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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Andrea Lucaroni Andrea Bottino Fabrizio Lamberti Giulia Mariani Andrea Marinelli Nicolò Boccardo Emanuele Gruppioni Lorenzo De Michieli Matteo Laffranchi Giacinto Barresi Rehab Technologies Lab Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genoa Italy Centro Protesi INAIL Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro Bologna Italy

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The embodiment (the sensation that an external object is part of one’s body scheme) is one of the processes targeted by the research in bionic prosthetics as a potential strategy to increase the acceptance of artificial limbs. This study investigates how one of the components of the embodiment, the body ownership (the sensation of possessing the external object), may be affected by a secondary task (possibly competing for cognitive resources requested for the embodiment itself) in a virtual exergame for prosthetic training. In this preliminary study participants, without impairments, are asked to perform activities of pick-and-place in two conditions by using a digital model of the Hannes bionic hand: (i) one is cognitively challenging, requiring to memorize the sequence of objects to move before it disappears; (ii) the other is non-challenging for the subject’s memory, with a constantly visible sequence. In both conditions, each subject is asked to catch objects according to the presented sequence. According to questionnaire data, we noticed how this procedure is feasible. Interestingly, the proprioceptive drift (the person is asked to indicate where they perceive their own real limb without looking at it) in a subsequent Rubber Hand Illusion session with the real bionic hand seems highlighting that the working memory-engaging secondary task can impair the embodiment process in terms of body ownership of the robotic hand. Probably, the memory task actually requires a type of cognitive resources that are necessary for the body ownership process itself during the multisensory stimulation. This allows for envisioning further investigations and novel design concepts for prosthetic training paradigms.

关键词： Training Solid modeling Visualization Biological system modeling Memory management Propioception Virtual environments

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Latency versus Transmission Power Trade-off in Free-Space Optical (FSO) Satellite Networks with Multiple Inter-Continental Connections

arXiv

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arXiv 2023年

作者： Liang, Jintao Chaudhry, Aizaz U. Chinneck, John W. Yanikomeroglu, Halim Kurt, Gunes Karabulut Hu, Peng Ahmed, Khaled Martel, Stephane Lab Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Poly-Grames Research Center Department of Electrical Engineering Polytechnique Montreal MontrealQCH3T 1J4 Canada OttawaONK1A 0R6 Canada MDA Sainte-Anne-de-BellevueQCH9X 3Y5 Canada

In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program. The results reveal that, for satellite transmission power limits set at 0.5 W, 0.3 W, and 0.1 W, the average total network latency for all five inter-continental connections studied in this work levels off at 339 ms, 361 ms, and 542 ms, respectively. Furthermore, the corresponding LISL ranges required to achieve these average total network latency values are 4500 km, 3000 km, and 1731 km, respectively. Different limitations on satellite transmission power exhibit varying effects on average total network latency (over 100 time slots), and they also induce differing changes in the corresponding LISL ranges. In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms. © 2023, CC BY-NC-ND.

关键词： Satellites

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Achievable Diversity Order of HARQ-Aided Downlink NOMA systems

arXiv

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arXiv 2022年

作者： Shi, Zheng Zhang, Chenmeng Fu, Yaru Wang, Hong Yang, Guanghua Ma, Shaodan The Institute of Physical Internet The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The School of Telecommunications Engineering Xidian University Xi’an710000 China The Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China

The combination between non-orthogonal multiple access (NOMA) and hybrid automatic repeat request (HARQ) is capable of realizing ultra-reliability, high throughput and many concurrent connections particularly for emerging communication systems. This paper focuses on characterizing the asymptotic scaling law of the outage probability of HARQ-aided NOMA systems with respect to the transmit power, i.e., diversity order. The analysis of diversity order is carried out for three basic types of HARQ-aided downlink NOMA systems, including Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR). The diversity orders of three HARQ-aided downlink NOMA systems are derived in closed-form, where an integration domain partition trick is developed to obtain the bounds of the outage probability specially for HARQ-CC and HARQ-IR-aided NOMA systems. The analytical results show that the diversity order is a decreasing step function of transmission rate, and full time diversity can only be achieved under a sufficiently low transmission rate. It is also revealed that HARQ-IR-aided NOMA systems have the largest diversity order, followed by HARQ-CC-aided and then Type I HARQ-aided NOMA systems. Additionally, the users’ diversity orders follow a descending order according to their respective average channel gains. Furthermore, we expand discussions on the cases of power-efficient transmissions and imperfect channel state information (CSI). Monte Carlo simulations finally confirm our analysis. © 2022, CC BY.

关键词： Monte Carlo methods

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Innovative dust detection and efficient cleaning of PV Panels: A CNN‑RF approach using I–V curve data transformed into RGB mosaics

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Energy Conversion and Management: X 2025年 27卷

作者： Safia Babikir Bashir Mena Maurice Farag Abdul-Kadir Hamid Ali A. Adam Ramesh C. Bansal Nsilulu T Mbungu A. Elnady Ahmed G. Abo-Khalil Mousa Hussein Smart Grid Research Group Sustainable Energy and Power Systems Research Center Research Institute of Sciences and Engineering (RISE) Department of Electrical Engineering University of Sharjah Sharjah 27272 United Arab Emirates Smart Automation and Communication Technologies Research Center (SACT) University of Sharjah 27272 Sharjah United Arab Emirates Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa Department of Electrical and Communication Engineering United Arab Emirates University Al Ain United Arab Emirates

Photovoltaic (PV) panels are vital for renewable energy generation, yet their efficiency is critically hindered by environmental challenges such as dust accumulation, especially in arid regions like the UAE. Dust buildup can reduce efficiency by up to 30% within a month, threatening the sustainability of solar power, which is projected to supply 10% of global energy by 2030. Existing cleaning methods are unsustainable, consuming an estimated 10 billion gallons of water annually, enough to meet the drinking needs of 2 million people, necessitating the development of a cost-effective, resource-efficient alternative. This research presents a novel machine learning-based system to automate dust detection and optimize cleaning, significantly reducing water consumption while improving power generation efficiency. The methodology transforms I-V curve electrical parameters into RGB mosaic images, enabling precise classification of operational states such as normal operation, dust accumulation, shading, and faults without relying on external imaging devices. The system is built on a hybrid model combining Convolutional Neural Networks (CNN) and Random Forest (RF) classifiers (CNN-RF), where the CNN extracts high-level features from RGB mosaic images, and the RF classifier accurately categorizes operational states. Upon detecting dust accumulation, a secondary CNN-RF model classifies the severity into low, moderate, or heavy, guiding an optimized cleaning process that minimizes water usage while maintaining cleaning effectiveness. The primary CNN-RF model achieved 100% accuracy in classifying operational states using RGB mosaic images, surpassing the 97% accuracy achieved by I–V curve-based methods. Furthermore, the secondary CNN-RF model for dust severity classification attained an accuracy of 98% using RGB mosaic images, compared to only 68% when using traditional I–V curves, highlighting the superior performance of RGB mosaic images in detecting fine-grained dust levels. T

关键词： Automated Cleaning Convolutional Neural Networks Dust Detection Machine Learning Photovoltaics

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Afnet: Local Aggregation and Context Fusion Network for Plant Point Cloud Part Segmentation

SSRN

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SSRN 2024年

作者： Qi, Yanyu Guo, Ruohao Li, Zhenbo Qu, Liao Niu, Dantong College of Information and Electrical Engineering China Agricultural University Beijing100083 China National Innovation Center for Digital Fishery Beijing100083 China School of Intelligence Science and Technology Peking University Beijing100871 China Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural Affairs Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States BAIR Lab University of California BerkeleyCA94709 United States

Convolution-like models have emerged in recent years to address diverse point cloud processing tasks. However, a notable challenge persists in effectively capturing subtle spatial relationships and contextual semantics essential for achieving a thorough comprehension of point clouds. In this work, we propose AFNet, a local aggregation and context fusion network, to capture complicated shape structures, global semantics and contextual representations. Initially, a novel plug-and-play convolution operator is designed to model local points, leveraging both low-level positional relations and high-level feature correlations. This operator employs element-wise multiplication to aggregate transformed positions and features, followed by a reduction function for information abstraction from neighbors. By utilizing the aforementioned convolution as a foundational operator, a deep residual architecture is constructed, wherein each layer adopts one-shot connection strategy. Consequently, global semantics and complicated shapes can be fully obtained in an elegant manner. Furthermore, this work presents PlantSeg3D, a point cloud dataset of 3,600 3D plant models for plant phenotyping, to alleviate the scarcity of plant datasets in the community. Extensive experiments showcase that AFNet performs well on several public benchmarks across different tasks, surpassing previous state-of-the-art approaches with an additional increase of +4.1 mIoU. The impressive performance on both part segmentation and classification tasks demonstrates noteworthy generalization capability. © 2024, The Authors. All rights reserved.

关键词： Convolution

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A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack

arXiv

引用

arXiv 2023年

作者： Li, Zhuoxuan Korovin, Iakov Shi, Xinli Gorbachev, Sergey Gorbacheva, Nadezhda Huang, Wei Cao, Jinde The School of Mathematics Southeast University Nanjing210096 China The Scientific Research Institute of Multiprocessor Computer Systems Southern Federal University Taganrog347928 Russia The School of Cyber Science & Engineering Southeast University Nanjing210096 China The Russian Academy of Engineering Moscow125009 Russia The Intelligent Transportation System Research Center Southeast University Nanjing210096 China The School of Mathematics Southeast University Nanjing210096 China Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry. © 2023, CC BY.

关键词： Complex networks

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Dynamic neural network with memristive CIM and CAM for 2D and 3D vision

arXiv

引用

arXiv 2024年

作者： Zhang, Yue Zhang, Woyu Wang, Shaocong Lin, Ning Yu, Yifei He, Yangu Wang, Bo Jiang, Hao Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Zhang, Xumeng Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100049 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Institute of the Mind The University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based Computing-In-Memory (CIM) and Content-Addressable Memory (CAM) circuits, respectively. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets, which not only achieves accuracy on par with software but also a 48.1% and 15.9% reduction in computational budget. Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption. © 2024, CC BY.

关键词： Memristors

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Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies

arXiv

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arXiv 2023年

作者： Kashif, Muhammad Rashid, Muhammad Al-Kuwari, Saif Shafique, Muhammad eBrain Lab Division of Engineering New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Center for Quantum and Topological Systems NYUAD Research Institute New York University Abu Dhabi United Arab Emirates Department of Computer Engineering Umm Al Qura University Makkah Saudi Arabia Qatar Center for Quantum Computing College of Science and Engineering Hamad bin Khalifa University Qatar

Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parameter values, PQCs often exhibit barren plateaus (BP). These plateaus, characterized by vanishing gradients with an increasing number of qubits, hinder optimization in quantum algorithms. In this paper, we analyze the impact of state-of-the-art parameter initialization strategies from classical machine learning in random PQCs from the aspect of BP phenomenon. Our investigation encompasses a spectrum of initialization techniques, including random, Xavier (both normal and uniform variants), He, LeCun, and Orthogonal methods. Empirical assessment reveals a pronounced reduction in variance decay of gradients across all these methodologies compared to the randomly initialized PQCs. Specifically, the Xavier initialization technique outperforms the rest, showing a 62% improvement in variance decay compared to the random initialization. The He, Lecun, and orthogonal methods also display improvements, with respective enhancements of 32%, 28%, and 26%. This compellingly suggests that the adoption of these existing initialization techniques holds the potential to significantly amplify the training efficacy of Quantum Neural Networks (QNNs), a subclass of PQCs. Demonstrating this effect, we employ the identified techniques to train QNNs for learning the identity function, effectively mitigating the adverse effects of BPs. The training performance, ranked from the best to the worst, aligns with the variance decay enhancement as outlined above. This paper underscores the role of tailored parameter initialization in mitigating the BP problem and eventually enhancing the training dynamics of QNNs. © 2023, CC BY.

关键词： Machine learning

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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Explainable Unsupervised Multi-Sensor Industrial Anomaly Detection and Categorization

Explainable Unsupervised Multi-Sensor Industrial Anomaly Det...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Mina Ameli Philipp Aaron Becker Katharina Lankers Markus van Ackeren Holger Bähring Wolfgang Maaß Cognitive Assistants Systems Department DFKI GmbH Saarbrücken Germany Lab Department deZem GmbH Berlin Germany Research & Development – Data Science Department Schott AG Mainz Germany Development Department SEITEC GmbH Erfurt Germany Smart Service Engineering Department DFKI GmbH Saarbrücken Germany

Real-time Anomaly Detection is of great importance in industrial applications in order to have high-quality production and avoid downtime or failure of the system. In this paper, we study the application of anomaly detection over the multivariate data collected from Glass Production Industry. Our experiments utilize and compare different Unsupervised multivariate time series Anomaly Detection and Localization algorithms that have already demonstrated significant results on the state-of-the-art data sets. We propose a two-level multivariate anomaly detection approach that not only detects anomalous events in the production line but also categorize the different type of anomalies based on statistical pattern recognition. Furthermore, we localize the anomalous sensors by utilizing Explainable-AI approaches to help better decision-making in glass production monitoring. In this work, we propose an efficient pipeline for Anomaly Detection, Categorization and Localization which the experiments show promising results.

关键词： Location awareness Machine learning algorithms Time series analysis Pipelines Production Glass Real-time systems

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