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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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Depthwise Separable Axial Asymmetric Wavelet Convolutional Neural Networks

SSRN

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SSRN 2024年

作者： Wang, Tonghao Gao, Shijiao Huo, Yukang Cattani, Piercarlo Mei, Shuli College of Information and Electrical Engineering China Agricultural University Beijing100083 China Department of Computer Control and Management Engineering Sapienza - Università di Roma Roma Italy

Reinterpreting wavelet multi-resolution analysis as CNN methods to endow them with the capacity for high-level semantic feature extraction has emerged as a research topic in deep sparse representations. We explore the fundamental operations of wavelet transform and convolution from a projective standpoint, then design a highly interpretable, exceptionally lightweight, and fully learnable deep neural network architecture known as Depthwise Separable Axial Asymmetric Wavelet Convolutional Neural Networks (DSAWCN). This network acquires wavelets filters customized for specific image tasks, thus providing adaptive and efficient multi-scale feature extraction strategies. We examined the behavior and the impact of the parameters in the proposed method using three general texture image datasets and four bark texture image datasets. The findings indicate that this exceptionally lightweight network surpasses current wavelet convolutional networks in terms of classification accuracy and achieves performance comparable to some eminent CNN models. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 260-272页

作者： Dalong Jiang Congcong Li Zifan Lin Yuntian Wu Hongjuan Pei Magdalena Zielinska Hongwei Xiao School of Computer and Control Engineering Yantai UniversityYantai 264005ShandongChina Hebei Agricultural University Baoding 071001HebeiChina Hebei Key Laboratory of Agricultural Big Data Baoding 071001HebeiChina Department of Electrical and Electronic Engineering University of Western AustraliaPerth 6000Australia BeiGene Guangzhou Biologics Manufacturing Co. LtdGuangzhou 510555China Neuroscience and Intelligent Media Institute Communication University of ChinaBeijing 100024China Department of Systems Engineering University of Warmia and MazuryOlsztyn 004889Poland College of Engineering China Agricultural UniversityBeijing 100083China

In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slices under constant and controlled temperature and relative humidity were carried *** results were validated with experimental *** results of the simulation show that the Quadratic model fitted well to the moisture ratio and the material temperature data trend with average relative errors of 5.9%and 8.1%,***,the results of the simulation considering shrinkage show that the moisture and temperature distributions during drying are closer to the experimental data than the results of the simulation disregarding *** material moisture content was significantly related to the shrinkage of dried *** and relative humidity significantly affected the volume shrinkage of carrot *** volume shrinkage increased with the rising of the constant temperature and the decline of relative *** model can be used to provide more information on the dynamics of heat and mass transfer during drying and can also be adapted to other products and dryers devices.

关键词： carrot drying numerical simulation heat and mass transfer shrinkage

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Quantitative Inertia Estimation Method for Wind Farm Based on AFF-RLS 8

Quantitative Inertia Estimation Method for Wind Farm Based o...

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8th International Conference on control Science and Systems engineering, ICCSSE 2022

作者： Qin, Hao Cao, Yongji Zhang, Hengxu Gao, Zhimin Zhang, Xiaoning Li, Changgang Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Technical University of Denmark Kgs. Department of Electrical Engineering Lyngby Denmark School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781665484138

In order to guarantee the frequency security and stability of the power system with high proportional of wind generation, virtual inertia control techniques are gradually being promoted and applied. However, there is still a lack of methods and techniques to accurately estimate the equivalent virtual inertia of wind farms in real time, which makes it difficult to quantitatively evaluate the effect of virtual inertia control and to dynamically monitor the inertia level of power systems. Therefore, in this paper, an AR model with eXogenous input is applied to the time-varying characteristics of the virtual inertia of wind farm, and a recursive least squares identification algorithm based on a fuzzy control algorithm with an adaptive forgetting factor is proposed to estimate the virtual inertia of wind farms online. The proposed identification model and solution algorithm have superior tracking ability and good convergence. All that is required is to obtain perturbation data on the frequency and active power of the point of common coupling from the phasor measurement unit. A case study is carried out to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Least squares approximations

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Fast Disentangled Slim Tensor Learning for Multi-view Clustering

arXiv

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arXiv 2024年

作者： Xu, Deng Zhang, Chao Li, Zechao Chen, Chunlin Li, Huaxiong Department of Control Science and Intelligence Engineering Nanjing University Nanjing210093 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalable to large-scale data. (2) Although some methods address it by introducing bipartite graphs, they may result in sub-optimal solutions caused by an unstable anchor selection process. (3) They generally ignore the negative impact of latent semantic-unrelated information in each view. To tackle these issues, we propose a new approach termed fast Disentangled Slim Tensor Learning (DSTL) for multi-view clustering . Instead of focusing on the multi-view graph structures, DSTL directly explores the high-order correlations among multi-view latent semantic representations based on matrix factorization. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Subsequently, two slim tensors are constructed with tensor-based regularization. To further enhance the quality of feature disentanglement, the semantic-related representations are aligned across views through a consensus alignment indicator. Our proposed model is computationally efficient and can be solved effectively. Extensive experiments demonstrate the superiority and efficiency of DSTL over state-of-the-art approaches. The code of DSTL is available at https://***/dengxu-nju/DSTL. Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix factorization

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PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning

arXiv

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arXiv 2022年

作者： Martone, Simone Manigrasso, Francesco Lamberti, Fabrizio Morra, Lia Department of Control and Computer Engineering Politecnico di Torino Italy

Semantic image interpretation can vastly benefit from approaches that combine sub-symbolic distributed representation learning with the capability to reason at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. LTNs replace the classical concept of training set with a knowledge base of fuzzy logical axioms. By defining a set of differentiable operators to approximate the role of connectives, predicates, functions and quantifiers, a loss function is automatically specified so that LTNs can learn to satisfy the knowledge base. We focus here on the subsumption or isOfClass predicate, which is fundamental to encode most semantic image interpretation tasks. Unlike conventional LTNs, which rely on a separate predicate for each class (e.g., dog, cat), each with its own set of learnable weights, we propose a common isOfClass predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the current formulation by grounding abstract concepts as parametrized class prototypes in a high-dimensional embedding space, while reducing the number of parameters required to ground the knowledge base. We show how this architecture can be effectively trained in the few and zero-shot learning scenarios. Experiments on Generalized Zero Shot Learning benchmarks validate the proposed implementation as a competitive alternative to traditional embedding-based approaches. The proposed formulation opens up new opportunities in zero shot learning settings, as the LTN formalism allows to integrate background knowledge in the form of logical axioms to compensate for the lack of labelled examples. PROTO-LTN was implemented in Tensorflow and is available at https://***/FrancescoManigrass/***. Copyright © 2022, The Authors. All rights r

关键词： Tensors

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Optimizing High Resolution Wide Field of View Optical Design for Long Wave Infrared Objective

Optimizing High Resolution Wide Field of View Optical Design...

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Electrical engineering (ICEENG), 12th International Conference on

作者： Hossam Aboalia Sherif Hussein Alaaeldin Mahmoud Control Systems Department Optoelectronics and Automatic Military Technical College Cairo Egypt Artificial Intelligence Department Computer Engineering Military Technical College Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

Optical system is a basic component of infrared (IR) imaging systems that affect the image quality of the observed field of view (FOV). Responsible for collecting IR radiation from the scene and projecting it onto detector pixels. The optical system's characteristics, such as lens material, focal length, and optical diameter, critically impact the amount of transmitted IR radiation. The design process aims to eliminate and limit aberrations to enhance performance. Increasing spatial resolution and widening the FOV are crucial for image quality improvement. In this study, we present a unique high-resolution wide FOV optical design for an infrared objective operating in the long-wave infrared (LWIR) band $(\mathbf{8}-\mathbf{12}\ \boldsymbol{\mu} \mathbf{m})$ . Utilizing the time-tested Cooke triplet structure as the foundation, we enhance its capabilities through the implementation of a splitting methodology. This approach allows for improved control over aberrations and enhances the optical design's overall performance. Ansys Zemax OpticStudio software was employed to optimize radii of curvatures and thicknesses based on the proposed merit function operands. Our concept makes use of a small pixel size $(\mathbf{15}\ \mu \mathbf{m})$ to achieve an expansive FOV of 18° along with a high spatial resolution of 31 cycles/mm. This makes the design a highly suitable candidate for diverse remote sensing observation applications.

关键词： Integrated optics Image quality Optical design Optical detectors Optical imaging Size measurement Software

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Exploiting Robots as Healthcare Resources for Epidemics Management and Support Caregivers 10

Exploiting Robots as Healthcare Resources for Epidemics Mana...

引用

10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Napoli, Christian Napoli, Christian Ponzi, Valerio Puglisi, Adriano Russo, Samuele Tibermacine, Imad Eddine Department of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Medical Surgical Sciences and Translational Medicine Sapienza University of Rome Via di Grottarossa 1035 Roma00189 Italy Department of Psychology Sapienza University of Rome via dei Marsi 78 Roma00185 Italy

The COVID-19 emergency has exposed the fragility of many Health Care Systems around the world. Two major critical factors have been related to the management of critical care accesses and the availability of healthcare operators. COVID-like diseases are generally transmitted by airborne pathogens that grant a high contagion rate and rapidity. Unfortunately, health operators and medical doctors are the designated first victims of such epidemics. Infected operators (symptomatic or not) must be put at rest due to the potential contagion risk for the patients. In the midst of global health catastrophes such as the COVID-19 pandemic, the healthcare sector is always looking for new ways to improve patient care while reducing the risk of disease transmission. This study takes an innovative approach to the integration of robotic technology in hospital settings in order to increase operational efficiency, protect healthcare personnel, and improve patient outcomes. This study digs into the strategic deployment of robotic resources, with an emphasis on their function in epidemic control and front-line caregiver assistance. We look at how these robots may do different duties, decreasing human-to-human contact and the possibility of viral transmission. We pave the road for a safer, more efficient, and resilient healthcare environment by using the potential of robots as important healthcare resources. © 2024 Copyright for this paper by its authors.

关键词： COVID-19

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Attention-Based Lightweight Network for PCB Defect Data Augmentation

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Journal of computers (Taiwan) 2024年第6期35卷 185-199页

作者： Zhang, Hong-Yan Wang, Xin Zhao, Jian-Wei College of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China Department of Computer Information Engineering Baoding Vocational and Technical College Baoding071052 China

Performance of deep learning-based PCB (Printed Circuit Board) surface defect detection networks is often limited by the depth of feature extraction networks and the quality of training data. While significantly increasing network parameters can only slightly enhance system performance, optimizing training data can improve network performance without adding computational overhead. Therefore, this paper proposes a data augmentation network to enhance detection accuracy. First, an autoencoder generator is designed to enhance the feature fitting capability of the latent space. Second, a generative adversarial structural loss function is introduced, and an adversarial training method with different learning rates for the generator and discriminator is employed. Finally, experimental results demonstrate that this method enhances the diversity of PCB defect data and effectively improves the detection network’s recognition accuracy. © 2024 Codon Publications. All rights reserved.

关键词： Adversarial machine learning

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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 510-524页

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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