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Inkjet-printing assisted engineering of patternable zinc anode-based electrochromic devices

Materials Reports(Energy) 2024年第4期4卷 40-47,I0001页

作者： Sheng Qiu Yanan Zhao Kai Wang Jinbin Luo Rui Wang Xinwei Jiang Jingwei Chen Abdulhakem Y.Elezzabi Wu Zhang Hao Jia Haizeng Li Key Laboratory of Eco-Textiles Ministry of EducationJiangnan UniversityWuxiJiangsu214122China School of Materials Science and Engineering Ocean University of ChinaQingdaoShandong266100China Ultrafast Optics and Nanophotonics Laboratory Department of Electrical and Computer EngineeringUniversity of AlbertaAlbertaT6G 2V4Canada National Innovation Center of Advanced Dyeing&Finishing Technology Tai'anShandong 271000China School of Energy and Power Engineering Shandong UniversityJinanShandong 250061China

Zn anode-based electrochromic devices(ZECDs)stand out as a highly promising technology in the upcoming era of multifunctional electronic devices,offering a blend of electrochromic capabilities and energy storage functions within a single transparent ***,significant challenges persist in achieving efficient patterning,ensuring long-term stability,and fast color-switching kinetics for these *** this study,heterogeneous tungsten oxide nanowires(W_(17)O_(47)/Na_(0.1)WO_(3),WNOs)are formulated into inkjet printing ink to assemble patternable *** heterogeneous electrode structure of WNO enables a highly capacitive-controlled mechanism that promotes fast electrochromic/electrochemical ***,by utilizing a three-dimensional MXene mesh modified substrate,the inkjet-printed ZECDs exhibit a wide optical modulation range of 69.13%,rapid color-changing kinetics(t_(c)=4.1 s,t_(b)=5.4 s),and highly reversible capacities of 70 mAh cm^(-2)over 1000 *** scalable strategy develops the patterned electrodes with a wide optical modulation range and substantial energy storage properties,offering promising prospects for their application in next-generation smart electronics.

关键词： Zn anode-based electrochromic devices Tungsten oxide nanowires Patterned electrodes Inkjet printing Electrolyte optimization

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Evaluating Negative Sampling Approaches for Neural Topic Models

arXiv

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arXiv 2025年

作者： Adhya, Suman Lahiri, Avishek Sanyal, Debarshi Kumar Das, Partha Pratim School of Mathematical and Computational Sciences Indian Association for the Cultivation of Science 2A & 2B Raja S C Mullick Road Kolkata700032 India Department of Computer Science Ashoka University Haryana Sonipat131029 India Department of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India

Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of "learn-to-compare". The goal of this approach is to add robustness to deep learning models to learn better representation by comparing the positive samples against the negative ones. Despite its numerous demonstrations in various areas of computer vision and natural language processing, a comprehensive study of the effect of negative sampling in an unsupervised domain like topic modeling has not been well explored. In this paper, we present a comprehensive analysis of the impact of different negative sampling strategies on neural topic models. We compare the performance of several popular neural topic models by incorporating a negative sampling technique in the decoder of variational autoencoder based neural topic models. Experiments on four publicly available datasets demonstrate that integrating negative sampling into topic models results in significant enhancements across multiple aspects, including improved topic coherence, richer topic diversity, and more accurate document classification. Manual evaluations also indicate that the inclusion of negative sampling into neural topic models enhances the quality of the generated topics. These findings highlight the potential of negative sampling as a valuable tool for advancing the effectiveness of neural topic models. © 2025, CC BY.

关键词： Contrastive Learning

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Multi-Variate Time Series Prediction of Traffic and Users for Dynamic RRH-BBU Mapping in C-RAN

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Fan Wang, Shanshan Zhou, Jieyu Pan, Haoye Zhou, Conghao Yang, Wang Lyu, Feng Zhang, Yaoxue Central South University School of Computer Science and Engineering Changsha410083 China Rutgers University Department of Computer Science PiscatawayNJ United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada BNRist Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Cellular operators face significant challenges in cutting operating expenses while maintaining the quality of service (QoS) for users due to growing network traffic and dynamic user connections. These challenges are addressed by the cloud radio access network (C-RAN) architecture, which includes a centralized pool of baseband units (BBUs) and distributes them from remote radio heads (RRHs). The key to improving C-RAN performance is to dynamically allocate large-scale RRHs to different BBUs in real time. In this paper, we propose a user behavior-aware RRH-BBU mapping framework to improve the performance of large-scale C-RANs by predicting RRH traffic and users in advance. First, we propose a Multivariate RRH time series Prediction Model (MRPM) that captures the spatio-temporal patterns in the data to predict the traffic volume and the number of users of RRHs, which represents key indicators of RRH connection states. Second, we formulate the RRH-BBU mapping as a Markov decision process problem to optimize cost and QoS by considering BBU utilization, BBU energy consumption, RRH migration frequency, and BBU load balancing. Third, we propose a prediction-based RRH-BBU mapping scheme (PB-RBM) to find the optimal RRH-BBU mapping strategy by leveraging the prediction information of MRPM. In the PB-RBM algorithm, we employ an A3C algorithm to learn the mapping policy and group the RRHs based on a defined popularity metric to reduce the state and action space of the reinforcement learning algorithm. Finally, extensive experiments are conducted on a real-world dataset, and our algorithm is compared with several matching algorithms, such as ACKTR, heuristic, etc., to demonstrate its superiority, especially reducing 17.5% in RMSE compared to the best-performing baseline. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Leveraging Multi-modal Sensing for Robotic Insertion Tasks in R&D Laboratories 19

Leveraging Multi-modal Sensing for Robotic Insertion Tasks i...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Butterworth, Aaron Pizzuto, Gabriella Pecyna, Leszek Cooper, Andrew I. Luo, Shan University of Liverpool The Department of Computer Science LiverpoolL69 3BX United Kingdom University of Liverpool The Department of Chemistry LiverpoolL69 3BX United Kingdom King's College London The Department of Engineering LondonWC2R 2LS United Kingdom

ISBN: (纸本)9798350320695

Performing a large volume of experiments in Chemistry labs creates repetitive actions costing researchers time, automating these routines is highly desirable. Previous experiments in robotic chemistry have performed high numbers of experiments autonomously, however, these processes rely on automated machines in all stages from solid or liquid addition to analysis of the final product. In these systems every transition between machine requires the robotic chemist to pick and place glass vials, however, this is currently performed using open loop methods which require all equipment being used by the robot to be in well defined known locations. We seek to begin closing the loop in this vial handling process in a way which also fosters human-robot collaboration in the chemistry lab environment. To do this the robot must be able to detect valid placement positions for the vials it is collecting, and reliably insert them into the detected locations. We create a single modality visual method for estimating placement locations to provide a baseline before introducing two additional methods of feedback (force and tactile feedback). Our visual method uses a combination of classic computer vision methods and a CNN discriminator to detect possible insertion points, then a vial is grasped and positioned above an insertion point and the multi-modal methods guide the final insertion movements using an efficient search pattern. Through our experiments we show the baseline insertion rate of 48.78% improves to 89.55% with the addition of our 'force and vision' multi-modal feedback method. © 2023 IEEE.

关键词： Location

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Artificial Immune Systems Approach for Surface Reconstruction of Shapes with Large Smooth Bumps 23rd

Artificial Immune Systems Approach for Surface Reconstructi...

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23rd International Conference on Computational science, ICCS 2023

作者： Gálvez, Akemi Fister, Iztok You, Lihua Fister, Iztok Iglesias, Andrés Department of Applied Mathematics and Computational Sciences University of Cantabria Santander39005 Spain Faculty of Pharmaceutical Sciences Toho University 2-2-1 Miyama Funabashi274-8510 Japan Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia National Center for Computer Animation Faculty of Media and Communication Bournemouth University PooleBH12 5BB United Kingdom

ISBN: (纸本)9783031360268

Reverse engineering is one of the classical approaches for quailty assessment in industrial manufacturing. A key technology in reverse engineering is surface reconstruction, which aims at obtaining a digital model of a physical object from a cloud of 3D data points obtained by scanning the object. In this paper we address the surface reconstruction problem for surfaces that can exhibit large smooth bumps. To account for this type of features, our approach is based on using exponentials of polynomial functions in two variables as the approximating functions. In particular, we consider three different models, given by bivariate distributions obtained by combining a normal univariate distribution with a normal, Gamma, and Weibull distribution, respectively. The resulting surfaces depend on some parameters whose values have to be optimized. This yields a difficult nonlinear continuous optimization problem solved through an artificial immune systems approach based on the clonal selection theory. The performance of the method is discussed through its application to a benchmark comprised of three examples of point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Weibull distribution

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Driver Status Monitoring System with Feedback from Fatigue Detection and Lane Line Detection 15

Driver Status Monitoring System with Feedback from Fatigue D...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Yan, Kai Zhao, Chaoyue Shen, Chengkang Wang, Peiyan Wang, Guoqing 2nd Research Institute of Caac Research Centre Chengdu China Ai Research Centre Beijing China Nanjing University School of Electronic Science and Engineering Nanjing China Purdue University Department of Electrical and Computer Engineeering West Lafayette United States University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9781665464994

Automobiles have become an indispensable part of life for both business and pleasure in today's society. Because of the long-term continuous work, fatigue presents a great danger to ride-sharing and truck drivers. Therefore, this paper aims to design a device that provides valuable feedback by evaluating driver status and surroundings. A gradient judgment is made through lane detection and face detection. When a dangerous condition is detected, the driver will be alerted by music and audio announcements with different degrees. The system also has two additional functions. First, a digital record-keeping to assist the professional driver. The other is a security system that if a stranger starts the car, a text message will be sent to the owner's phone. Compared with those in previous works, the proposed system's efficacy and efficiency are validated qualitatively and quantitatively in driver fatigue detection. © 2022 IEEE.

关键词： Face recognition

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Two-Server Private Information Retrieval with High Throughput and Logarithmic Bandwidth

Two-Server Private Information Retrieval with High Throughpu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Huang, Cheng Yang, Anjia Liu, Dongxiao Lu, Rongxing Shen, Xuemin Sherman University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Jinan University College of Cyber Security Guangzhou510632 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada Pazhou Lab Guangzhou China

ISBN: (纸本)9798350345384

In this paper, we propose a high-throughput, logarithmic-bandwidth private information retrieval (PIR) scheme, which enables a client to securely retrieve any record from a database replicated on two remote servers without revealing the record index. Specifically, based on the puncturable pseudorandom function (PPRF), a client first leverages randomized mapping to locate a logarithmic-size punctured PRF key capable of replacing a randomized set of arbitrary size while concealing a chosen element within the set. With the key, the client then generates corresponding PIR queries for two servers, achieving logarithmic communication complexity. Upon receiving the queries, the servers independently perform level-I PPRF evaluations on partitioned databases, i.e., buckets, to obtain intermediate results, and cooperatively perform level-II PPRF evaluations on these results to provide fast PIR responses. The two-level recursive approach can significantly reduce the servers' computational complexity from linear to sublinear, ensuring high throughput. Finally, we conduct a comprehensive security analysis and develop a proof-of-concept prototype to demonstrate the efficiency and security of the proposed PIR scheme. © 2023 IEEE.

关键词： Bandwidth

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Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading 30th

Efficient Compute Resource Sharing of RISC-V Packed-SIMD Us...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Takata, Shogo Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan

ISBN: (纸本)9783031856372

AI tasks are gaining popularity in the area of IoT and edge devices. To run such tasks on devices, QNNs are used because of their reduced size and ability to be computed with simple integer arithmetic. There have been many implementations to support such a network format. However, when considering thread-level parallelism to speedup the program, many often implement a multi-core architecture or clusters which needs to copy all resources for each core. In this paper, we introduce a new RISC-V Out-of-Order Simultaneous Multi-Threading core "B4SMT" with RISC-V Packed-SIMD extension for evaluation. We also show that even a single executor could increase the performance of a 1D median filter by over 100×, and a matrix multiplication by over 30 on more than 16 threads efficiently. Furthermore, we suggest that other infrequently used executors may be placed as a shared resource efficiently in an SMT core. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Median filters

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Towards the speed enhancement of association rule mining algorithm for intrusion detection system 10th

Towards the speed enhancement of association rule mining alg...

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10th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2019 and 9th World Congress on Information and Communication Technologies, WICT 2019

作者： Dasgupta, Sarbani Saha, Banani Department of MCA Techno International Newtown Block-DG Action Area 1 New town Kolkata700156 India Computer Science and Engineering Department2 University of Calcutta JD Block Sector III Salt Lake City Kolkata700098 India

ISBN: (纸本)9783030493387

Intrusion detection system is a device or a software application which is used to monitor network traffic data for suspicious activity and alert the system administrator about any malicious activity or network policy violation that has occurred. Among the several techniques proposed for anomaly detection in network audit data, data mining techniques are used for efficient analysis of network audit data to detect any abnormalities occurred due to specific types of attacks. Association rule mining algorithm an unsupervised data mining algorithm has been applied for analysis of network audit data for detecting anomalies. Due to rapid increase of internet based services, cyber security has become a challenging problem. In this paper, a frame work using association rule mining algorithm, has been proposed for detecting suspicious activity in network traffic data. Further in order to increase the speed of processing for large size network traffic data, big data processing tool Apache Spark has been used. Among the several association rule mining algorithm FP growth algorithm has been used to generate attack rules that will detect malicious attack on network audit data. For the purpose of the experiment the Kyoto dataset which is available freely online has been used. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Association rules

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Differentially Private Wireless Federated Learning With Integrated Sensing and Communication

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IEEE Transactions on Wireless Communications 2025年

作者： Hu, Shuyan Yuan, Xin Ni, Wei Wang, Xin Hossain, Ekram Vincent Poor, H. Department of Communication Science and Engineering Shanghai200433 China Tongji University College of Electronics and Information Engineering Shanghai201804 China Commonwealth Scientific and Industrial Research Organization Data61 MarsfieldNSW2122 Australia University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 2N2 Canada Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

This paper develops a novel framework for differentially private (DP) wireless federated learning (FL) with integrated sensing and communication (ISAC). In this framework, which is referred to as DP-ISAC-FL, wireless devices sense data and upload the trained local models using ISAC technique. The local training can take place concurrently with sensing at each device. We analyze the convergence upper bound of DP-ISAC-FL and rigorously capture the impact of device selection (for model training), time allocation between sensing/training and model uploading for the selected devices, and the allocations of channels, modulations, and transmit powers. We also develop an algorithm that enforces the convergence of DP-ISAC-FL by minimizing the convergence upper bound in an OFDMA system with discrete modulations. The beamforming for sensing, device selection, and the allocations of time, subchannels, modulations, and transmit powers are jointly optimized using successive convex approximation (SCA), adapting to the channels and computing capabilities of the devices. Experiments on multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) show that DP-ISAC-FL with optimal allocations can significantly improve the learning convergence and accuracy under different privacy levels, e.g., by 7% and 18%, compared with its benchmarks. This is attributed to 68% more sensing data that DP-ISAC-FL can admit for model training. © 2002-2012 IEEE.

关键词： Differential privacy

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