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Constructed TiO_(2)/WO_(3)heterojunction with strengthened nano-trees structure for highly stable electrochromic energy storage device

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Journal of Advanced Ceramics 2023年第3期12卷 634-648页

作者： Lili Zhao Zhuoan Cai Xiaoyang Wang Wenbo Liao Simin Huang Lingyun Ye Jilie Fang Chunxing Wu Hao Qiu Lei Miao School of Chemical Engineering and Energy Technology Dongguan University of TechnologyDongguan 523808China Guangxi Key Laboratory of Information Materials Engineering Research Center of Electronic Information Materials and DevicesMinistry of EducationSchool of Materials Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Guangdong Provincial Key Laboratory of Distributed Energy Systems Dongguan 523808China

Tungsten trioxide(WO_(3))has been widely regarded as a prospective bifunctional material due to its electrochromic and pseudocapacitive properties,while still facing the dilemma of inadequate cycle stability and trapping-induced ***,inspired by the trees-strengthening approach,a unique titanium dioxide(TiO_(2))nanorod arrays strengthened WO_(3)nano-trees(TWNTs)heterojunction was rationally designed and *** sharp contrast to the transmittance modulation(ΔT)attenuation of primary WO_(3)nano-trees during cycling,the TWNTs film showed not only excellent electrochromic performance but also fascinating cycle stability(77.35%retention of the initialΔT after 10,000 cycles).Besides,the trapping-induced degradation could be easily rejuvenated by a potentiostatic de-trapping *** electrochromic energy storage device(EESD)was further assembled based on the TWNTs film to deliver excellentΔT(up to 79.5%at 633 nm),fast switching speed(tc/tb=1.9 s/14.8 s),extremely high coloration efficiency value(443.4 cm^(2)·C^(−1)),and long-term cycle stability(over 10,000 charge/discharge cycles).This innovative study provided in-depth insights into the electrochromism nature and a significant step in the realization of stable electrochromic-energy storage application,paving the way for multifunctional smart windows as well as next-generation optoelectronic devices.

关键词： electrochromic(EC) WO_(3)nano-trees cycle stability TiO_(2)nanorod arrays(TiO_(2)NRAs) energy storage

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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Rollick Games: A Formal Language Based Platform for Location-Based Pervasive Games 1

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15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Anestis, George Gioldasis, Nektarios Karasavvidis, Stefanos Palioudakis, Tzanis Moumoutzis, Nektarios Christodoulakis, Stavros Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Greece School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

In this paper, we present the Rollick Games platform and its Pervasive Game Modeling Language that enables the description of location-based pervasive games using a GUI provided by the Game Studio web app. The runtime environment (Pervasive Game Execution Environment) is responsible for generating the Pervasive Game Execution Language from a game model and loading it to an instance of the game's Execution Engine. The Execution Environment includes a set of agents responsible for monitoring the players’ context and interaction with other game elements and among each other. The Engine follows an event-driven paradigm and uses an event loop waiting for events to be dispatched from the agents to carry out its computations based on its current state, context, and event data. The proposed approach allows for both sharing and reusing game models, significantly reducing time to market and development costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Modeling languages

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EdgeAnchor: A Rapid and Balanced File Storage Strategy at the Network Edge 29

EdgeAnchor: A Rapid and Balanced File Storage Strategy at th...

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29th IEEE International Conference on Parallel and distributed systems, ICPADS 2023

作者： Liu, Han Xie, Xingrui Zhang, Zhuopu Cheng, Geyao Luo, Lailong Guo, Deke National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9798350330717

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers' storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively. © 2023 IEEE.

关键词： dynamic network edge file storage load balance rapid indexing

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PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

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Stone Soup: No Longer Just an Appetiser 26

Stone Soup: No Longer Just an Appetiser

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26th International Conference on information Fusion, FUSION 2023

作者： Hiscocks, Steven Barr, Jordi Perree, Nicola Wright, James Pritchett, Henry Rosoman, Oliver Harris, Michael Gorman, Roisín Pike, Sam Carniglia, Peter Vladimirov, Lyudmil Oakes, Benedict Defence Science and Technology Laboratory Cyber and Information Systems Salisbury United Kingdom Defence Research and Development Canada Radar Sensing and Exploitation Ottawa Canada University of Liverpool Electrical Engineering & Electronics Cdt in Distributed Algorithms Liverpool United Kingdom

ISBN: (纸本)9798890344854

This paper announces version 1.0 of Stone Soup: the open-source tracking and state estimation framework. We highlight key elements of the framework and outline example applications and community *** Soup is engineered with modularity and encapsulation at its heart. This means that its many components can be put together in any number of ways to build, compare, and assure almost any type of multi-target tracking and fusion algorithm. Since its inception in 2017, it has aimed to provide the target tracking and state estimation community with an open, easy-to-deploy framework to develop and assess the performance of different types of trackers. Now, through repeated application in many use cases, implementation of a wide variety of algorithms, multiple beta releases, and contributions from the community, the framework has reached a stable *** announcing this release, we hope to encourage additional adoption and further contributions to the toolkit. We also acknowledge and express appreciation for the many contributions of time and expertise donated by the tracking and fusion community. © 2023 International Society of information Fusion.

关键词： State estimation

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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$\mu$μ-DDRL: A QoS-Aware distributed Deep Reinforcement Learning Technique for Service Offloading in Fog Computing Environments

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IEEE Transactions on Services Computing 2023年第1期17卷 47-59页

作者： Mohammad Goudarzi Maria A. Rodriguez Majid Sarvi Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems University of Melbourne Parkville VIC Australia School of Computing and Information Systems University of Melbourne Parkville VIC Australia Department of Infrastructure Engineering University of Melbourne Parkville VIC Australia

Fog and Edge computing extend cloud services to the proximity of end users, allowing many Internet of Things (IoT) use cases, particularly latency-critical applications. Smart devices, such as traffic and surveillance cameras, often do not have sufficient resources to process computation-intensive and latency-critical services. Hence, the constituent parts of services can be offloaded to nearby Edge/Fog resources for processing and storage. However, making offloading decisions for complex services in highly stochastic and dynamic environments is an important, yet difficult task. Recently, Deep Reinforcement Learning (DRL) has been used in many complex service offloading problems; however, existing techniques are most suitable for centralized environments, and their convergence to the best-suitable solutions is slow. In addition, constituent parts of services often have predefined data dependencies and quality of service constraints, which further intensify the complexity of service offloading. To solve these issues, we propose a distributed DRL technique following the actor-critic architecture based on Asynchronous Proximal Policy Optimization (APPO) to achieve efficient and diverse distributed experience trajectory generation. Also, we employ PPO clipping and V-trace techniques for off-policy correction for faster convergence to the most suitable service offloading solutions. The results obtained demonstrate that our technique converges quickly, offers high scalability and adaptability, and outperforms its counterparts by improving the execution time of heterogeneous services.

关键词： Edge computing Internet of Things Quality of service Trajectory Servers Stochastic processes Costs

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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EdgeAnchor: A Rapid and Balanced File Storage Strategy at the Network Edge

EdgeAnchor: A Rapid and Balanced File Storage Strategy at th...

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International Conference on Parallel and distributed systems (ICPADS)

作者： Han Liu Xingrui Xie Zhuopu Zhang Geyao Cheng Lailong Luo Deke Guo Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers’ storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively.

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