intermodal interference between the intrinsic polarization fiber core modes are investigated by contrast in two interferometers based on the Mach-Zehnder(M-Z)and Sagnac interference *** addition,the RI sensing characteristics of the two interferometers are studied by successively filling the three layers air holes closest to the elliptical core in the *** results show that the M-Z interference between LP_(01)and LP_(11)mode in the same polarized direction is featured with the incremental RI sensing sensitivity as the decreasing interference length,and the infilled scope around the elliptical core has a weak correlation with the RI sensing *** to the high birefringence of LP11 mode,the Sagnac interferometer has better RI sensing performance,the maximum RI sensing sensitivity of 12000 nm/RIU is achieved under the innermost one layer air holes infilled with RI matching liquid of RI=1.39 at the pre-setting EC-PCF length of 12 cm,which is two orders of magnitude higher than the M-Z interferometer with the same fiber *** series of theoretical optimized analysis would provide guidance for the applications in the field of biochemical sensing.

关键词： Sagnac PCF Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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Multi-Scale Exact and Opposition-Based Learning Sine Cosine Algorithm for Discounted {0-1} Knapsack Problems

SSRN

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SSRN 2022年

作者： Kang, Yan Wang, Haining Pu, Bin Huang, Xin Liu, Jiansong Yang, Xuekun National Pilot School of Software Yunnan university Yunnan Kunming650000 China College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China

The discounted {0-1} knapsack problem (DKP) is one of the NP-hard problems, which is more challenging to obtain optimal item combinations than the classical backpack *** and meta-heuristic algorithms have different merits in solving the DKP of different scales, and the existing methods still lack the balance of exact and fuzzy *** this paper, an exact hybrid algorithm efficiently solves the DKP by fusing greedy strategies, opposition-based learning (OBL), and the sine cosine algorithm (SCA) on a different ***, initial populations are generated by balancing randomness and exactness. Furthermore, a forward-inverse SCA search is proposed to jump out of the local optimum by utilizing the worst and best solutions alternating based on the OBL. A multi-strategy mutation operator and a dynamic adaptive strategy are proposed to efficiently balance exploration and exploitation and adjust the number of mutated items, ***, extensive experiments are conducted on 30 publicly available datasets compared with the greedy algorithm, 3 SCAs, and 5 competitive baseline methods. The experiment results demonstrate our method obtains state-of-the-art performance. © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Design, techno-economic evaluation, and experimental testing of grid connected rooftop solar photovoltaic systems for commercial buildings

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Frontiers in Energy Research 2024年 12卷

作者： Habib, Salman Tamoor, Muhammad Gulzar, Muhammad Majid Chauhdary, Sohaib Tahir Ahmad, Hasnain Alqahtani, Mohammed Khalid, Muhammad Control and Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering and Technology Government College University Faisalabad Faisalabad Pakistan King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical and Computer Engineering College of Engineering Dhofar University Salalah Oman Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences PIEAS Islamabad Pakistan Department of Industrial Engineering and Center for Engineering and Technology Innovations King Khalid University Abha Saudi Arabia Dhahran Saudi Arabia

This study aims to investigate the potential of rooftop solar photovoltaic systems for commercial buildings. Helio-Scope software is utilized to perform simulations to determine the ideal rooftop area for photovoltaic panels. The efficiency of photovoltaic systems is impacted by the shading effects of photovoltaic modules installed in parallel rows. To enhance energy output, the optimal distance between rows is determined, and it is found that 5-feet inter-row spacing provides the best results. The simulation results indicate that with 5-feet inter-row spacing, photovoltaic system has an energy generation of 371.6 MWh, specific yield of 1508.0 kWh/kWp, performance ratio of 82.1%, solar access rate of 98.9%, total solar resource fraction of 96.3% and a total irradiance of 1655.9 kWh/m2. The annual nameplate energy is 425.1 MWh, output energy at irradiance levels is 423.1 MWh, optimal DC output is 378.5 MWh, inverter output is 373.5 MWh, and total energy delivered to the national power grid is 371.6 MWh. The average daily DC inverter input power is 158881.5110 W and the average daily AC inverter output power is 152231.6311 W, showing an inverter efficiency of approximately 95.93%. Moreover, detailed testing of the installed PV system is performed on-site to make sure that equipment’s performance guarantees are achieved, the system is properly installed and its configuration is suitable for commercial operations. The maximum daily output energy generation of an installed photovoltaic (PV) system is 1.33 MWh, and its average energy generation is 1.09 MWh. The voltage of all strings is within the rated range of the inverter, with a maximum voltage of 835 V and a minimum of 698 V, as tested by PV string open-circuit voltage. The inverter efficiency test is also performed, with a maximum efficiency of 98.83% and fill factors ranging from 81.37% to 82.34%. The payback period of a photovoltaic system is 4.22 years and LCOE is 0.0229$/kWh. PV system saved 215569.818 metric to

关键词： Solar power generation

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Local Neighbor Propagation on Graphs for Robust Feature Matching

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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A Topology Independent Fast Rerouting Scheme with Hop-by-Hop Forwarding Can Achieve a Full Protection

SSRN

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SSRN 2022年

作者： Geng, Haijun Meng, Zhuo Zhang, Han Shi, Xingang Wang, Zhiliang Yin, Xia Chen, Meng Guo, Yingya The School of Automation and Software Engineering Shanxi University School of Computer and Information Technology China The Institute for Network Sciences and Cyberspace Tsinghua University China The Department of Computer Science and Technology Tsinghua University China The School of Big Data and Software Engineering Chongqing University China The College of Computer and Data Science Fuzhou University China

To support critical applications or guarantee a high quality of service, Internet Service Providers (ISPs) generally deploy a technique called routing protection algorithm for local fast rerouting. Alternate next hops are stored alongside with the default next hops in a router’s forwarding table, and can be immediately activated to invoke a loop free repair path in face of link or node failures, or used for multipath transmission. However, the existing routing protection methods either cannot protect all possible single-failure scenarios in the network or needs the assistance of an auxiliary address. In response to the above problems, the present study proposes a single-failure routing protection algorithm based on hop by hop routing (SPA). The algorithm has the following three advantages: (1) supports hop-by-hop forwarding, (2) supports incremental deployment, and (3) can protect all possible singlefailure scenarios in the network as long as network keep connected. Extensive simulations show that, compared to the state-of-the-art schemes which are adopted by the industry, SPA not only can handle arbitrary single-failure situations in the network but also has a small path stretch. These advantages make SPA a good candidate for practical solution for IP fast reroute when network failure occurs. © 2022, The Authors. All rights reserved.

关键词： Internet service providers

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Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus

arXiv

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arXiv 2023年

作者： Cheng, Huqiang Xie, Mengying Yang, Xiaowei Lü, Qingguo Li, Huaqing Key Laboratory of Dependable Services Computing in Cyber Physical Society-Ministry of Education College of Computer Science Chongqing University Chongqing401331 China School of Software Engineering South China University of Technology Guangzhou510006 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

In the intricate dance of multi-agent systems, achieving average consensus is not just vital–it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make sensitive information be revealed. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in initial several iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. © 2023, CC BY.

关键词： Consensus algorithm

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Enhanced Small Object Detection Neural Network 2020

Enhanced Small Object Detection Neural Network

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2020 International Conference on Aviation Safety and Information Technology, ICASIT 2020

作者： Liu, Lin Fan, Jiahao Xu, Cong College of Software Engineering Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Limited Company Jilin China

ISBN: (纸本)9781450375764

Faster-RCNN is an vital deep learning object detection algorithm. Nevertheless, the small object detection effect of Faster-RCNN, which does not use multi-layer feature map, is not good enough. In this paper, a new network architecture called enhanced small object detection neural network (ESOD) is proposed. Two advancements are proposed in ESOD. Firstly, a innovative multilayer combining network which integrating multi-layer feature map is designed to leverage multi-layer convolutional information. Thereupon, the ability of small object detection can be improved. Secondly, the scale-transfer layer (STL) is introduced in the fusion of features which are in high layer and features which are in low layer, which extends the size of the features by reducing the quantity of aisles. In this way, the semantic features of distinct size are acquired without additional parameters. Extensive experimental results on PASCAL VOC 2007 and 2012 datasets indicate that, compared with several commonly used algorithms, ESOD brings better small object detection ability than existing methods for object detection. © 2020 ACM.

关键词： Object detection

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