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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Optimization-Method-Inspired Update Scheme for Neural Network Assisted Control Tuning of PMSMs 26

Optimization-Method-Inspired Update Scheme for Neural Networ...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Yin, Zhenxiao Zhao, Hang Lu, Pei Shen, Yang Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350317589

The derivative of the control increment concerning the output of the neural network (NN) stands as a pivotal factor within the NN-assisted control tuning approach for permanent magnet synchronous motors (PMSMs). However, the significance of selecting an appropriate formula lacks explanation. Through an analysis of gradient-based optimization methods, the underlying principle behind the update of control increments becomes evident. Consequently, the design of the control tuning scheme incorporates gradient descent with and without momentum, as well as accelerated momentum. These techniques are applied to formulate the update law for the backpropagation neural network (BPNN) assisted control. Subsequently, these devised methodologies are integrated into the BPNN framework for the purpose of comparison. Finally, the results are validated in experiments. © 2023 IEEE.

关键词： Optimization

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Quality Estimation of Electronic Educational Resources 2

Quality Estimation of Electronic Educational Resources

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2nd International Conference on Technology Enhanced Learning in Higher Education, TELE 2022

作者： Karamzina, Anastasia Silnova, Svetlana Ufa State Aviation Technical University Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9781665470070

The transition of modern higher education to online learning is analyzed, the relevance of estimation the quality of electronic educational resources is substantiated. Using the Ishikawa diagram, problems were identified in estimation the quality of electronic educational resources. Criteria and procedure for estimation the quality of electronic educational resources were proposed, modeling of the process of estimation the quality of electronic educational resources was carried out. The requirements for conducting an expert estimation of the quality of electronic educational resources, for a user estimation of the quality of electronic educational resources have been determined. Quality estimation of electronic educational resources in the area of training 'Quality Management' was carried out. © 2022 IEEE.

关键词： E-learning

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Arbitrarily Scalable Environment Generators via Neural Cellular Automata 37

Arbitrarily Scalable Environment Generators via Neural Cellu...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhang, Yulun Fontaine, Matthew C. Bhatt, Varun Nikolaidis, Stefanos Li, Jiaoyang Robotics Institute Carnegie Mellon University United States Thomas Lord Department of Computer Science University of Southern California United States

ISBN: (纸本)9781713899921

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns. We include the source code at https://***/lunjohnzhang/warehouse_env_gen_nca_public. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Multipurpose robots

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A Novel Coupling Control Method for 6-DOF AUVs based on Multi-Motor Cooperation 26

A Novel Coupling Control Method for 6-DOF AUVs based on Mult...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Lu, Pei Yin, Zhenxiao Zhao, Hang Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350317589

The speed difference among underwater motors can induce either strong or weak coupling effects among different control loops within six-degree-of-freedom (6-DOF) autonomous underwater vehicles (AUVs). However, this issue is rarely accorded specialized attention. This paper utilizes the impact of the yaw dimension on roll angle control as an example, and proposes a novel coupling control method. This method hinges on collaborative action among multiple motors to mitigate the amplitude of speed difference fluctuations while introducing a coupling compensator in the feedforward loop. The efficacy of this method in enhancing the stability and robustness of motion control, as well as its superiority over conventional decoupling control methods, are substantiated through both simulation and experimentation. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Comparison of Structural Optimization for PMSMs Based on Various Machine Learning Methods 49

Comparison of Structural Optimization for PMSMs Based on Var...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Li, Fang Zhao, Hang Yin, Zhenxiao Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350331820

Existing sensitivity analysis methods suffer from issues such as small differentiation in parameter sensitivity and slow computational speed. To solve these problems, three machine learning methods, namely Ridge regression, Lasso regression, and Elastic Net, are investigated in this paper. First, a PMSM's performance is analyzed using Finite Element Method (FEM) to identify the variation range of the structural parameters that require optimization. Following that, a finite element sample database is established based on this range using the Design of Experiment (DOE). Next, the variable selection results of PMSM optimization are compared using different machine learning methods and traditional machine sensitivity analysis method. Finally, the performances of the PMSM optimized based on the three machine learning methods are evaluated. The results indicates that Lasso Regression proves to be the most effective method. It achieves rapid convergence and effectively identifies the most sensitive parameter, enhancing the optimization efficiency of PMSMs while retaining the global optimum. © 2023 IEEE.

关键词： Sensitivity analysis

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Walking-age estimator based on gait parameters 12

Walking-age estimator based on gait parameters

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Kuroda, Tomohito Okamoto, Shogo Akiyama, Yasuhiro Tokyo Metropolitan University Department of Computer Science Hino Japan Shinshu University Dept. Mechanical Engineering and Robotics Ueda Japan

ISBN: (纸本)9798350340181

Human gait motions differ depending on age. We estimated peoples' age using kernel regression analysis with reported height and weight and representative gait parameters as explanatory variables. The samples were Japanese women aged in their 20s-70s represented in an open database. The estimated ages exhibited a correlation coefficient of 0.65 with the actual ages, and the mean absolute error was 17.7 years. Estimation errors were significantly greater for older adults than for young people. Given that the method involves gait parameters that can be measured using wearable devices, such as inertial measurement units, this technique provides an accessible way for people to independently monitor their physical health in daily life. © 2023 IEEE.

关键词： Regression analysis

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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