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Editorial Message: Special Issue on Advances Fuzzy Methods on Control, Robotics and Applications

International Journal of Fuzzy Systems 2024年第1期26卷 331-332页

作者： Chuang, Chen-Chia Jeng, Jin-Tsong Department of Electrical Engineering National Ilan University Ilan County Yilan City Taiwan Department of Computer Science and Information Engineering National Formosa University Yunlin County Huwei Township Taiwan

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Image-Based Automatic Energy Meter Reading Using Deep Learning

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computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

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Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

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computers, Materials & Continua 2022年第11期73卷 2275-2291页

作者： Prasanalakshmi Balaji B.Sri Revathi Praveetha Gobinathan Shermin Shamsudheen Thavavel Vaiyapuri Department of Computer Science King Khalid UniversitySaudi Arabia School of Electrical Engineering Vellore Institute of TechnologyIndia Department of Computer Science Jazan UniversitySaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical *** Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare *** recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image *** medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial *** newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death *** on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this *** context,primarily image quality enhancement procedures like noise removal and contrast normalization are *** the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are *** extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant *** selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar *** proposed model is evaluated in terms of accuracy,precision,recall,and f-

关键词： Deep belief neural network mayfly optimization gaussian filter contrast normalization grey level variance local binary pattern discrete wavelet transform

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Automatic Skull Shape Completion of Defective Skulls Using Transformers for Cranial Implant Design 2

Automatic Skull Shape Completion of Defective Skulls Using T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Resmi, S. Singh, Rimjhimpadam Palaniappan, Kannappan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electrical Engineering and Computer Science University of Missouri MO United States

Cranioplasty is a surgical method that restores the aesthetic and protecting function of a damaged skull by implanting material into the damaged *** and accurate design of patient specific cranial implants is very much required in the process of *** time consumption for designing and manufacturing of patient specific cranial implant has become an obstruction for cranioplasty procedures. Hence, a fully automatic and fast design of cranial implant becomes very important. The cranial implant design processmainly comprises of two steps. The former step concentrates on the automatic skull shape completion of defective skulls to fill the gaps and the cracks created in the skull. While the second step computes the difference between defective input and the completed skull for generating the implant. Currently computer aided design is used for the skull shape completion task which is a time consuming process. The application of deep learning techniques may result to faster and accurate skull shape completionwhich can be used for the design of patient specific cranial implants. This work proposes a novel approach combining 3D U-Net with Transformers for the automatic skull shape completion *** are using a vision transformer in the encoder section of the 3D U-Net architecture to consider the volumetric skull reconstruction as sequence- to-sequence prediction problem and to efficaciously grasp the global contextual information. The work also compares its performance with the famous variants of 3D U-Net deep network model namely, 3D U-Net and 3D U-Net with attention. From the values resulted for the dice coefficient metric it is clear that the proposed 3D U-Net with transformer approach performs better than the other two models on test images. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer aided design

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FedSTGNN: A Federated Spatio-Temporal Graph Neural Network 15

FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yoon, Heeyong Chon, Kang-Wook Kim, Min-Soo Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of School of Computer Science and Engineering Koreatech Cheonan Korea Republic of School of Computing Kaist Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Owing to the explosive growth of the Internet of Things (IoT), there have been vast volumes of sensor-generated time series data in various locations. A lot of network usage occurs through these locations to process the sensor-generated data. To reduce network usage, the point of data processing gradually shifts from the central cloud to servers at the edge of the Internet, called edge servers. To cover the paradigm shift, there has been a challenging issue in training a neural network model, which aggregates the data generated by different locations and causes a massive amount of transmitted data across the network. Federated learning successfully resolves this issue by exchanging model parameters in the edge server through the central cloud. Meanwhile, Spatio-Temporal Graph Neural Networks (STGNNs) have gained much attention for analyzing time series data transmitted from several locations in IoT environments. Despite the increasing number of STGNN models that have been examined, the integration of STGNNs and federated learning remains underexplored. This paper presents FedSTGNN, a framework that seamlessly adapts arbitrary STGNN models to edge computing environments. The proposed method partitions the spatio-temporal graph with a mathematical definition and then trains each partitioned data with an arbitrary STGNN model on edge servers separately. Then, it aggregates all the model parameters in the central cloud with dramatically reduced network usage. Through experiments, we demonstrate that the FedSTGNN framework could train the model with a reduced amount of network communication by utilizing the advantages of edge computing and a slight loss in accuracy. © 2024 IEEE.

关键词： Spatio-temporal data

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Development of a Knowledge-Based System for Diagnosis and Treatment of Obesity

Development of a Knowledge-Based System for Diagnosis and Tr...

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2023 Workshop on Advances in Computational Intelligence, ACI 2023

作者： Agegne, Abebaw Salau, Ayodeji Olalekan Belay, Ayalew Singh, Anupam Asnake, Nigus Wereta Braide, Sepiribo Lucky Department of Computer Science Debark University Debark Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Computer Science AAU University Addis Ababa Ethiopia Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Information Technology Debark University Debark Ethiopia Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria

Obesity is a major public health issue that affects both industrialized and developing countries. Obesity is a varied and complex issue that necessitates diagnosis and treatment. Various research projects have attempted to create and develop a knowledge-based system (KBS). Physicians’ workload and medical errors could be reduced by using a KBS. The goal of this study was to create a KBS that can guide clinicians and patients through the diagnosis and treatment of obesity. A case-based reasoning approach is applied in this study. Case-based reasoning is a general artificial intelligence paradigm that has been studied in the context of improving human decision-making and has gotten a lot of interest in the development of KBSs. To acquire data, a purposive sampling technique is employed. The explicit knowledge is collected from the literature. To develop the prototype, JCOLIBRI software is used. In the development of the knowledge base system, 243 obesity cases were used. During evaluation, six experts were involved. User acceptance testing, case retrieval in terms of precision and recall, and case similarity testing are all used to assess the system’s performance. The user acceptance testing conducted showed that 90% of the evaluators found the system to be easy to use, efficient with respect to time and speed of the system. With precision and recall, the performance of the system is 71% and 80% respectively. The study found that there is a shortage of physicians and medical experts who can diagnose and treat obesity. That means that a single physician or medical doctor will not be able to treat a large number of people in a short amount of time. The proposed system in this study could help to alleviate the shortage of experts who can diagnose and treat obese people. The system helps to decrease the amount of work that physicians have to do by allowing them to delegate the same patient cases to other experts. © 2024 Copyright for this paper by its authors.

关键词： Case based reasoning

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Optimizing Different Methods of Fabricating 3D Printed UV Blocking Sheets for Solar Cells 1

Optimizing Different Methods of Fabricating 3D Printed UV Bl...

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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： Alhindi, Amjad Fawzi Elatab, Nazek Baawad, Lujain Khalid Bahabry, Rabab R. Allam, Fahad Laboratory Electrical and Computer Engineering Computer Electrical Mathematical Science and Engineering Thuwal Saudi Arabia College of Science Department of Physical Sciences Jeddah21589 Saudi Arabia Materials Science and Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9798331519445

This study investigates the fabrication of innovative UV-blocking sheets that effectively transmit visible light while simultaneously obstructing harmful ultraviolet (UV) radiation, utilizing Cerium Oxide (CeO2) and Zinc Oxide (ZnO) nanoparticles as UV-blocking agents. Three distinct fabrication methods were explored: The Powder Method, where nanoparticles were incorporated directly into polydimethylsiloxane (PDMS);the Ball milling Mixture method, which involved mixing nanoparticles through ball milling;and the Solvent-Based Mixture method, where nanoparticles were dissolved, mixed, and dried;before incorporation into PDMS. Samples were produced using a 3D printing technique, which is Direct Ink Writing (DIW) and characterized for their optical properties via UV-Vis spectroscopy. The results indicated CeO2 demonstrated superior UV-blocking capabilities (a maximum blockage of ~99%) compared to ZnO, while maintaining high transmittance for visible light (a maximum of ~90%). Notably, the solvent-based method exhibited better results than the ball milling method;however, the powder method had the best results. Therefore, this investigation offers a promising solution for solar panel protection that aligns with Saudi Arabia's Vision 2030 energy objectives. The relentless exposure of devices, particularly solar cells, to harmful UV radiation poses a formidable challenge and leads to annual degradation rates of between −0.8% and −4.9%, besides other factors. These findings underscore the critical importance of developing effective UV-blocking solutions. ©2024 IEEE.

关键词： Polydimethylsiloxane

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Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models

Pruning then Reweighting: Towards Data-Efficient Training of...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yize Zhang, Yihua Liu, Sijia Lin, Xue Department of Electrical and Computer Engineering Northeastern University Boston United States Department of Computer Science and Engineering Michigan State University East Lansing United States

ISBN: (纸本)9798350368741

Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion training has often been overlooked. In this work, we investigate efficient diffusion training from the perspective of dataset pruning. Inspired by the principles of data-efficient training for generative models such as generative adversarial networks (GANs), we first extend the data selection scheme used in GANs to DM training, where data features are encoded by a surrogate model, and a score criterion is then applied to select the coreset. To further improve the generation performance, we employ a class-wise reweighting approach, which derives class weights through distributionally robust optimization (DRO) over a pre-trained reference DM. For a pixel-wise DM (DDPM) on CIFAR-10, experiments demonstrate the superiority of our methodology over existing approaches and its effectiveness in image synthesis comparable to that of the original full-data model while achieving the speed-up between 2.34× and 8.32×. Additionally, our method could be generalized to latent DMs (LDMs), e.g., Masked Diffusion Transformer (MDT) and Stable Diffusion (SD), and achieves competitive generation capability on ImageNet. © 2025 IEEE.

关键词： data reweighting data-efficient training diffusion model

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Single-Phase Direct PWM Bipolar Buck–Boost AC–AC Converter Without Commutation Problem

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2023年第6期11卷 5940-5953页

作者： Ahmed, Hafiz Furqan Ali Khan, Ashraf Abdoli, Iman Zaabi, Omar Al the Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan the Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X7 Canada the Department of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

— The direct pulsewidth modulation (PWM) ac–ac converters are seeing rapid development due to their single-stage power conversion with reduced footprints, due to the elimination of intermediate dc-link capacitor. However, they only provide unipolar voltage gain and fixed frequency operation and use the bidirectional voltage-blocking devices without natural inductor current free-wheeling path, restricting their practical applications. This article proposes a direct PWM bipolar and step-variable frequency buck–boost ac–ac converter. The proposed converter is fabricated by connecting input–output LC filters and a small value film capacitor to six unidirectional voltage-blocking devices, which are arranged as a three-leg inverter module. It can provide noninverting and inverting buck–boost voltage operations with a symmetric bipolar voltage gain of ±d/(1 − d) and can also change the frequency of output voltage in discrete steps, same as that of single-phase matrix converters (MCs). It has no open-circuit risk of inductor due to the presence of natural current free-wheeling path during PWM dead time. Therefore, it is unaffected by the commutation problem of conventional direct ac–ac converters. The proposed converter can realize high-quality input and output due to the presence of second-order input–output filters with continuous input–output currents. A detailed analysis is provided based on proposed switch modulation strategies and confirmed through extensive experimental tests on a laboratory-fabricated hardware. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Matrix converters

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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