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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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Many-Objective Optimization Evolutionary Algorithm Based on Dynamic Region Partitioning 4

Many-Objective Optimization Evolutionary Algorithm Based on ...

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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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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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Multimodal Sentiment Recognition Based on Sentiment Fusion Transformer

Multimodal Sentiment Recognition Based on Sentiment Fusion T...

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

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

Multimodal emotion recognition (MER), which relies on its role in processing and analysing comments posted on social media and identifying the corresponding target emotion states, has a very important position in education, social media and especially in the field of HCI. In order to solve the problem of emotion recognition for video, text and other forms of data in social media, we propose a Transformer with feature. The Transformer is able to take into account the combination of different feature relations and extract global keys, so it is introduced for long-range sequential context extraction, and the residual connections are placed after the Transformer to prevent information collapse. In addition, this paper uses a multi-headed attention mechanism to combine the extracted different feature laws, and finally the fused feature vectors are fed into the classifier to classify the sentiment and output it to achieve multimodal sentiment recognition in a multimodal context.

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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H control for General Conformable Polynomial Fuzzy Models

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International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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