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The purpose of this study was expected to simultaneously monitor the surface roughness of each solidified layer, the surface roughness of the metal powder, the outline of the solidified layer, and the height difference between the solidified layer and the metal powder.

In the proposed approach, color images with red, green and blue fringes are used to measure the shape of the built object using a three-step phase-shift algorithm and phase-unwrapping method. In addition, the surface roughness is extracted from the speckle information in the captured image using a predetermined autocorrelation function.

The feasibility and accuracy of the proposed system were validated by comparing it with a commercial system for an identical set of samples fabricated by a selective laser melting process. The maximum and minimum errors between the two systems are approximately 24% and 0.8%, respectively.

In the additive manufacturing field, the authors are the first to use fringe detection technology to simultaneously measure the profile of the printed layer and its surface roughness.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

This study aims to examine how open innovation can be effective in changing organizational inertia to create business model innovation and improve firm performance. It also seeks to explore whether the existence of open innovation has a mediating effect and influence.

This study constructs a theoretical model to explore the relationship between latent variables and uses a questionnaire to collect research data. In the conceptual framework, organizational inertia is a second-order latent variable and comprises three first-order latent variables: insight inertia, action inertia, and psychological inertia. Open innovation is also a second-order latent variable, and consists of two first-order latent variables: outbound and inbound open innovation. To clarify the relationship between these latent variables, structural equation modeling (SEM) is used to test the goodness of fit of the theoretical model and research hypotheses. This study uses 141 small to medium-sized manufacturing enterprises (SMEs) in Taiwan as the research subjects.

The SEM analysis revealed that open innovation has a significant mediating effect on the relationship between organizational inertia and business model innovation, and the relationship between organizational inertia and firm performance; business model innovation also has a positive influence on firm performance.

This study contributes the empirical analysis of SMEs to illustrate the role of open innovation on business model innovation processes.

This study aims to analyse the impact of e-paper on many existing industries including paper, publishing, book distribution, display, handheld device manufacturers and content service providers. Flexible display has been studied by many institutes, firms and market research companies. Some believe that e-paper is an exceptional application for flexible display, and the need for flexible display development for handheld devices and cloud-based e-book content is indisputable.

This study uses the Delphi technique and STEEP (Sociological, Technological, Economic, Environmental and Political aspects) with a panel to analyse a business model and the opportunity for the development of e-paper in Taiwan up to the year 2020.

The study concludes that e-paper content and customised digital services are an essential part of e-paper development, while hardware and cloud data are no more than a mechanism to show, compute and store data. Thus, whether the screen of a handheld device is flexible may not be of importance. Although e-paper will affect the display industry, it will not substitute for handheld devices and traditional bookstores.

This research can be used as a reference for government, academics, industry and international investors.

The purpose of this paper is to apply analysis of public discourses on Ze Xiao to explore and interpret the power relationships shaping inequality in admission to public junior high schools in urban China.

This study first introduces the rise of Ze Xiao as an educational phenomenon in China. It then elucidates power relationships in public school admission by analyzing continuities and changes in stakeholders’ interaction in public school admission. It concludes by discussing educational reform for equal public school admission in urban China. Data were collected from written and spoken texts about public school admission, including newspaper articles from the 1980s to the 2000s, policy documents and interviews with relevant stakeholders.

Findings demonstrate that multi-layered power relationships caused diverse inequalities in admission to public secondary education in urban China. These are represented by political and institutional privileges and an imbalance in education development during the social transition from a profit-driven approach in the 1990s to a balance-centered one after 2000. Arguably, there is a necessity to further promote a systematic reform to terminate the privileges and imbalance for an equal and balanced public secondary education in urban China post-2015.

This study attempts to make a contribution toward reconstructing the meaning of inequality in admission to public junior high schools in urban areas by revealing the power relationships among stakeholders constituted through their interactions in public education during the different stages of socio-economic development in urban China.

The purpose of this paper is to prove the importance of both cluster leadership and identification on cluster innovation.

The case studies presented in this study involve a cluster by micro-enterprises in Yilan, Taiwan. Data collected during interviews, observations and secondary data provide understanding and practices for the impact of cluster identification on cluster innovation.

This study proved: first, the importance of cluster identification on innovation by representing the need of consensus and collaboration of the members in conducting innovation actions; and second, the cluster identification is influenced by the cluster leadership by showing high satisfaction of the leader, close interaction between the members and high identification with the cluster.

This study predicts the ongoing cluster innovation activities will be achieved due to the transformational leadership and high cluster identification.

This study enriches the factors of cluster innovation accomplishment and proposes the important of cluster identification, which has not been discussed much in the past.

A chaotic system is a nonlinear deterministic system that displays complex, noisy‐like and unpredictable behavior. The interest in chaotic systems lies mostly upon their complex, unpredictable behavior, and extreme sensitivity to initial conditions as well as parameter variations. Based on wavelet neural network's (WNN) online approximation ability, the purpose of this paper is to propose an adaptive Gaussian wavelet neural control (AGWNC) system to control a chaotic system.

The proposed AGWNC system is composed of a wavelet neural controller and a compensation tangent controller. The wavelet neural controller utilizes a Gaussian WNN to mimic an ideal controller, and the compensation tangent controller is designed to compensate the approximation error between the ideal and the wavelet neural controllers. The controller parameters of the proposed AGWNC can online tune in the Lyapunov sense, thus the uniformly ultimately bounded stability of closed‐loop system can be guaranteed.

The proposed AGWNC system is applied to a chaotic system. Simulation results are used to demonstrate the effectiveness and performance of the proposed AGWNC scheme. Simulation results show that not only the favorable control performance can be achieved but also the control efforts without any chattering phenomena. Moreover, all controller parameters can be online tuning by the derived adaptive laws based on the Lyapunov function.

The proposed AGWNC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable in uniform ultimate boundedness; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the AGWNC system can achieve favorable tracking performance.

The DC‐DC converters which convert one level of electrical voltage to the desired level are widely used in many electrical peripherals. During the past two decade, many different control laws have been developed. The proportional‐integral (PI) control and sliding‐mode control have been carried out for the DC‐DC converters since they are simple to implement and easy to design. However, its performance using PI control and sliding‐mode control is obviously quite limited. The purpose of this paper is to a self‐tuning nonlinear function control (STNFC) propose for the DC‐DC converters. The adaptation laws of the proposed STNFC system are derived in the sense of Lyapunov function, thus not only the controller parameters can be online tuned itself, but also the system's stability can be guaranteed.

In general, the accurate mathematical models of the DC‐DC converters are difficult to derive. This paper proposes a model‐free STNFC design method. Since the proposed STNFC uses a simple fuzzy system with three fuzzy rules base to implement the control law, the computational loading of the fuzzy inference mechanism is slight. So the proposed STNFC system is suitable for the real‐time practical applications. The controller parameters of the proposed STNFC system can online tune in the Lyapunov sense, thus the stability of closed‐loop system can be guaranteed.

The proposed STNFC system is applied to a DC‐DC converter based on a field‐programmable gate array chip. The experimental results are provided to demonstrate the proposed STNFC system can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response.

The proposed STNFC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the successful development of STNFC system without heavy computational loading. The parameter‐learning algorithm is design based on the Lyapunov stability theorem to guarantee the system stability; the successful applications of the STNFC system to control the forward DC‐DC converter. And, the proposed STNFC methodology can be easily extended to other DC‐DC converters.

The purpose of this paper is to develop a robot vision system for surface defect detection of 3D objects. It aims at the ill‐defined qualitative items such as stains and scratches.

A robot vision system for surface defect detection may counter: high surface reflection at some viewing angles; and no reference markers in any sensed images for matching. A filtering process is used to separate the illumination and reflection components of an image. An automatic marker‐selection process and a template‐matching method are then proposed for image registration and anomaly detection in reflection‐free images.

Tests were performed on a variety of hand‐held electronic devices such as cellular phones. Experimental results show that the proposed system can reliably avoid reflection surfaces and effectively identify small local defects on the surfaces in different viewing angles.

The results have practical implications for industrial objects with arbitrary surfaces.

Traditional visual inspection systems mainly work for two‐dimensional planar surfaces such as printed circuit boards and wafers. The proposed system can find the viewing angles with minimum surface reflection and detect small local defects under image misalignment for three‐dimensional objects.