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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 989-1002页

作者： Li, Dandan Xia, Jiaxing Li, Jiangfeng Xiao, Changjiang Stankovic, Vladimir Stankovic, Lina Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai201804 China China University of Mining and Technology Artificial Intelligence Research Institute Jiangsu Xuzhou221008 China Tongji University College of Surveying and Geo-Information Shanghai200092 China University of Strathclyde Department of Electronic and Electrical Engineering GlasgowG1 1XJ United Kingdom

Predicting the electricity consumption of individual appliances, known as appliance-level energy consumption (ALEC) prediction, is essential for effective energy management and conservation. Despite its importance, research in this area is limited and faces several challenges: 1) the correlation between the usage of different appliances has rarely been considered for ALEC prediction;2) a learnable strategy for obtaining the optimal correlation between different appliance behaviors is lacking;and 3) it is difficult to accurately quantify the usage relationship among different appliances. To address these issues, we propose a graph-based temporal-spatial network that employs a learnable adjacency matrix for appliance-level load prediction in this work. The network comprises a temporal graph convolutional network (TGCN) and a learnable adjacency matrix that enables us to utilize correlations between appliances and quantify their relationships. To validate our approach, we compared our model with six others: a TGCN model with a fixed adjacency matrix where all elements are set to 0;a TGCN model with a fixed adjacency matrix where all elements are set to 0.5, except for the diagonal;a TGCN model with a randomly generated adjacency matrix, except for the diagonal;an Aug-LSTM model;a model with ResNetPlus architecture;and a feed-forward deep neural network. Five houses in four datasets: AMPDs, REFIT, UK-DALE, and SC-EDNRR are utilized. The metrics used in this study include root mean square error, explained variance score, mean absolute error, F-norm and coefficient of determination. Our experiments have validated the accuracy and practicality of our proposed approach across different datasets. © 2024 IEEE.

关键词： Prediction models

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A frequency-domain index for quickly selecting optimal down-sampling factor in photoacoustic imaging 17

A frequency-domain index for quickly selecting optimal down-...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Tang, Shihao Wan, Min Li, Jiani Zhang, Yameng Tao, Ling Li, Weitao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China Department of Computer Engineering Nanjing Institute of Technology No.1 Hongjing Avenue Jiangsu Nanjing211167 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Photoacoustic microscopy (PAM) has emerged as a rapidly advancing non-invasive medical imaging method in recent years. However, slow imaging speed has impeded its widespread adoption in clinical applications. In certain scenarios, sparse spatial sampling is essential for PAM so there is a trade-off between spatial resolution and imaging speed. To address this limitation, we propose a frequency domain index based on cumulative power difference (CPD) to determine rapidly the optimal down-sampling factors. In this study, the structural images of mice ears were acquired by the PAM system. Subsequently, the optimal down-sampling factor was determined through CPD analysis of these images via interpolation. Finally, the correlation between cumulative power difference and the image quality loss curve were analyzed. The results indicate that the quality of the reconstructed images decreases with the increasing down-sampling factor. Moreover, the cumulative power difference is an effective tool for rapid assessment of reconstruction image quality degradation due to varying down-sampling factors. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Dual Polarized Stack Antenna for Ku Band Satellite Communication with Capacitively Coupled Feeding

Dual Polarized Stack Antenna for Ku Band Satellite Communica...

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Asia-Pacific Conference on Microwave

作者： Mon-Li Chang Sung-Nien Hsieh Hsiang-En Hsieh Ding-Bing Lin Hsi-Tseng Chou 1Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

This study proposes a dual-polarization array antenna design applied to the Ku band low-orbit satellite communication system. The application scenario of this design can be used in the uplink and downlink frequency bands of satellite communication. The downlink frequency band is designed from 11 GHz to 12 GHz, and the uplink frequency band is designed from 13.75 GHz to 14.6 GHz. In addition, this paper also adds a dual-polarization antenna architecture. In satellite communication, the antenna polarization of the ground equipment uses a dual polarization antenna architecture. In the design of the receiver, the signals of two different polarizations can be processed separately. Use signal processing to get the correct signal.

关键词： Microwave antennas Satellite antennas Receivers Microwave devices Signal processing Microwave communication Downlink Antenna feeds Ku-band Uplink

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Time-Division Online Speculative Inference for Cost-Effective Intelligent Internet of Things 10

Time-Division Online Speculative Inference for Cost-Effectiv...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Du, Yao Wang, Zehua Leung, Cyril Leung, Victor C.M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9798350373011

Intelligent Internet of Things (IIoT), a network paradigm, is an interconnection of intelligent edge devices, empowered by machine learning models. The recent emergence of large language models (LLMs) opens a new path towards IIoT. Although device models are generally lightweight and suitable for edge devices with limited processing power, their performance is less impressive compared to LLMs running on large servers. Server models offer superior performance with the cost of significant computational resources for model inference. To realize cost-effective IIoT, we introduce a novel collaborative inference framework: the online speculative inference enabled by device collaboration and time divisions. We aim to find the minimum number of servers required for the immediate processing of arrived inference requests. To solve the problem, we propose an evolving directed acyclic graph along with a Proof-of-Deletion mechanism for cost reduction and privacy protection. Based on computer simulations using heterogeneous LLMs, it is found that time-division online speculative inference is a promising approach towards cost-effective IIoT. © 2024 IEEE.

关键词： Adversarial machine learning

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Iterative Learning Control - Algorithms, Applications and Future Research Directions 63

Iterative Learning Control - Algorithms, Applications and Fu...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rogers, Eric Chu, Bing Moore, Kevin Oomen, Tom Tan, Ying University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom Colorado School of Mines Department of Electrical Engineering United States Eindhoven University of Technology Department of Mechanical Engineering Netherlands Delft University of Technology Department of Mechanical Engineering Netherlands University of Melbourne Department of Mechanical Engineering Australia

ISBN: (纸本)9798350316339

This paper gives a tutorial on iterative learning control nearly five decades after what is widely regarded as the first substantive paper in the literature. The focus is on algorithm development under a number of general headings (linear, optimization, frequency domain, and nonlinear), together with supporting experimental validation/industrial applications and also applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Enhancing Students' Learning Performance by Combining Flipped Learning and Online Formative Assessment Platform

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International Journal of engineering Education 2023年第2期39卷 409-419页

作者： Cheng, Shu-Chen Cheng, Yu-Ping Huang, Yueh-Min Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology No. 1 Nantai St Yongkang District Tainan City71005 Taiwan Department of Engineering Science National Cheng Kung University No. 1 Dasyue Rd East District Tainan City701m Taiwan

Flipped learning is allowed students to increase opportunities for active learning in the learning process. Although previous studies have shown that there was a positive impact of flipped learning on class, few studies have combined flipped learning with formative assessment to explore students' learning performance in terms of programming. Hence, this study adopted a quasi-experimcntal design to recruit 121 students for 15 weeks of experimental activities. The experimental group used a combination of flipped learning and formative assessment to explore whether their learning performance was higher than that of the control group using flipped learning. In addition, this study established an online formative assessment platform to provide the experimental group with online formative assessment tests. According to the results, the learning performance of the experimental group in the second test and the third test was significantly higher than that of the control group. On the other hand, the experimental group could still achieve better learning performance in the more difficult units (units 7 to 12). In addition, the researchers showed that when the duration of the experiment was longer and the experimental group of students continued to use the online formative assessment platform to administer the test, their learning performance in terms of programming was also significantly enhanced. Therefore, this study demonstrated the effectiveness and contribution of combining flipped learning with formative assessment in terms of programming. © 2023 TEMPUS Publications.

关键词： Students

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Occupancy estimation in smart buildings using predictive modeling in imbalanced domains

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10917-10929页

作者： Guo, Jiaxun Amayri, Manar Najar, Fatma Fan, Wentao Bouguila, Nizar Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G1T7 Canada Grenoble Institute of Technology Grenoble France The Department of Computer Science and Technology Huaqiao University Xiamen China

This paper introduces a novel approach for occupancy estimation in smart buildings. In particular, we focus on the challenging yet common situation where the amount of training data is small and imbalanced (i.e. the classes are not approximately equally represented). Our model is based on two parts namely predictive modelling, performed via the inverted Dirichlet mixture (IDMM), and an over-sampling approach that we propose. The first part, in which the main goal is to tackle the small training data problem, concerns the calculation of the predictive distribution of the IDMM by marginalizing over its parameters, with their posterior distributions, which are estimated by a Bayesian variational inference approach that we develop. Based on over-sampling, the second part can be viewed as a complement to tackling the imbalanced domains problem. Extensive experiments and simulations involving synthetic data as well as real data extracted from smart buildings sensors show the merits of our statistical framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Intelligent buildings

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Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

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Intelligent Automation & Soft Computing 2023年第8期37卷 1531-1551页

作者： Hang Yang Xunbo Li Witold Pedrycz School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 1H9Canada

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply *** study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of *** hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN *** model improves the power supply to a more considerable extent with the least number of power bank *** main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic *** overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or ***’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment ***,the cellular automata(CA)is utilized to enhance the convergence *** case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization *** experiments are conducted on four benchmark functions to validate the effectiveness of the proposed *** experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)*** results show that the proposed energy coverage optimization method based on

关键词： Industrial wireless sensor network hybrid power bank deployment model:energy supply coverage optimization artificial bee colony algorithm radio frequency numerical function optimization

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Industrial Automation Information Analogy for Smart Grid Security

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computers, Materials & Continua 2022年第5期71卷 3985-3999页

作者： Muhammad Asif Ishfaq Ali Shahbaz Ahmad Azeem Irshad Akber Abid Gardezi Fawaz Alassery Habib Hamam Muhammad Shafiq Department of Computer Science National Textile UniversityFaisalabad37610PunjabPakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Industrial automation or assembly automation is a strictly monitored environment,in which changes occur at a good *** are many types of entities in the focusing environment,and the data generated by these devices is *** addition,because the robustness is achieved by sensing redundant data,the data becomes *** data generating device,whether it is a sensing device or a physical device,streams the data to a higher-level deception device for calculation,so that it can be driven and configured according to the updated *** the emergence of the Industry 4.0 concept that includes a variety of automation technologies,various data is generated through numerous ***,the data generated for industrial automation requires unique Information Architecture(IA).IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all *** understand its applicability,we used an example smart grid *** smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the *** addition,in a smart grid system,it needs to report changes on either side of the system,i.e.,consumers and suppliers configure and reconfigure the system according to the *** this article,we propose an analogy of a physical phenomenon.A point charge is used as a data generating device,the streamline of electric flux is used as a data flow,and the charge distribution on a closed surface is used as a ***,the intensity changes are used in the physical process,e.g.,the smart *** analogy is explained by metaphors,and the structural mapping framework is used for its theoretical *** proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows,definition changes(deterministic and non-deterministic),events,and i

关键词： Industry 4.0 information architecture industrial automation

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