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Web based maintenance work support by neural networks - Detection and wear estimation of components in wind energy turbines 16

Web based maintenance work support by neural networks - Dete...

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16th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Zeitler, Waldemar Quandt, Moritz Stern, Hendrik Freitag, Michael BIBA - Bremer Institut für Produktion and Logistik GmbH University of Bremen Hochschulring 20 Bremen28359 Germany University of Bremen Faculty of Production Engineering Badgasteiner Straße 1 Bremen28359 Germany

Mobile assistance systems in demanding wind turbine maintenance processes can help to reduce the work time of individual tasks. One such task is the measurement of carbon brushes, which need replacement when mechanical abrasions occur. We developed an image-based detection of different types of carbon brushes using an autoencoder network. After the detection, we adjusted the alignment of each carbon brush to get the height to length ratio, without additional depth information, for a reasonable estimation of the mechanical abrasion and a suggestion for a possible replacement. This leads to a more efficient workflow and less manual work during maintenance procedures. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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Electronics and Renewable systems (ICEARS), international conference on

作者： A. Vinitha S. Jeevitha N.B. Mahesh Kumar Sathish Kumar L Saravanan D Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamilnadu India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Virudhunagar Tamilnadu India Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Tamil Nadu India School of Computing Science and Engineering (SCOPE) VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset.

关键词： YOLO Technological innovation Renewable energy sources Accuracy Computational modeling Real-time systems Time measurement Object recognition Optimization Standards

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Estimation of Affective State based on Keystroke and Typing Vibration during computer-Mediated Communication 14

Estimation of Affective State based on Keystroke and Typing ...

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14th international joint conference on Biomedical Engineering systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired systems and Signal Processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Hasegawa, Kei Miyamoto, Hikaru Ashida, Yuki Ban, Yuki Fukui, Rui Inazawa, Masahiro Warisawa, Shin'ichi Univ Tokyo Grad Sch Frontier Sci Dev Human & Engn Environm Studies Kashiwa Chiba Japan

ISBN: (纸本)9789897584909

In recent years, the use of computer-mediated communication (CMC), that is, communication among people through computers, has increased. Knowing the message sender's affective state is essential for understanding the contents of the message correctly. However, it is difficult to interpret this state because of the non-availability of nonverbal information from the sender during CMC. Although attempts have been performed to estimate affective state, there is a challenge of high measurement load. In this paper, we propose an estimation of valence and arousal using keyboard input and typing vibration information as a method to estimate the sender's affective state with a low measurement load during CMC. We conducted experiments to obtain keyboard input and typing vibration information for estimating valence and arousal. This estimation was performed by extracting features from the information using a support vector machine, and cross-validation was conducted to verify our method. Therefore, the valence and arousal were estimated at accuracies of 69.8% and 71.1%, respectively, for unlearned participants' data.

关键词： Affective Computing computer-Mediated Communication Keyboard Support Vector Machine

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Merging Realms: Classical and Quantum-Enhanced Deep Learning Techniques for CIFAR-10 Classification

Merging Realms: Classical and Quantum-Enhanced Deep Learning...

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Computing, Semiconductor, Mechatronics, Intelligent systems and Communications (COSMIC), international conference on

作者： Sai Jayanth Bobbili S Chandrappa V. Keshava Reddy Guru Prasad M S Deepa K Mansi Sharma Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engineering Jain(Deemed-to-be University) Bengaluru India Department of ECE VTU India Department of ECE NMAMIT Nitte

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

Classical and quantum-enhanced deep learning techniques are revolutionizing areas of artificial intelligence. This leads to substantial innovations in various domains, including the image processing. The CIFAR-10 dataset is a widely used and benchmarked dataset for assessing the performance of deep learning models. In this proposed method, the CIFAR-10 dataset is used for image classification in an effort to improve model performance. The modern and strong models are employed, such as CNN, InceptionV3, DenseNet, and a Hybrid Quantum-DenseNet. The precision, recall, accuracy, loss, and F1-score metrics are used in this analysis to assess these models. From the analysis, it is observed that CNN and Hybrid Quantum-DenseNet attained 77%-78% accuracy. The InceptionV3 reached the maximum accuracy of 90.15%. The performance of the developed hybrid model was exceptionally well across a range of parameters. DenseNet achieved an accuracy of up to 84%.

关键词： Deep learning Semiconductor device modeling measurement Technological innovation Accuracy Quantum algorithm Mechatronics Computational modeling Merging Intelligent systems

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OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics

OpenCapBench: A Benchmark to Bridge Pose Estimation and Biom...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Yoni Gozlan Antoine Falisse Scott Uhlrich Anthony Gatti Michael Black Jennifer Hicks Scott Delp Akshay Chaudhari Stanford University Max Planck Institute for Intelligent Systems

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Pose estimation has promised to impact healthcare by enabling more practical methods to quantify nuances of human movement and biomechanics. However, despite the inherent connection between pose estimation and biomechanics, these disciplines have largely remained disparate. For example, most current pose estimation benchmarks use metrics such as Mean Per joint Position Error, Percentage of Correct Keypoints, or mean Average Precision to assess performance, without quantifying kinematic and physiological correctness - key aspects for biomechanics. To alleviate this challenge, we develop OpenCapBench to offer an easy-to-use unified benchmark to assess common tasks in human pose estimation, evaluated under physiological constraints. OpenCapBench computes consistent kinematic metrics through joints angles provided by an open-source musculoskeletal modeling software (OpenSim). Through OpenCapBench, we demonstrate that current pose estimation models use keypoints that are too sparse for accurate biomechanics analysis. To mitigate this challenge, we introduce SynthPose, a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypoints for accurate kinematic analysis through the use of synthetic data. Incorporating such fine-tuning on synthetic data of prior models leads to twofold reduced joint angle errors. Moreover, OpenCapBench allows users to benchmark their own developed models on our clinically relevant cohort. Overall, OpenCapBench bridges the computer vision and biomechanics communities, aiming to drive simultaneous advances in both areas.

关键词： Biomechanics measurement Computational modeling Biological system modeling Pose estimation Kinematics Benchmark testing Physiology Data models Synthetic data

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Design and modeling of a New File Transfer Architecture to Reduce Undetected Errors Evaluated in the FABRIC Testbed 25

Design and Modeling of a New File Transfer Architecture to R...

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Abstracts of the 2025 ACM SIGMETRICS international conference on measurement and modeling of computer systems

作者： Prateek Jain Arash Sarabi Abraham Matta Violet R. Syrotiuk Department of Computer Science Boston University Boston MA USA School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA

ISBN: (纸本)9798400715938

Ensuring data integrity for petabyte-scale file transfers is critical for scientific applications. As packet sizes increase, so does the likelihood of undetected errors. Multi-Level Error Detection (MLED) is a recursive architecture that leverages in-network resources to reduce undetected error probability (UEP) in file transfer. MLED organizes communication in layers at different levels, with each layer implementing configurable policies. Through experimentation on the FABRIC testbed, we show that while the traditional---transport and data link layer---error detection results in corrupt file transfers requiring retransmission, MLED detects and corrects these errors at intermediate levels, when an adversarial error model is used. MLED thus achieves a 100% gain in goodput reaching over 800 Mbps on a single connection with no appreciable increase in delay.

关键词： CRC

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1st international Symposium, ToM for Teams 2021

1st International Symposium, ToM for Teams 2021

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1st international Symposium, ToM for Teams 2021

ISBN: (纸本)9783031216701

The proceedings contain 14 papers. The special focus in this conference is on ToM for Teams. The topics include: Development of Emergent Leadership measurement: Implications for Human-Machine Teams;should Agents Have Two systems to Track Beliefs and Belief-Like States?;preface;operationalizing Theories of Theory of Mind: A Survey;sequential Theory of Mind modeling in Team Search and Rescue Tasks;integrating Machine Learning and Cognitive modeling of Decision Making;overgenerality from Inference in Perspective-Taking;using Features at Multiple Temporal and Spatial Resolutions to Predict Human Behavior in Real Time;route Optimization in Service of a Search and Rescue Artificial Social Intelligence Agent;knowledge of Self and Other Within a Broader Commonsense Setting;constructivist Approaches for Computational Emotions: A Systematic Survey;social Cognition Paradigms ex Machinas.

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Analog Dopplegangers: Twinning with Deep Continuous-Time Recurrent Neural Networks

Analog Dopplegangers: Twinning with Deep Continuous-Time Rec...

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international joint conference on Neural Networks (IJCNN)

作者： John C. Gallagher Eric T. Matson Electrical and Computer Enginnering University of Cincinnati Cincinnati OH USA Computer and Information Technology Purdue University West Lafayette IN

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Digital computers are built of analog circuit components that "twin" conceptual digital entities such as switches or logic gates. Employing such "analog doppelgangers" to twin conceptual discrete components modulo thresholding and clocking constraints was a necessary step in implementing modern digital computer. These digital computers have gone on to be so successful that in many circles the word "digital" has almost become synonymous with "computational". We may see some of this effect even the term "digital twin". This paper speculatively addresses the idea that the twin need not be digital in the strictest sense so long as it actually models, indistinguishably, the behavior of some other system. We will draw from previous work using fully-recurrent continuous time and continuous valued neural networks to twin optimal controllers for a simplified legged-locomotion agent. We will then argue that the use of "analog doppelgangers" to twin systems directly has benefits and utility that strictly digital twins may not possess in equal measure. Finally, we will examine and discuss what types of practical problems might be positively impacted by this alternative view.

关键词： Digital computers Recurrent neural networks Computational modeling Switches Logic gates Analog circuits Digital twins

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Simple Attention-Based Mechanism for Multimodal Emotion Classification

Simple Attention-Based Mechanism for Multimodal Emotion Clas...

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international conference on Automation and Computing (ICAC)

作者： Mazen Elabd Sardar Jaf School of Computer Science University of Sunderland Sunderland United Kingdom

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Recent advances in attention-based machine learning models have significantly enhanced performance in various classification tasks, including emotion recognition. In this work, we introduce two novel multimodal architectures that leverage attention-based techniques for emotion classification. We introduce a state-of-the-art attention-based multimodal architecture and a baseline architecture. Our state-of-the-art architecture utilises attention-based unimodal models to extract contextualised embeddings from each modality and an attention-based fusion technique. Performance metrics and rigorous error analysis indicate that unimodal systems trained solely on text or speech data underperform compared to our multimodal system, which integrates both modalities. Furthermore, our system significantly outperforms existing state-of-the-art multimodal systems using the same modalities by nearly 4%.

关键词： measurement Emotion recognition Automation Error analysis computer architecture Machine learning Context modeling

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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...

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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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