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Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

Computer Modeling in engineering & Sciences 2024年第2期138卷 1183-1206页

作者： Xinzheng Wang Bing Guo Yan Shen College of Computer Science Sichuan UniversityChengdu610065China School of Information Science and Engineering Guilin University of TechnologyGuilin541004China School of Control Engineering Chengdu University of Information TechnologyChengdu610225China

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,*** with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is *** makes building models to predict students’performance accurately in such an environment even *** paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course *** experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

关键词： Computer education performance prediction deep learning

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Multi-Level Parallel Network for Brain Tumor Segmentation

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Computer Modeling in engineering & Sciences 2024年第4期139卷 741-757页

作者： Juhong Tie Hui Peng School of Software Engineering Chengdu University of Information TechnologyChengdu610225China

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and *** convolution neural networks(CNNs)have recently improved glioma segmentation ***,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation ***,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor *** also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep *** proposed method is trained and validated on the BraTS 2020 training and validation *** the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,*** with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

关键词： Convolution neural network brain tumor segmentation parallel network

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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 369-380页

作者： Tianyu Wang Fan Zhou Yangjie Wu Jun Zhao Wei Wang IEEE the School of Control Science and Engineering Dalian University of Technology the School of Information Science and Engineering Dalian Polytechnic University

As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.

关键词： Ensemble learning industrial energy prediction multi-working condition structural deep network transfer learning

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A game incentive mechanism for energy efficient federated learning in computing power networks

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Digital Communications and Networks 2024年第6期10卷 1741-1747页

作者： Xiao Lin Ruolin Wu Haibo Mei Kun Yang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengduChina Chengdu Tongfei Technology Co.LtdChengduChina

Computing Power Network(CPN)is emerging as one of the important research interests in beyond 5G(B5G)or *** paper constructs a CPN based on Federated Learning(FL),where all Multi-access Edge Computing(MEC)servers are linked to a computing power center via wireless *** this FL procedure,each MEC server in CPN can independently train the learning models using localized data,thus preserving data ***,it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC *** address these issues,we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC ***,we formulate a comprehensive algorithm to jointly optimize the communication resource(wireless bandwidth and transmission power)allocations and the computation resource(computation capacity of MEC servers)allocations while ensuring the local accuracy of the training of each MEC *** numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.

关键词： Computing power network Federated learning Energy efficiency Stackelberg game Resource allocation

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Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

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Journal of Systems engineering and Electronics 2024年第3期35卷 509-531,F0002页

作者： OU Xinwei CHEN Zhangxin ZHU Ce LIU Yipeng School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Deep neural networks(DNNs)have achieved great success in many data processing ***,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power *** this paper,we focus on low-rank optimization for efficient deep learning *** the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network *** the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast *** model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,*** a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and *** addition to summary of recent technical advances,we have two findings for motivating future *** is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network *** other is a spatial and temporal balance for tensorized neural *** accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

关键词： model compression subspace training effective rank low rank tensor optimization efficient deep learning

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Scale effect removal and range migration correction for hypersonic target coherent detection

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Journal of Systems engineering and Electronics 2024年第1期35卷 14-23页

作者： WU Shang SUN Zhi JIANG Xingtao ZHANG Haonan DENG Jiangyun LI Xiaolong CUI Guolong School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar ***,with the increasing requirement of far-range detection,the time bandwidth product,which is corresponding to radar’s mean power,should be promoted in actual ***,the echo signal generates the scale effect(SE)at large time bandwidth product situation,influencing the intra and inter pulse integration *** eliminate SE and correct RM,this paper proposes an effective algorithm,i.e.,scaled location rotation transform(ScLRT).The ScLRT can remove SE to obtain the matching pulse compression(PC)as well as correct RM to complete CI via the location rotation transform,being implemented by seeking the actual rotation *** to the traditional coherent detection algorithms,Sc LRT can address the SE problem to achieve better detection/estimation *** last,this paper gives several simulations to assess the viability of ScLRT.

关键词： hypersonic target detection coherent integration(CI) scale effect(SE)removal range migration(RM)correction scaled location rotation transform(ScLRT)

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Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

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Defence technology（防务技术） 2024年第5期35卷 275-288页

作者： Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic *** methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative *** paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse *** results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

关键词： Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection Outlier detection

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Real-Time Dwell Scheduling Based on a Unified Pulse Interleaving Framework for Phased Array Radar

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Tsinghua Science and technology 2024年第5期29卷 1540-1553页

作者： Ting Cheng Luqing Liu Zhongzhu Li Siyu Heng School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 614000China

Dwell scheduling is a key for phased array radar to realize multi-function and it becomes especially challenging in complex tactical *** this manuscript,a real-time radar dwell scheduling algorithm based on a unified pulse interleaving framework is proposed.A unified pulse interleaving framework that can realize pulse interleaving analysis for phased array radars with different receiving modes is put forward,which greatly improves the time utilization of the *** on above framework,a real-time two-stage approach is proposed to solve the optimization problem of dwell *** importance and urgency criteria are guaranteed by the first pre-schedule stage,and the desired execution time criterion is improved at the second stage with the modified particle swarm optimization(PSO).Simulation results demonstrate that the proposed algorithm has better comprehensive scheduling performance than up-to-date algorithms that consider the pulse interleaving technique for both single beam and multiple beams receiving ***,the proposed algorithm can realize dwell scheduling in realtime.

关键词： phased array radar dwell scheduling receiving mode pulse interleaving particle swarm optimization

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Quantum color image encryption: Dual scrambling scheme based on DNA codec and quantum Arnold transform

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Chinese Physics B 2025年第1期34卷 235-244页

作者： Tao Cheng Run-Sheng Zhao Shuang Wang Kehan Wang Hong-Yang Ma School of Sciences Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform(QAr T) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QAr T to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of “Monkey”, “Flower”, and “House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate(NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity(UACI) is 33.41%,and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

关键词： DNA codec quantum Arnold transform quantum image encryption algorithm

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Global receptive field transformer decoder method on quantum surface code data and syndrome error correction

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Chinese Physics B 2025年第3期34卷 267-276页

作者： Ao-Qing Li Ce-Wen Tian Xiao-Xuan Xu Hong-Yang Ma Jun-Qing Liang School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China School of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum computing has the potential to solve complex problems that are inefficiently handled by classical ***,the high sensitivity of qubits to environmental interference and the high error rates in current quantum devices exceed the error correction thresholds required for effective algorithm ***,quantum error correction technology is crucial to achieving reliable quantum *** this work,we study a topological surface code with a two-dimensional lattice structure that protects quantum information by introducing redundancy across multiple qubits and using syndrome qubits to detect and correct ***,errors can occur not only in data qubits but also in syndrome qubits,and different types of errors may generate the same syndromes,complicating the decoding task and creating a need for more efficient decoding *** address this challenge,we used a transformer decoder based on an attention *** mapping the surface code lattice,the decoder performs a self-attention process on all input syndromes,thereby obtaining a global receptive *** performance of the decoder was evaluated under a phenomenological error *** results demonstrate that the decoder achieved a decoding accuracy of 93.8%.Additionally,we obtained decoding thresholds of 5%and 6.05%at maximum code distances of 7 and 9,*** results indicate that the decoder used demonstrates a certain capability in correcting noise errors in surface codes.

关键词： quantum error correction surface code transformer decoder

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