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arXiv 2023年

作者： Zhang, Chengjie Li, Liangsheng Lu, Guodong Yuan, Haidong Duan, Runyao School of Physical Science and Technology Ningbo University Ningbo315211 China Science and Technology on Electromagnetic Scattering Laboratory Beijing100854 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Institute for Quantum Computing Baidu Research Beijing100193 China

Most previous efforts of quantum error correction focused on either extending classical error correction schemes to the quantum regime by performing a perfect correction on a subset of errors, or seeking a recovery operation to maximize the fidelity between a input state and its corresponding output state of a noisy channel. There are few results concerning quantum error pre-compensation. Here we design an error pre-compensated input state for an arbitrary quantum noisy channel and a given target output state. By following a procedure, the required input state, if it exists, can be analytically obtained in single-partite systems. Furthermore, we also present semidefinite programs to numerically obtain the error pre-compensated input states with maximal fidelities between the target state and the output state. The numerical results coincide with the analytical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Error correction

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Distributed Load Shedding via Differentially Private Average Consensus Algorithm

Distributed Load Shedding via Differentially Private Average...

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Chinese Control and Decision Conference, CCDC

作者： Zixin Qiao Fanghong Guo Xiping Pan Yue Sun Lei Wang Shanghai Institute of Computing Technology Shanghai China Department of Automation Zhejiang University of Technology Hangzhou China Shandong Institute of Blockchain Jinan China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665478977

This paper presents a distributed load shedding strategy for islanded active distribution power system, where the average consensus algorithm acts as a key tool to discover the required global information for load shedding. For privacy concerns, we propose a new differentially private average consensus algorithm, where a distributed shuffling mechanism based on the Pailler cryptosystem is established to initialize the initial states for the subsequent stage of running standard average consensus. For the proposed algorithm, both rigorous provable differential privacy and accuracy guarantee are established, with an improved privacy/accuracy trade-off than the existing relevant frameworks. Particularly, it is shown that given any expected differential privacy levels, the achievable mean-square computation error is inversely proportional to the network size, in contrast with the optimal error achieved in the literature that is independent of the network size. A numerical example and an application to the distributed load shedding for an IEEE 33-bus power system are used to validate the effectiveness of the proposed approach.

关键词： Privacy Differential privacy Load shedding Consensus algorithm Power systems Standards

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Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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Segmentation Model Approaches using Cardiac Magnetic Resonance Images: A Review

TechRxiv

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TechRxiv 2022年

作者： Khan, Amjad Nfai, Dayang Wang, Yin Chai Lim, P.C. Ullah, Faheem Javid Ahmad, Tariq Deapartment of Computing Abasyn University Islamabad Campus Pakistan UNIMAS Sarawak Malaysia School of Automation and Control Engineering SCUT China Department of Computer Science University of Science and Technology Bannu Pakistan School of Information and Communications Engineering Guilin University China

Automatic segmentation solution is the process of detecting and extracting information to simplify the representation of Cardiac Magnetic Resonance images (CMRI) of Left Ventricle (LV) contour. This segmented information, using CMR images, helps to reduce the segmentation error between expert and automatic segmented contours. The error represents missing region values calculated in percentages after segmenting a cardiac LV contour. This review paper will discuss the major three segmentation approaches, namely manual approach, semiautomatic, and fully automatic, along with the segmentation models, namely image-based models, region-based models, edge-based models, deformable-based models, active shape-based models (ASM), active contour-based models (ACM), level set-based models (LSM), and variational LSM (VLSM). The review deeply explains the performance of segmentation models using different techniques. Furthermore, the review compares 122 studies on segmentation model approaches, i.e., 16 from 2004 to 2010, 40 from 2011 to 2016, and 63 from 2017 to 2021, and 3 other related studies were conducted for LV contour segmentation, cardiac function, area-at-risk (AAR) identification, scar tissue classification, oedema tissue classification, and identification via presence, size, and location. Given the large number of articles on CMR-LV images that have been published, this review conducted a critical analysis and found a gap for researchers in the areas of LV localization, LV contour segmentation, cardiac function, and oedoema tissue classification and segmentation. Regarding critical analysis, this paper summarised a research gap and made useful suggestions for new CMR-LV researchers. although a timely reviewed study can lead to cardiac segmentation challenges, which will be discussed in each review section. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Distributed Compressed Sensing Based on Local Transformer Network

SSRN

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SSRN 2023年

作者： Zhou, Yu Xie, Wei Yang, Yingkai Sun, Shilong Zhang, Xiao Qin, Jun Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Department of Computer Science South-Central Minzu University Wuhan430074 China

Among compressed sensing algorithms, Distributed Compressed Sensing (DCS) stands out for its ability to jointly reconstruct multiple signals, making it a focal point of interest for researchers and engineers , thanks to its outstanding efficiency in signal reconstruction. In contrast to conventional approaches that depend on a priori estimates, like joint sparsity of signals, deep neural networks leverage their robust feature extraction and sequence modeling abilities to automatically capture inter-signal dependencies. This confers them with the benefits of faster solving speed and superior reconstruction accuracy. Most of the current methods, however, do not fully utilize the global dependency information between signals, while also ignoring the detailed features within the signals. Therefore, this paper proposes a Local Transformer Network for DCS (LTN-DCS). It utilizes the self-attention mechanism to extract global contextual information between the reconstructed signal sequences, while also addressing local redundancy partitioning within the signal sequences for finer-grained information capture. This approach generates probabilistic estimates of the non-zero element positions in each signal, forming the foundation for selecting basis atoms from the sampling matrix. Finally, the least-squares method is used to achieve the signal reconstruction. Through extensive experiments, the results show that our proposed method outperforms state-ofthe art DCS methods. © 2023, The Authors. All rights reserved.

关键词： Signal reconstruction

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Minimum displacements for cloth-obstacle penetration resolving 37

Minimum displacements for cloth-obstacle penetration resolvi...

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37th Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2016

作者： Sun, Liming Nyberg, Timo R. Xiong, Gang Ye, Juntao NLPR-LIAMA Institute of Automation CAS China Cloud Computing Center CAS Dongguan China SKL-MCCS Institute of Automation CAS China School of Automation Foshan University China Department of Industrial Engineering and Management Aalto University Finland

Pre-existing penetrations often show up in many applications, particularly in garments fitting. The popular continuous collision detection (CCD) based methods are incapable of handling them, as there is no history information to rely on. On the other hand, surfaces of human bodies have normals defined to designate their orientation (i.e. front- and back-face), which are totally overlooked by CCD methods (thus they are orientation-free). In this paper we present a history-free method for separating two penetrating meshes, given that one of them represents a rigid object and has clarified surface orientation. This method computes all edge-face (E-F) intersections with discrete collision detection, and identifies illegal vertices with the help of surface orientation, and then builds a number of penetration stencils. On response, the stencil vertices are relocated into a penetration-free state, via a global displacement minimizer. The proposed algorithm outperforms existing methods for handling solid/cloth collisions, thus is an effective tool for applications like virtual-try-on and example-based garment animation synthesis. © 2016 The Eurographics Association.

关键词： Animation

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Fractional Order AGC Design for Power Systems via Artificial Gorilla Troops Optimizer

Fractional Order AGC Design for Power Systems via Artificial...

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International Conference on Power Electronic Drives

作者： Sneha V Sah Vivek Prakash Pawan Kumar Pathak Anil Kumar Yadav School of Automation Banasthali Vidyapith Rajasthan India Department of Technical Education Government of Rajasthan Government Polytechnic Tonk India Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical Engineering National Institute of Technology Hamirpur Hamirpur (H.P.) India

Due to parameter uncertainty, vague loading behavior, and nonlinearities of the interconnected power system (IPS), various controllers come into the picture to deal with automatic generation control (AGC). The critical role of AGC is to maintain a balance among power demands and generations in IPS. This paper deals with the implementation of fractional order PID (FOPID) controller for AGC in two areas reheat thermal type IPS. To obtain the controller parameters, a recently invented metaheuristic technique named Artificial Gorilla Troops optimizer (GTO) is proposed. To replicate the practical scenarios nonlinearities such as generation rate constraints (GRC) and generation dead band (GDB) are considered in the proposed work. A comparative analysis of four state-of-the-art metaheuristic approaches, as well as the performance evaluation of the proposed controller against parametric uncertainties, have been performed. Finally, the obtained results reveal the superior performance of the proposed control scheme.

关键词： Uncertain systems Renewable energy sources Uncertainty Power demand Metaheuristics Automatic generation control Robustness

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System for String Monitoring in small Experimental PV Plants

System for String Monitoring in small Experimental PV Plants

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International Conference on Electrical Machines, Drives and Power Systems (ELMA)

作者： Nikolay Valov Boris Evstatiev Tsvetelina Mladenova Vladislav Hinkov Nicolay Mihailov Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Center for Computing and Information Services University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria

This paper presents a system for real-time measurement of electrical and physical parameters of a small (12kWp) photovoltaic power plant. This experimental photovoltaic installation is located in the urban area of Ruse, on the territory of the University of Ruse. A system was designed that measures the energy produced by each solar array in shaded locations commonly found in urban environments, as well as environment parameters, solar radiation, PV cell temperature and even infrared images of the facility. The collected data will be used to model and forecast energy production from solar panels under urban conditions.

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Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis

arXiv

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arXiv 2024年

作者： Lawan, Adamu Pu, Juhua Yunusa, Haruna Umar, Aliyu Lawan, Muhammad School of Computer Science and Technology Beihang University Beijing China National Research Institute for Teaching Materials in Information Science and Tech. China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computing University of Portsmouth Portsmouth United Kingdom Department of Information and Communication Technology Federal University Gusau Nigeria

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN’s effectiveness, outperforming state-of-the-art (SOTA) baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huarong Zhao Jinjun Shan Xing Li Hongnian Yu School of Internet of Things Engineering Jiangnan University Wuxi P. R. China Department of Earth and Space Science and Engineering York University Toronto ON Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning P. R. China School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

关键词： Uncertainty Event detection Soft sensors Delay effects Mathematical models Data models Steady-state Vehicle dynamics Autonomous vehicles Cybernetics

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