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CSEE Journal of Power and Energy Systems 2024年第2期10卷 584-592页

作者： Zhi Ding Tianyao Ji Mengshi Li Q.H.Wu school of electric Power engineering South China University of Technology(SCUT)Guangzhou 510641China

In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.

关键词： Hadamard matrix mathematical morphology morphological clustering power quality disturbance Walsh theory

Location of Asymmetric Ground Fault Using Virtual Injected Current Ratio and Two-stage Recovery Strategy in Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 151-161页

作者： Haiting Shan Luliang Zhang Q.H.Wu Mengshi Li school of electric Power engineering South China University of Technology(SCUT)Guangzhou 510641China

Sparse measurements challenge fault location in distribution *** paper proposes a method for asymmetric ground fault location in distribution networks with limited measurements.A virtual injected current vector is formulated to estimate the fault line,which can be reconstructed from voltage sags measured at a few buses using compressive sensing(CS).The relationship between the virtual injected current ratio(VICR)and fault position is deduced from circuit analysis to pinpoint the ***,a two-stage recovery strategy is proposed for improving reconstruction accuracy of the current vector,where two different sensing matrixes are utilized to improve the *** proposed method is validated in IEEE 34 node test *** results show asymmetric ground fault type,resistance,fault position and access of distributed generators(DGs)do not significantly influence performance of our *** addition,it works effectively under various scenarios of noisy measurement and line parameter *** on 134 node test feeders prove the proposed method is also suitable for systems with more complex structure.

关键词： Distribution networks fault location reconstruction accuracy two-stage recovery virtual injected current ratio

Design of a novel hybrid quantum deep neural network in INEQR images classification

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Chinese Physics B 2024年第6期33卷 230-238页

作者：王爽王柯涵程涛赵润盛马鸿洋郭帅 school of Information and control engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

关键词： quantum computing image classification quantum–classical hybrid neural network quantum image representation interpolation

Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

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Chinese Physics B 2024年第4期33卷 275-283页

作者：程涛赵润盛王爽王睿马鸿洋 school of Sciences Qingdao University of TechnologyQingdao 266033China school of Information and control engineering Qingdao University of TechnologyQingdao 266033China

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum *** this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from *** the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of ***,we use the MNIST dataset to test the potential of the *** results indicate that the HQCCNN has good performance in solving classification *** binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.

关键词： parameterized quantum circuits quantum machine learning hybrid quantum-classical convolutional neural network

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

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Chinese Physics B 2024年第4期33卷 285-295页

作者：赵润盛马鸿洋程涛王爽范兴奎 school of Sciences Qingdao University of TechnologyQingdao 266033China school of Information and control engineering Qingdao University of TechnologyQingdao 266033China

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)*** this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation *** experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,***,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,*** results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

关键词： readout errors quantum generative adversarial networks bit-flip averaging method fault tolerant mechanisms

Recurrent neural network decoding of rotated surface codes based on distributed strategy

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Chinese Physics B 2024年第4期33卷 322-330页

作者：李帆李熬庆甘启迪马鸿洋 school of Information and control engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction is a crucial technology for realizing quantum *** computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding *** error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise *** this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes *** decoding algorithm is based on renormalization group decoding and recurrent neural network *** recurrent neural network is trained through the ResNet architecture to improve its decoding *** we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,*** results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

关键词： quantum error correction rotated surface code recurrent neural network distributed strategy

Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms

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Chinese Physics B 2024年第6期33卷 262-270页

作者：陈庆辉姬宇欣王柯涵马鸿洋纪乃华 school of Information and control engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

关键词： quantum error correction topological quantum stabilizer code reinforcement learning attention mechanism

electricity Pricing with Continuous-time Commodity Model and Non-monotonic Load Curve

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 272-280页

作者： Lijia Han Yue Li Haoyong Chen Ningning Lu Mathematical and Physical Science school North China Electric Power UniversityBeijing 102206China school of electric Power engineering South China University of TechnologyGuangzhou 510641China

Under the "carbon peak and carbon neutrality"background,clean energy enters the market on a large scale,so it is very important to study the pricing theory of *** bright spot of this paper is that the load curve is more *** the original version of electricity pricing theory based on continuous time commodity model,the load curve is supposed to be a monotonic function with time for simplicity of derivation,which is not *** overcome this defect,load curves are supposed to be in shapes of a unimodal model and multimodal cases in this paper,which are more in conformity with the actual load curve in real-world power *** study cases are used to demonstrate the rationality of the proposed *** results together with the authors'previous work provide a complete pricing scheme for continuous-time electricity commodity model.

关键词： Functional optimization market equilibrium spot pricing variational method

An adaptive 3D reconstruction method for asymmetric dual-angle multispectral stereo imaging system on UAV platform

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Science China(Information Sciences) 2024年第8期67卷 285-299页

作者： Chen WANG Xian LI Yanfeng GU Zixu WANG school of Electronics and Information engineering Harbin Institute of Technology Beijing Institute of control and Electronic Technology

A multispectral imaging system often cannot capture 3D spatial information owing to hardware limitations, which diminishes the effectiveness across various domains. To address this problem, we have developed a multispectral stereo imaging system along with an adaptive 3D reconstruction algorithm. Unlike existing unmanned aerial vehicle stereo imaging systems, our multispectral stereo imaging system uses two multispectral cameras with asymmetric spectral bands positioned at different angles. This design enables the acquisition of a higher number of bands and lateral spatial information while maintaining a lightweight structure. This system introduces challenges such as large geometric distortions and intensity differences between multiple bands. To accurately recover 3D spatial information, we propose an adaptive 3D reconstruction method. This method employs a position and orientation system-assisted projection transformation and a normalized threshold adjustment strategy. Finally, mutual information is used to reconstruct the multispectral images densely, effectively addressing nonlinear differences and generating a comprehensive multispectral point cloud. Our stereo system was used for two real data collections in different regions, and the efficacy of the proposed 3D reconstruction method was validated by comparing it with existing methods and commercial software.

关键词： 3D reconstruction feature extraction multispectral images stereo imaging system unmanned aerial vehicle

A Mean-Field Game for a Forward-Backward Stochastic System With Partial Observation and Common Noise

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 746-759页

作者： Pengyan Huang Guangchen Wang Shujun Wang Hua Xiao the school of control Science and engineering Shandong University the school of Statistics and Mathematics Shandong University of Finance and Economics IEEE the school of Management Shandong University the school of Mathematics and Statistics Shandong University

This paper considers a linear-quadratic(LQ) meanfield game governed by a forward-backward stochastic system with partial observation and common noise,where a coupling structure enters state equations,cost functionals and observation ***,to reduce the complexity of solving the meanfield game,a limiting control problem is *** virtue of the decomposition approach,an admissible control set is *** a filter technique and dimensional-expansion technique,a decentralized control strategy and a consistency condition system are derived,and the related solvability is also ***,we discuss an approximate Nash equilibrium property of the decentralized control ***,we work out a financial problem with some numerical simulations.

关键词： Decentralized control strategy ϵ-Nash equilibrium forward-backward stochastic system mean-field game partial observation