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APFA:Ameliorated Pathfinder Algorithm for Engineering Applications

Journal of Bionic Engineering 2024年第3期21卷 1592-1616页

作者： Keyu Zhong Fen Xiao Xieping Gao Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal UniversityChangsha410081China School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China School of Computer Science Xiangtan UniversityXiangtan411105China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China

Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food *** algorithm has the characteristics of a simple structure and high ***,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation *** paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization ***,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the ***,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst ***,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence *** three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the *** performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic *** numerical experimental results demonstrated the superiority of APFA.

关键词： Pathfinder algorithm Swarm intelligent Metaheuristic Engineering problems

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An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

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Journal of Bionic Engineering 2024年第3期21卷 1541-1566页

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific *** many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching *** address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED *** proposed algorithm first stores non-dominated optimal solutions found so far into an ***,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index *** mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant ***,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto *** this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching *** proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance ***,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED ***,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs

关键词： Dynamic economic emission dispatch Multi-objective optimization Golden jackal Euclidean distance index

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An Analysis on the Training Mode of Master Students in Artificial Intelligence Field for Electronic information Professional Degree—Take hunan Normal University as an Example 8th

An Analysis on the Training Mode of Master Students in Artif...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Fu, Weina Liu, Shuai Institute of Information Science and Engineering Hunan Normal University Hunan Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha410081 China

ISBN: (纸本)9783031211638

The ability of independent innovation in the field of artificial intelligence is a key element to occupy the commanding heights of future technology and talent competition in China. The cultivation of artificial intelligence talents, especially the cultivation high-end talent, it is essential to promote the development of artificial intelligence industry. At present, the market for artificial intelligence talents is in great demand. There is a shortage of high-end artificial intelligence talents, the weak strength of artificial intelligence teachers in universities, and the incomplete education system for talent training and many other issues. This paper combines some measures taken by hunan Normal University for graduate students in the field of artificial intelligence in the degree of electronic information, the necessity and feasibility of artificial intelligence training senior talents are discussed for social needs in the field of artificial intelligence, The training goals and methods of postgraduate students in the field of artificial intelligence under the professional category of electronic information major are clarified, and the construction of artificial intelligence field is planned. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Professional aspects

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The Problems and Analysis of Artificial Intelligence Specialty Construction in Universities Under the Present Situation of Artificial Intelligence Development 8th

The Problems and Analysis of Artificial Intelligence Special...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

ISBN: (纸本)9783031211638

China’s independent innovation ability in the field of artificial intelligence is a key link to occupy the commanding heights of future science and technology and talent competition. The cultivation of artificial intelligent talents is crucial to promote the development of the artificial intelligent industry. There is a large demand for artificial intelligence talents in the market, but the development of artificial intelligence in colleges and universities is still faced with many problems, such as the construction of related disciplines and majors has just started, the strength of artificial intelligence teachers in colleges and universities is weak, the talent training is mainly based on postgraduate education and the education system is not perfect. This paper analyzes the current cultivation situation of artificial intelligence major in undergraduate colleges and vocational colleges, points out the shortcomings in the construction of artificial intelligence major, and gives some suggestions. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Personnel training

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation 48

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Wenjing Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc Beijing100085 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods. © 2023 IEEE.

关键词： Convolutional neural network Deep refinement Multi-decision Ultrasound image segmentation

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Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model 9th

Novelty Detection-Based Automated Anomaly Identification via...

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9th CCF Conference on Big Data, BigData 2021

作者： Liu, Lianye Liu, Jinping Wu, Juanjuan Zhou, Jiaming Cai, Meiling Hunan Meteorological Science Institute Hunan Changsha410007 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China

ISBN: (纸本)9789811697081

Novelty detection (ND) is a crucial task in machine learning to identify anomalies in the test data in some respects different from the training data. As an anomaly detection method, novelty detection only uses normal samples for model learning, which can well fit most of the natural scenes that the amount of abnormal samples is in fact strongly insufficient, such as network intrusion detection, industrial fault detection, and so on, due to the rareness of abnormal events or the high cost of abnormal samples collection. This paper proposes a reconstruction-based ND scheme by introducing an optimized deep generative model (ODGM), which combines the concept of Variational Auto-encoder (VAE) and the generative adversarial network (GAN) model jointly to efficiently and stably learn the essential characteristics from normal samples. A novelty index is established by combining signal reconstruction loss and feature loss between the original signal of the reconstructed signal based on the ODGM on normal samples for anomaly point identification in the test data. The effectiveness and superiority of the proposed model is validated and compared with other representative deep learning-based novelty detection models on two public data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Design and Analysis of a Modified ZNN with Dynamic Parameter for Time Varying Generalized Sylvester Equation

Design and Analysis of a Modified ZNN with Dynamic Parameter...

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2022 International Conference on Machine Learning, Cloud computing and intelligent Mining, MLCCIM 2022

作者： Zhang, Yongsheng Chen, Jifeng He, Yongjun Peng, Hao Li, Ping College of Information and Mechatronics Engineering Hunan International Economics University Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410205 China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

The time varying generalized Sylvester equation (TVGSE) is widely used in many domains such as mathematics, engineering and control system. Differing from the zeroing neural network (ZNN) and varying parameter ZNN (VPZNN), a novel neural network algorithm based on a novel time-varying parameter, named modified VPZNN (MVPZNN), is presented and studied for handling TVGSE in this work. In particular, the proposed MVPZNN possesses faster convergence capacity when activated by the linear and sign-bi-power activation function, respectively. Numerical simulation indicates that MVPZNN has superiority in convergence rate in coping with TVGSE compared with fixed parameter ZNN and other VPZNN models. © 2022 IEEE.

关键词： Neural networks

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Fixed-Parameter Tractability for Book Drawing with Bounded Number of Crossings per Edge 15th

Fixed-Parameter Tractability for Book Drawing with Bounded...

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15th International Conference on Algorithmic Aspects in information and Management, AAIM 2021

作者： Liu, Yunlong Li, Yixuan Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Hunan Xiangjiang Artificial Intelligence Academy Changsha China

ISBN: (纸本)9783030931759

The problem book drawing with a bounded number of crossings per edge asks, given a graph G= (V, E) and two integers k, b, whether there is a k-page book drawing of G such that the maximum number of crossings per edge is at most b. In this paper, we study this problem from a parameterized complexity viewpoint. Specifically, we investigate the problem parameterized by both the maximum number b of crossings per edge and the vertex cover number τ of G, and show that this parameterized problem admits a kernel of size (3 b+ 1 ) · 2O(τ) and admits a fixed-parameter tractable algorithm running in time O((3b+1)·2(3b+2)·2O(τ)+τ·|V| ). Together with our previous result for fixed-order book drawing with a bounded number of crossings per edge (COCOA 2020), our result provides a more complete answer to a question posed by Bhore et al. (J. Graph Alg. Appl. 2020). © 2021, Springer Nature Switzerland AG.

关键词： Parameterization

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Pseudo Multi-Source Domain Extension and Selective Pseudo-Labeling for Unsupervised Domain Adaptive Medical Image Segmentation 48

Pseudo Multi-Source Domain Extension and Selective Pseudo-La...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Xiaokang Wang, Zhiqiang Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Xiangnan University College of Medical Imaging Laboratory and Rehabilitation Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

Unsupervised domain adaptation (UDA) attracts extra attention in medical image processing because no additional labels are required when adapting to different distributions. In this work, we propose a novel unsupervised domain adaptation framework named as Domain Expansion and PseudoLabeling (DEPL). We extend the domain of a labeled source domain data to four different distributed domains and use adversarial learning to align the image appearance level and feature level from the four different domains to the unlabeled target domain. In addition, we propose a selective pseudolabeling mechanism, namely using strong confidence pseudolabeling to boost model performance. We evaluate our model for the MR to CT adaptation segmentation task on the public dataset MMWHS. Compared to seven other state-of-the-art segmentation methods, our DEPL achieves the best Dice similarity coefficient by 82.4%, which is at least 3.9% higher than the other UDA segmentation methods. © 2023 IEEE.

关键词： Adversarial learning domain extension Image segmentation Selective pseudo-labeling Unsupervised domain adaptive

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On book thickness parameterized by the vertex cover number

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Science China(information Sciences) 2022年第4期65卷 112-113页

作者： Yunlong LIU Jie CHEN Jingui HUANG School of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University

Dear editor,Book thickness for graphs forms a major theme in graph theory and has a broad application in the fields of sorting permutations, fault tolerant VLSI design, parallel computing, and others. The book Thickness problem, even if the vertex order is taken as part of the input, was shown to be NP-complete in general [1, 2]. Just because of this, parameterized algorithms have been proposed to deal with it [3].

关键词： Book Thickness Vertex Cover Number Linear Order 1-page embedding graph kernel

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