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15th IEEE International Conference on ASIC, ASICON 2023

作者： Shao, Xilong Ma, Xuejiao Li, Gang Wenzhou University College of Electrical and Electronic Engineering Wenzhou China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou China

ISBN: (纸本)9798350312980

Arbiter Physical Unclonable Function (APUF), as a typical representative of strong PUF, suffers from limitations in hardware cost and randomness, severely constraining its application in the field of resource-constrained Internet of Things (IoT). This paper investigated the mechanism of MOSFET threshold loss and proposed a lightweight strong APUF structure of two kinds of MOSFETs. This structure utilizes the threshold loss that occurs when PMOS transmits low level and NMOS transmits high level, greatly extending the delay deviation of the rising and falling edges, thus enhancing the randomness of the PUF and passing 9 NIST randomness subtests. Each delay unit of the two proposed PUF circuits consists of only 16 MOS transistors, which is 8 transistors less than the delay unit of APUF. The full-custom layout area of the proposed delay unit is 4.68 μm2 in TSMC 65 nm process, reducing the area by 35.8% compared to the traditional APUF. © 2023 IEEE.

关键词： APUF Hardware Security Physical Unclonable Function Threshold Loss

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Fabrication progress detection for concrete T-girders based on improved Yolov4

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第2期176卷 85-95页

作者： Liang, Dong Yang, Liu Ma, Chuankui Yu, Yang College of Civil Engineering and Transportation Hebei University of Technology Tianjin China Henan Provincial Communications Planning & Design Institute Co. Ltd Henan Zhengzhou China College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

Large precast concrete girder plants have many processes, long cycles and a large amount of data. This study proposes an improved Yolov4 object detection algorithm with a spatio-temporal relationship to detect each fabrication process of precast concrete girders. It realises the digitisation of the fabrication information of traditional precast concrete girder plants. Initially, adding upsampling and convolution layers to the Yolov4 base model enhances the feature extraction ability of the algorithm at different fabrication stages of precast concrete girders. The spatio-temporal relationship is adopted to determine the fabrication progress of precast concrete girders with identical features but are at various fabrication stages. Finally, this research conducts an application analysis of an actual precast concrete girder plant. The analysis result indicated that the improved Yolov4 algorithm significantly raises the mean average precision and average intersection over union in recognition. Besides, the spatio-temporal relationship effectively solves error detection problems caused by the similar appearance at different fabrication stages. This method provides practical support for digitising the fabrication data of traditional precast girder plants. © 2023 ICE Publishing. All rights reserved.

关键词： Fabrication

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Based on Transfer Learning a Performance Analysis and Comparative Study of CNN for Accurate Plant Disease Detection

Based on Transfer Learning a Performance Analysis and Compar...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Nithin, S. Nishanth, M.S. Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept of Computer Science and Engineering Banglore India Global Academy of Technology Dept of Artificial Intelligence and Data Science Banglore India Global Academy of Technology Department of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350300826

This paper investigates the use of Convolutional Neural Networks (CNNs) for plant disease diagnosis, utilizing the notion of transfer learning to develop a deep CNN network at a low cost. The dataset utilized includes 30,000 pictures from 18 distinct categories. Several architectural models, including ResNet50, ResNet50V2, VGG16, VGG19, EfficientNetB0, MobileNet, MobileNetV2, NASNetMobile, and DenseNet, were created using transfer learning methodologies. The models are trained on the target plant disease dataset while keeping the features learnt from large-scale image datasets like ImageNet. Performance criteria such as accuracy, precision, and recall are used to assess the models' accuracy in detecting various plant diseasesx. Furthermore, the research investigates the effect of dropout on accuracy and other model parameters in the context of transfer learning. Finally, the outcomes of a tailored model with and without batch normalization are compared. © 2023 IEEE.

关键词： Convolutional neural networks

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Soft Sensor Method based on Quality-related Virtual Sample Generation and Sample-weighted Learning 13

Soft Sensor Method based on Quality-related Virtual Sample G...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Dong, Shuang Jin, Huaiping Wang, Bin Yang, Biao Liu, Haipeng Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China

ISBN: (纸本)9798350361674

In process industry, data-driven soft sensor often faces the problem of data shortage in modeling due to factors such as high cost of label samples acquisition and high data repetition rate. The virtual sample generation (VSG) method has been proposed for data augmentation to solve the above problems. Most of the conventional generative models cannot generate virtual samples with labeled data, at the same time, previous data augmentation methods have ignored the quality differences of the generated virtual samples themselves. Thus, this paper proposes a soft sensor method based on quality-related virtual sample generation and sample-weighted learning (QRVSG-SWL). Firstly, this method combines the respective advantages of variational autoencoder (VAE) and generative adversarial network (GAN) to generate labeled virtual samples. Secondly, a prediction model is constructed using virtual samples to calculate the prediction accuracy and distribution differences on real labeled data. Then, sample similarity calculation under supervised latent *** performed. Finally, model learns virtual sample weights. The effectiveness of the proposed method is validated by the industrial chlortetracycline (CTC) fermentation process. © 2024 IEEE.

关键词： Generative adversarial networks

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A Deep Learning Soft Sensor Based on Domain-Invariant Features Extraction and Online Local Adaptation 13

A Deep Learning Soft Sensor Based on Domain-Invariant Featur...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Wang, Yelei Jin, Huaiping Wang, Bin Yang, Biao Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China

ISBN: (纸本)9798350361674

data-driven approaches, especially those based on deep learning, have received much attention in quality prediction in the modern process industry. However, in practice, industrial data are often generated in the form of data streams. As operating conditions and process parameters change with the environment, industrial data are dynamic, resulting in degraded model prediction performance. Although the method based on deep transfer learning can automatically extract domain-invariant features in various tasks to adapt to data distribution changes. However, current deep transfer learning methods cannot handle online data streams with dynamic distribution change characteristics. To solve this problem, a deep learning soft sensor method based on domain-invariant features and online local adaptation (DIF-OLA) is proposed. The implementation of the method consists of an offline phase and an online phase. In the offline phase, domain-invariant features are extracted to build a domain-adaption model. In the online phase, a local fine-tuned update strategy is introduced to make the prediction model better adapted to the new data streams. The effectiveness of the method is verified by the dataset of an actual industrial debutanizer column process. The experimental results indicate that compared to traditional adaptation soft sensor methods, the model has improved performance metrics of MAE, RMSE and R2 by 41.14%,40.94%, and 30.94% respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Binary Optimization Using Hybrid Owl Optimization for Biomarker Selection from Cancer datasets 3

Binary Optimization Using Hybrid Owl Optimization for Biomar...

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3rd IEEE International Conference on technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Sahu, Bibhuprasad Madhavi, Chakradharamahanthi Pani, Sasmita Ravindra, Jvr Department of Artificial Intelligence and Data Science Hyderabad India Balasore College of Engineering and Technology Department of Computer Science and Engineering Odisha Balasore India Department of Electronics & Communication Engineering Hyderabad India

ISBN: (纸本)9798350397819

The enhancement of medical technology generates a massive amount of disease data. For accurate disease detection, feature subset selection plays an important role in solving different classification problems. The selection of a good feature subset enhances the accuracy of the machine learning model and reduces the training time. However, it becomes tricky and challenging to identify the optimal feature subset from the high-dimensional datasets. Different hybrid models are proposed by various researchers to deal with these types of issues. The Owl optimization algorithm proves its efficiency in selecting the optimal features. Keeping an eye on the performance of this stochastic optimization, a binary version of the hybrid Owl optimization algorithm with simulating annealing is proposed to detect the biomarker features from the cancer datasets. K nearest neighbors (KNN) are used as a wrapper to find the best biomarkers. Eight benchmark datasets were employed for the proposed model's performance evaluation. The results show that the proposed model is significantly better than binary Owl optimization, and other counterparts using various performance matrices such as accuracy, selection of best biomarkers, and computational time. The proposed model Owl-SA performs with an accuracy of 100% in the case of CNS and lymphoma datasets. And it retains the accuracy of 99.14%, 97.16%, 98.41%, 97.94%, 98.93%,98.32% for breast, colon, ALL-AML, ovarian, SRBCT, and MLL respectively. © 2023 IEEE.

关键词： Biomarkers

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A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

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Computers, Materials & Continua 2022年第8期72卷 2797-2810页

作者： Waleed Shahjehan Abid Ullah Syed Waqar Shah Imran Khan Nor Samsiah Sani Ki-Il Kim Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Center for Artificial Intelligence Technology Faculty of Information Science&TechnologyUniversiti KebangsaanKajang43000Malaysia Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base ***,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power *** order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains *** sparse digital precoding problem is generated by utilizing the analog precoding ***,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)*** results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

关键词： 5G mmwave precoding massive mimo complexity

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Investigation of Control Algorithm for Electromechanical Vehicle Active Anti-Roll Bar

Investigation of Control Algorithm for Electromechanical Veh...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Zhang, Zien Rahman, Abdul Hadi Abd Zulkarnain, Noraishikin Center for Artificial Intelligence Technology Faculty of Information Science & Technology Selangor Bangi Malaysia Faculty of Engineering & Built Environment Department of Electrical Electronic & Systems Engineering Selangor Bangi Malaysia

ISBN: (纸本)9798350381290

The present paper aims to address the issue of improving the roll stability of vehicles by employing an active anti-roll bar (AARB) controller. The AARB controller is established using MATLAB/SIMULINK based on the principle of Proportional-Integral-Derivative (PID) Controller, and the CARSIM software is used to build a testing environment. The evaluation is conducted through specific indicators of vehicle roll stability. A comparative simulation was conducted on the control system by changing the vehicle speed. The joint simulation of SIMULINK and CARSIM software verified the feasibility and correctness of the established model and control system. The simulation results analyzed using MATLAB software show that the AARB control system can improve the vehicle's stability. The significance of the AARB control algorithm for other active safety controllers in vehicles was also briefly explained. We highlight the future research direction of the control algorithm for the AARB controller on the importance and impact of intelligent algorithms on future research. © 2023 IEEE.

关键词： Controllers

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CNN Based Plant Leaf Disease Detection Using Raw Leaf Images for Efficient Plant Health Monitoring in Agricultural IoT

CNN Based Plant Leaf Disease Detection Using Raw Leaf Images...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Shibu, Pooja Reddy, Gangireddy Narendra Kumar St.Joseph College of Engineering and Technology Department of Artificial Intelligence and Data Science Kerala Palai686579 India National Institute of Technology Calicut Department of ECE Kerala Kozhikode673601 India

ISBN: (纸本)9798350376135

Agriculture is a major economic driver of both the high and low income countries across the globe. Hence it is important to modernize agricultural practices by incorporating modern technological developments as a continuous process. Among the various challenges, plant leaf diseases pose a significant threat, leading to reduced agricultural yields and economic losses. Early detection of these diseases is crucial for timely intervention, yet traditional methods relying on human visual inspection are often delayed, incomplete, and unreliable. To address this, our study employs a Convolutional Neural Network (CNN) to analyze plant leaf images taken from New plant dataset, resulting in a highly accurate model for detecting various leaf diseases of Apple, Potato, Strawberry and Tomato. The developed CNN model achieved an accuracy of 90.91%, offering a promising tool for improving agricultural productivity by minimizing disease-induced crop damage. © 2024 IEEE.

关键词： Agricultural economics

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MEC-IoT-Healthcare: Analysis and Prospects

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Computers, Materials & Continua 2023年第6期75卷 6219-6250页

作者： Hongyuan Wang Mohammed Dauwed Imran Khan Nor Samsiah Sani Hasmila Amirah Omar Hirofumi Amano Samih M.Mostafa College of Computer Science Guangdong University of Science and TechnologyDongGuan523083China Department of Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600BangiSelangorMalaysia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their *** health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare *** of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right ***,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent *** the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big *** article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other ***,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health *** comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

关键词： IoT mobile-edge computing cloud computing e-health

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