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5th International Conference on Advancement in Data science, E-Learning and Information Systems, ICADEIS 2023

作者： Syamsuar, Dedy Oktaharisetia, Muhammad Angga Witarsyah, Deden Luthfi, Ahmad Dell, Peter Permana, Yudistira Sira Antoni, Darius Bina Nusantara University School of Information Systems Information Systems Department Indonesia Universitas Bina Darma Program Pasca Sarjana Palembang Indonesia Telkom University School of Industrial Engineering Bandung Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Indonesia Curtin University School of Management and Marketing Faculty of Business and Law Australia Politeknik Sekayu Teknik Informatika Sekayu Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350303414

ERP stands for enterprise resource planning. It is an information system that is all rolled into one, is very flexible and adaptable, and optimizes business operations while also centralizing all of the company's data. This study aims to empirically investigate the elements that affect users' intention and usage of enterprise resource planning (ERP). This research incorporates self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A quantitative technique was taken, and as a result, 143 replies that might be used were effectively collected. After that, the information was put through a structural equation modelling partial least square (SEM-PLS) analysis. According to the findings of this research, self-efficacy appears to have a significant influence on all endogenous variables (EE, BI, and UB), as indicated by t-values of 3.951, 7.573, and 5.492, respectively. In addition, the analysis also indicated that PE (4.317), EE (2.397), and HM (3.084) are likewise statistically and substantively significant to BI. The result shows that the R2 for BI and UB indicate a moderate impact of 0.603 and 0.521, respectively. It is suggested that the model has valuable applications in academic and real-world contexts. © 2023 IEEE.

关键词： Enterprise resource planning

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Modeling of Measurement Error in Financial Returns Data

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Mijatović, Aleksandar School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistics University of Warwick United Kingdom

In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period, we develop a stochastic model which models the true log-returns and maximum via a Lévy process and the data as a measurement error there-of. The main technical difficulty of trying to infer this model, for instance Bayesian parameter estimation, is that the joint transition density of the return and maximum is seldom known, nor can it be simulated exactly. Based upon the novel stick breaking representation of [12] we provide an approximation of the model. We develop a Markov chain Monte Carlo (MCMC) algorithm to sample from the Bayesian posterior of the approximated posterior and then extend this to a multilevel MCMC method which can reduce the computational cost to approximate posterior expectations, relative to ordinary MCMC. We implement our methodology on several applications including for real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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Conceptual Design and Kinematic Analysis of Hybrid Parallel Robot for Accurate Position and Orientation

Conceptual Design and Kinematic Analysis of Hybrid Parallel ...

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SICE Annual Conference

作者： K. M. Asy Ahmed B. Zaky Haitham El-Hussieny Hiroyuki Ishii Mahmoud Elsamanty Mechatronics and Robotics Department School of Innovative Design Engineering Egypt Japan University for Science and Technology Alexandria Egypt Production and Mechanical Design Engineering Department Faculty of Engineering Menoufia University Computer Science and Information Technology Programs (CSIT) Egypt Japan University for Science and Technology Alexandria Egypt Electrical Department Faculty of Engineering at Shoubra Benha University Cairo Egypt School of Creative Science and Engineering Waseda University Japan Mechanical Department Faculty of Engineering at Shoubra Benha University Cairo Egypt

The proposed work presents an innovative hybrid parallel robot, combining a delta robot with a spherical parallel manipulator (SPM) to achieve 6-DoF motion. The conceptual design was developed using SolidWorks, showcasing agile movement with required angles. A comprehensive electrical layout design was initiated for ensuring efficient system control, integrating various components such as actuators, sensors, and control units for seamless operation. A forward kinematic model was mathematically developed and implemented in MATLAB, generating trained data by simulating the robot’s motion for various input parameters. The trained data represents the robot’s position and orientation for different movement types, including linear, circular, pure, and mixed multiple orientation movements. The robot’s performance was evaluated using metrics such as position accuracy, orientation error, and response time. This study analyzed the robot’s kinematics for precise position and orientation control, conducting tests with square path and helix trajectory movements, as well as sudden and sinusoidal wave motor angle changes. Results demonstrated the robot’s platform moved with a pure roll angle, fixed yaw and pitch angles with repetitive roll angle changes, and a small range of roll and pitch angle with a yaw angle, achieving less than 0.2% error in position accuracy and orientation. These findings offer valuable insights for designing and controlling hybrid parallel robots for high-accuracy positioning, highlighting the importance of a well-designed electrical layout, a thorough understanding of the kinematic model, and rigorous performance evaluation for effective system control.

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Design of a customised BB8 Robot companion

Design of a customised BB8 Robot companion

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IEEE SENSORS

作者： Mia Innes Emanuele Lindo Secco Robotics Laboratory School of Mathematics Computer Science and Engineering Liverpool Hope University UK

In this paper we present the outcome and development of a customized replica of a tv character droid known as "BB8". The proposed design is using a low-cost Arduino-based hamster drive mechanism which allows to wirelessly control the movement of the robotic body through an app and a HC-08 Bluetooth 4.0 module. The robot’s design also embeds a set of acoustic and visual devices and *** command the robot moves in specific directions or performs specific movements, as well as reacts with acoustic feedback; a light sensor also triggers visual feedback in the head of the *** proposed system is characterized by a simple and low-cost design, and it inherently allows a set of multiple uses, from rehabilitation to Human Robot Interaction applications.

关键词： Performance evaluation Visualization TV Bluetooth Human-robot interaction Robot sensing systems Acoustics

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Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

arXiv

引用

arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic Forgetting

arXiv

引用

arXiv 2024年

作者： Li, Wei Zhang, Jingyang Heng, Pheng-Ann Gu, Lixu School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong

Generalist segmentation models are increasingly favored for diverse tasks involving various objects from different image sources. Task-Incremental Learning (TIL) offers a privacy-preserving training paradigm using tasks arriving sequentially, instead of gathering them due to strict data sharing policies. However, the task evolution can span a wide scope that involves shifts in both image appearance and segmentation semantics with intricate correlation, causing concurrent appearance and semantic forgetting. To solve this issue, we propose a Comprehensive Generative Replay (CGR) framework that restores appearance and semantic knowledge by synthesizing image-mask pairs to mimic past task data, which focuses on two aspects: modeling image-mask correspondence and promoting scalability for diverse tasks. Specifically, we introduce a novel Bayesian Joint Diffusion (BJD) model for high-quality synthesis of image-mask pairs with their correspondence explicitly preserved by conditional denoising. Furthermore, we develop a Task-Oriented Adapter (TOA) that recalibrates prompt embeddings to modulate the diffusion model, making the data synthesis compatible with different tasks. Experiments on incremental tasks (cardiac, fundus and prostate segmentation) show its clear advantage for alleviating concurrent appearance and semantic forgetting. Code is available at https://***/jingyzhang/CGR. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Platform Urbanism for Resident Safety: A Real-Time Predictive Microclimate Risk Monitoring and Alert System

SSRN

引用

SSRN 2025年

作者： Hossain, Sk Tahsin Yigitcanlar, Tan Nguyen, Kien Xu, Yue City 4.0 Lab School of Architecture and Built Environment Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Electrical Engineering and Robotics Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Computer Science Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia

Urban microclimates significantly impact public health, liveability, and emergency preparedness, yet traditional weather alert systems often rely on centralised data that fails to capture the spatial variability of conditions within cities. This study presents an innovative platform that integrates ArcGIS GeoEvent Server with AIoT-driven predictive analytics to deliver hyper-localised weather alerts. The platform’s adaptable design enables the incorporation of location-specific AI/ML-based predictive models and context-aware notifications, tailored to at-risk populations based on their location, health conditions, and disabilities. Featuring three GeoEvent Services and two ArcGIS Notebook Schedules, the system facilitates real-time data ingestion, processing, predictive modelling, and dissemination. A case study in Brisbane, Australia, demonstrates its capacity to address microclimatic variability, revealing significant differences in thermal stress and precipitation patterns across suburbs. Unlike traditional city-wide alerts, the platform provides targeted notifications prioritising vulnerable groups such as children, the elderly, and individuals with chronic conditions or disabilities. This approach enhances urban resilience and offers a scalable framework for applications in health monitoring, emergency response, and public safety. © 2025, The Authors. All rights reserved.

关键词： Emergency services

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Route Control of Four-rotor UAV based on Brain-computer Interface in Virtual Reality 12

Route Control of Four-rotor UAV based on Brain-Computer Inte...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Niu, Lan Bin, Jianxiong Wang, Junkongshuai Zhan, Gege Jiang, Haifeng Dong, Zhiyan Zhang, Lihua Kang, Xiaoyang Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Province Yiwu City China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

This article presented an experiment to determine the effect of dynamic 3D vision-evoked modules on the performance of the system we designed to steer a four-rotor unmanned aerial vehicle (UAV). Nowadays, UAV system is widely used in various industries, and UAV control has always been a focus of research. The Brain-computer Interface (BCI) system was based on the well-known steady-state visually evoked potentials (SSVEP). The specificity of the interface was the integration of dynamic 3D paradigms in virtual reality. In order to achieve a more immersive and interactive scene, VR scenes were moving synchronously with the UAV. Users can achieve remote control of four-rotor UAV through virtual scenarios. Ten subjects participated in the experiment and operated the UAV to complete a specific track. The dynamic paradigm based on the experimenter's feedback may indeed affect their attention, we proposed an effective solution to overcome this problem. The detailed results of these experiments were reported in this paper and we discussed the possible causes of performance loss under such conditions. © 2022 IEEE.

关键词： Brain computer interface

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An Overview on Economic Analysis of Internet of Everything

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Ding, Ningning Ouyang, Xiaomin Gao, Lin Huang, Jianwei Xing, Guoliang Guangzhou511453 China Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage Guangdong Shenzhen518172 China The Chinese University of Hong Kong Department of Information Engineering 999077 Hong Kong

The Internet of Everything (IoE) integrates people, processes, data, and things into a unified network, enabling highly interconnected systems with enhanced performance in terms of delay, reliability, and connection density. With the advent of next-generation networks, IoE has garnered significant attention for its transformative potential across various domains. However, while most existing studies focus on developing enabling technologies and deployment frameworks, the economic dimensions of IoE systems remain critically underexplored, despite being essential for ensuring their sustainability and scalability. This paper addresses this gap by providing a comprehensive review of the economic analysis of IoE systems. Specifically, we categorize existing research into four key aspects: information collection, information transmission, information fusion, and service application. We identify major economic challenges and propose modeling approaches, including game-theoretic and learning-based methods. Furthermore, we explore diverse IoE application scenarios, such as metaverse solutions and large-scale industrial systems, and summarize prevailing business patterns in the industry. By highlighting the economic dimension as a core component of IoE systems, this survey offers valuable insights into future research directions and outlines critical open challenges. © 1998-2012 IEEE.

关键词： Internet of Everything

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Federated Learning in Competitive EV Charging Market

Federated Learning in Competitive EV Charging Market

引用

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Chenxi Sun Chao Huang Biying Shou Jianwei Huang Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Computer Science The University of California Davis China Department of Management Science City University of Hong Kong Hong Kong China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) Shenzhen China

Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations’ operation in terms of price competition has yet to be fully understood. This paper aims to fill this gap by modeling the strategic interactions between two charging stations and EV owners as a multi-stage game. Each station first decides its FL participation strategy and charging price, and then individual EV owners decide their charging strategies. The game analysis involves solving a non-concave problem and by decomposing it into a piece-wise concave program we manage to fully characterize the equilibrium. Based on real-world datasets, our numerical results reveal an interesting insight: even if FL improves QoS, it can lead to smaller profits for both stations. The key reason is that FL intensifies the price competition between charging stations by improving stations’ QoS to a similar level. We further show that the stations will participate in FL when their data distributions are mildly dissimilar.

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