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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Liu, Yuxiang Wang, Zhaojian Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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Detection of Weak Signal Amplitude of Winding Deformation Based on Duffing Oscillator

Detection of Weak Signal Amplitude of Winding Deformation Ba...

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第40届中国控制会议

作者： Sheng-min Li Yuan-yuan He Yue-yue Che Xu-xia Sun Academy of Automation & Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online,this paper proposes a method to detect the amplitude of the response signal by using Duffing ***,in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process,a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is ***,using this method to process the two sets of measured response signals of online detection,the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5.69%,which meets the requirements of the power industry standard,which proves that the method used in this article is It is feasible in winding deformation detection.

关键词： On-line detection of winding deformation Weak signal detection Duffing vibrator

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Reference-point-based Brain Storm Optimization Algorithm for Many-Objective Optimization

Reference-point-based Brain Storm Optimization Algorithm for...

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第32届中国过程控制会议(CPCC2021)

作者： Yali Wu Xiaoxiao Quan Jinjin Qi Yulong Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Brain storm optimization algorithm,as a new type of swarm intelligence optimization algorithm,has attracted the attention of many researchers as soon as it was put ***,it has been applied to various many-objective,multi-modal and practical engineering *** this paper,a brain storm optimization algorithm based on reference points was proposed to solve many-objective optimization ***,a series of reference points are introduced into the target space,and the population is clustered in an adaptive clustering method centered on the reference point poles to guide the evolution of the ***,the idea based on decomposition is combined with the mechanism of brain storm optimization algorithm to solve many-objective optimization ***,a mechanism to update the external archive set based on the strategy of enhanced dominance and weighted boundary intersection is proposed to maintain the convergence and diversity of the *** simulation results on the DTLZ series of test function sets show that the proposed algorithm based on reference points is effective.

关键词： Brain Storm Optimization Algorithm Reference Point Many-objective optimization problems adaptive cluster

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A New Global Optimization Method Based on Simplex Branching for Solving a Class of Non-Convex QCQP Problems

arXiv

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arXiv 2023年

作者： Zhang, Bo Gao, YueLin Liu, Xia Huang, XiaoLi School of Civil and Hydraulic Engineering Ningxia University Yinchuan750021 China School of Mathematics and Statistics Ningxia University Yinchuan750021 China Ningxia province key laboratory of intelligent information and data processing North Minzu University Yinchuan750021 China School of mathematics and information science North Minzu University Yinchuan750021 China

Quadratic constrained quadratic programming problems often occur in various fields such as engineering practice, management science, and network communication. This article mainly studies a non convex quadratic programming problem with convex quadratic constraints. Firstly, based on our existing results, the problem is reconstructed as an equivalent problem with a simple concave quadratic objective function in the result space, with a convex feasible domain. A global optimization algorithm for solving equivalent problems is proposed based on a branch and bound framework that can ensure the global optimality of the solution. This algorithm combines effective relaxation processes with branching processes related to new external approximation techniques. Finally, the theoretical feasibility of the algorithm was analyzed. Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

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MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuyan Fang Yu Liu Song Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan University of Science and Technology Wuhan China

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules.

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Robust Remote Sensing Scene Classification with Multi-View Voting and Entropy Ranking

arXiv

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arXiv 2023年

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Tcgfusion: A Network for Pet-Mri Fusion Based on Gan and Transformer

SSRN

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SSRN 2022年

作者： Fan, Chao Chen, Zhixiang Lin, Hao Wang, Xiao School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China

We provide an end-to-end unsupervised training model called TCGFusion: a network for PET-MRI fusion based on GAN and transformer. We set the generator to a dual-path layout to assure the gathering of both global and detailed image information. To guarantee the coarse scale picture extraction and prevent the high computational cost, the global path T-Path utilizes Grid-former (a residual grid transformer structure). The detailed path C-Path is DenseNet, and its multi-cascade structure captures image details well. Additionally, we set up a dual adversarial mechanism between the fused image and the source image, creating a dual discriminator to determine the distribution of structural similarity between the fused result and the source image, and using patch-SSIM to reinforce the semantic constraints between them and promote the image details. According to our extensive studies on public datasets, medical image fusion demonstrate the superiority of our TCGFusion compared to the state-of-the-art image fusion algorithms. © 2022, The Authors. All rights reserved.

关键词： Semantics

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A Method of Realizing External Network Access to Intranet for Embedded Web Server

A Method of Realizing External Network Access to Intranet fo...

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Electronic information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Taiguo Qu Pengju Tang Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China Key Laboratory of Intelligent Control Technology for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China

ISBN: (纸本)9781665464024

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects.

关键词： Performance evaluation Uniform resource locators Smart agriculture Web servers Software Real-time systems Organ transplantation

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Dfe-Net: Dual-Branch Feature Extraction Network for Enhanced Segmentation in Skin Lesion

SSRN

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SSRN 2022年

作者： Fan, Chao Yang, Litao Lin, Hao Qiu, Yingying School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China

Skin lesion segmentation is a critical method for extracting pathological information from dermoscopy images, which is of great significance for lesion location, recognition, monitoring and treatment. Due to the varied sizes of lesions, different forms and colors, and blurring boundaries, the present detection methods are unable to accurately anticipate local features, which has an impact on the segmentation accuracy. Therefore, the Dual-branch Feature Extraction Network (DFE-Net) is suggested in this paper. We designed two types of encoders based on the Transformer and Efficient Channel Attention (ECA) module, one for extracting the global feature and another for the local feature, before fusing and decoding. Simultaneously, the skip connections from the Enhanced ECA Feature Extraction Modules to decoders are designed to reduce feature loss during decoding, this will restore the boundaries and local features to the greatest extent possible. We test the model on three publicly available skin lesion datasets: PH2, ISIC-2016, and ISIC-2018. The results show that our model is superior to other previous methods, and the segmentation characteristics are more accurate representations of the actual lesion. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Unsupervised Time Series Anomaly Detection under data Contamination 12

Unsupervised Time Series Anomaly Detection under Data Contam...

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12th International Conference on information Technology in Medicine and Education, ITME 2022

作者： Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350310153

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE. © 2022 IEEE.

关键词： Contamination

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