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

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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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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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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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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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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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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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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Within the Protection of COVID-19 Spreading: A Face Mask Detection Model Based on the Neutrosophic RGB with Deep Transfer Learning

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Neutrosophic Sets and Systems 2022年 50卷 320-335页

作者： Khalifa, Nour Eldeen Loey, Mohamed Chakrabortty, Ripon K. Taha, Mohamed Hamed N. Department of Information Technology Faculty of Computers & Artificial Intelligence Cairo University Cairo12613 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt School of Engineering & IT UNSW Canberra at ADFA Australia

COVID-19’s fast spread in 2020 compelled the World Health Organization (WHO) to declare COVID-19 a worldwide pandemic. According to the WHO, one of the preventative countermeasures against this type of virus is to use face masks in public places. This paper proposes a face mask detection model by extracting features based on the neutrosophic RGB with deep transfer learning. The suggested model is divided into three steps, the first step is the conversion to the neutrosophic RGB domain. This work is considered one of the first trails of applying neutrosophic RGB conversion to image domain, as it was commonly used in the conversion of grayscale images. The second step is the feature extraction using Alexnet, which has been small number of layers. The detection model is created in the third step using two traditional machine learning algorithms: decision trees classifier and Support Vector Machine (SVM). The Simulated Masked Face dataset (SMF) and the Real-World Mask Face dataset (RMF) are merged to a single dataset with two categories (a face with a mask, and a face without a mask). According to the experimental results, the SVM classifier with the True (T) neutrosophic domain achieved the highest testing accuracy with 98.37%. © 2022

关键词： COVID-19

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network 3

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

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3rd International Conference on Computer Applications technology, CCAT 2024

作者： Jiang, Xin Chen, Zhi-Chao Sun, Zheng Liu, Ming-Zhe Chongqing University of Technology School of Artificial Intelligence Chongqing401135 China Chongqing University Cancer Hospital Department of Radiology Chongqing400030 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu610000 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

ISBN: (纸本)9798331539795

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring. © 2024 IEEE.

关键词： Radiotherapy

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