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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Huang, Min Wang, Wen Lin, Zheyuan Tesema, Fiseha B. Ji, Shanshan Gu, Jason Wan, Minhong Song, Wei Li, Te Zhu, Shiqiang The Intelligent Robotics Research Center Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University Canada

ISBN: (纸本)9781665481090

Continuously measuring the speaking state of users with robot in a human-robot Interaction(HRI) system improves metrics of interaction quality. Meanwhile, mainstream active speaker detection (ASD) algorithms emphasize achieving high AUCs at frame level in the AVA-Active Speaker dataset and pay less attention to get real-time performance in robotic systems. In this paper, we propose a model named FSDNet to keep a high AUC score in the AVA-Active Speaker dataset while reducing time cost, our model increase AUC score by 0.1% compared with the State-Of-The-Art and need only 75% running time. Furthermore, we put forward an architecture with a time-related prediction function to make our algorithm more effective and generative in interactive robotic systems. The code is released at https://***/huangmin9966/FSDNet-RealTimeArch. © 2022 IEEE.

关键词： Human robot interaction

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

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Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning 19

Network Parameter Control in Cellular Networks through Graph...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Forsberg, Albin Larsson Nikou, Alexandros Feljan, Aneta Vulgarakis Tumova, Jana Stockholm Sweden Kth Division of Robotics Perception and Learning Department of Electrical Engineering and Computer Science Stockholm Sweden Digital Futures United States

ISBN: (纸本)9798350320695

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective. © 2023 IEEE.

关键词： Graphic methods

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An Adaptive Superposition Point Process Model with Neuronal Encoding Engagement Identification 46

An Adaptive Superposition Point Process Model with Neuronal ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Mingdong Wang, Mingyi Wang, Yiwen Robotics and Autonomous Systems Thrust Hkust Guangzhou China Hkust Hong Kong Department of Chemical and Biological Engineering Hkust Hong Kong Department of Electronic and Computer Engineering Hkust Hong Kong Hkust Guangzhou China

ISBN: (纸本)9798350371499

Neuronal encoding is realized by modulating firing rates in response to various encoding factors, including external stimuli, behaviors, and complex neural interactions. The neuronal encoding engagement of various factors are dynamic, which reflects how neurons aggregate different information. The process of uncovering the extent to which these factors contribute to neuronal encoding over time is neuronal encoding engagement identification. Brain-machine interface (BMI) establishes a closed-loop framework to investigate how neurons response to different encoding factors. Since neurons don't fully participate in encoding one specific factor, accurate encoding engagement identification contributes to leveraging encoding and decoding in more naturalistic BMI application scenarios. However, previous works focus on modeling and estimating tuning properties instead of analyzing the neuronal information aggregation. We develop a dual adaptive superposition point process filter (DASPPF), which explicitly incorporates various encoding factors. DASPPF not only enables decoding kinematics but also identifies the engagement of individual kinematics encoding and functional neural connectivity encoding. DASPPF is validated on numerical simulations of monkey circle-tracking tasks. The proposed method can effectively promote decoding performance and uncover how neurons engage themselves in different effects with point process observation, which may help enhance the development of neurotechnologies. © 2024 IEEE.

关键词： Decoding

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A Computational Model for Estimating NMDA Properties from Local Field Potential Spectra 17th

A Computational Model for Estimating NMDA Properties from ...

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17th International Conference on Brain Informatics, BI 2024

作者： Mancini, Gabriele Martínez-Cañada, Pablo Panzeri, Stefano Hamburg Germany Istituto Italiano di Tecnologia Genoa Italy Department of Pharmacy and Biotechnology University of Bologna Bologna Italy Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

ISBN: (纸本)9789819632930

The balance between excitatory (E) and inhibitory (I) neural activity is crucial for brain function and is often disrupted in brain disorders. Recent model studies suggest that the slope of the frequency spectra of aggregate measures of neural activity, such as local field potentials (LFPs) or electroencephalograms (EEGs), can be used to predict the E/I ratio. However, these existing models overlook NMDA receptors, which are critical for cognitive functions like working memory and are implicated in disorders such as schizophrenia and Alzheimer’s. Here we use neural network simulations to show that the presence and strength of NMDA receptors have a major influence on the spectral slope of aggregate neural activity, particularly for lower frequencies, suggesting that spectral slopes of aggregate neural activity can be used to estimate NMDA receptor strength. We also find that estimates are better when using slopes computed from persistent activity after stimulus offset, a time when NMDA receptors affect most network dynamics. Moreover, when gamma oscillations are generated by the network, NMDA modulation affects the gamma peak spectral properties. The results suggest that the spectral slope of aggregate measures of neural activity such as LFPs or EEGs may be used as a biomarker of NMDA function or dysfunction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural network models

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GN-CNN-LSTM: Financial Market Prediction With Gaussian Noise Embedded CNN LSTM 11

GN-CNN-LSTM: Financial Market Prediction With Gaussian Noise...

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11th International Symposium on Telecommunication, IST 2024

作者： Abbasi, Mahsa Kargar, Masoud Ahmadian, Fatemeh Noormohammadzadehmaleki, Deniz Arandan, Amirhossein Hosseini, Nayer Seyed Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Robotics and Soft Technologies Research Center Tabriz Branch Islamic Azad University Tabriz Iran

ISBN: (纸本)9798350356250

Accurate financial market prediction is challenging due to volatility and complex patterns. This study presents a novel approach that injects Gaussian noise into a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model, enhancing prediction accuracy. While denoising techniques such as Autoencoders, Wavelet Transform, Filtering Techniques, and Generative Adversarial Networks (GANs) are commonly used, this research demonstrates that injecting noise during preprocessing significantly improves the performance of the CNNLSTM model. Empirical results using historical data show that the CNN-LSTM model with injected Gaussian noise outperforms traditional models, with notable improvements in MAE and RMSE metrics. Specifically, in SHOMP, SHI, HSI, and XAUUSD, the value of MAE in different sequences improved to 0.017,0.052,0.030, and 0.021, respectively, highlighting the effectiveness of the GN-CNN-LSTM model. These findings underscore the potential of noise-injected deep learning models, instilling optimism for achieving more reliable financial market predictions. © 2024 IEEE.

关键词： Commerce

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Open-Source, Low-Cost, and Bimodal Amateur Radio Communication Paradigm for Scientific CanSats 6

Open-Source, Low-Cost, and Bimodal Amateur Radio Communicati...

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6th International Conference on robotics, Control and Automation engineering, RCAE 2023

作者： Chun, Carrington Kihei, Billy Chakravarty, Sumit Tanveer, M. Hassan Kennesaw State Univeristy Department of Robotics and Mechatronics Engineering MariettaGA United States Kennesaw State Univeristy Department of Electrical and Computer Engineering MariettaGA United States

ISBN: (纸本)9798350315301

To facilitate the development of accessible space-bound robotic explorers, this work demonstrates a novel communication architecture intended for scientific CanSats. As low-cost terrestrial simulation platforms for orbital satellites, CanSats provide a valuable proving ground for new technologies or techniques. CanSats are also a considerably more accessible technological frontier than orbital CubeSats, as CanSats are lower in cost to develop and retain fewer operational requirements. Multiple low-cost CanSats and a simple Base Station were developed using Commercial-Off-The-Shelf (COTS) components. The bimodal modulation technique utilizes Amplitude Shift Keying (ASK) as a means for transferring data packets between the devices, while the delay between multiple ASK trains modulates additional Pseudo Morse Code (PMC) information that is audibly decodable with a handheld amateur radio receiver or digitally with a Software Defined Radio (SDR). The presented framework could be a valuable addition to telemetry beacons for orbital satellites that are seeking to engage the open-source or amateur radio communities. © 2023 IEEE.

关键词： Costs

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Trash Detection in Water Bodies Using Yolo With Explainable AI Insight

Trash Detection in Water Bodies Using Yolo With Explainable ...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Shafaq Rasheed Amal Mirza Muhammad Saad Saeed Muhammad Haroon Yousaf Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation Taxila Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Waste disposal into water bodies is a serious concern for environmental engineers, often resulting in urban flooding, soil degradation in agricultural areas, and freshwater pollution. Additionally, trash accumulation in water bodies threatens ma-rine life by introducing contaminants through storm drains, rivers, and other channels. Furthermore, the unique character-istics of underwater environments, including high noise levels, low visibility, and color variation, pose challenges for trash detection. Thus, addressing this issue is crucial for environmental sustainability. The work focuses on both floating and submerged trash in water bodies. Typically, the detection of trash on water bodies is impacted by environmental variability, diverse appearance, and background clutter. These conditions also make usual object detection techniques less effective. Deep learning methods have shown promise in handling challenging visual tasks, therefore researchers have turned to them to get over these challenges. In this work, we focus on applying different versions of YOLO to the problem of water trash detection. To assess these models' performance, we use four different datasets: Trash Flow rate Dataset, DeepTrash, FloW, and TrashICRA. Each dataset represents various trash types under different conditions such as underwater trash and floating trash. Our study attempts to identify the best method suitable for the detection of aquatic waste. Our results highlight the effectiveness of YOLO variants in identifying the presence of trash in water ecosystems. By leveraging Grad-CAM we gain insights into the interpretability and transparency of deep learning models thus enabling Explainable AI. This approach will also contribute to the understanding and trustworthiness of artificial systems deployed for trash detection applications by providing a transparent view of decision making process of deep learning models.

关键词： Deep learning YOLO Visualization Explainable AI Storms Biological system modeling Soil Water pollution Waste disposal robotics and automation

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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Design of the Interior Permanent Magnet Synchronous Motor for Electric Vehicles Using Grain-Oriented Electrical Steel

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Transactions of the Korean Institute of Electrical Engineers 2022年第7期71卷 939-944页

作者： Kwon, Min-Su Lim, Dong-Kuk Seo, Hyeon-Uk Dept. of Electrical Electronic and Computer Engineering University of Ulsan Korea Republic of Dept. of Robotics & Mechatronics Korea Institute of Machinery Materials Korea Republic of

In this paper, the interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs) using grain-oriented electrical steel (GO) is proposed. It was confirmed through the B-H characteristic curve and the B-W characteristic curve that the GO has superior magnetic characteristic compared to the non-grain-oriented electrical steel (NO). In order to maximize the effect of GO, the GO was partially applied to the stator teeth that has a uniform magnetic flux direction through magnetic path analysis of the EV traction motor. It has been confirmed that the output and various characteristics of the motor have improved with the application of GO. In particular, the iron loss was decreased by 44.83% in no-load condition. Moreover, the average torque and the efficiency were increased by 5.26%, 0.29%, respectively and the iron loss was decreased by 42.45% in load condition. The result of this study shows that the partial application of the GO in IPMSM improves the performance of the motor, effectively © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Traction motors

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