检索结果-内蒙古大学图书馆

Write-Once-Read-Many-Times Nonvolatile Memory Characteristics of Sol-Gel Hafnium Zirconium Oxide

IEEE Transactions on Electron Devices 2024年第5期71卷 3285-3292页

作者： Jhang, Wun-Ciang Hsieh, Tsung-Chun Zhang, Xuan-Zhi Qiu, Zi-Rong Hsu, Chih-Chieh Joodaki, Mojtaba National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology The Graduate School of Electronic Engineering Douliu64002 Taiwan Constructor University School of Computer Science and Engineering Bremen28759 Germany

In this article, write-once-read-many-times (WORM) memory behavior of HfZrO (HZO) ferroelectric material is demonstrated. A stoichiometric Hf0.5Zr0.5O2 thin film prepared using a sol-gel process is used as a resistive switching (RS) layer. The top and bottom electrodes are Al and n+-Si, respectively. The Al/HZO/n+-Si memory shows a low current of 10-11 A at a read voltage of 0.6 V in the OFF state. A writing process occurs when the voltage on Al electrode increases to ∼2.8 V. The ratio of the OFF-state resistance (Roff) to ON-state resistance (Ron) is 106. Both data retention and stress tests indicate that Roff and Ron can remain the same for over 104s. Moreover, data stability is further confirmed at an elevated temperature of 85 °C. The switching speed of the HZO memory is 40 ns, corresponding to a power consumption of 0.98 nJ. The RS mechanism is investigated, and the carrier conduction mechanisms are studied by the analysis of current-voltage characteristics at different temperatures. © 1963-2012 IEEE.

关键词： X ray scattering

来源：评论

学校读者我要写书评

暂无评论

The secret of the magic gourd(Ⅰ):biomass from various organizations of gourds as a sustainable source for high-performance supercapacitors

引用

science China Chemistry 2025年第3期68卷 908-923页

作者： Shaoqing Zhang Xuchun Wang Tianming Lv Baixue Dong Jiqi Zheng Yang Mu Miao Cui Ting Zhang Changgong Meng College of Chemistry and Materials Engineering Anhui Science and Technology UniversityBengbu 233000China Anhui Province Quartz Sand Purification and Photovoltaic Glass Engineering Research Center Anhui Science and Technology UniversityBengbu 233000China Instrumental Analysis Center Dalian University of TechnologyDalian 116024China School of Chemical Engineering Zhejiang University of TechnologyHangzhou 310014China College of Environment and Chemical Engineering Dalian UniversityDalian 116622China School of Chemical Engineering Dalian University of TechnologyDalian 116024China

The classic cartoon“Calabash Brothers”describes a story of seven brothers born from a magic gourd uniting to defeat powerful *** order to explore the secret of the magic gourd,several transition metals were used to synthesize metal silicates(CMSi)by planted gourd leaves(GLs)and then the C-MSi materials were used to fabricate supercapacitor electrodes and devices with superior electrochemical *** integrating theoretical calculations and experimental results,the supercapacitor electrodes and devices obtained from the combination of transition metals with amorphous carbon exhibit superior electrochemical *** detail,in the three-electrode system,the Na OH etched materials(C-MSi)exhibited better electrochemical performance(for instance,as for C-CdSi,607 F g^(-1)at 0.5 A g^(-1)and the capacitance retention of 98.2%after 10,000cycles)than the unetched ones(i-C-MSi).Hybrid supercapacitor(HSC)devices also achieve very excellent electrochemical *** C-CdSi//AC as an example,the areal specific capacitance with 691 mF cm^(-2)at 2 mA cm^(-2),the energy density with 5.04 Whm^(-2)at the power density of 22.2 Wm^(-2)and the cycle stability with 87.3%after 6,000 *** approach is very versatile and was also applied to produce many hierarchically structured metal-silicate materials of other biomass precursors,including roots,vines,flowers,fruits and seeds of the planted ***,it is a potential way to prepare transition metal silicates using biomaterials for the enhancement of electrochemical performance and improvement of energy storage and ***,this paper preliminarily reveals the secret of the magic gourd.

关键词： gourd leaves alkali etching metal silicates supercapacitor electrochemical performance

来源：评论

学校读者我要写书评

暂无评论

Research on stock trend prediction method based on optimized random forest

引用

CAAI Transactions on Intelligence technology 2023年第1期8卷 274-284页

作者： Lili Yin Benling Li Peng Li Rubo Zhang School of Computer and Science and Technology Harbin University of Science and TechnologyHarbinChina Department of Computer Science and Engineering Dalian Nationalities UniversityDalianChina

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

关键词： ensemble learning finance random forest random search technical indicator

来源：评论

学校读者我要写书评

暂无评论

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

引用

science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

来源：评论

学校读者我要写书评

暂无评论

A Novel CAPTCHA Recognition System Based on Refined Visual Attention

引用

computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

来源：评论

学校读者我要写书评

暂无评论

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

引用

computer Modeling in engineering & sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

来源：评论

学校读者我要写书评

暂无评论

Self-Attention Mechanism-Based Activity and Motion Recognition Using Wi-Fi Signals

引用

China Communications 2024年第12期21卷 92-107页

作者： Kabo Poloko Nkabiti Chen Yueyun Tang Chao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingGuangdong 528399China

Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of ***,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential ***,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi *** Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI *** were carried out to evaluate the performance of the proposed Sole-SAM *** experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.

关键词： CSI human activity and motion recognition Sole-SAM Wi-Fi

来源：评论

学校读者我要写书评

暂无评论

Physics-based battery SOC estimation methods:Recent advances and future perspectives

引用

Journal of Energy Chemistry 2024年第2期89卷 27-40,I0003页

作者： Longxing Wu Zhiqiang Lyu Zebo Huang Chao Zhang Changyin Wei College of Mechanical Engineering Anhui Science and Technology UniversityChuzhou 233100AnhuiChina Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and ScienceXiangyang 441053HubeiChina School of Internet Anhui UniversityHefei 230039AnhuiChina School of Mechanical and Electrical Engineering Guilin University of Electronic TechnologyGuilin 541004GuangxiChina

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric *** far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various ***,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced *** this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced ***,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully ***,future perspectives of the current researches on physics-based battery SOC estimation are *** insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

关键词： Lithium-ion batteries State of charge Electrochemical model Battery management system

来源：评论

学校读者我要写书评

暂无评论

A Neural Network Slab Quality Prediction Analysis Based on a Hybrid Intelligent Optimisation Algorithm

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2025年第3期52卷 673-683页

作者： Li, Yiran Zhang, Chunna School of Applied Technology University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

To address the problem of inaccurate prediction of slab quality in continuous casting, an algorithm based on particle swarm optimisation and differential evolution is proposed. The algorithm combines BP neural network prediction method. Firstly, the factors affecting the quality of the slab are analysed and a sufficient number of sample sets are extracted for training;Secondly, the particle swarm optimisation algorithm is optimised, and then the BP neural network is optimised and the chaos mechanism is introduced to increase the continuity of motion of the particles. The population is partitioned hierarchically, and different types of particles are processed separately to improve the global search capability of the algorithm. The differential evolution algorithm is also integrated to optimise cross-selection and increase particle diversity. Finally, a prediction model is built to predict and analyse the sample data. In the experiment, three algorithms are tested for different benchmark functions, namely the comparison of convergence, stability and searchability. The optimisation time comparison is completed, the target curve fit test is performed and the slab quality test results are checked. The experimental results show that the PSO-EIDE algorithm has strong optimisation ability, fast convergence speed and obvious improvement in stability and accuracy. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Data Secure Storage Mechanism for IIoT Based on Blockchain

引用

computers, Materials & Continua 2024年第3期78卷 4029-4048页

作者： Jin Wang Guoshu Huang R.Simon Sherratt Ding Huang Jia Ni School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

关键词： Blockchain IIoT data storage cryptographic commitment

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：