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Ionic liquid-wrapped MXene film with bowl-like structures for highly integrated micro-supercapacitor array with ultrahigh output voltage

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Nano Research 2023年第4期16卷 4926-4932页

作者： Cai-Yun Ren Sheng-You Qiu Jing-Ru Zhai Ke-Qi Zhang Jia-Xing Lu Jian Gao Chuang Wang Yong-Chao Zhang Xiao-Dong Zhu State Key Laboratory Base of Eco-Chemical Engineering Institute of Chemical TechnologyQingdao University of Science&TechnologyQingdao 266042China School of Chemistry and Chemical Engineering Harbin Institute of TechnologyHarbin 150080China School of Materials Sun Yat-sen UniversityShenzhen 518107China

Recent researches in the development of in-plane micro-supercapacitor(MSC)have been dedicated to advancing its energy density in a finite storage ***,the low ion-accessible surface area of plane electrode material has been the bottleneck limiting the energy output of ***,we design a stable ionic liquid(IL)-MXene microemulsion system in virtue of tween-20(TW20),gathering IL-TW20 microdroplets around MXene *** microemulsion can adhere on the current collector and form the dense MXene-TW20-IL *** IL as spacer and electrolyte is retained in the interlamination of MXene with the elimination of the TW20 during heat treatment,which enlarges the ion-accessible surface area of the MXene ***,the MSC based on the flexible MXene-IL film exhibits a high areal capacitance(44.6 mF·cm^(−2)),a high areal and volumetric energy density(50.7μW·h·cm^(−2)and 39 mW·h·cm^(−3)),surpassing most of the reported MXene-based *** the MSC unit can be arbitrarily integrated in terms of various energy and voltage *** instance,the integrated array with thirty MSC units in series realizes a high voltage output of 90 V,widening the range of application in next-generation microelectronic devices.

关键词： microemulsion system ionic liquid-wrapped MXene films micro-supercapacitors extra-high voltage integration

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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A Novel Integrated Wireless Charging System Based on On-board Charger with Less Components 5

A Novel Integrated Wireless Charging System Based on On-boar...

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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Liu, Jinhao Na, Tuopu Ning, Lei Li, Kangcheng Sun, Haowei Zhao, Bo Harbin Institute of Technology School of Instrumentation Science and Engineering Harbin China Heilongjiang University of Technology School of Modern Manufacturing Engineering Harbin China

ISBN: (纸本)9798350355253

Wireless power transfer (WPT) system is one of important electric vehicles (EVs) charging methods. While, the receiving coil and converter of WPT increase the volume, cost and weight. This paper proposes an integrated WPT system using the components of on-board charger (OBC). The ac-side filtering inductor of OBC is shared as one inductor of LCC compensation topology for receiver. And LCC compensation topology can reduce the volume of receiving coil. To make the power density of integrated system higher, all the components of OBC are reused as the receiver converter of WPT. This paper introduces the topology of proposed system, on-board charging and wireless charging modes. Furthermore, bidirectional function can be achieved for two charging modes. Finally, the simulations are done, and the simulation results verify the effectiveness of the proposed integrated charging system. © 2024 IEEE.

关键词： Charging stations

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A Simple Analysis and Discussion of Poland Longwave Broadcasting Station Antenna

A Simple Analysis and Discussion of Poland Longwave Broadcas...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Wu, Chao Qiu, Shuang Qiu, Jinghui Denisov, Olensandr Molebny, Vasyl Department of Microwave Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Macau China

ISBN: (纸本)9798350364774

With the profound and complex changes of the international situation, the longwave communication has more and more important application value and strategic significance. And longwave can realize a large range of communication and even global communication at the low end of longwave frequency because of its advantages of low propagation loss, strong diffraction ability, etc. Therefore, longwave is widely used in a lot of fields, such as sea navigation, underground communication, underwater submarine communication, and so on. As one of the most important components of the longwave communication system, the performance of longwave antennas (LWAs) play a decisive role. In recent years, the research of LWAs mainly focuses on mechanical antenna and the traditional antenna form is less studied. In this paper, the world's most famous Poland longwave broadcasting station antenna is briefly discussed and analyzed and the development direction of the traditional longwave antenna is proposed. © 2024 IEEE.

关键词： Broadcasting antennas

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HIT-MI &T Lab’s Submission to CCMT 2023 Automatic Post-editing Task 19th

HIT-MI &T Lab’s Submission to CCMT 2023 Automatic Post-edi...

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19th China Conference on Machine Translation, CCMT 2023

作者： Zhang, Rui Yuan, Jinghao Huang, Hui Yang, Muyun Zhao, Tiejun Research Center on Language Technology School of Computer Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819978939

Automatic post-editing (APE) aims to automatically correct the outputs of the machine translation system by learning from manual correction examples. We present the system developed by HIT-MI &T Lab for the CCMT 2023 APE task of Chinese-English direction. We use mBART as the backbone model, and explore different techniques to create synthetic data, including domain selection, forward translation and data augmentation via large language model. Multi-model ensemble is also adopted in our final system. The experiment results on the development set demonstrate the effectiveness of our proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine translation

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Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 824-835页

作者： Min Yang Zhenwei Wang Qiancheng Xu Chengming Li Ruifeng Xu Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenChina School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaUSA School of Intelligent Systems Engineering Sun Yat‐Sen UniversityGuangzhouChina School of Computer Science Harbin Institute of Technology(Shenzhen)ShenzhenChina

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable ***,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic *** this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training *** learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation *** experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual *** reproducibility,we release the code and data publicly at:https://***/siat‐nlp/HSEMEC‐code‐data.

关键词： deep learning emotional conversation generation semantic‐emotional memory

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Scientometric analysis of research trends on solid oxide electrolysis cells for green hydrogen and syngas production

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Frontiers in Energy 2024年第5期18卷 583-611页

作者： Shimeng Kang Zehua Pan Jinjie Guo Yexin Zhou Jingyi Wang Liangdong Fan Chunhua Zheng Suk Won Cha Zheng Zhong School of Science Harbin Institute of TechnologyShenzhen 518055China Department of New Energy Science and Technology College of Chemistry and Environmental EngineeringShenzhen UniversityShenzhen 518060China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Department of Mechanical Engineering Seoul National UniversitySeoul 08826Republic of Korea

Solid oxide electrolysis cell(SOEC)is a promising water electrolysis technology that produces hydrogen or syngas through water electrolysis or water and carbon dioxide *** hydrogen or syngas can be produced by SOEC with renewable ***,SOEC has attracted continuous attention in recent years for the urgency of developing environmentally friendly energy sources and achieving carbon *** on 1276 related articles retrieved from the Web of science(WoS)database,the historical development of SOECs are depicted from 1983 to 2023 in this *** co-occurrence networks of the countries,source journals,and author keywords are ***,three main clusters showing different content of the SOEC research are identified and ***,the scientometric analysis and the content of the high-cited articles of the research of different topics of SOECs:fuel electrode,air electrode,electrolyte,co-electrolysis,proton-conducting SOECs,and the modeling of SOECs are also *** results show that co-electrolysis and proton-conducting SOECs are two popular directions in the study of *** paper provides a straightforward reference for researchers interested in the field of SOEC research,helping them navigate the landscape of this area of study,locate potential partners,secure funding,discover influential scholars,identify leading countries,and access key research publications.

关键词： solid oxide electrolysis cell(SOEC) scientometric review knowledge network material development H_(2)O-CO_(2)co-electrolysis modeling

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Application of machine learning models in groundwater quality assessment and prediction:progress and challenges

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Frontiers of Environmental science & engineering 2024年第3期18卷 25-55页

作者： Yanpeng Huang Chao Wang Yuanhao Wang Guangfeng Lyu Sijie Lin Weijiang Liu Haobo Niu Qing Hu School of Environment Harbin Institute of TechnologyHarbin 150090China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Engineering Innovation Center of SUSTech(Beijing) Southern University of Science and TechnologyBeijing 100083China Technical Center for Soil Agriculture and Rural Ecology and EnvironmentMinistry of Ecology and EnvironmentBeijing 100012China Chinese Academy of Environmental Planning Beijing 100043China

Groundwater quality assessment and prediction(GQAP)is vital for protecting groundwater *** GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally ***,the application of machine learning(ML)in GAQP(GQAPxML)has been widely studied due to ML’s reliability and *** many GQAPxML publications exist,a thorough review is *** review provides a comprehensive summary of the development of ML applications in the field of ***,the workflow of ML modeling is briefly introduced,as are data preparation,model development,model evaluation,and model ***,299 publications related to the topic are filtered,mainly through ML ***,many aspects of GQAPxML,such as publication trends,the spatial distribution of study areas,the size of data sets,and ML algorithms,are discussed from a bibliometric *** addition,we review in detail the well-established applications and recent findings for several subtopics,including groundwater quality assessment,groundwater quality modeling using groundwater quality parameters,groundwater quality spatial mapping,probability estimation of exceeding the groundwater quality threshold,groundwater quality temporal prediction,and the hybrid use of ML and physics-based ***,the development of GQAPxML is explored from three perspectives:data collection and preprocessing,model building and evaluation,and the broadening of model *** review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.

关键词： Groundwater quality assessment Groundwater quality prediction Machine learning Groundwater modeling

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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