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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Li, Wenlong Chen, Hanning Wang, Guopeng Song, Lizhe Xia, Yelin Wang, Zhixue Jia, Kun School of Artificial Intelligence Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China The Open University of China Beijing China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China School of Control Science and Engineering Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd. Beijing China

ISBN: (纸本)9798350384062

Multi objective optimization evolutionary algorithms (MOEAs) play a crucial role in addressing multi-objective optimization problems (MOPs) in the field of artificial intelligence. However, MOEAs often struggle to simultaneously maintain good convergence and diversity in Many-objective optimization problems (MaOPs). To address this problem, this paper proposes a Many-objective optimization evolutionary algorithm based on dynamic region partitioning approach (MaOEA-DRP). After non-dominated sorting, the solutions are initially uniformly partitioned into regions based on Euclidean distance. Within each region, at most one solution closest to the ideal point is selected. Subsequently, dynamic region partitioning is applied to the remaining individuals. In each iteration, a solution closest to the ideal point is chosen within the obtained region until the population size is satisfied. Experimental results indicate that MaOEA-DRP outperforms other algorithms significantly in terms of performance on the test problems. © 2024 IEEE.

关键词： Evolutionary algorithms

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Antiepileptic capacity of c-tDCS: A computational modeling study

Antiepileptic capacity of c-tDCS: A computational modeling s...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Hossein Soroushi Fariba Bahrami Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Transcranial Direct-Current Stimulation (tDCS), as a safe and non-invasive neuromodulator, has been recently the center of attention for the treatment and management of neurological disorders. However, the intervening mechanisms of tDCS are not completely understood, and in consequence, it was not possible or advisable to utilize it as an effective treatment. In this work, by integrating a neural mass model of the thalamocortical system, which can generate and induce different types of seizures, and the physiological aspects of the interaction between tDCS and the brain, such as permissible current and how it must be involved in different neural populations, we have proposed a basic model. Then, by connecting several units of the basic model and applying a learning rule to calculate properly the connectivity weights between units, a multi-zone model is established. Our simulation results explained the ever-increasing connectivity between pre-and post-stimulation, an accepted feature of tDCS. Then, we described computationally (based on bifurcation analysis) how this change in connectivity can lead to a reduction in epileptic susceptibility.

关键词： Neurological diseases Computational modeling Simulation Sociology Limit-cycles Bifurcation Brain modeling

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Edge and feature points based video intra-frame passive-blind copy-paste forgery detection

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 637-645页

作者： Kang, Guang-Yu Feng, Ya-Pei Wang, Ren-Kui Lu, Zhe-Ming School of Control and Mechanical Engineering Tianjin Chengjian University Tianjin300222 China College of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Aeronautics and Astronautics Zhejiang University Hangzhou310027 China

One of the most prevalent video content forgery is to clone (copy and paste) one region of the frame, for instance, to add, change or delete significant information in the video scene. In this paper, a passive-blind method for detecting homology video intra-frame copy-paste forgery and identify the duplicated regions in the tampered frame is presented. This method is implemented in two main stages: extraction of edge lines and matching of feature points. The cloned area detected by our method has the same edge lines as the original one. Based on the cosine similarity, matching and clustering of feature points reflect the relationship between the original source block and the forged one very well. Without any priori thresholds or so-called empirical values, our matching method for local descriptors is efficient in obtaining an accurate forgery localization. As a passive authentication method, experimental results demonstrate that it is effective in detecting and locating cloned region blocks and has its own advantage of identifying the size and shape of the duplicated region. ©2021 ISSN 2414-8105 (Online).

关键词： Cloning

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Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems

Toward an Interaction-Driven Framework for Modeling Big Data...

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24th Eurographics Conference on Visualization, EuroVis 2022

作者： Benvenuti, D. Fiordeponti, G. Cheng, H. Catarci, T. Fekete, J.D. Santucci, G. Angelini, M. Battle, L. Department of Computer Control and Management Engineering Antonio Ruberti University of Rome "La Sapienza" Rome Italy University of Maryland College ParkMD United States Aviz Inria Saclay France Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States

ISBN: (纸本)9783038681854

Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Data visualization

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Ultra-short term power load prediction based on gated cycle neural network and XGBoost models 2

Ultra-short term power load prediction based on gated cycle ...

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2021 2nd International Conference on computer Science and Communication Technology, ICCSCT 2021

作者： Li, Xiaojin Huang, Yueyang Shi, Yuanbo College of Computer Communication and Engineering Liaoning Shihua University Liaoning Fushun113001 China School of Information and Control Engineering Liaoning Shihua University Liaoning Fushun113001 China

Load forecasting is an important guarantee for power system design, planning and operation. In order to further improve the accuracy of power load forecasting, a combined forecasting model based on gating recurrent neural network (GRU) and limit gradient lifting (XGBoost) is proposed. Firstly, according to the load data and the input structure of GRU and XGBoost models, the data are preprocessed, and the preprocessed data are input into the corresponding model respectively. Then, the model is weighted by entropy weight method to obtain the predicted value of the final combination model. Finally, the first mock exam is used to compare the combination forecasting model with the common forecasting models. The results show that the proposed method can effectively combine the advantages of the two models, and take into account the continuous time series and discontinuous feature variables, which is more accurate than the single model and the common forecasting models. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Recurrent neural networks

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computer Vision-control-Based CNN-PID for Mobile Robot

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computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

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Multimodal Feature Fusion and Emotion Recognition Based on Variational Autoencoder

Multimodal Feature Fusion and Emotion Recognition Based on V...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Yuan Wang Xinyu Guan School of Control and Computer Engineering North China Electric Power University Beijing China College of Intelligent Equipment Shandong University of Science and Technology Tai’an China

Multimodal emotion recognition is one of the mainstream frontier directions in the field of AI, and has potential significant application value and wide application in related application scenarios involving intelligent science and human-computer interaction. Since the connection of semantic information features between contexts is a factor that needs to be focused on in multimodal emotion recognition (ER), although the CNN model can predict the information contained in the time series, it does not fully consider. The special relationship between semantic information has certain limitations. The CMN uses the semantic information that GRU can store in the memory unit. However, the practical application of this model is only to dynamically monitor the emotional changes of both parties over a period of time. There are deficiencies in the dialogue process involved. The recognition model based on variational autoencoder and multi-modal feature fusion constructed in this paper extracts rich contextual semantic information through Bi-LSTM, and uses the attention mechanism to analyze the multi-modal features extracted by multiple speakers. Features are fused to obtain important features, which effectively solves the problems mentioned above. The MFF-VAE recognition model proposed in this article effectively combines the extended feature extraction of horizontal multiple participants with the complementarity and connection of vertical semantic information, providing the next level of research direction for further improving recognition accuracy in the field of multimodal emotion recognition.

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qincheng Lv Xiaofeng Liu Jie Li Rongrong Ni Pujun Xue Siyang Song College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter U.K.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals’ facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions.

关键词： Three-dimensional displays Interference Signal processing Acoustics Speech processing

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DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations

arXiv

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arXiv 2023年

作者： Liu, Qingtao Cui, Yu Ye, Qi Sun, Zhengnan Li, Haoming Li, Gaofeng Shao, Lin Chen, Jiming College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Computer Science National University of Singapore 15 Computing Dr 117418 Singapore

Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep reinforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been paid to hand-object interaction representations for high-level generalization. In this paper, we propose a novel geometric and spatial hand-object interaction representation, named DexRep, to capture object surface features and the spatial relations between hands and objects during grasping. DexRep comprises Occupancy Feature for rough shapes within sensing range by moving hands, Surface Feature for changing hand-object surface distances, and Local-Geo Feature for local geometric surface features most related to potential contacts. Based on the new representation, we propose a dexterous deep reinforcement learning method DexRepNet to learn a generalizable grasping policy. Experimental results show that our method outperforms baselines using existing representations for robotic grasping dramatically both in grasp success rate and convergence speed. It achieves a 93% grasping success rate on seen objects and higher than 80% grasping success rates on diverse objects of unseen categories in both simulation and real-world ex eriments © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Hybrid Clustering Solutions Fusion based on Gated Three-way Decision

Hybrid Clustering Solutions Fusion based on Gated Three-way ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kaixiang Yang Yifan Shi Zhiwen Yu Zhijie Zhong Jichao Bi Mengzhi Wang School of Computer Science&Engineering South China University of Technology Guangzhou China College of Engineering Huaqiao University Quanzhou China Zhejiang Institute of Industry and Information Technology Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China

Consensus clustering methods provide better performance by fusing multiple clustering solutions in terms of accuracy, robustness and stability. However, most current methods suffer from different challenges: i) the high-dimensional problem; ii) limitations of single clustering method; iii) the optimal number of clusters selecting for a certain validity measure; iv) redundant clustering candidate attributes. To overcome the above limitations, we propose a hybrid clustering solutions fusion method based on gated three-way decision (HCFG) for data analysis. By integrating multiple clustering solutions and executing information fusion, HCFG enjoys four properties: (1) multiple random subspace generation strategy is utilized to generate diverse low-dimensional subspaces effectively; (2) a fusion framework that considers characteristics of both the soft clustering and hard clustering methods is designed, in which potential boundary of feature attribute sets is explored; (3) the optimal number of clusters is set by utilizing multiple clustering validity indices; (4) clustering solutions is considered as attributes and a gated three-way decision method is proposed to adaptively conduct attribute reductions. Extensive comparative experiments on 24 real-world data sets demonstrates the effectiveness and superiority of HCFG. Moreover, nonparametric tests are conducted to compare HCFG with multiple consensus clustering methods.

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