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Paraelectric Doping Simultaneously Improves the Field Frequency Adaptability and Dielectric Properties of Ferroelectric Materials: A Phase-Field Study

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Computers, Materials and Continua 2024年第1期81卷 213-228页

作者： Wang z. Cao J. Liu z. z.ao Y. School of Materials Science and Engineering Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-Performance Al/Mg Alloy Materials North University of China Taiyuan 030051 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang 110004 China

Recent years, the polariz.tion response of ferroelectrics has been entirely studied. However, it is found that the polariz.tion may disappear gradually with the continually applied of electric field. In this paper, taking K0.48Na0.52NbO3(KNN) as an example, it was demonstrated that the residual polariz.tion began to decrease when the electric field frequency increased to a certain extent using a phase-field methods. The results showed that the content of out-of-plane domains increased first and then decreased with the increase of applied electric field frequency, the maximum polariz.tion disappeared at high frequencies, and the hysteresis loop became elliptical. In order to further study the abnormal changes of hysteresis loops of ferroelectrics under high electric field frequency, we analyz.d the hysteresis loop and dielectric response of solid solution 0.1SrTiO3-0.9K0.48Na0.52NbO3. It was found that the doped hysteresis loop maintained its shape at higher frequency and the dielectric constant increased. This kind of doping has a higher field frequency adaptability, which has a key guiding role in improving the dielectric properties of ferroelectric thin films and expanding the frequency application range of ferroelectric nano memory. © 2024 The Authors. Published by Tech Science Press.

关键词： dielectric properties doping electric field frequency Ferroelectric ceramics phase field method

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PUNet: A Semi-Supervised Anomaly Detection Model for Network Anomaly Detection Based on Positive Unlabeled Data

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Computers, Materials and Continua 2024年第1期81卷 327-343页

作者： Long G. z.ang z. Faculty of Computing Harbin Institute of Technology Harbin 150000 China

Network anomaly detection plays a vital role in safeguarding network security. However, the existing network anomaly detection task is typically based on the one-class z.ro-positive scenario. This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set. This phenomenon is known as prediction drift. Additionally, the rarity of anomaly data, often masked by normal data, further complicates network anomaly detection. To address these challenges, we propose the PUNet network, which ingeniously combines the strengths of traditional machine learning and deep learning techniques for anomaly detection. Specifically, PUNet employs a reconstruction-based autoencoder to pre-train normal data, enabling the network to capture potential features and correlations within the data. Subsequently, PUNet integrates a sampling algorithm to construct a pseudo-label candidate set among the outliers based on the reconstruction loss of the samples. This approach effectively mitigates the prediction drift problem by incorporating abnormal samples. Furthermore, PUNet utiliz.s the CatBoost classifier for anomaly detection to tackle potential data imbalance issues within the candidate set. Extensive experimental evaluations demonstrate that PUNet effectively resolves the prediction drift and data imbalance problems, significantly outperforming competing methods. © 2024 The Authors. Published by Tech Science Press.

关键词： candidate set CatBoost Network anomaly detection representation learning

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Graph Attention Residual Network Based Routing and Fault-Tolerant SchedulingMechanism for Data Flow in Power Communication Network

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Computers, Materials and Continua 2024年第1期81卷 1641-1665页

作者： Lin z. z.ng z. Yu Y. Ren Y. Qiu X. Chen J. State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China State Grid Jiangsu Electric Power Co. Ltd Nanjing 210008 China

For permanent faults (PF) in the power communication network (PCN), such as link interruptions, the timesensitive networking (TSN) relied on by PCN, typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability, which often limits TSN scheduling performance in fault-free ideal states. So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism (GRFS) for data flow in PCN, which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding (CQF) model and fault recovery method, which reduces the impact of faults by simplified scheduling configurations of CQF and fault-tolerance of prioritiz.ng the rerouting of faulty time-sensitive (TS) flows; considering that PF leading to changes in network topology is more appropriately solved by doing routing and time slot injection decisions hop-by-hop, and that reasonable network load can reduce the damage caused by PF and reserve resources for the rerouting of faulty TS flows, an optimiz.tion model for joint routing and scheduling is constructed with scheduling success rate as the objective, and with traffic latency and network load as constraints; to catch changes in TSN topology and traffic load, a D3QN algorithm based on a multi-head graph attention residual network (MGAR) is designed to solve the problem model, where the MGAR based encoder reconstructs theTSNstatus into feature embedding vectors, and a dueling network decoder performs decoding tasks on the reconstructed feature embedding vectors. Simulation results show that GRFS outperforms heuristic fault-tolerance algorithms and other benchmark schemes by approximately 10%in routing and scheduling success rate in ideal states and 5% in rerouting and rescheduling success rate in fault states. © 2024 The Authors. Published by Tech Science Press.

关键词： deep reinforcement learning fault tolerance graph attention network Time-sensitive network

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Robust Deep Image Watermarking: A Survey

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Computers, Materials and Continua 2024年第1期81卷 133-160页

作者： Luo Y. Tan X. Cai z. College of Computer National University of Defense Technology Changsha 410005 China

In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces, resilient to various attacks. These methods, characteriz.d by a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analysis of deep watermarking’s core technologies, current status, and prospective trajectories, evaluating recent scholarly contributions across diverse frameworks. It concludes with an overview of the technical hurdles and prospects, providing essential insights for ongoing and future research endeavors in digital image watermarking. © 2024 The Authors. Published by Tech Science Press.

关键词： data protection deep neural network Deep image watermarking multimedia security

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Short-Term Wind Power Prediction Based on WVMD and Spatio-Temporal Dual-Stream Network

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Computers, Materials and Continua 2024年第1期81卷 549-566页

作者： z.ao Y. Ruan Y. Peng z. School of Computer and Science Nanjing University of Information Science and Technology Nanjing 210044 China

As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimiz.tion control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimiz.tion Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set using Variational Mode Decomposition (VMD) to eliminate the nonstationarity and obtains Intrinsic Mode Functions (IMFs). Here Whale Optimiz.tion Algorithm (WOA) is applied to optimise the key parameters of VMD, namely the number of mode components K and penalty factor a. Finally, incorporating attention mechanism (AM), Squeez.-Excitation Network (SENet), and Bidirectional Gated Recurrent Unit (BiGRU), it constructs the dual-stream network (DSN) for short-term wind power prediction. Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generaliz.tion performance. The relevant code is available at https://***/ruanyuyuan/*** (accessed on 20 August 2024). © 2024 The Authors.

关键词： dual-stream network variational mode decomposition (VMD) whale optimiz.tion algorithm (WOA) Wind power prediction

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DeepSurNet-NSGA II: Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

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Computers, Materials and Continua 2024年第1期81卷 229-249页

作者： Ibrayev S. Omarov B. Ibrayeva A. Momynkulov z. Joldasbekov Institute of Mechanics and Engineering Almaty 050000 Kazakhstan Department of Mathematical and Computer Modeling International Information Technology University Almaty 050000 Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty 050000 Kazakhstan

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II (Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II) for solving complex multi-objective optimiz.tion problems, with a particular focus on robotic leg-linkage design. The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II, aiming to enhance the efficiency and precision of the optimiz.tion process. Through a series of empirical experiments and algorithmic analyses, the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods, underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands. The methodology encompasses a detailed exploration of the algorithm’s configuration, the experimental setup, and the criteria for performance evaluation, ensuring the reproducibility of results and facilitating future advancements in the field. The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimiz.tion but also highlight its potential as a transformative tool in engineering and design optimiz.tion. By bridging the gap between complex optimiz.tion challenges and achievable solutions, this research contributes valuable insights into the optimiz.tion domain, offering a promising direction for future inquiries and technological innovations. © 2024 The Authors. Published by Tech Science Press.

关键词： deep learning genetic algorithm Multi-objective optimiz.tion surrogate model walking robots

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Research on Fine-Grained RecognitionMethod for Sensitive Information in Social Networks Based on CLIP

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Computers, Materials and Continua 2024年第1期81卷 1565-1580页

作者： z.ang M. Shan F. Liu M. Wang z. School of Computer Science Zhongyuan University of Technology Zhengzhou 450007 China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001 China

With the emergence and development of social networks, people can stay in touch with friends, family, and colleaguesmore quickly and conveniently, regardless of their location. This ubiquitous digital internet environment has also ledtolarge-scaledisclosureof personal *** tothe complexity andsubtletyof sensitive information, traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data, thus weakening the deep connections between text and images. In this context, this paper adopts the CLIP model as a modality discriminator. By using comparative learning between sensitive image descriptions and images, the similarity between the images and the sensitive descriptions is obtained to determine whether the images contain sensitive information. This provides the basis for identifying sensitive information using different modalities. Specifically, if the original data does not contain sensitive information, only single-modality text-sensitive information identification is performed; if the original data contains sensitive information, multimodality sensitive information identification is conducted. This approach allows for differentiated processing of each piece of data, thereby achieving more accurate sensitive information identification. The aforementioned modality discriminator can address the limitations of existing sensitive information identification technologies, making the identification of sensitive information from the original data more appropriate and precise. © 2024 The Authors. Published by Tech Science Press.

关键词： Deep learning multi-modal fusion sensitive information recognition social networks

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High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

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Computers, Materials and Continua 2024年第1期81卷 183-211页

作者： Njoum M. Sulaiman R. Shukur z. Qamar F. Center of Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia Computer Science and Cyber Security Faculty of Engineering and Information Technology BirZeit University Ramallah P.O. Box 14 BirZeit Palestine

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data. This property makes it difficult for steganalysts’ powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation. However, using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations. In addition, these numbers may cluster in certain ranges. The hidden data in these clustered pixels will reduce the image quality, which steganalysis tools can detect. Therefore, this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image. This paper employs the Adelson-Velsky and Landis (AVL) tree data structure algorithm to implement the randomiz.tion pixel selection technique for data concealment. The AVL tree algorithm provides several advantages for image steganography. Firstly, it ensures balanced tree structures, which leads to efficient data retrieval and insertion operations. Secondly, the self-balancing nature of AVL trees minimiz.s clustering by maintaining an even distribution of pixels, thereby preserving the stego image quality. The data structure employs the pixel indicator technique for Red, Green, and Blue (RGB) channel extraction. The green channel serves as the foundation for building a balanced binary tree. First, the sender identifies the colored cover image and secret data. The sender will use the two least significant bits (2-LSB) of RGB channels to conceal the data’s siz. and associated information. The next step is to create a balanced binary tree based on the green channel. Utiliz.ng the channel pixel indicator on the LSB of the green channel, we can conceal bits in the 2-LSB of the red or blue channel. The first four levels of the data structure tree will mask the data siz., while subsequent levels will conceal the remaining digits of s

关键词： AVL tree capacity Image steganography imperceptibility peak signal-to-noise ratio (PSNR) pixel random selection (PRS)

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A DiscreteMulti-Objective Squirrel Search Algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed

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Computers, Materials and Continua 2024年第1期81卷 1757-1787页

作者： z.ng L. Ding z. Shi J. Wang S. School of Electrical and Electronic Engineering Hubei University of Technology Wuhan 430068 China Hubei Key Laboratory for High-Efficiency Utiliz.tion of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan 430068 China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang 441100 China

In the manufacturing industry, reasonable scheduling can greatly improve production efficiency, while excessive resource consumption highlights the growing significance of energy conservation in production. This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed (DHPFSP-VPS), considering both theminimum makespan and total energy consumption (TEC) as objectives. A discrete multi-objective squirrel search algorithm (DMSSA) is proposed to solve the DHPFSPVPS. DMSSA makes four improvements based on the squirrel search algorithm. Firstly, in terms of the population initializ.tion strategy, four hybrid initializ.tion methods targeting different objectives are proposed to enhance the quality of initial solutions. Secondly, enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm, making it more suitable for discrete scheduling problems. Additionally, regarding the search strategy, six local searches are designed based on problem characteristics to enhance search capability. Moreover, a dynamic predator strategy based on Q-learning is devised to effectively balanceDMSSA's capability for global exploration and local exploitation. Finally, two speed control energy-efficient strategies are designed to reduce TEC. Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies. The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem. © 2024 The Authors. Published by Tech Science Press.

关键词： Distributed heterogeneous permutation flowshop problem energy-efficient squirrel search algorithm muli-objective optimiz.tion variable processing speed

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Numerical Study of Flow Features Around Submerged Circular and Square Piles at Flat and Scoured Beds Using OpenFOAM

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Journal of Ocean University of China 2024年第4期23卷 897-914页

作者： WANG Chaolin WU Guoxiang WANG Dianhe DU Shengtao z.ANG z.iyong JIN Heng z.U David z. LIANG Bingchen Donghai Academy School of Civil&Environmental Engineering and Geography ScienceNingbo UniversityNingbo 315211China Key Laboratory of Estuary and Coast of z.ejiang Province and Zhejiang Institute of Hydraulics and EstuaryHangzhou 310020China Shandong Provincial Key Laboratory of Ocean Engineering and College of EngineeringOcean University of ChinaQingdao 266100China Shandong Water Conservancy Vocational College Rizhao 276826China School of Mechanical and Energy Engineering Ningbotech UniversityNingbo 315211China

Elucidating the flow features around piles in local scouring processes is crucial for studies of local scouring mechanisms and scour depth *** study details the flow turbulence characteristics of two submerged piles that are determined by solving the Navier-Stokes equations with the improved delayed detached eddy simulation *** model is verified by comparing experimental and numerical results for hydrodynamic parameters with the literature for both square-crossing piles(SCPs)and circular-crossing piles(CCPs).Original topographies of flat and scoured beds(i.e.,the initial and equilibrium scouring stages)are based on experimental results obtained by the authors in the present *** and CCP flow features in the scouring process are *** results indicate that during the scouring process,the time-averaged drag coefficient and root mean square(rms)of the lift coefficient increase linearly in the CCP test,while the rms of the lift coefficient in the SCP test decreases ***,the minimum pressure coefficient is always located in the upstream corners in the SCP case but moves from 72.5°to 79.5°when the scour hole is completely developed in the CCP *** flow behind the pile,which is generated by separated boundary layers above the top face of the pile,can reach the sand bed and turn the separated shear layers into patches of small vortices in the near-wake ***,the high shear stress z.nes are mainly at the scour edges under scoured-bed conditions.

关键词： flat bed scoured bed local scour OpenFOAM bed shear stress

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