• International journal of advanced smart convergence
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• 제8권2호
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• 한국정보시스템학회지:정보시스템연구
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• 제28권2호
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• pp.129-152
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• 2019

Purpose The purpose of this study is to empirically identify the risky behavior of mobile device users using the Internet of Things on a situational perspective. Design/methodology/approach This study made a design of the research model based on model of the determinants of risk behavior. Data were collected through a survey including hypothetical scenario. SmartPLS 2.0 was used for the structural model analysis and t-test was conducted to compare the between normal and situational behavior. Findings The results were as follows. First, the central roles of risk propriety and risk perception were verified empirically. Second, we identified the role of locus of control as a new factor of impact on risky behavior. Third, mobile risk propensity has been shown to increase risk perception. Fouth, it has been shown that risk perception does not directly affect risky behavior and reduce the relationship between mobile risk propensity and risk behavior. According to the empirical analysis result, Determinants of risk behavior for mobile users were identified based on a theoretical framework. And it raised the need to pay attention to the impact of locus of control on risk behavior in the IS security field. It provided direction to the approach to risky behavior of mobile device users. In addition, this study confirmed that there was a possibility of taking risky behavior in the actual decision-making.

• 디지털산업정보학회논문지
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• 제15권1호
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• Smart Structures and Systems
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• 제23권4호
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.613-623
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• 2021

This study aims to identify the effect of blue ocean leadership style on strategic decision making and it also aims to examine the mediating role of organizational politic and moderating role of emotional intelligence in the Government Link Companies (GLCs) in Malaysia. In order to achieve the objective of the study, a research framework had been developed to establish a relationship among the variables of the study based on resource-based view theory. Questionnaire method was used to collect the data form middle to top level employees in GLCs. All the items in the study's variables were assessed using the 5-point Likert scale. A stratified random sampling technique was used to identify the sample for this study. Data was derived from 135 middle to top level employees, which were involved in decision making process. The data was analyzed using the SPSS and the SmartPLS 3.0 software. This supplemented the theory surrounding blue ocean leadership styles and strategic decision making. The study also identified several avenues for further research by using different research methods and examining the impact of strategic decision making in different contexts.

• Smart Structures and Systems
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• 제26권6호
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• pp.735-751
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• 2020

Identification of structure parameters is crucial in Structural Health Monitoring (SHM) context for activities such as model validation, damage assessment and signal processing of structure response. In this paper, guided waves generated by piezoelectric transducers are used for in-situ and non-destructive structural parameter estimation based on Bayesian approach. As Bayesian approach needs iterative process, which is computationally expensive, this paper proposes a method in which an analytical model is selected and developed in order to decrease computational time and complexity of modeling. An experimental set-up is implemented to estimate three target elastic and geometrical parameters: Young's modulus, Poisson ratio and thickness of aluminum and steel plates. Experimental and simulated data are combined in a Bayesian framework for parameter identification. A significant accuracy is achieved regarding estimation of target parameters with maximum error of 8, 11 and 17 percent respectively. Moreover, the limitation of analytical model concerning boundary reflections is addressed and managed experimentally. Pulse excitation is selected as it can excite the structure in a wide frequency range contrary to conventional tone burst excitation. The results show that the proposed non-destructive method can be used in service for estimation of material and geometrical properties of structure in industrial applications.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.16-25
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• 2020

The operation of a wastewater treatment plant (WWTP) is a complex task which requires to consider several aspects: adapting to always changing influent composition and volume, ensuring treated effluents quality complies with local regulations, ensuring dissolved oxygen levels in biological reaction tanks are sufficient to avoid anoxic conditions etc. all of it while minimizing usage of chemicals and power consumption. The traditional way of managing WWTPs consists in having employees on the field measure various parameters and make decisions based on their judgment and experience which holds various concerns such as the low frequency of data, errors in measurement and difficulty to analyze historical data to propose optimal solutions. In the case of activated sludge WWTPs, parts of the treatment process can be automated and controlled in order to satisfy various control objectives. The models developed by the International Water Association (IWA) have been extensively used worldwide in order to design and assess the performance of various control strategies. In this work, we propose to review most recent WWTP automation initiatives around the world and identify most currently used control parameters and control architectures. We then suggest a framework to select WWTP model, control parameters and control scheme in order to develop and benchmark control strategies for WWTP automation.

• 융합경영연구
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• 제8권2호
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• pp.1-12
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• 2020

Purpose: The rise of the phrase Work Life Balance was bought up in 1986 when amid many Americans there was prevalence of detrimental work place practices like neglecting families, leisure activities and friends in order to achieve their study place goals. The significance of work-life balance has been gaining ground in recent years to grasp a wider range of groups, including students. Searching and finding a balance can be complex and challenging for many individuals and students. Research design, data and methodology: Through this paper we will explore how students balance the competing demands of work, study, and social activities. Several factors have increased imbalances within Educational organizations, and technology specifically has been influential. However, technology also provides a novel solution to this organizational performance management issue. A Study-Life Optimization model (SLO) is suggested, which incorporates information systems, analytics, and decision support into a Smart Service System. A general framework for this model, detailing data collection, measurement, and ethical issues is explained briefly. Results: Outcomes include improved WLB, greater perceived quality of life, and increased Educational organizational performance. Conclusions: This paper contributes to the relevant literature as it pays attention to the various students' of varying lifestyles school-work-personal lives. Findings of this study will provide a meaningful of the Work/school-life balance issues faced by students. The research could be helpful to the various stakeholders of a University, the curriculum designers, program coordinators etc.

• 로봇학회논문지
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• 제16권3호
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• Smart Structures and Systems
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• 제28권6호
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.