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Thermal Comfort Model Established by Using Machine Learning Strategies Based on Physiological Parameters in Hot and Cold Environments

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Indoor Air 2024年第1期2024卷 9427822-9427822页

作者： Ho, Tseng-Fung Tsai, Hsin-Han Chuang, Chi-Chih Lee, Dasheng Huang, Xi-Wei Chen, Hsiang Cheng, Chin-Chi Kuo, Yaw-Wen Chou, Hsin-Hung Hsiao, Wei-Han Yang, Ching Hsu Li, Yung-Hui Department of Industrial Engineering and Management National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung City411030 Taiwan Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Department of Electrical Engineering National Chi Nan University Nantou54561 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou54561 Taiwan Department and Institute of Electrical Engineering Chang Gung University No. 259 Wenhua 1st Rd. Guishan Dist Taoyuan City33302 Taiwan Department of Emergency Medicine Hsinchu Mackay Memorial Hospital No. 690 Sec. 2 Guangfu Rd. East Dist Hsinchu City300041 Taiwan AI Research Center Hon Hai Research Institute Taipei City114699 Taiwan

The air-conditioning systems have become an indispensable part of our daily life for keeping the quality of life. However, to improve the thermal comfort and reduce energy consumption is crucial to use the air conditioners effectively with rapid development of artificial intelligence technology. This study explored the correlation between the response of human physiological parameters and thermal sensation voting (TSV) to evaluate the comfort level among various cold and hot stimulations. The variations of the three physiological parameters, which were body surface temperature, skin blood flow (SBF), and sweat area on the skin surface, and TSV values were all positively correlated with the stimulation amount under the stimulation of cold wind, hot wind, and heat radiation, but the relationship was not completely linear. Among the three physiological parameters, the forehead skin temperature has the closest relationship with TSV, followed by the SBF and sweat. Among three stimulations, the cold wind stimulation causes the closest relationship between TSV and forehead temperature, followed by the radiation and hot wind stimulations. Through three different machine learning models, namely, random forest (RF) model, support vector machine (SVM) model, and neural network (NN) model, the stimulation of cold wind, hot wind, and heat radiation was applied to investigate the variation of the three physiological parameters as the input of the models. Moreover, the models were evaluated and verified by TSV. The results revealed that among the three different machine learning methods, RF had the best accuracy. The established thermal comfort models can predict the real-Time user's thermal comfort feeling, so that air-conditioning equipment's performance can be optimized to create a healthy and energy-saving comfortable environment. © 2024 Tseng-Fung Ho et al.

关键词： Thermal comfort

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Space Domain Based Cooperative and Adaptive Cruise Control for Enhanced Fuel Efficiency on Rolling Terrain

SSRN

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SSRN 2024年

作者： Lei, Mingyue Wang, Haoran Li, Duo Li, Zhenning Dhamaniya, Ashish Hu, Jia Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Environmental Engineering University of California Los AngelesCA90024 United States State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Transportation Engineering & Planning Section Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Key Laboratory of Road and Traffic Engineering of the Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China

Ecological Cooperative and Adaptive Cruise Control (Eco-CACC) is widely focused to enhance sustainability of CACC. However, state-of-the-art Eco-CACC studies are still facing challenges in adopting on rolling terrain. Furthermore, they cannot ensure both ecology optimality and computational efficiency. Hence, this paper proposes a nonlinear optimal control based Eco-CACC controller. It has the following features: i) enhancing performance across rolling terrains by modeling in space domain;ii) enhancing fuel efficiency via globally optimizing all vehicle's fuel consumptions;iii) ensuring computational efficiency by developing a differential dynamic programming-based solving method for the non-linear optimal control problem;iv) ensuring string stability through theoretically proving and experimentally validating. The performance of the proposed Eco-CACC controller was evaluated. Results showed that the proposed Eco-CACC controller can improve average fuel saving by 37.67% at collector road and about 17.30% at major arterial. © 2024, The Authors. All rights reserved.

关键词： Dynamic programming

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Low-Profile 3D Printed Transmit-array for Wide-Angle Beam Scanning at Ka-Band

Low-Profile 3D Printed Transmit-array for Wide-Angle Beam Sc...

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International Conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Álvaro. F. Vaquero Sérgio Matos Manuel Arrebola Jorge R. Costa João Felício Carlos Fernandes Nelson J. G. Fonseca Dept. Electrical Engineering Universidad de Oviedo Gijón Spain Institute de Telecomunicações (IT) Lisbon Portugal Dept. Information Science and Technology University Institute of Lisbon (ISCTE-IUL) Lisbon Portugal Centro de Investigação Naval (CINAV) Escola Naval Almada Portugal Dept. Electrical and Computer Engineering Instituto Superior Tecnico (IST) Univ. of Lisbon Portugal European Space Agency Antenna and Sub-Millimeter Waves Section Noordwijk The Netherlands

Low-cost and compact antennas for wide-angle beam scanning in the mm-Wave band play a key role in the development of the next generation of terrestrial and satellite communication systems. Phased array antennas are currently getting a lot of attention for their excellent scanning with a low profile and mature manufacturing process [1]. However, the power consumption and losses due to their complex feeding network and large count of active components are still their main drawbacks. Both issues are overcome using spatially fed antennas (SFA) such as reconfigurable reflectarray (RA) or transmitarray (TA) antennas. These solutions avoid the feeding network, and the beam scanning is obtained using power-efficient controlling elements (PIN, MEMs, Liquid Crystal, etc.) [2]. However, they are still more costly and complex than solutions based on passive periodic structures combined with mechanical scanning (mainly reflector-based antennas, RA, or TA).

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Unsupervised Social Bot Detection via Structural Information Theory

arXiv

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arXiv 2024年

作者： Peng, Hao Zhang, Jingyun Huang, Xiang Hao, Zhifeng Li, Angsheng Yu, Zhengtao Yu, Philip S. School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China State Key Laboratory of Public Big Data Guizhou University No. 2708 South Section of Huaxi Avenue Huaxi District Guizhou Province Guiyang City550025 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China College of Science University of Shantou No. 243 University Road Shantou515063 China School of Computer Science and Engineering Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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Smart grid security through fusion-enhanced federated learning against adversarial attacks

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Attia Shabbir Habib Ullah Manzoor Ahmed Zoha Zahid Halim Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan James Watt School of Engineering University Of Glasgow Glasgow G12 8QQ United Kingdom Department of Electrical Engineering University of Engineering and Technology Lahore Pakistan Machine Intelligence and Affective Systems Laboratory National Yunlin University of Science and Technology 123 University Road Section 3 Douliu Yunlin 64002 Taiwan

The proliferation of smart grids introduces significant challenges for energy networks in managing and securing the vast data they generate. Federated Learning (FL) provides a cost-effective and privacy-preserving framework for distributed model training, addressing critical concerns around customer data privacy and security. However, FL remains vulnerable to adversarial threats, particularly data poisoning attacks, which can significantly impair model performance. This study presents a novel data poisoning attack and proposes a mitigation framework tailored for resource-constrained smart grids. The Centroid-Based Anomaly Aware Federated Averaging (CBAA-FedAvg) framework is introduced, achieving a Mean Absolute Percentage Error (MAPE) of 2.7 percent, closely aligning with baseline performance while maintaining robustness. CBAA-FedAvg integrates advanced fusion techniques, including parameter quantization (from 32-bit floating point to 8-bit fixed point) and dynamic clustering, to minimize computational complexity and optimize data processing. Furthermore, an automatic stopping criterion optimizes convergence, reducing energy consumption and computation time. Experimental results demonstrate that CBAA-FedAvg exhibits remarkable resilience to both data and model poisoning attacks, leveraging fusion strategies to enhance security and efficiency. This framework provides a scalable and effective solution for improving the fusion, security, and operational efficiency of smart grids.

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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SelfDN: Adaptive Self-Denoising for multi-view 3D object detection

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Knowledge-Based Systems 2025年

作者： Yafei Qi Menghao Yang Yongmin Zhang Bing Xiong Yaping Liu Zhaoning Zhang School of Computer Science and Engineering Central South University computer building yuelu district Changsha 410073 Hunan China Changsha University of Science & Technology 960 2nd Section Wanjiali RD (S) Changsha 410073 Hunan China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 Guangdong China Computer Science and Technology National University of Defense Technology No. 137 Yanwachi Street Changsha 410073 Hunan China

Multi-view 3D object detection has achieved remarkable success comparable to LiDAR-based methods through the detection transformer, with query denoising approaches playing a pivotal role by addressing the challenges of false positives and sample imbalance, thereby significantly enhancing the accuracy and robustness of detection models. However, existing methods rely heavily on handcrafted noise patterns and overlook the spatial characteristics of multi-view 3D detection, which limits the performance of the methods. We present adaptive Self-Denoising (SelfDN), a simple yet effective training framework that automatically generates adaptive noise patterns based on the model’s current training state. In particular, we construct adaptive denoising queries based on a one-to-many assignment between predictions and ground truth during training. Based on these denoising queries, we propose two sampling strategies: Dynamic Noise Sampling (DNS) and Complementary Depth Sampling (CDS). Collectively, these strategies adaptively control both the number of denoising queries and the balance between positive and negative noise samples. The proposed SelfDN approach addresses the limitations of traditional manual denoising techniques through the optimization of adaptive learning states, thereby facilitating the effective mining of challenging and ambiguous samples. As a plug-and-play module, SelfDN can be integrated with existing DETR-like multi-view 3D detectors without any significant impact on computational resources during training. Extensive experiments on nuScenes and Argoverse 2 benchmarks demonstrate the effectiveness of our approach. With the ResNet-50 backbone, SelfDN achieves 47.9% mAP and 56.8% NDS on nuScenes validation set, outperforming previous the state-of-the-art RayDN method by 1.0%. In particular, for the strict distance threshold evaluation (AP 0.5 ), a score of 36.7% AP is observed for the car category, representing a 3.64% improvement over previous methods. On th

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Traceable random numbers from a nonlocal quantum advantage

arXiv

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arXiv 2024年

作者： Kavuri, Gautam A. Palfree, Jasper Reddy, Dileep V. Zhang, Yanbao Bienfang, Joshua C. Mazurek, Michael D. Alhejji, Mohammad A. Siddiqui, Aliza U. Cavanagh, Joseph M. Dalal, Aagam Abellán, Carlos Amaya, Waldimar Mitchell, Morgan W. Stange, Katherine E. Beale, Paul D. Brandão, Luís T.A.N. Booth, Harold Peralta, René Nam, Sae Woo Mirin, Richard P. Stevens, Martin J. Knill, Emanuel Shalm, Lynden K. Department of Physics University of Colorado BoulderCO80309 United States The National Institute of Standards and Technology BoulderCO80305 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Joint Quantum Institute National Institute of Standards and Technology University of Maryland 100 Bureau Drive GaithersburgMD20899 United States Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Quside Technologies S.L. Castelldefels Barcelona Spain ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Department of Mathematics University of Colorado BoulderCO80309 United States GaithersburgMD20899 United States Information Technology Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Physical Measurement Laboratory National Institute of Standards and Technology BoulderCO80305 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80305 United States Applied and Computational Mathematics Division National Institute of Standards and Technology BoulderCO80305 United States Quantum Engineering Initiative Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States

The unpredictability of random numbers is fundamental to both digital security [1, 2] and applications that fairly distribute resources [3, 4]. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators [5] are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators [6–9] can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here, for the first time, we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol is at the heart of a public traceable and certifiable quantum randomness beacon that we have launched [10]. Over the first 40 days of operation, we completed the protocol 7434 out of 7454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error times actual success probability bounded by 2−64. The generation of certifiable and traceable randomness represents one of the first public services that operates with an entanglement-derived advantage over comparable classical approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Random number generation

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End-To-End Audiovisual Feature Fusion for Active Speaker Detection

arXiv

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arXiv 2022年

作者： Tesema, Fiseha Berhanu Lin, Zheyuan Zhu, Shiqiang Song, Wei Gu, Jason Wu, Hong Interdisciplinary Innovation Research Institute Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Yuhang District China Electrical and Computer Engineering Dalhousie University HalifaxNS B3J 2X4 Canada School of Computer Science and Engineering University of Electronic Science and Technology of China Shahe Campus No.4 Section 2 North Jianshe Road Chengdu China Ocean College Zhejiang University 866 Yuhangtang Rd Xihu Hangzhou China

Active speaker detection plays a vital role in human-machine interaction. Recently, a few end-to-end audiovisual frameworks emerged. However, these models' inference time was not explored and are not applicable for real-time applications due to their complexity and large input size. In addition, they explored a similar feature extraction strategy that employs the ConvNet on audio and visual inputs. This work presents a novel two-stream end-to-end framework fusing features extracted from images via VGG-M with raw Mel Frequency Cepstrum Coefficients features extracted from the audio waveform. The network has two BiGRU layers attached to each stream to handle each stream's temporal dynamic before fusion. After fusion, one BiGRU layer is attached to model the joint temporal dynamics. The experiment result on the AVA-ActiveSpeaker dataset indicates that our new feature extraction strategy shows more robustness to noisy signals and better inference time than models that employed ConvNet on both modalities. The proposed model predicts within 44.41 ms, which is fast enough for real-time applications. Our best-performing model attained 88.929% accuracy, nearly the same detection result as state-of-the-art work. © 2022, CC BY.

关键词： Feature extraction

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Long Text Classification Based on BERT 5

Long Text Classification Based on BERT

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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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