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SSRN 2022年

作者： Zhong, keyu Xiao, Fen Gao, Xieping Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal University Changsha410081 China School of Mathematics and Statistics Hunan Normal University Changsha410081 China School of Computer Science Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

The OPF system for renewable energy power has presented characteristics such as extremely high security constraints and more obvious multimodal properties, its solution is a challenging task and requires more efficient methods. In this paper, an enhanced flower pollination algorithm, EFPA-SF is proposed to solve the OPF problem. In EFPA-SF, there are four improvements: firstly, a random step size in the process of cross-pollination is proposed to improve the global search ability of EFPA-SF. Secondly, in the stage of self-pollination, the global optimal pollen is introduced to improve the ability of exploitation–exploration balance of the algorithm. In addition, if pollen individual crosses boundaries, boundary constraint is carried out by the previous position and boundary position of pollen, which is instrumental to escape from local optimum. Finally, the superiority of feasible solutions (SF) constraints handling method is integrated into the algorithm to satisfy the feasibility of the solutions. To verify the effectiveness of the proposed algorithm, the optimization capability of EFPA is first investigated and analyzed by using the most recent IEEE CEC2021 benchmark functions. Then, EFPA-SF is used to solve three engineering design problems and the performance is compared with the state-of-the-art algorithms, the validation results reveal that the proposed algorithm outperforms state-of-the-art algorithms, and demonstrate that it can be used for constrained optimization as well as for unconstrained optimization problems with good generality. Finally, EFA-SF is applied to the OPF problem on a modified IEEE-30 bus system that incorporates stochastic wind, solar, and small hydro power in addition to conventional thermal power generation. The comprehensive investigation reveals that EFA-SF can provide competitive solutions to the OPF problem compared to popular algorithms. © 2022, The Authors. All rights reserved.

关键词： Constraint handling

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Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system

arXiv

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arXiv 2024年

作者： Dong, Li Jiang, Feibo Wang, Minjie Peng, Yubo Li, Xiaolong School of Computer Science Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Social Emergency Event Judgement based on BiLSTM-CRF 19

Social Emergency Event Judgement based on BiLSTM-CRF

引用

19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

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A Multi-level Approach to Learning Early Warning based on Cognitive Diagnosis and Learning Behaviors Analysis

A Multi-level Approach to Learning Early Warning based on Co...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Hua Ma Wen Zhao Zixu Jiang Peiji Huang Wensheng Tang Hongyu Zhang Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China Tencent Cloud Computing (Changsha) Co. Ltd School of Business Central South University Changsha China

Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.

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A Simple Analysis of Multimodal Data Fusion

A Simple Analysis of Multimodal Data Fusion

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jiangchang Cheng Yinglong Dai Yao Yuan Hongli Zhu College of Information Science and Engineering Hunan Normal University Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Liberal Arts and Sciences National University of Defense Technology Changsha China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Multimodal data fusion has a long research history since audio-visual speech recognition, which is inspired by the McGurk effect. Because of the limited model capacity of the traditional methods, multimodal data fusion researches are not so popular for a period. Recently, the advances of deep learning techniques open up new opportunities for the multimodal data fusion field. However, there is still a great gap in multimodal data processing ability between artificial intelligence and human beings. Many problems in multimodal data processing are still necessary to be researched. In this work, we propose to gain an insight into the information fusion level and apply different information fusion strategy to different situations. We analyze the different situations of the multimodal data fusion process and divide them into two categories, including consistent information fusion and contradictory information fusion. We demonstrate some toy examples of the different cases of the multimodal data fusion process.

关键词： Deep learning Data privacy Toy manufacturing industry Data integration Speech recognition Security History

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Negative information filtering algorithm based on text content in multimedia networks

引用

International Journal of Performability Engineering 2019年第11期15卷 3061-3071页

作者： Chen, Wenqing Fu, Weina College of Applied Technology China University of Labor Relations Beijing100000 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010012 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In the multimedia network environment, it is necessary to effectively filter negative information in the multimedia network and enhance the ability to mine and identify valid data. This paper presents a new algorithm of negative information filtering based on text content in multimedia networks. The principal component features of negative information in multimedia networks are extracted, and matched filters are designed to filter the negative information reasonably. All text contents and negative information are normalized and sorted. They are transformed into the same text format for classification and processing, and the filtering and detection of negative information are realized. Finally, based on the semantic features of text content, the support vector machine algorithm is used to extract negative information features from data. Experimental results show that the algorithm improves the filtering accuracy and performance for negative information in multimedia networks, and it has good application value. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Information filtering

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Learning to Learn Transferable Generative Attack for Person Re-Identification

arXiv

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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A many-objective evolutionary algorithm based on rotation and decomposition

引用

Swarm and Evolutionary Computation 2021年 60卷

作者： Zou, Juan Liu, Jing Yang, Shengxiang Zheng, Jinhua Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University XiangtanHunan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University XiangtanHunan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Evolutionary algorithms have shown their promise in addressing multiobjective problems (MOPs). However, the Pareto dominance used in multiobjective optimization loses its effectiveness when addressing many-objective problems (MaOPs), which are defined as having more than three objectives. This is because the Pareto dominance loses its ability to distinguish between individuals. In this paper, a many-objective evolutionary algorithm based on rotation and decomposition is proposed (MaOEA-RD) to overcome the shortcoming of insufficient selection pressure caused by the Pareto dominance. First, the coordinates system is rotated and a hyperplane is established to distinguish between the nondominated individuals. Then, a novel individual selection mechanism incorporating decomposition is adopted to maintain the diversity of the population. In order to compensate for the deficiency of the predefined reference vectors, a reference vector adjustment mechanism is proposed. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with nine state-of-the-art many-objective algorithms. © 2020 Elsevier B.V.

关键词： Evolutionary algorithms

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Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

arXiv

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arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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RIESZ TYPE THEOREMS FOR κ-PLURIHARMONIC MAPPINGS, INVARIANT HARMONIC QUASIREGULAR MAPPINGS AND HARMONIC QUASIREGULAR MAPPINGS

arXiv

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arXiv 2023年

作者： Chen, Shaolin Huang, Manzi College of Mathematics and Statistics Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application 421002 China Key Laboratory of High Performance Computing and Stochastic Information Processing College of Mathematics and Statistics Hunan Normal University Hunan Changsha410081 China

The main purpose of this paper is to develop some methods to improve and generalize the main results in a recent paper by Liu and Zhu (Adv. Math., 2023, i.e., [19]). The paper consists of two parts. In the first part, we discuss the Riesz type theorem in the setting of n-dimensional complex spaces for all n ≥ 1. In this part, we first introduce the family of κ-pluriharmonic mappings of the n-dimensional complex unit ball. Then we establish two Riesz type theorems for these mappings, which are the n-dimensional versions of Theorems 1.1 and 1.2 in [19], respectively. Furthermore, even when n = 1, our first result shows that the assumption of the real parts of the mappings not being negative (or being negative) in [19, Theorem 1.1] is redundant;and our second result illustrates that the assumption of "quasiconformality" on the mappings in [19, Theorem 1.2] can be replaced by the weaker one of "quasiregularity". In the second part, we investigate the Riesz type theorem in the setting of n-dimensional real spaces for all n ≥ 2. In this part, first, we prove a Riesz type theorem for invariant harmonic quasiregular mappings of the unit n-dimensional real ball. Our result indicates that (i) the range of the parameter p discussed in [19, Theorem 1.3] can be changed from (1, 2) to (1, ∞);(ii) the assumption of the first coordinate functions of the mappings being non-zero in [19, Theorem 1.3] is redundant. In this way, we complete the discussions carried out in [19, Theorems 1.3 and 1.4]. Second, we obtain a Riesz type theorem for harmonic K-quasiregular mappings of the unit n-dimensional real ball. Our result demonstrates that the range of the parameter p discussed in [15, Theorem 2.1] can be changed from (1, 2) to (1, ∞).MSC Codes 30H10, 30C62 Copyright © 2023, The Authors. All rights reserved.

关键词： Harmonic analysis

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