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Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

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Nano-Micro Letters 2022年第12期14卷 630-644页

作者： Shen Shen Jia Yi Zhongda Sun Zihao Guo Tianyiyi He Liyun Ma Huimin Li Jiajia Fu Chengkuo Lee Zhong Lin Wang Jiangsu Engineering Technology Research Center for Functional Textiles Jiangnan UniversityNo.1800 Lihu AvenueWuxiP.R.China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-Nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 100083P.R.China China National Textile and Apparel Council Key Laboratory of Natural Dyes Soochow UniversitySuzhou 215123People’s Republic of China School of Material Science and Engineering Georgia Institute of TechnologyAtlantaGA 30332-0245USA

Letter handwriting,especially stroke correction,is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the *** this study,a biodegradable and conductive carboxymethyl chitosan-silk fibroin(CSF)film is prepared to design wearable triboelectric nanogenerator(denoted as CSF-TENG),which outputs of V_(oc)≈165 V,I_(sc)≈1.4μA,and Q_(sc)≈72 mW cm^(−2).Further,in vitro biodegradation of CSF film is performed through trypsin and *** results show that trypsin and lysozyme have stable and favorable biodegradation properties,removing 63.1%of CSF film after degrading for 11 ***,the CSF-TENG-based human-machine interface(HMI)is designed to promptly track writing steps and access the accuracy of letters,resulting in a straightforward communication media of human and *** CSF-TENG-based HMI can automatically recognize and correct three representative letters(F,H,and K),which is benefited by HMI system for data processing and *** CSF-TENG-based HMI can make decisions for the next stroke,highlighting the stroke in advance by replacing it with red,which can be a candidate for calligraphy practice and ***,various demonstrations are done in real-time to achieve virtual and real-world controls including writing,vehicle movements,and healthcare.

关键词： Letter handwriting Triboelectric nanogenerator Biodegradable Human-machine interface Calligraphy practice

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D³APTS: Design of ECC-Based Authentication Protocol and Data Storage for Tactile Internet Enabled IoD System With Blockchain

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IEEE Transactions on Consumer Electronics 2023年第1期70卷 4239-4248页

作者： Sanjeev Kumar Dwivedi Mohammad Abdussami Ruhul Amin Muhammad Khurram Khan VIT-AP School of Computer Science and Engineering VIT-AP University Amaravati India Computer Science and Engineering Department International Institute of Information Technology Naya Raipur India Center of Excellence in Information Assurance King Saud University Riyadh Saudi Arabia

Tactile Internet, a fast and low-latency communication network, plays a crucial role in IoD applications for providing various features like precision control and realistic telepresence. The Internet of Drones (IoD) is a rapidly evolving concept that envisions a network of collaborating and communicating interconnected drones. Drones have diverse applications, including traffic monitoring, medicine, battlefield surveillance, logistics, and agriculture. However, the widespread deployment of drones raises significant security and privacy concerns. In this paper we have proposed secure data delivery authentication protocol for tactile Internet enabled IoD system $(\text {D}^{3}$ APTS). Also, using blockchain along with IoD for storing data will provide additional characteristics like data immutability. To secure communication among user, ground station server, and drone D3APTS employs one-way hash function and ECC cryptosystem. It resists various attacks like impersonation attacks, capturing attacks, session-key disclosure attacks, especially desynchronisation attacks which is important in case of IoD scenario. D3APTS also provides anonymity and unlinkability features. It is formally verified using the scyther security tool and ROR model. The performance analysis and comparison shows that D3APTS is better in terms of computation, communication overheads, security and functionality features.

关键词： Drones Security Blockchains Authentication Tactile Internet Computational modeling Servers

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Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Deep Reinforcement Learning for Multi-Objective Resource All...

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作者： Xu, Yuanyuan Zhu, Kun Xu, Hu Ji, Jiequ Hohai University College of Computer and Information Nanjing211100 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China University of Victoria Department of Electrical and Computer Engineering VictoriaV8P 5C2 Canada

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success ratio of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a contribution-based dual-clip proximal policy optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all subproblems are solved. Simulation results reveal that the proposed algorithm can outperform other algorithms in terms of the MOS and transmission success ratio. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Adaptive Autopilot: Constrained Drl for Diverse Driving Behaviors

Adaptive Autopilot: Constrained Drl for Diverse Driving Beha...

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International Conference on Intelligent Transportation

作者： Dinesh Cyril Selvaraj Christian Vitale Tania Panayiotou Panayiotis Kolios Carla Fabiana Chiasserini Georgios Ellinas CARS@Polito and Politecnico di Torino Torino Italy Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Computer Science KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Chalmers University of Technology Sweden

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.

关键词： Adaptation models Artificial neural networks Reinforcement learning Predictive models Semisupervised learning Autopilot Safety Driver behavior Intelligent transportation systems Vehicles

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models

Explainable AI for Healthcare: An Approach Towards Interpret...

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Information Technology (ACIT)

作者： Kanwal Zahoor Narmeen Zakaria Bawany Usman Ghani Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Institute of Business Administration Karachi Pakistan

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as ‘normal’ or ‘pneumonia’. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness.

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Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

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Intelligent Automation & Soft Computing 2023年第10期38卷 57-81页

作者： Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira African Center of Excellence in Internet of Things(ACEIoT) College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda Department of Computer Science College of Computing&Information SciencesMakerere UniversityKampala7062Uganda Directorate of Science Technology&Innovation(DSTI)Ministry of EducationMalawiLilongwe328Malawi Department of Computer&Software Engineering College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda National Council for Science&Technology(NCST) Government of RwandaKigali2285Rwanda

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby *** circuits,arson,and neglected charcoal stoves are the major causes of these *** methods,i.e.,satellites,are expensive to maintain and cause unnecessary ***,unit-smoke detectors are highly prone to false *** this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy *** crisp input parameters,namely:FIT and FIG��are *** show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression *** study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire *** a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false *** informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

关键词： Interval type-2(IT2)fuzzy systems mean absolute error(MAE) mean square error(MSE) root mean square

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Edge-assisted ML-aided Uncertainty-aware Vehicle Collision Avoidance at Urban Intersections

arXiv

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arXiv 2024年

作者： Selvaraj, Dinesh Cyril Vitale, Christian Panayiotou, Tania Kolios, Panayiotis Chiasserini, Carla Fabiana Ellinas, Georgios CARS@Polito Politecnico di Torino Torino Italy The KIOS Research Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multiaccess Edge Computing (MEC) platform of 5G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles’ trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted. © 2024, CC BY.

关键词： Recurrent neural networks

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Multi-Microgrid Based Coordinated Consensus Control for Demand Energy Management

Multi-Microgrid Based Coordinated Consensus Control for Dema...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Don Gamage Chathura Wanigasekara Abhisek Ukil Akshya Swain Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand German Aerospace Center (DLR) Institute for the Protection of Maritime Infrastructures Bremerhaven Germany

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

We propose a coordinated consensus-based approach for managing sudden peak demands in a multi-microgrid system (MMGS). Each micro-grid is equipped with a battery which will create a DC link. We introduce a mesh system that employs consensus-based coordination with droop coefficients to facilitate power sharing among neighbouring microgrids, ensuring load demand fulfilment while regulating frequency and voltage at the point of common coupling (PCC). The overall system comprises five interconnected microgrids forming a undirected communication graph. Furthermore, We analyze the system’s stability using mathematical techniques derived from graph theory. Simulation of the entire system is conducted in MATLAB/Simulink virtual environment. The simulation results demonstrate various operational modes and are compared with those obtained using conventional control methods.

关键词： Reactive power Simulation Virtual environments Microgrids Power system stability Probabilistic logic Stability analysis

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New Insights on Molecular Communication in Nano Communication Networks and Their applications 1

New Insights on Molecular Communication in Nano Communicatio...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Koshy, Akshay Mammen Jeerapan, Itthipon Manjunatha, C. Department of Computer Science Engineering RV College of Engineering Bengaluru560059 India Division of Physical Science Center of Excellence for Trace Analysis and Biosensor Prince of Songkla University Songkhla Hat Yai90110 Thailand Department of Chemistry RV College of Engineering Bengaluru560059 India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9781607685395

The advancement in nanotechnology has carved out a way to establish a powerful base to fall on for implementation of nanoscale machines having the capability to carry out significant yet trivial tasks. This impetus has led the researchers to continually bring up various solutions for implementing and executing nanoscale communications taking into consideration of biological as well as electromagnetic communication. The primary aims are expanding the abilities of nanoscale devices to integrated multiple composite tasks by reciprocated coordination, which can be carried out through this communication type. This paper describes the current research developments in nano communication networks, focusing on molecular communication (MC). MC makes use of molecules as an information transporter between nanoscale machines. Molecules help in enciphering messages to send between a sender and a receiver. MC has, thereby, become an inseparable part of the IONT (Internet of Nano-Things) as a communication model among nanoscale machines. This paper also gives a description of the design of MC systems and various channelling models used to address the various challenges and opportunities in this new upcoming and developing field. In addition, this review article is also inclusive of an investigative survey on the recent developments in this field. © The Electrochemical Society

关键词： Biochemical Communication Molecular Communication Molecular Propagation Systems Nano Networks Nanomachines

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A Toolbox for Supporting Research on AI inWater Distribution Networks

arXiv

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arXiv 2024年

作者： Artelt, André Kyriakou, Marios S. Vrachimis, Stelios G. Eliades, Demetrios G. Hammer, Barbara Polycarpou, Marios M. Faculty of Technology Bielefeld University Germany KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

Drinking water is a vital resource for humanity, and thus,Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling & generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms. © 2024, CC BY.

关键词： Potable water

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