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Thermoelectric enhancement of p-type Si_(80)Ge_(20) alloy via co-compositing of dual oxides:Respective regulation for power factor and thermal conductivity byβ-Ga_(2)O_(3) and SiO_(2) aerogel powders

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Journal of Advanced Ceramics 2023年第2期12卷 228-241页

作者： Huajun LAI Ying PENG Mengfei WANG Runze SHI Junliang CHEN Chengyan LIU Yifeng WANG Lei MIAO Haiqiao WEI Key Laboratory of Information Material Ministry of EducationGuangxi Key Laboratory of Information MaterialSchool of Materials Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Precision Navigation Technology and Application School of Information and CommunicationGuilin University of Electronic TechnologyGuilin 541004China School of Physical Science and Technology Guangxi UniversityNanning 530004China College of Materials Science and Engineering Nanjing Tech UniversityNanjing 210009China State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

Si-based thermoelectric(TE)materials are exhibiting remarkable perspectives in self-energized applications with their special ***,the relatively high total thermal conductivity(κ)prevents their TE ***,a strategy of co-compositing dual oxides was implemented for enhancing the TE properties of p-type Si_(80)Ge_(20) *** Ga2O_(3) was demonstrated to enhance the power factor(PF)due to the crystallization-induced effect of produced Ga by decomposition on SiGe *** with compositing SiO_(2) aerogel(a-SiO_(2))powder,not only introduced the fine amorphous inclusions and decreased the grain size of host matrix,but also various nano morphologies were formed,i.e.,nano inclusions,precipitations,twin boundaries(TBs),and *** with the eutectic Ge,hierarchical scattering centers impeded the phonon transport comprehensively(decreasing the phonon group velocity(a v)and relaxation time)for reducing the lattice-induced thermal conductivity(lκ).As a result,a minimumκof 2.38 W·m^(−1)·K^(−1) was achieved,which is significantly dropped by 32.6%in contrast with that of the pristine ***,a maximal dimensionless figure of merit(ZT)of 0.9 was achieved at 600℃,which is better than those of most corresponding oxide-composited Si-based bulks.

关键词： thermoelectric(TE)materials SiGe Ga_(2)O_(3) SiO_(2)aerogel(a-SiO_(2)) average velocity of sound hierarchical scattering

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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

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computer Systems science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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Event-Triggered Control for Obstacle Avoidance in Second-Order Nonlinear Multi-Agent Systems with Directed Graph 8

Event-Triggered Control for Obstacle Avoidance in Second-Ord...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhu, Zhipeng Zhang, Zhao Wang, Chenyu Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by using the event-triggered consensus algorithm, and experiments are given for verification. Secondly, an improved artificial potential field (IAPF) method is proposed to solve the obstacle avoidance problem, and comparison experiments are made, from which the experimental results show that the IAPF method is significantly better than the traditional artificial potential field (TAPF) method under the same conditions. © 2024 SPIE.

关键词： Directed graphs

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Application of a class of density peak clustering algorithm in short-term smart grid 8

Application of a class of density peak clustering algorithm ...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Wang, Chenyu Zhang, Zhao Zhu, Zhipeng Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the operation of power system. Density peaks clustering algorithm with k-nearest neighbors and weighted similarity can reduce the negative impact of global data on local data, avoid the collateral effect of errors and improve the accuracy of the model. It has better effect on local peak power change and global power forecast. In this paper, the density peaks clustering algorithm with k-nearest neighbors and weighted similarity are compared with DPC clustering method through simulation examples, and the effectiveness of this method is verified. © 2024 SPIE.

关键词： Clustering algorithms

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Adversarial Transfer Learning for Left Ventricle Segmentation in Cardiac CT Images

Adversarial Transfer Learning for Left Ventricle Segmentatio...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Alam, Intekhab Tripathi, Ramesh Chandra Raghavendra, R. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

adverse transfer learning for Left Ventricle Segmentation in Cardiac CT photos is an emerging method in clinical photograph analysis. It pursues to transfer the learned expertise from a source area to a goal area, that's, in any other case, steeply priced to annotate. That is achieved via schooling a segmentation version in the source area with adversarial education. The version then learns many functions which might be domain-agnostic and transferable to new responsibilities. At the target area, the version generalizes the features from the source and gives correct segmentation effects. In this paper, we gift experiments using opposed transfer mastering for left ventricle segmentation in cardiac CT pix. Our results show a significant improvement over using an everyday segmentation model trained on the goal domain. The outcomes are promising and inspire similar research on antagonistic switch getting to know in clinical image evaluation... © 2024 IEEE.

关键词： Adversarial analysis Learning promising Ventricle

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An Evaluation of Arithmetic and Huffman Coding in Data Compression & Source Coding

An Evaluation of Arithmetic and Huffman Coding in Data Compr...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Prakash, Febin Singh, Vaishali Saxena, Ashendra Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This paper considers the trouble of records compression and supply coding by evaluating the use of arithmetic and Huffman coding. Arithmetic coding is a good, most reliable coding approach used for statistics compression. It assigns shorter message lengths to symbols that appear extra often in the statistics movement and can reach better compression ratios than maximum different coding strategies. Huffman coding then uses a Tree information shape to manner the source facts and assigns a unique binary code to every symbol within the facts. It is quicker and easier to put in force than arithmetic coding and yields exact average compression ratios. Each coding strategy is compared and discussed in-depth in phrases of compression ratio overall performance, velocity of encode/decode, and complexity. From the assessment and dialogue, it's miles concluded that mathematics coding is a slightly better desire for records compression/supply coding than Huffman coding, with overall higher performance and compression ratio. In contrast, Huffman's coding change into discovered to be quicker and less complicated. © 2024 IEEE.

关键词： compression easier lengths quicker strategy

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Cross-Domain Collaborative Filtering: A Deep Neural Network Approach for Accurate and Diverse Recommendations 2

Cross-Domain Collaborative Filtering: A Deep Neural Network ...

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

作者： Goel, Chirag Sinha, Bam Bahadur Department of Computer Science and Engineering Indian Institute of Information Technology Jharkhand Ranchi834010 India

The rapid proliferation of data and the intricate nature of user behavior in the online realm have presented new hurdles for recommendation systems, which aim to suggest pertinent items to users. Among the notable challenges, ensuring the provision of accurate recommendations across diverse domains stands out. This research paper proposes an innovative solution to tackle this challenge by developing a cross-domain recommendation system that leverages the collaborative filtering technique to generate precise and varied recommendations spanning multiple domains. The system gathers user behavior and item attribute data from various domains and employs a collaborative filtering algorithm integrated with a deep neural network. The model makes use of a Neural Network with CReLU activation along with the embeddings of the user and the item followed by concealed layers. The output layer employs tanh activation to guarantee that recommendations fall within [-1, 1]. Adam optimizer, MSE loss, and an accuracy metric are utilised in training. This architecture captures user-item interactions effectively, resulting in precise personalised recommendations resulting an accuracy of 96.5% - 97.5%. To validate the effectiveness of the model, extensive evaluations are conducted on real-world datasets encompassing movies and books. The results demonstrate that the proposed system outperforms state-of-the-art recommendation systems in terms of accuracy, as measured by precision(89%), recall(90%), and AUC(0.74) scores. Furthermore, the system exhibits robust performance even in scenarios characterized by cold-start problems and data sparsity. © 2024 Elsevier B.V.. All rights reserved.

关键词： Collaborative filtering

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Scalable SubXPCA on a Distributed Platform Applied to Classification of Hyperspectral Images 8

Scalable SubXPCA on a Distributed Platform Applied to Classi...

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8th IEEE International Conference on information and Communication technology, CICT 2024

作者： Rupa, Bogolu Negi, Atul School of Computer and Information Sciences University of Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331531539

The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, extensively used and open-source distributed processing frameworks, which is proven effective in processing large volumes of remotely sensed hyperspectral images in a time-efficient manner. Open-source distributed processing frameworks have proven effective in processing large volumes of remotely sensed hyperspectral images quickly and efficiently. While computational power has been increasing, the rate of data accumulation is more than the processing capabilities. Therefore, more efficient algorithms such as dimensionality reduction are needed to process and get accurate performance for the application. This paper proposes an efficient and parallel spectral dimensionality reduction approach based on feature partitioning principal component analysis called scalable SubXPCA. We implemented scalable SubXPCA on a spark cluster distributed environment. We compared scalable SubXPCA against other distributed feature partitioning and various non-feature partitioning dimensionality reduction methods. Our experiments on different real and synthetic datasets of hyperspectral images confirm that SubXPCA classification performance is not only better than its competitors but also that the running time of SubXPCA is better in distributed processing than serial processing. As the size of the hyperspectral image dataset increased, SubXPCA showed a speed up factor of 5.7× and more in the spark cluster compared to the serial version. © 2024 IEEE.

关键词： Dimensionality reduction

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Data quantity governance for machine learning in materials science

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National science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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