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A Hybrid CNN-Transformer Approach for Continuous Fine Finger Motion Decoding from sEMG Signals

A Hybrid CNN-Transformer Approach for Continuous Fine Finger...

引用

IEEE international conference on Computational Intelligence and Virtual Environments for measurement systems and Applications

作者： Zihan Weng Xiabing Zhang Yufeng Mou Chanlin Yi Fali Li Pouya Bashivan Peng Xu School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Institute of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China Department of Physiology McGill University Montréal Canada

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

This work presents a novel approach that synergistically integrates convolutional neural networks (CNNs) and Transformer models for decoding continuous fine finger motions from surface electromyography (sEMG) signals. This integration capitalizes on CNNs’ proficiency in extracting rich temporal and spatial features from multichannel sEMG data and the Transformer’s superior capability in recognizing complex patterns and long-range dependencies. A significant advancement in this field is the use of a custom-developed Epidermal Electrode Array Sleeve (EEAS) for capturing high-fidelity sEMG signals, enabling more accurate and reliable signal acquisition than traditional methods. The decoded joint angles could be used in seamless and intuitive human-machine interaction in various applications, such as virtual reality, augmented reality, robotic control, and prosthetic control. Evaluations demonstrate the superior performance of the proposed CNN-Transformer hybrid architecture in decoding continuous fine finger motions, outperforming individual CNN and Transformer models. The synergistic integration of CNNs and Transformers presents a powerful framework for sEMG decoding, offering exciting opportunities for naturalistic and intuitive human-machine interaction applications. Its robustness and efficiency make it an ideal choice for real-world applications, promising to enhance the interface between humans and machines significantly. The implications of this research extend to advancing the understanding of human neuromuscular signals and their application in computing interfaces.

关键词： Human computer interaction Solid modeling Fingers Virtual environments Transformers Feature extraction Electromyography

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Arabic Speech Recognition for Industrial Commands: A Review

Arabic Speech Recognition for Industrial Commands: A Review

引用

Embedded systems and Artificial Intelligence (ESAI), international conference on

作者： Naouar Laaidi Abderrahim Ezzine Mohamed Lamrini Hassan Satori Departement of Computer Sciences FSDM LISAC Laboratory USMBA University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The integration of speech recognition technology in industrial environments has significantly enhanced humanmachine interaction, offering hands-free control over machinery in complex and noisy settings. This review focuses on the application of speech recognition for industrial command systems, with a particular emphasis on stochastic approaches such as Hidden Markov Models and their extensions. We examine the challenges posed by noisy environments, linguistic variability, and dialectal diversity, especially in Arabic, as well as the opportunities offered by advanced methods like deep learning. The paper also highlights emerging hybrid models that combine traditional and modern techniques to improve accuracy and robustness. Finally, the review outlines future research directions for optimizing speech recognition in industrial settings.

关键词： Deep learning Accuracy Reviews Computational modeling Scalability Hidden Markov models Stochastic processes Speech recognition Real-time systems Noise measurement

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Sim-to-Real Domain Adaptation for Deformation Classification

Sim-to-Real Domain Adaptation for Deformation Classification

引用

IEEE international conference on systems, Man and Cybernetics

作者： Joel Sol Jamil Fayyad Shadi Alijani Homayoun Najjaran Faculty of Electrical and Computer Engineering University of Victoria Victoria BC Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Deformation detection is vital for enabling accurate assessment and prediction of structural changes in materials, ensuring timely and effective interventions to maintain safety and integrity. Automating deformation detection through computer vision is crucial for efficient monitoring, but it faces significant challenges in creating a comprehensive dataset of both deformed and non-deformed objects, which can be difficult to obtain in many scenarios. In this paper, we introduce a novel framework for generating controlled synthetic data that simulates deformed objects. This approach allows for the realistic modeling of object deformations under various conditions. Our framework integrates an intelligent adapter network that facilitates sim-to-real domain adaptation, enhancing classification results requiring limited real data from deformed objects. We conduct experiments on domain adaptation and classification tasks and demonstrate that our framework improves sim-to-real classification results compared to the simulation baseline. Our code is available here.

关键词： measurement computer vision Visualization Quality assurance Accuracy Deformation Transformers Robustness Safety Synthetic data

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Temporal Encoding for Sequential Recommendation

Temporal Encoding for Sequential Recommendation

引用

international joint conference on Neural Networks (IJCNN)

作者： Zhichao Zhong Ling Luo Xiaohan He Hao Li University of Melbourne University of Hunan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In recommender systems, accurately modeling dynamic user preferences is crucial, especially for Internet enterprises with large volumes of user data. Recent sequential recommender systems based on neural networks have excelled in generating highly personalized recommendations by analyzing interaction sequences to infer user interests. However, these models often neglect temporal information, a significant part of user behavior sequences. We argue that temporal dynamics, like evolving user preferences, can potentially influence user decisions. Apart from user preference, item popularity can also contribute to a user-item interaction at a specific timestamp. To address the gap in modeling temporal information, we draw inspiration from the Fourier Series to propose a novel method for converting timestamps into temporal encodings, unveiling latent features suitable for deep networks. Our model, TE4SRec, employs this method with bidirectional attention networks to meld both sequential and temporal data from user histories. In this work, extensive experiments demonstrate that TE4SRec outperforms current state-of-the-art models across four diverse benchmark datasets, as evidenced by common evaluation metrics. An additional ablation study highlights the marked improvements attributed to our temporal encoding method. Furthermore, the visualizations of temporal approximations in TE4SRec illustrate its proficiency in effectively learning and interpreting the temporal dynamics of item popularity.

关键词： measurement Neural networks Data visualization Benchmark testing Encoding Data models Internet

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Factorized models in neural architecture search: Impact on computational costs and performance

Factorized models in neural architecture search: Impact on c...

引用

international joint conference on Neural Networks (IJCNN)

作者： Unai Garciarena Alexander Mendiburu Roberto Santana Intelligent Systems Group University of the Basque Country San Sebastian Spain Intelligent Systems Group University of the Basque Country San Sebastián Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The current quest for environmentally sustainable AI models has led to NAS algorithms that not only pursue model accuracy, but also energy efficiency in a simultaneous manner, which increases the architecture optimization problem complexity. In this paper, we aim at simplifying this problem by using factorization. More specifically, we analyze the fitness landscapes of NAS from the point of view of search distributions related to the accuracy and computational complexity of the neural network architectures. For these search distributions, we extract probability factorizations and compute a number of descriptors of the search difficulty for methods based on probability distributions. Our results reveal that each objective can induce search distributions with different strengths of interactions between variables. Therefore, methods that intend to optimize the computational cost of NAS can require different types of factorizations than those focused on the accuracy.

关键词： Ranking (statistics) Accuracy Computational modeling Neural networks computer architecture Search problems Probability distribution

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Neural Network Control for Dynamics of a 3DOF Robot Arm

Neural Network Control for Dynamics of a 3DOF Robot Arm

引用

IEEE international conference on Smart Instrumentation, measurement and Applications (ICSIMA)

作者： Erwin Susanto Estananto Sony Sumaryo Basuki Rahmat School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

This paper discusses the use of neural networks as an alternative to replace PID plus computed torque control in a 3DOF arm robot system. The neural network model used is multilayer perceptron with 3 inputs, 3 outputs and 30 hidden layers activated with the scaled gradient descent function. The training results show an R squared value of around 0.564, but the root mean square error of end effector external coordinate trajectories shows performance that does not exceed the computed torque control. The neural network model uses generated I/O data, and divide it for training, validation and testing 70%, 15% and 15% percent respectively. However, the graphically response of the neural network control system is better than that of PID plus computed torque control for the dynamic robot system. Therefore, further research is needed to improve the performance of this model by selecting appropriate architecture, hidden layer number and activating function.

关键词： Training Computational modeling Robot kinematics Torque control computer architecture Brain modeling Control systems

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Green and Low Carbon Load Side Carbon Emission measurement in Power systems Based on Opportunity Constraints

Green and Low Carbon Load Side Carbon Emission Measurement i...

引用

China international conference on Electricity Distribution, CICED

作者： Zhiyuan Xu Liang Cheng School of Control and Computer Engineering North China Electric Power University Beijing China TCA Lab Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368352

In the context of global warming, traditional carbon measurement methods have failed to completely capture the carbon emission dynamics of the power system under various operating states, resulting in biased carbon emission data. Moreover, there is still significant research space for measurement models on the power load side. To improve the accuracy and real-time performance of carbon emission measurement, this study designs a power system load side carbon emission measurement model based on opportunity constraints. The proposed model is built on opportunity constraints and fairness principles to allocate carbon emission responsibilities on the load side. On this basis, considering the dynamic characteristics of the power system, a two-stage carbon emission measurement model is raised, which uses dynamic carbon emission measurement method and node carbon potential method to calculate carbon emissions in the first and second stages, respectively. The outcomes validated that the carbon emissions of Unit 1 in the fixed carbon emission factor model were 620.07 tons, while they were reduced to 470.11 tons in the proposed model. After implementing demand response measures, the total carbon emissions of the system during peak electricity usage decreased by 3130 kg. The fixed carbon emission factor econometric model, traditional carbon market model, and the proposed model resulted in a total carbon emissions of 18306.61 tons, 16939.79 tons, and 19045.21 tons, respectively. Therefore, the proposed model performs better in regulating demand, can more accurately measure carbon emissions on the load side of the power system, and contribute to promoting green and low-carbon development of the energy system.

关键词： Power measurement Biological system modeling Power system dynamics Energy measurement Carbon dioxide Extraterrestrial measurements Time measurement Real-time systems Global warming Load modeling

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LTMVS: Liquid Temperature measurement with Vibration Sensing Using Smartphones

LTMVS: Liquid Temperature Measurement with Vibration Sensing...

引用

Artificial Intelligence of Things and systems (AIoTSys), international conference on

作者： Qingyuan Fu Jiamei Cao Ziyan Zhang Tianhao Zhao Xingfa Shen School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

The real-time measurement and monitoring of liquid temperatures is a critical aspect in diverse fields, including food inspection, medical pharmaceuticals, and industrial machinery. Conventional methods of liquid temperature measurement predominantly depend on invasive external temperature measurement devices. These devices are not only inconvenient for daily utilization but also carry the risk of liquid contamination. Smartphones, recognized for their portability and array of built-in sensors, present an innovative avenue for liquid temperature measurement. However, the use of smartphones for this purpose encounters obstacles in the creation of precise mathematical models and the extraction of informative features. In this study, we introduce a novel method dubbed LTMVS (Liquid Temperature measurement with Vibration Sensing), grounded on the principle of liquid viscosity. This approach employs the smartphone's vibration motor to generate vibration signals and conducts pattern matching on the echo produced by the cup wall, facilitating non-invasive temperature measurement. We executed extensive experiments using the LTMVS system to measure maltose within a temperature range of 32°C to 63°C. The average absolute error was approximately 1.74°C, and the average absolute error percentage was around 3.91%. These results validate the efficacy and precision of this approach in liquid temperature measurement.

关键词： Temperature measurement Vibrations Temperature sensors Viscosity Liquids Feature extraction Sensors Pollution measurement Smart phones Sensor arrays

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Detection of Safety Helmets Using YOLOv5s Model

Detection of Safety Helmets Using YOLOv5s Model

引用

Sustainable Computing and Smart systems (ICSCSS), international conference on

作者： Urmila Pilania Manoj Kumar Sarika Chaudhary Vivaan Mittal Computer Science & Technology Manav Rachna University Faridabad India Computer Science & Technology Computer Science and Engineering DPG Institute of Technology and Management Gurgaon India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

With the increasing number of vehicles, these days the road accident is becoming very common in society. The manual detection of helmet is very time-consuming and requires a lot of manpower. In order to overcome the abovementioned issue, an automated technique is required detect whether the helmet is worn by the rider or not. This research proposes a helmet detection model for checking whether the riders are wearing helmets or not. The research utilizes the YOLOv5s deep learning object detection algorithm to identify rider's helmets. In this research work, various combinations of learning rates, levels, and epochs are explored to assess the model performance and identify the optimal model. Performance metrics such as F1 score, precision, recall, and Mean Average Precision (mAP) are computed to evaluate the effectiveness of the proposed system. To validate the experimental results, the proposed model is compared with the recent existing research works.

关键词： YOLO Deep learning Training measurement Head Road accidents Computational modeling

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Automatic Reconstruction of Noisy Electroencephalography (EEG) Channels with Transformer-Based Architectures for Sustainable systems

Automatic Reconstruction of Noisy Electroencephalography (EE...

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international conference on Computing, Engineering, and Design (ICCED)

作者： Nibras Abo Alzahab Ahmad Adnan Bsis Biomedical Nour Alshawa Department of information Engineering Marche Plotichnic University Ancona Italy Engineering Tishreen University Latakkia Syria School of Biomedical Engineering Indian Institute of Technology (BHU) Varanasi India

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

In the field of Brain-computer Interface (BCI), electroencephalography, commonly referred to as EEG, is a neuroimaging technology that experiences widespread application. EEG signals, on the other hand, are susceptible to a wide variety of noises and artifacts, which can lead to a decrease in the quality of the data. Using transformer-based architectures, including attention-based models, the purpose of this research is to establish an automatic approach for reconstructing noisy EEG channels. There were three different noise scenarios that were taken into consideration, and they were zero-mashing, noise masking, and zero-noise masking. The reconstructing model combines convulsion neural networks (CNN) with a multi-head self attehtion transformer model. We designed the model to balance both performance and sustainability considerations. The findings indicate that the model was able to accomplish a high level of reconstructing skills, as indicated by the value of R 2 , which is equivalent to 0.8563 within a margin of error of 0.0543. In addition, considerations of sustainability were taken into account for the actual execution where the model size was restricted to less than 0.5 MB.

关键词： Computational modeling Biological system modeling Noise computer architecture Brain modeling Transformers Electroencephalography Brain-computer interfaces Noise measurement Sustainable development

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