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Enhancing pseudocapacitive behavior of CoFe-LDH through MWCNTs conductive network: a strategy for ultra-stable supercapacitors

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Diamond and Related Materials 2025年 157卷

作者： Qihui Niu Suna Zhang Lijun Wang Yongmin Qiao Jianguang Xu Jing Li Wenqin Li Yong Zhu Yang Li Huaqing Xie School of Energy and Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai 201209 China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai 201209 China Shanghai Shanshan Technology Co. Ltd. Shanghai 201209 China Ningbo Dema Billion Intelligent Technology Co. Ltd. Cixi Ningbo Zhejiang Province 315300 China

Layered double hydroxide (LDH) holds a prospective position in the realm of electrode materials for supercapacitors, due to its distinctive layered structure. However, its inherent low conductivity hinders its possible utilization in supercapacitors. In this study, we synthesized CoFe-LDH nanosheets and introduced multi-walled carbon nanotubes (MWCNTs) to construct MWCNTs wrapped CoFe-LDH nanocomposites. The results show that the CoFe-LDH/MWCNTs nanocomposite has a specific capacitance of 752.5 F/g under a current density of 1 A/g. The potential electrochemical capability of the CoFe-LDH/MWCNTs nanocomposite is excited via the construction of the conductive network of MWCNTs. High energy density (71 Wh/kg) and power density (9800 W/kg) are generated in an asymmetric supercapacitor with CoFe-LDH/MWCNTs nanocomposite as electrochemical active material, showing excellent cycle stability of 88.9 % capacitance remaining even after 10,000 cycles. These results indicate that MWCNTs wrapped CoFe-LDH composites are promising candidates for high performance supercapacitors.

关键词： Multiwalled carbon nanotubes (MWCN)

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On the value of imbalance loss functions in enhancing deep learning-based vulnerability detection

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Expert Systems with Applications 2025年 291卷

作者： Xiaoxue Ma Yanzhong He Jacky Keung Cheng Tan Chuanxiang Ma Wenhua Hu Fuyang Li School of Science and Technology Hong Kong Metropolitan University Hong Kong China School of Computer Science Wuhan University Wuhan Hubei China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan Hubei China Department of Computer Science City University of Hong Kong Hong Kong China School of Computer Science Hubei University Wuhan Hubei China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan Hubei China

Software vulnerability detection is crucial in software engineering and information security, and deep learning has been demonstrated to be effective in this domain. However, the class imbalance issue, where non-vulnerable code snippets vastly outnumber vulnerable ones, hinders the performance of deep learning-based vulnerability detection (DLVD) models. Although some recent research has explored the use of imbalance loss functions to address this issue and enhance model efficacy, they have primarily focused on a limited selection of imbalance loss functions, leaving many others unexplored. Therefore, their conclusions about the most effective imbalance loss function may be biased and inconclusive. To fill this gap, we first conduct a comprehensive literature review of 119 DLVD studies, focusing on the loss functions used by these models. We then assess the effectiveness of nine imbalance loss functions alongside cross entropy (CE) loss (the standard balanced loss function) on two DLVD models across four public vulnerability datasets. Our evaluation incorporates six performance metrics, with results analyzed using the Scott-Knott effect size difference (ESD) test. Furthermore, we employ interpretable analysis to elucidate the impact of loss functions on model performance. Our findings provide key insights for DLVD, which mainly include the following: the LineVul model consistently outperforms the ReVeal model; label distribution aware margin (LDAM) loss achieves the highest Precision, while logit adjustment (LA) loss yields the best Recall; Class balanced focal (CB-Focal) loss excels in comprehensive performance on extremely imbalanced datasets; and LA loss is optimal for nearly balanced datasets. We recommend using LineVul with either CB-Focal loss or LA loss to enhance DLVD outcomes. Our source code and datasets are available at https://***/YanzhongHe/DLVD-ImbalanceLossEmpirical .

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Travel Time Prediction for Multi-Airport Systems Via Multiclass Queuing Networks

Travel Time Prediction for Multi-Airport Systems Via Multicl...

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Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Kailin Chen Shaoyu Wang Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Guangdong China Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

In this paper, we consider predicting travel time for aircraft operated in multi-airport systems by modeling and simulating a multiclass queuing network, which can systematically capture the complicated coupling relationship among multiple airports and terminal airspace and the complex nature of flight trajectories following different traffic flow patterns. In this multiclass queuing network model, each class of queuing network, named a class of customers, is modeled with the data of a traffic flow pattern, which is identified for a cluster of flight trajectories. Airports and airspace sectors are correspondingly modeled as networked servers with nonhomogeneous and time-varying arrival rate, service rate and server capacity to serve those classes of customers following their specific routing probabilities. Then, all of the parameters for setting up the multiclass queuing network model can be properly estimated using historical 4D flight trajectory data. To illustrate the superiority of this model, both average travel time for each class of customers, i.e., aircraft following a particular flow pattern, and the arrival time for an individual flight are predicted via simulations of a multiclass queuing network, and furthermore, compared with the real travel time. A typical example of a multi-airport system, the Guangdong-Hong Kong-Macau Greater Bay Area in China, is utilized to showcase the prediction performance of the proposed multiclass queuing network simulation model. The simulation experiments of the case study demonstrate that the proposed model well fits this multi-airports system. For most of the time periods, the percentage error (PE) of simulated average travel time and real average travel time is less than 5%. The travel time prediction for a random individual flight can achieve around 1% of the percentage error in terms of point estimation.

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VFL: A Verifiable Federated Learning with Privacy-Preserving for big data in Industrial IoT

arXiv

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arXiv 2020年

作者： Fu, Anmin Zhang, Xianglong Xiong, Naixue Gao, Yansong Wang, Huaqun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK United States Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Due to the strong analytical ability of big data, deep learning has been widely applied to model the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Summary of Scene Text Detection and Recognition

Summary of Scene Text Detection and Recognition

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yao Qin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In recent years, scene text recognition has received much attention, and has a wealth of application scenarios, such as: photo translation, image retrieval, scene understanding and so on. However, the text in the scene is also faced with many problems, such as: light changes, deformation text, text string recognition under background noise interference, text skew and degree of curvature, and a large number of artistic fonts. Solving the above problems will always be a challenging thing. This paper reviews some recent work on text detection, text recognition and end-to-end text detection and recognition, and finally looks forward to the future development direction.

关键词： Industrial electronics Image recognition Text recognition Interference Market research Noise measurement Strain

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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年第Part1期332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Transfer learning with active queries for relational data modeling across multiple information networks 25th

Transfer learning with active queries for relational data mo...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Chen, Ke-Jia Zhang, Kai Jiang, Xi-Lin Wang, Yunyun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9783030041816

This paper studies the relationship prediction problem in multi-network scenarios, aiming to overcome the network sparsity challenge where the labeled data (connected node pairs) are much less than the unlabeled data (unconnected node pairs). The TAQIL framework is proposed by using transfer learning to get knowledge from the related source networks and then use active learning to query the labels of the most informative instances from the oracle in the target network. A new query function is also proposed in order to better use the parameters output by the transfer learning method. The alternate use of transfer learning and active learning allows adaptive transfer of knowledge across multiple networks to mitigate cold start and meantime improve the prediction accuracy with active queries in the target network. The experimental results on both non-network datasets and network datasets demonstrate the significant improvement in prediction accuracy compared with several benchmark methods and related state-of-art methods. © Springer Nature Switzerland AG 2018.

关键词： Forecasting

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Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

Accelerated Latent Factor Analysis for Recommender Systems v...

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IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models data models Sparse matrices Acceleration Recommender systems Convergence

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A Deep Temporal Collaborative Filtering Recommendation Framework via Joint Learning from Long and Short-Term Effects

A Deep Temporal Collaborative Filtering Recommendation Frame...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Qianqian Ji Xiaoyu Shi Mingsheng Shang Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Recommendations based on deep learning technologies are attracting increasingly attentions from academia and industry recently, due to it has the powerful ability of data representation learning, and can capture the complex and non-linear user-item interaction patterns. Previous studies on deep learning based recommenders heavily emphasize on learning the short-term or long-term temporal effects in separate ways. Furthermore, most of the existing recommenders studied on temporal dynamics hidden in user-item interactions by using ratings or review texts solely, without utilizing these heterogeneous side information in a comprehensive manner. As a result, they own a limited ability to exploit all of available user-item interaction data. To address the above issues, we propose a temporal collaborative filtering recommendation framework with utilizing deep learning technologies, which can capture the drift of users' preferences and items' attributes over time from available heterogeneous feedback. For studying the rich sentiment information about particular item features hidden in reviews and capturing the short-term temporal dynamics in user preference, a parallel recurrent neural network (RNN) coupled in the last layer is presented. With consideration of the life cycle of each item, we further propose an item-based time evolution model as a supplement to enrich the long-term temporal effects when recommendation. In addition, a modified cosine similarity function is incorporated to produce the personalized candidate list for each user. Finally, extensive experimental are conducted on Amazon's three datasets. They indicate that the proposed method outperforms the other state-of-the-art recommenders in terms of precision and recall when doing the Top-n recommendation task.

关键词： Hidden Markov models Recommender systems Analytical models Deep learning Predictive models Collaboration Bidirectional control

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Theoretical Analysis on Index System of Ecological Balanced Fertilization for Three Crops

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Meteorological and Environmental Research 2020年第5期11卷 91-95页

作者： Shuojin WANG Changhong MI Zhiping HUANG Shutian LIU Xianda HOU Hongyan ZHENG Jian DING Ling LU Yanlin HOU Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology Nanning Normal UniversityNanning 530001China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal UniversityNanning 530001China Key Laboratory of Environment Change and Resources Use in Beibu Gulf(Nanning Normal University) Nanning 530001China Agro-Environmental Protection Institute Ministry of Agriculture and Rural AffairsTianjin 300191China

This paper analyzed theoretical basis of ecological balanced fertilization by data mining method based on the relationships among relative abundance-deficiency values of soil nutrients,optimum fertilization amounts and nutrient conversion rates of N,P and K for rice,wheat and maize in column *** results and conclusions were as follows:①the scientific basis of guiding fertilization by the relative abundance-deficiency value of soil nutrient was insufficient.②The scientific basis for predicting fertilizer amount according to nutrient conversion rate was sufficient.③There was consistency among the relative abundance-deficiency values of soil nutrients,indicating that the soil nutrient balance was important to the production.④There was some consistency among the optimum fertilizer amounts,indicating that N,P and K fertilizer should be balanced.⑤There was some consistency among nutrient conversion rates,indicating that nutrient uptake was consistent,which required the consistency among relative abundance-deficiency values of soil *** there was inconsistency,it needed to be supplemented by means of fertilization.⑥The relative abundance-deficiency value of soil nutrient and optimum fertilizer amounts of other nutrients were negatively correlated mostly,indicating that if the relative abundance-deficiency value of soil nutrient was high,the others also should be high,resulting in optimum fertilizer amounts of other nutrients decreased.⑦The nutrient conversion rate was negatively correlated mostly with the optimal fertilizer amounts of other nutrients,indicating that more fertilizer amount of one nutrient lowered its own conversion rate,mean while also reduced the conversion rates of other *** embodied the unity and balance of nutrient absorption by the crops.

关键词： Optimum yield Optimum fertilization amount Nutrient conversion rate Fertilizer utilization ratio Relative abundance-deficiency value of soil nutrient

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