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4th International Conference on Wireless Communications and Applications, ICWCA 2020

作者： Wang, Yawen Qian, Qian Feng, Yong Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9789811651632

Traditional particle swarm optimization algorithm has some disadvantages, such as slow convergence speed and easy to fall into local extremes. In order to improve the performance, an improved adaptive particle swarm optimization algorithm with a two-way learning method is proposed. First, the algorithm adaptively adjusts the algorithm according to the iteration periods of the optimization process. Specifically, the inertia weight and the value of the learning factor are changed nonlinearly, so as to better balance the search behavior of the particles in the group;Second, the idea of beetle search is introduced into the particle swarm algorithm to form a new two-way learning mechanism, which overcomes the limitations of the traditional particle swarm algorithm and help to increase the diversity of the population. In this way, the search scope is expanded, and the search accuracy of the algorithm is enhanced. Finally, the simulation is carried out on several multi-dimensional functions, and compared with other two related algorithms. The experimental results show that under the same experimental conditions, the improved algorithm has obvious advantages in optimization ability and convergence speed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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Sparrow search algorithm based on cosine similarity and random multi-chaotic perturbation 6

Sparrow search algorithm based on cosine similarity and rand...

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6th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Li, Guangyang Qian, Qian Feng, Yong Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510656635

Aiming at the problems of sparrow algorithm (SSA), such as easy to fall into local extremum, uneven initial population distribution and slow convergence in late iteration, a sparrow search algorithm (CMSSA) was proposed, which combined cosine similarity and random multi-chaotic disturbance. The algorithm firstly integrates the reverse learning strategy and initializes the population by using the population uniform adjustment strategy of cosine similarity to ensure the uniformity and richness of the population, so that the algorithm can better search for the global optimal solution. Secondly, a random selection mechanism of multi-chaos local search strategy is used to take advantages from different disturbance states of multiple chaos models. Randomly selected chaos maps are used in each iteration to perturb individuals and help SSA get rid of local extremums. Simulation results show that compared with other intelligent algorithms,CMSSA achieves better results in robustness and optimization accuracy. © 2022 SPIE.

关键词： Stochastic systems

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Flower Pollination Algorithm Based on Beetle Antennae Search Method 4th

Flower Pollination Algorithm Based on Beetle Antennae Search...

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4th International Conference on Wireless Communications and Applications, ICWCA 2020

作者： Zhou, Jiayi Qian, Qian Fu, Yunfa Feng, Yong Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9789811651632

In this paper, a new flower pollination algorithm based on beetle antennae search method (TBFPA) is proposed to deal with the slow convergence speed problem of traditional flower pollination algorithm. Specifically, after the new individuals are generated by the way of flower pollination algorithm, the beetle antennae search method is used to search the nearby solution space of the new individuals, thus increasing the probability to reach better solutions and speeding up the convergence speed of the algorithm. In addition, a new uniform distribution sampling method is used to generate the initial population so as to spread the initial solutions all over the solution space. In order to verify the effectiveness of TBFPA, different standard functions are tested between TBFPA and FPA algorithms and the results proved that TBFPA has faster convergence speed and stronger global searchability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Beetle antennae search algorithm Flower pollination Function optimization

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Intrusion Detection Based on Deep Belief Network and Stacking Method 3

Intrusion Detection Based on Deep Belief Network and Stackin...

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3rd International Conference on computer Communication and Network Security, CCNS 2022

作者： Wang, Jia Bao Miao, Xiang Hua Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510660113

After data preprocessing, the feature dimension of NSL-KDD dataset increases from 42 dimensions to 122 dimensions. High dimensional data will make it more difficult for the model to learn the characteristics of the data, and there will be a lot of redundant data in the data set. Therefore, this paper uses the deep belief network to reduce the dimension of the characteristics of the intrusion detection data set after data preprocessing, and uses the stacking algorithm as the classifier to construct the intrusion detection model. Through comparative experiments, it is proved that the model has good performance in the four evaluation indexes of accuracy, precision, recall and F1 score, and effectively improves the performance of intrusion detection model. © 2022 SPIE.

关键词： Intrusion detection

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R2F-UGCGAN: a regional fusion factor-based union gradient and contrast generative adversarial network for infrared and visible image fusion

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Optica Acta: International Journal of Optics 2023年第1期70卷 52-68页

作者： Yuning Xie Gang Liu Rui Xu Durga Prasad Bavirisetti Haojie Tang Mengliang Xing a School of Automation Engineering Shanghai University of Electric Power Shanghai People's Republic of China b Faculty of Information Technology and Electrical Engineering Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

ABSTRACTABSTRACTTo efficiently preserve texture and target information in source images, an image fusion algorithm of Regional Fusion Factor-Based Union Gradient and Contrast Generative Adversarial Network (R2F-UGCGAN) is proposed. Firstly, an adaptive gradient diffusion (AGD) decomposition algorithm is designed to extract representative features. A pair of infrared (IR) and visible (VIS) images are decomposed by AGD to obtain low-frequency components with salient targets and high-frequency components with rich edge gradient information. Secondly, In the high-frequency components, principal component analysis (PCA) is used for fusion to obtain more detailed images with texture gradients. R2F-UGCGAN is used to fuse the low-frequency components, which can effectively ensure good consistency between the target region and the background region. Therefore, a fused image is produced, which inherits more thermal radiation information and important texture details. Finally, subjective and objective comparison experiments are performed on TNO and RoadScene datasets with state-of-the-art image fusion methods. The experimental results of R2F-UGCGAN are prominent and consistent compared to these fusion algorithms in terms of both subjective and objective evaluation.

关键词： Image fusion infrared visible generative adversarial network PCA adaptive gradient diffusion

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Asymmetric Modulated Predictive Current Control for Dual Three-Phase PMSM With Improved Performance

Asymmetric Modulated Predictive Current Control for Dual Thr...

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International Conference on Power Electronics and Motion Control (IPEMC)

作者： Ze Li Xingyu Zheng Jinhui Xia Hangan Liu Xiaonan Gao Jose Rodriguez Liling Wang Zhaoyan Zhang Zhiheng Liu College of Electronic and Information Engineering Hebei University China School of Automation Southeast University China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Engineering Universidad San Sebastian Chile

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Dual three-phase PMSM (DTP-PMSM) has gained significant attention benefiting from their exceptional reliability and high torque density. However, DTP-PMSM exhibits poor steady-state performance and large calculation amount with the conventional finite-control-set predictive current control. To address these concerns, an asymmetric modulated predictive current control (AM-PCC) for DTP-PMSM is presented. Therein, the three-phase decomposition and deadbeat control scheme are combined to eliminate the weighting factor and optimize the execution efficiency. An asymmetric modulation strategy is presented and integrated into PCC to reduce the current harmonics and switching frequency. Extensive simulation tests are conducted to demonstrate the effectiveness of the proposed AM-PCC scheme.

关键词： Current control Torque Switching frequency Dynamics Modulation Harmonic analysis Power electronics

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Adaptive Prediction of Resources and Workloads for Cloud Computing Systems with Attention-based and Hybrid LSTM

Adaptive Prediction of Resources and Workloads for Cloud Com...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Bi, Jing Ma, Haisen Yuan, Haitao Xu, Kangyuan Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high performance in predicting resource usage and workload sequences. Much noise implicit in the original sequences of resources and workloads is another reason for their low performance. To address these problems, this work proposes a hybrid prediction model named SABG that integrates an adaptive Savitzky-Golay (SG) filter, Attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. SABG adopts an adaptive SG filter in the data pre-processing to eliminate noise and extreme points in the original time series. It uses bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Then, it utilizes an attention mechanism to explore importance of different data dimensions. SABG aims to predict resource usage and workloads in highly variable traces in cloud computing systems. Extensive experimental results demonstrate that SABG achieves higher-accuracy prediction than several benchmark prediction approaches with datasets from Google cluster traces. © 2022 IEEE.

关键词： Time series

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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface Technology" as an Example 6th

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th International Conference on computer Science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of Things (AIOT), the course "Microcomputer Principles and Interface Technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. The innovative teaching mode of this course has achieved significant results in innovative practice. This innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. This teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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A Hybrid Deep Learning Method for Network Attack Prediction

A Hybrid Deep Learning Method for Network Attack Prediction

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Bi, Jing Xu, Kangyuan Yuan, Haitao Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Precise real-time prediction of the number of future network attacks cannot only prompt cloud infrastructures to fast respond to them and protect network security, but also prevents economic and business losses. In recent years, neural networks, e.g., Bi-direction Long and Short Term Memory (LSTM) and Temporal Convolutional Network (TCN), have been proven to be suitable for predicting time series data. Attention mechanisms are also widely used for the time series prediction. In this work, we propose a novel hybrid deep learning prediction method by combining the capabilities of a Savitzky-Golay (SG) filter, TCN, Multi-head self attention, and BiLSTM for the prediction of network attacks. This work first adopts a SG filter to eliminate noise in the raw data. It applies TCN to extract short-term features from the sequences. It then adopts multi-head self attention to capture intrinsic connections among features. Finally, this work adopts Bi-LSTM to extract bi-directional and long-term correlations in the sequences. Experimental results with a real-life dataset show that the proposed method outperforms several typical algorithms in terms of prediction accuracy. © 2022 IEEE.

关键词： Forecasting

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LOCALLY FREE WEIGHT SHARING FOR NETWORK WIDTH SEARCH 9

LOCALLY FREE WEIGHT SHARING FOR NETWORK WIDTH SEARCH

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9th International Conference on Learning Representations, ICLR 2021

作者： Su, Xiu You, Shan Huang, Tao Wang, Fei Qian, Chen Zhang, Changshui Xu, Chang School of Computer Science Faculty of Engineering The University of Sydney Australia SenseTime Research Department of Automation Tsinghua University Beijing China

Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluator to rank the performance w.r.t. different width. Nevertheless, current methods mainly follow a manually fixed weight sharing pattern, which is limited to distinguish the performance gap of different width. In this paper, to better evaluate each width, we propose a loCAlly FrEe weight sharing strategy (CafeNet) accordingly. In CafeNet, weights are more freely shared, and each width is jointly indicated by its base channels and free channels, where free channels are supposed to locate freely in a local zone to better represent each width. Besides, we propose to further reduce the search space by leveraging our introduced FLOPs-sensitive bins. As a result, our CafeNet can be trained stochastically and get optimized within a min-min strategy. Extensive experiments on ImageNet, CIFAR-10, CelebA and MS COCO dataset have verified our superiority comparing to other state-of-the-art baselines. For example, our method can further boost the benchmark NAS network EfficientNet-BO by 0.41% via searching its width more delicately. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep neural networks

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