• Journal of Korea Society of Industrial Information Systems
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• v.14 no.1
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• pp.43-55
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• 2009

We consider a Direct Input Output Manufacturing System(DIOMS) which has a number of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the SIR machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.546-549
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• 2022

In recent years, to prevent the increase in accidents at industrial sites, various innovative technologies from the 4th industrial era have been incorporated into the construction administration to promote the advancement of safety management. As a result, smart safety management systems using intelligent monitoring that prevent and manage risks in industrial sites in real time are attracting attention. Smart safety management systems provide users with real-time, remote monitoring of factors such as noise, gas concentration fine dust concentration, building material quality, building tilt, and RFID-based worker access through sensors located everywhere. This paper presents a method for collecting and monitoring various data for smart safety management systems via Zigbee communication using Raspberry Pi.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.273-286
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• 2022

An HD map is essential in the automated driving of level 4 and above to generate the vehicle's global path since it contains road information and each road's lane information. Therefore, all the road elements in the HD map must be correctly defined to construct the correct road network necessary to generate the global path. But unfortunately, it is not difficult to find various errors even in the most recent HD maps. Hence, a preprocessing algorithm has been developed to detect and correct errors in the HD map. This error detection and correction result in constructing the correct road network for use in global path planning. Furthermore, the algorithm was tested on real roads' HD maps, demonstrating its validity.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.67-75
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• 2023

This paper deals with the operation of air taxis, which is one of the latest innovative technologies aimed at solving the issue of traffic congestion in cities. A key challenge for the successful introduction of the technology and efficient operation is a vertiport location problem. This paper employs a discrete choice model to calculate choice probabilities of transportation modes for each route, taking into account factors such as cost and travel time associated with different modes. Based on this probability, a mathematical formulation to maximize the utilization rate for air taxi is proposed. However, the proposed model is NP-hard, effective and efficient solution methodology is required. Compared to previous studies that simply proposed the optimization models, this study presents a solution methodology using the cross-entropy algorithm and confirms the effectiveness and efficiency of the algorith through numerical experiments. In addition to the academic excellence of the algorithm, it suggests that decision-making that considers actual data and air taxi utilization plans can increase the practial usability.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.119-133
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• 2012

Recent rise of social media channel is changing social and economic paradigm and is being used as an effective communication in marketing. The following research analyzes the most employed social marketing tools such as portal advertisement, blogs, and SNS channels to effectively execute social media marketing from performance indicator and ICSI perspective, analyzes each channel's characteristics and results based on Korea distribution companies' case studies and suggests a framework to effectively use each channel. Portal site advertisements are the most effective channel to draw customers with new information and are thus linked to profit by corporations with excessive budget and workforce. Blogs target a specific range of customers providing quality information and knowledge thus improving a corporation's and its product's trustworthiness, spread the word by allowing customers to scrap the information, form social groups and synthesize ideas, events, new contents and social involvement with loyal customers. SNS channels allow customers to get involved in real time information and events, grow through network by the power of customers, react immediately to customers' needs, and execute real-time market and customer reports. Though national corporations currently rely heavily on portal site advertisements, insightful marketing professionals are showing financial results with blog and SNS. In the future, based on a precise understanding of each channel's benefits and expected results, and with a focus on flexibility, timeliness and integrated use of each channel, a portfolio of dynamic marketing as a maximizing strategy could be synthesized.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.147-175
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• 2020

This study analyzed the effects that digital experience factors influence on purchase intention and the purchase. The study targeted an online shopping mall with a strong digital experience value among industries. The research model was derived by adding variables to independent and mediating variables according to the industry context of online shopping which is based on the theoretical background and previous studies. Product variety, price efficiency, convenience and conversation were used by terms of digital marketing mix as independent variables. Personalization has been very important factor in online shopping malls, and therefore added as a independent variable. Flow has been added as a mediating variable. Purchase and purchase intention has been used as dependent variables. For empirical testing of established research models and generalization of research results, research was conducted on online shopping malls where digital experiences are important. To do this, a survey was conducted for existing users of online shopping malls. In hypothesis testing, the hypothesis was established that product diversity, price efficiency, convenience, conversation and personalization influenced the intention to purchase online shopping. In particular, the product diversity and conversation variable were tested as the most influential factors on purchase intention. For price efficiency and personalization there were no statistically significant effect. Flow has been shown to be a partial mediator between Product variety and purchase intention in online shopping. In particular, in the case of personalization, it was tested to have a significant influence on purchase intention only when there was a flow experience called pleasure and immersion. This is because the flow experience of pleasure and immersion has played a full mediating role and significantly has affected the purchase intention, because the consumers themselves have to carry out the overall purchase journey without human help due to the nature of online. In the digital experience economy, since consumers are mostly digital consumers, where communication and sharing are the basics, they have been conducting digital word-of-mouth communication and sharing naturally before purchasing. Based on these results, theoretical and practical implications were suggested.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.29-44
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• 2009

A variety of research efforts, using advanced wireless communication technologies, have been made to develop more reliable traffic information system. This study presents a novel decentralized traffic information system based on vehicle infrastructure integration (VII). A major objective of this study was also to devise a methodology for determining appropriate spacing of roadside equipment (RSE) to fully exploit the benefits of the proposed VII-based traffic information system. Evaluation of travel time estimation accuracy was conducted with various RSE spacings and the market penetration rates of equipped vehicle. A microscopic traffic simulator, VISSIM, was used to obtain individual vehicle travel information for the evaluation. In addition, the ANOVA tests were conducted to draw statistically significant results of simulation analyses in determining the RSE spacing. It is expected that the proposed methodology will be a valuable precursor to implementing capability-enhanced next generation traffic information systems under the forthcoming ubiquitous transportation environment.

• The Journal of Society for e-Business Studies
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• v.13 no.2
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• pp.101-115
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• 2008

Recently in the human society, the crime using small arms has increased. Also, many accidents happen because of incomplete management of chemicals and radiation. Accordingly, it is necessary to manage dangerous objects by tracing the position of dangerous objects and rapidly providing the correct information for them. This paper presents a web-based system for real-time monitoring of dangerous objects using RFID in order to overcome the limitations and problems of current dangerous objects management techniques. In this paper, we define the architecture for web-based dangerous objects monitoring system and the scheme for storing information of a dangerous object in the RFID tag. We also implement the web-based monitoring system and present the execution result of the system. The proposed real-time monitoring system is composed of the dangerous objects monitoring server which manages information of dangerous objects and controls them, the dangerous objects monitoring middleware which is mediator between dangerous objects and the server, the RFID reader which reads information of dangerous objects from RFID tags attached to the objects and the database which stores information, status and position of dangerous object. The proposed system manages diverse dangerous objects such as small arms, radiation and harmful chemicals based on the position of them using RFID, so the user can check dangerous objects when they are checked in and checked out and the user can acquire the real-time position information of them through the system. Furthermore, the user can visually monitor dangerous objects through web browser from any where and at any time because the system is web-based system and it provides graphical user interface.