检索结果-内蒙古大学图书馆

Hawaii International Conference on System sciences

作者： Chongyang Chen Sesia J. Zhao Tianjiao Cong Kem Z. K. Zhang Matthew K. O. Lee University of Science and Technology of China-City University of Hong Kong Joint Program Management School Anhui University School of Management University of Science and Technology of China Department of Information Systems City University of Hong Kong

ISBN: (纸本)9781509019816

Compared with other information technologies (e.g., desk computers), smartphones are being used anytime anywhere, which shows the unique phenomenon of extensive exposure. However, to date, the information system literature still doesn't fully understand the association between extensive exposure and information technology usage. The purpose of this paper is to investigate whether and how extensive exposure motivates users to use smartphone addictively from the lens of mere exposure effect. Particularly, we propose convenience, habit and enjoyment to describe the mere exposure effect, as well as incorporating concentration into our research model. Results of an online survey with 384 respondents suggest that convenience facilitates the formation of habit and drives higher degrees of concentration and enjoyment. Habit also generates users' reactions of concentration and enjoyment, which further result in smartphone addiction. We believe that this study opens up new avenues for a better understanding of smartphone addiction. Implications and limitations are discussed.

关键词： Happiness Smartphone Habits information technology DESK Consumers Online Surveys lens Lenses

来源：评论

学校读者我要写书评

暂无评论

Secure Handwritten Protocol for Protecting Integrity of Digital Evidences

Secure Handwritten Protocol for Protecting Integrity of Digi...

引用

2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Hsu, Chien-Lung Lin, Wei-Qian Tso, Sheng-Hsin Graduate Institute of Management Chang Gung University Taoyuan Taiwan Department of Information Management Chang Gung University Taoyuan Taiwan Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University Taoyuan Taiwan Healthy Aging Research Center Chang Gung University Taoyuan Taiwan Department of Visual Communication Design Ming-Chi University of Technology Taoyuan Taiwan Chang-Gung Memorial Hospital Taiwan Department of Information Management National Central University Taoyuan Taiwan

ISBN: (纸本)9798350389210

With the advancement of technology, people constantly use IT through the Internet. In this modern living environment, many individuals exploit this technological landscape for illegal gains and criminal activities. Judicial investigation units gather relevant evidence from the crime scene and process it when a crime occurs. When the medium used in criminal activity involves information technology, it is referred to as digital evidence. Unlike traditional evidence, which leaves traces if moved or altered, digital evidence can be copied without any detectable changes, posing challenges to maintaining its integrity and accuracy. This article explores how digital identification can ensure that the content of a report matches the evidence seized at the scene. It also addresses how the validity of digital evidence, as presented by the investigating authority, can be upheld in court. Additionally, it discusses the use of digital evidence and electronic handwritten signatures to enhance evidence preservation measures. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

An Augmented Reality Game's User Experience and Its Effect on Immersion Leading to Intention

An Augmented Reality Game's User Experience and Its Effect o...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Overview of Bearing Fault Diagnosis Based on Deep Learning

Overview of Bearing Fault Diagnosis Based on Deep Learning

引用

Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Xue-Ling Zou Kai-Xu Han Wei Chien Xin-Yan Gan Liu-Yue Shi School of Electric and Information Engineering Beibu Gulf University Qinzhou China School of Information Management and Master’s Program Lunghwa University of Science and Technology Taoyuan City Taiwan Guangxi University of Chinese Medicine Nanning China

Deep learning has excellent learning and feature classification abilities, extracting features in massive, multi-source, and high-dimensional measurement data. It also diagnoses and generalizes models without relying on manual intervention. Thus, it is widely used in equipment fault diagnosis. We introduce the common models of deep learning: deep confidence network, convolutional neural network, and automatic encoder. Using deep learning, the fault diagnosis of rolling bearings is executed, and the result provides the basis for future development.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Integration of the Use of Conventional technology Towards Smart Home technology for People in the City of Jakarta

Integration of the Use of Conventional Technology Towards Sm...

引用

International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Febian Wijaya Lintang Gerald Christian Ongko Griselda Valerie Michelle Benedicta Vieri Aska Juneio Sembiring Zaldy Raditya Ananta Takaaki Hosoda Ford Lumban Gaol Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

引用

Energies 2023年第22期16卷 7660-7660页

作者： Hsu, Chao-Chung Jiang, Bi-Hai Lin, Chun-Cheng Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research. © 2023 by the authors.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

引用

International Conference of Computer science and information technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：