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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Kawang Utomo Ford Lumban Gaol Takaaki Hosoda Information Systems Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Surveys Industries Data analysis Games Companies User experience Software Hardware Internet Augmented reality

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction 11

Using LSTM and Regression Analysis for Railway Passenger Tra...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Tey, Fu Jie Tuan, Chih-Chi Cai, Cheng-En Wu, Ming-Xuan Wu, Yun-Sheng Wu, Tin-Yu National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan

ISBN: (纸本)9781665492324

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data. © 2022 IEEE.

关键词： Long short-term memory

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Lower bounds for minimizing two types of unnecessary costs in the film and television industry

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Computers and Operations Research 2024年 170卷

作者： Lin, Lie-Fen Wang, Jen-Ya Master's Program in Statistics and Actuarial Science Feng Chia University Taiwan Taiwan Department of Information Management National Taichung University of Science and Technology Taiwan Taiwan

To optimize film and television production costs, we aim to minimize unnecessary expenditures such as the idle time of top-paid actors and the rental periods of expensive equipment. By scheduling scenes that require these resources on consecutive days, significant cost reductions are achieved. In this study, a branch-and-bound algorithm with two lower bounds has been developed to serve as a benchmark for evaluating other metaheuristic algorithms. The novelty of these lower bounds stems from their set-based operations, which avoid time-consuming computations involving large sparse matrixes. The computational results confirm that the proposed lower bounds effectively reduce these costs. Clearly, this approach can be extended to other industries with similarly complex job compositions, such as scenarios involving multiple small tasks in a single job. © 2024 Elsevier Ltd

关键词： Branch-and-bound algorithm Lower bound Multi-agent scheduling Overlap effect Set operation Talent scheduling

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joint green dynamic order batching and picker routing problem using PSO with global worst experience

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Applied Soft Computing 2024年 154卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Kang, Jia-Rong Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Management Tatung University Taipei104 Taiwan

With the increasing importance of various countries’ initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically;and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm's previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing. © 2024 Elsevier B.V.

关键词： Carbon footprint

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An Improved Vision Transformer Architecture-Based Computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiang-Rui Huang Huai-Jhan Ke Szu-Yu Kuo Liang-Bi Chen Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species.

关键词： Computer vision Electric potential Accuracy Computational modeling Computer architecture Computer applications Flowering plants Network architecture Transformers Consumer electronics

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Multi band Antenna for Notebook Computer Device

Multi band Antenna for Notebook Computer Device

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Chien, Wei Tsai, Shangte Lunghwa University of Science and Technology Department of School of Information Management and Master's Program Taiwan Business School Yulin Normal University China

ISBN: (纸本)9781665437554

The printed inverted-F antenna is used to design a multi-band antenna, in which the frequency includes LTE, WLAN, and GPS frequency. The printed inverted-F antenna is applied in the environment and architecture of the 14-inch notebook computer. The design frequency of the antenna is 0.7-5 GHz. The usable frequency band includes three different systems: LTE, WLAN, and GPS. They comply with the wireless network specifications in the industry. Based on the analysis data such as current distribution and antenna field type, the variability and difference of the antenna in the environment are analyzed and compared. © 2021 IEEE.

关键词： Microwave antennas

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Quality Analysis the E-PASTI System to Determine the Condition of the System and information Quality

Quality Analysis the E-PASTI System to Determine the Conditi...

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2019 International Conference of science and information technology in Smart Administration, ICSINTeSA 2019

作者： Nugraha, Bayu Marleny, Finki Dona Sfenrianto, Sfenrianto Edwina, Cinthya Information System Program Faculty of Science and Technology Sari Mulia University South Kalimantan Banjarmasin Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

The purpose of this study is to evaluate the quality of secretarial, financial and staffing information systems at the Directorate General of AHU, known as the E-PASTI system. There are two variables analysed, namely system quality and information quality. Data collection using questionnaires that distributed to 123 employees who used the E-PASTI system but returned 93 questionnaires. Data quality analyst uses descriptive quantitative method. The results of the study are several conditions related to the system and information quality of E-PASTI. The quality can be a reference for the AHU Directorate General to improve the quality of E-PASTI that suits the needs of employees. © Published under licence by IOP Publishing Ltd.

关键词： Personnel

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation 11

A Genetic Algorithm to Optimize Cargo Stability in Container...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hsieh, Chi-Jung Wu, Kuang-Sung Wu, Tin-Yu Tey, Fu Jie Chen, Yi-Kai Ho, Jye Yi National Ilan University Department of Computer Science and Information Engineering Ilan County Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665492324

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method. © 2022 IEEE.

关键词： Genetic algorithms

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Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

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Computers and Industrial Engineering 2024年 189卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Chang, Yung-Sheng Chang, Chun-Hsiang Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

In smart factories, automated material handling system (AMHSs) replace manual material handling to increase production efficiency, in which stockers serve as the temporary storage for work-in-process inventories. Furthermore, costly machines and complex processes in advanced manufacturing lead to the need for multiple reentrant processes of the same workstation. However, previous works on scheduling problems rarely considered the function of stockers and reentrant processes, and their approaches were mostly based on metaheuristic algorithms. Recent advances in artificial intelligence enable the possibility of solving scheduling problems using deep reinforcement learning (DRL). Therefore, this work investigates the reentrant hybrid flow shop scheduling problem with stockers (RHFS2) inspired by AMHSs, and solves it by DRL. Firstly, the states, actions, and rewards of the Markov decision process for this problem are designed;and then, two deep Q network (DQN) approaches based on the actions for determining machines and jobs, respectively, are proposed. Simulation results demonstrate that our proposed DQN approaches outperform for finding better solutions of different-scale problems than classical metaheuristic algorithms. © 2024 Elsevier Ltd

关键词： Reinforcement learning

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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

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Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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