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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lala, Betty Kala, Srikant Manas Rastogi, Anmol Dahiya, Kunal Hagishima, Aya Kyushu University Interdisciplinary Graduate School of Engineering Sciences Fukuoka Japan Osaka University Mobile Computing Laboratory Graduate School of Information Science and Technology Japan Indian Institute of Technology Hyderabad India Indian Institute of Technology Delhi India

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Indoor thermal comfort in smart buildings has a significant impact on the health and performance of occupants. Consequently, machine learning (ML) is increasingly used to solve challenges related to indoor thermal comfort. Temporal variability of thermal comfort perception is an important problem that regulates occupant well-being and energy consumption. However, in most ML-based thermal comfort studies, temporal aspects such as the time of day, circadian rhythm, and outdoor temperature are not considered. This work addresses these problems. It investigates the impact of circadian rhythm and outdoor temperature on the prediction accuracy and classification performance of ML models. The data is gathered through month-long field experiments carried out in 14 classrooms of 5 schools, involving 512 primary school students. Four thermal comfort metrics are considered as the outputs of Deep Neural Networks and Support Vector Machine models for the dataset. The effect of temporal variability on school children's comfort is shown through a 'time of day' analysis. Temporal variability in prediction accuracy is demonstrated (up to 80%). Furthermore, we show that outdoor temperature (varying over time) positively impacts the prediction performance of thermal comfort models by up to 30%. The importance of spatio-temporal context is demonstrated by contrasting micro-level (location specific) and macro-level (6 locations across a city) performance. The most important finding of this work is that a definitive improvement in prediction accuracy is shown with an increase in the time of day and sky illuminance, for multiple thermal comfort metrics. © 2022 IEEE.

关键词： Energy efficiency

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Phase-bias-blind computational ghost imaging with optical phased array

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Optics Express 2025年第10期33卷 21132-21141页

作者： Hirata, Sota Matsuda, Naoki Komatsu, Kento Tanemura, Takuo Horisaki, Ryoichi Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

We propose a method for computational ghost imaging (CGI) with an optical phased array (OPA), wherein phase biases on the phase shifters are intrinsically unknown. In our method, an object is illuminated with a number of light fields from the OPA using random phase shifts, and the light from the object is captured by a single-pixel detector. The object and the phase biases are simultaneously estimated from the measured intensity signals. We numerically demonstrate the proposed method. Furthermore, we present complex amplitude field acquisition in OPA-based CGI with intensity measurements performed without reference light by simply adding an iris to the detector. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computational Imaging

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An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

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Information Processing in Agriculture 2022年第1期9卷 38-47页

作者： Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computing and PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil

In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

关键词： Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development

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GUI: A Comprehensive Dataset of Global Urban Infrastructure Based on Geospatial Visual Foundation Models 24

GUI: A Comprehensive Dataset of Global Urban Infrastructure ...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Han, Zhenyu Zhang, Xin Xi, Yanxin Luo, Yan Xia, Tong Li, Yong Department of Electronic Engineering BNRist Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science University of Helsinki Helsinki Finland Media Lab Massachusetts Institute of Technology Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400711077

The substantial social and financial costs of infrastructure identification impede in-depth analyses of sustainable urban design, especially in developing countries. In this paper, we present a novel framework with interactive web visualization based on geospatial visual foundation models. Leveraging this framework, we examine the urban infrastructure information in 1,178 cities worldwide, covering 93, 088 km2 areas. Cross-validation reveals that the overall accuracy of identified infrastructure achieves 67.0%. It sheds light on the sustainable development of cities and exposes the stark inequity in urban infrastructure provision for vulnerable populations. The identified urban infrastructure dataset of this study are available at https://***/tsinghua-fib-lab/GUI, and the interactive web application is at https://***/yz7xbfy3. © 2024 Copyright held by the owner/author(s).

关键词： Artificial Intelligence

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An IoT-Based Microservice Platform for Virtual Puppetry Performance

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IEEE Access 2023年 11卷 103014-103032页

作者： Luo, Helin Lin, Yi-Bing Liao, Chen-Chi Huang, Yung-Hui Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan Tainan National University of the Arts Graduate Institute of Animation and Film Art Tainan720 Taiwan National Yang Ming Chiao Tung University Department of Computer Science and Engineering Hsinchu300 Taiwan China Medical University College of Humanities and Sciences Taichung406 Taiwan National Cheng Kung University Miin Wu School of Computing Tainan701 Taiwan Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan

This study proposes a web-based real-time remote-controlled virtual puppetry (avatar) performance platform called AvatarTalk to combine traditional Taiwanese puppetry, the Internet of Things (IoT), and 3D avatar models. AvatarTalk enables puppeteers to control avatar puppets on the web using either motion capture gloves or camera-based image recognition. This transformative performance approach not only facilitates remote performances and multi-screen presentations across various devices but also introduces a fresh gesture interpretation technique for dance artists. AvatarTalk supports a mechanism that can accommodate new control and puppet devices from other approaches through the IoT-based microservice concept. We develop a calibration procedure to enable the accurate capture of hand gestures to manipulate the movements of virtual puppets, empowering them to perform fundamental traditional puppetry poses such as nodding, bowing, and synchronized hand movements. We have conducted experiments to show the accuracy of AvatarTalk calibration. Our study indicates that AvatarTalk can almost detect the right gestures (98.75%-100% recall) and very seldom mistake the wrong gestures (90.8%-100% precision). Additionally, we also provide the mechanism to measure the delays of controlling the puppets. An analytic model is proposed to design the delay times of the messages between the control device of a puppeteer and the AvatarTalk server. In the current AvatarTalk implementation, if the message delay does not exceed 0.1 seconds, four puppeteers can synchronize their actions if the elapsed time between two actions of a puppeteer is longer than 0.3 second. © 2013 IEEE.

关键词： Internet of things

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Diff-Control: A Stateful Diffusion-based Policy for Imitation Learning

Diff-Control: A Stateful Diffusion-based Policy for Imitatio...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiao Liu Yifan Zhou Fabian Weigend Shubham Sonawani Shuhei Ikemoto Heni Ben Amor Computing and Augmented Intelligence Arizona State University USA Department of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

While imitation learning provides a simple and effective framework for policy learning, acquiring consistent action during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation at data curation stage or altering the model itself, both of which do not fully address the scalability of consistent action generation. To overcome this limitation, we introduce the Diff-Control policy, which utilizes a diffusion-based model to learn action representation from a state-space modeling viewpoint. We demonstrate that diffusion-based policies can acquire statefulness through a Bayesian formulation facilitated by ControlNet, leading to improved robustness and success rates. Our experimental results demonstrate the significance of incorporating action statefulness in policy learning, where Diff-Control shows improved performance across various tasks. Specifically, Diff-Control achieves an average success rate of 72% and 84% on stateful and dynamic tasks, respectively. Notably, Diff-Control also shows consistent performance in the presence of perturbations, outperforming other state-of-the-art methods that falter under similar conditions. Project page: https://***/

关键词： Training Torque Imitation learning Scalability Perturbation methods Process control Sensor systems and applications Robustness Intelligent sensors Intelligent robots

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Spiking Transformer with Experts Mixture 38

Spiking Transformer with Experts Mixture

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Zhaokun Lu, Yijie Jia, Yanhao Che, Kaiwei Niu, Jun Huang, Liwei Shi, Xinyu Zhu, Yuesheng Li, Guoqi Yu, Zhaofei Yuan, Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Peng Cheng Laboratory China College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Peking University China Institute for Artificial Intelligence Peking University China Institute of Automation Chinese Academy of Sciences China Deep NeuroCognition Lab I2R and CFAR Agency for Science Technology and Research Singapore

Spiking Neural Networks (SNNs) provide a sparse spike-driven mechanism which is believed to be critical for energy-efficient deep learning. Mixture-of-Experts (MoE), on the other side, aligns with the brain mechanism of distributed and sparse processing, resulting in an efficient way of enhancing model capacity and conditional computation. In this work, we consider how to incorporate SNNs' spike-driven and MoE's conditional computation into a unified framework. However, MoE uses softmax to get the dense conditional weights for each expert and TopK to hard-sparsify the network, which does not fit the properties of SNNs. To address this issue, we reformulate MoE in SNNs and introduce the Spiking Experts Mixture Mechanism (SEMM) from the perspective of sparse spiking activation. Both the experts and the router output spiking sequences, and their element-wise operation makes SEMM computation spike-driven and dynamic sparse-conditional. By developing SEMM into Spiking Transformer, the Experts Mixture Spiking Attention (EMSA) and the Experts Mixture Spiking Perceptron (EMSP) are proposed, which performs routing allocation for head-wise and channel-wise spiking experts, respectively. Experiments show that SEMM realizes sparse conditional computation and obtains a stable improvement on neuromorphic and static datasets with approximate computational overhead based on the Spiking Transformer baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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Simulation-Based POLCA Integrated QRM Approach for Smart Manufacturing 2nd

Simulation-Based POLCA Integrated QRM Approach for Smart Man...

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2nd International Conference on Industry 4.0 and Advanced Manufacturing, I-4AM 2022

作者： Kumar, Sandeep Petchrompo, Sanyapong Ahmed, Tanveer Jain, Amit Kumar IILM Graduate School of Management Uttar Pradesh Greater Noida201306 India Department of Mathematics Faculty of Science Mahidol University Rama VI Rd. Bangkok10400 Thailand Department of Computer Science and Engineering Bennett University Uttar Pradesh Greater Noida201310 India School of Computing Engineering and the Built Environment Glasgow Caledonian University Glasgow United Kingdom

ISBN: (纸本)9789811905605

Digitalisation and Industry 4.0 have enabled real-time interaction of customers and suppliers with manufacturing systems. However, this requires a high level of reactivity in the value chain to sustain in a competitive economy. As a result, Quick Response Manufacturing (QRM) in dynamic conditions such as wide product variety, change in demand, volatile market conditions, uncertainty in supply, and machine failures is critical in capturing the benefits of digitalisation in industries. To empower QRM, Paired-cell Overlapping Loops of Cards with Authorization (POLCA) is suitable for a manufacturing environment with a wide variety and highly dynamic conditions. The combination of QRM and POLCA provides a significant competitive advantage through their quick response to dynamic conditions. Such a combination is not reported in the literature. Accordingly, this paper proposes a simulation-based POLCA integrated with the QRM approach for smart manufacturing. The offered approach evaluates a dynamic sequence of jobs at each machine to meet the due date and at the same time control the material flow to improve system performance. The approach is illustrated with the help of a complex manufacturing scenario. A simulation-based technique is used to evaluate the system performance. Further, the performance of the approach under dynamic conditions is analysed. Results show that improved system performance can be achieved by considering QRM and POLCA in dynamic conditions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Industry 4.0

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Automatic Adaptation Rule Optimization via Large Language Models

arXiv

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arXiv 2024年

作者： Ishimizu, Yusei Li, Jialong Xu, Jinglue Cai, Jinyu Iba, Hitoshi Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adaptation rules is often a challenge because it essentially involves searching the optimal design in a complex (variables) space. In response, this paper attempt to employ large language models (LLMs) as a optimizer to construct and optimize adaptation rules, leveraging the common sense and reasoning capabilities inherent in LLMs. Preliminary experiments conducted in SWIM have validated the effectiveness and limitation of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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A novel hybrid technique using fuzzy logic, neural networks and genetic algorithm for intrusion detection system

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Measurement: Sensors 2023年 30卷

作者： Ishaque, Mohammed Johar, Md Gapar Md Khatibi, Ali Yamin, Muhammed School of Graduate Studies Management and Science University Shah Alam Malaysia Information Technology and Innovation Centre Management and Science University Shah Alam Malaysia School of Computing ANU Colleage of Engineering Computing and Cybernetics The Australian National University Canberra Australia

As the number of people who use the internet continues to rapidly increase, security has become an increasingly important concern in the online world. Many researchers in the past have developed detection systems to identify and detect intruders using data mining. These systems can be found in use today. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time a novel intrusion detection system is proposed that will ensure the safety of data communication by locating any unauthorized users and efficiently identifying any unwanted visitors to wireless networks. In this paper, a hybrid algorithm is proposed for the removal of uncertainty and the prediction of outcomes. Fuzzy logic is utilized in the process of removing uncertainty, whereas neural networks are utilized for the purpose of prediction. The Genetic Algorithm is utilized in order to effect improvements in the accuracy of prediction results. Experiments have been carried out in order to evaluate the proposed intrusion detection system, which has an overall detection accuracy of 99.12 %. The proposed model's performance has been put to evaluation utilizing tenfold cross-validation, which has been completed. © 2023 The Authors

关键词： Genetic algorithms

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