• Knowledge Management Research
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• v.20 no.2
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• pp.59-86
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• 2019

As the era of the 4th Industrial Revolution evolves, not only private companies but also government agencies and institutions in public sector are adopting cloud computing services converged by new information technologies such as IoT, big data, and artificial intelligence to strengthen competitiveness and create new business values. The purpose of this study is to investigate the relationship between innovation characteristics, innovation resistance, and acceptance of innovative technologies from the perspective of cloud computing services in the public sector. In this study, we collected the survey data from 190 employees of IT division in the public sector, and analyzed the causal relationship between innovation characteristics, technostress, innovation resistance, and intention to adopt the cloud computing service that they perceived. As a result of the analysis, we demonstrated that innovation characteristics, technostress have significant effect on innovation resistance and acceptance intention, and that top executive commitment and innovation resistance also have significant effect on acceptance intention. This study provides meaningful practical implications for the staffs preparing for adoption of cloud computing services and the executives who make the final decision in public sector.

• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.7-19
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• 2021

Purpose: This paper aims to investigate (1) whether the interviewed auditors conduct higher quality audit than the non-interviewed auditors and (2) whether the frequency of audit engagement interviews has an impact on audit quality. Research design, data, and methodology: Using a sample of Chinese A-share listed firms between 2011 and 2019, this paper empirically tests the effect of audit engagement interviews on auditor's behavior. We collect the data of audit engagement interviews on the CICPA's website. We use OLS regression, fixed-effect model and random-effect model to examine the association between audit engagement interviews and audit quality. Results: Findings indicate that the audit quality of the interviewed auditors is significantly greater than that of the non-interviewed auditors. The frequency of the audit engagement interviews is positively associated with audit quality. The interviewed auditors spend significantly more time on the audit. Furthermore, the positive association between audit engagement interviews and audit quality only exists in non-Big 4 auditors. Conclusions: Our findings provide evidence for the effectiveness of audit regulation enforcement. The results suggest that in an emerging market with weak legal systems, preventive regulations such as audit interviews have a deterrent effect and are necessary in alleviating information asymmetry and improving information environment.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.2
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• pp.14-22
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• 2021

A plan for the development of reliability-based ROK amphibious assault vehicles is proposed. By analyzing the development case of the U.S. EFV, considerations for the successful development of the next-generation Korea Forces amphibious assault vehicle are presented. If the vehicle reliability can be improved to the level of the fourth highest priority electric unit for power units, suspensions, decelerators, and body groups, which have the highest priority among fault frequency items, a system level MTBF of 36.4%↑ can be achieved, and the operational availability can be increased by 3.5%↑. The next-generation amphibious assault vehicles must fulfill certain operating and performance requirements, the underlying systems must be built, and sequencing of the hybrid engine and the modular concept should be considered. Along with big-data- and machine-learning-based failure prediction, machine maintenance based on augmented reality/virtual reality and remote maintenance should be used to improve the ability to maintain combat readiness and reduce lifecycle costs.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.833-843
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• 2021

This research examines the impact of audit quality on the extent to which firm-specific information is integrated with a firm's share price - which is determined inversely using stock price synchronicity. The study sample consists of non-financial companies listed on the Amman Stock Exchange i.e., the Jordanian Stock Market, from 2014-2018. After examining 810 firm-year observations from Jordanian industrial companies listed on the ASE, during the study period, we find that the companies using one of the BIG4 audit firms for auditing have less synchronous and more informative stock prices, suggesting high-quality audit improved governance and reduce information asymmetry between firms' insiders and investors which enhances the capitalization of firm's specific information into the stock price, thus less synchronous and more informative stock return. The findings remain consistent over 2 separate measurements of stock price synchronicity (Market and Industry model and Market Model) and show robustness for fixed effect tests. Our multivariate regression results are also robust after controlling for a number of features at the firm level with potential associations with stock price synchronicity. These include the firm size, leverage, return on assets (ROA), and market to book value (MBV).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• Food Science and Industry
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• v.50 no.2
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• pp.60-73
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• 2017

Recently, the whole world is facing an unprecedented moment of opportunity, so-called The Fourth Industrial Revolution. As emphasized in the World Economic Forum held in January of 2016 at Davos, the Fourth Industrial Revolution is not merely a changes of technological devices. The fundamental of the revolution is new, innovative, and visionary business models which change the whole systems dramatically. One of the greatest challenges is to feed an expected population of 9 billion by 2050 in a impactful way. The system should be sustainable as well as beneficial in improving the lives of people in the food chain along with the ecological health of environment. The technological advances of the Fourth Industrial Revolution are expected to improve our food system. The smart farm technology such as precision planting and irrigation techniques will improve the yields of food materials. The smart food transportation and logistics systems will substantially improve the safety and human nutrition. The adaptation the Fourth Industrial Revolution technology will induce the smart supply chains, smart production, and smart products in food industry due to its flexibility and standardization. This will lead the manufactures to adapt to customers' changing product specifications and traceable services in a timely manner.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.1-6
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• 2021

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

• Safety and Health at Work
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• v.12 no.1
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• pp.1-9
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• 2021

This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.495-502
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• 2020

In this study, the prevalence of osteoporosis was predicted based on 10 independent variables such as age, weight, and alcohol consumption and 4 tree-based machine-learning models, and the performance of each model was compared. Also the model with the highest performance was used to check the performance by clearing the independent variable, and Area Under Curve(ACU) was utilized to evaluate the performance of the model. The ACU for each model was Decision tree 0.663, Random forest 0.704, GBM 0.702, and XGBoost 0.710 and the importance of the variable was shown in the order of age, weight, and family history. As a result of using XGBoost, the highest performance model and clearing independent variables, the ACU shows the best performance of 0.750 with 7 independent variables. This data suggests that this method be applied to predict osteoporosis, but also other various diseases. In addition, it is expected to be used as basic data for big data research in the health care field.