• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.327-332
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• 2016

This study is a leading research project to develop an automatic grade decision making algorithm of a 6-years-old fresh ginseng. For this work, we developed a Ginseng image acquiring instrument which can take 4-direction's images of a Ginseng at the same time and obtained 245 jingen images using the instrument. The 12 parameters were extracted for each image by a manual way. Lastly, 4 parameters were selected depending on a Ginseng grade classification criteria of KGC Ginseng research institute and a survey result which a distribution of averaging 12 parameters. A pattern recognition classifier was used as a support vector machine, designed to "k-class classifier" using the OpenCV library which is a open-source platform. We had been surveyed the algorithm performance(Correct Matching Ratio, False Acceptance Ratio, False Reject Ratio) when the training data number was controlled 10 to 20. The result of the correct matching ratio is 94% of the $1^{st}$ ginseng grade, 98% of the $2^{nd}$ ginseng grade, 90% of the $3^{rd}$ ginseng grade, overall, showed high recognition performance with all grades when the number of training data are 10.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1435-1446
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• 2021

Wind and flood damage caused by typhoons causes a lot of damage to the Korean Peninsula every year. In order to minimize damage, a preliminary analysis of damage estimation and evacuation routes is required for rapid decision-making. This study attempted to develop an analysis module that can provide necessary information according to the disaster stage. For use in the preparation stage, A function to check past typhoon routes and past damage information similar to typhoon routes heading north, a function to extract isolated dangerous areas, and a function to extract reservoir collapse areas were developed. For use in the early stages of response and recovery, a function to extract the expected flooding range considering the current flooding depth, a function to analyze expected damage information on population, buildings, farmland, and a function to provide evacuation information were included. In addition, an automated web map creation method was proposed to express the analysis results. The analysis function was developed and modularized based on Python open source, and the web display function was implemented based on JavaScript. The tools developed in this study are expected to be efficiently used for rapid decision-making in the early stages of monitoring against storm and flood damage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.74-84
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• 2016

Open Source Software (OSS) is a new paradigm for software development. The system is based on the notion of giving software (including sources) away for free, and making money on services, customizing and maintenance. For these reasons, many software companies have considered adopting and using OSS in Software R&D. A variety of factors may influence the use of decision making of OSS. The objective of this study was to explore the significant factors affecting the use decision of OSS and the job performance of OSS usage in software R&D. A research model was suggested based on the TOE Framework and Information Systems Success Model. These findings show that technical benefits of OSS have significant effects on OSS use. The technical benefits of OSS, and organization context, in turn, have significant effects on the use of OSS. On the other hand, the technical risks of OSS and the environment context have no effects on OSS use. In addition, OSS use and user satisfaction have significant effects on the individual job performance. This research contributes towards advancing the theoretical understanding of the OSS Benefits and Performance in Software Development.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.19-28
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• 2005

It is true that internet provides consumers with an efficient way to search information with minimal effort and cost, which facilitates better decision making. Especially, previous studies revealed that the online word-of-mouth marketing is widely used as a source of consumers' information seeking and purchase decision making. Even with this importance of the online word-of-mouth communication on internet few researches have systematically addressed the issue. This study investigates the effect of consumers' motives on perceived usefulness of word-of-mouth marketing in online shopping mall contents. The results are as follows: First, choice uncertainty, perceived sacrifice, and social pressure play an important role for perceived usefulness of word-of-mouth marketing. Second, perceived usefulness has directly affected consumers' quality perception. Thus, it is essential for internet companies to find ways to encourage their customers to engage in word-of-mouth communication on their websites.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.154-163
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• 2017

Research collaboration is regarded as core source to lead various innovations in all countries. This paper compares and analyzes the performance of Industry-University-Government Research Institutes (GRI) collaboration based on the four types of research collaborations; GRI-GRI, Industry-GRI, University-GRI and Industry-University-GRI. So this paper will show which collaboration type has the best work on each R&D step. We use four R&D steps; research, development, commercialization and overall. We also evaluate the performance of research collaboration of GRIs based on the collaboration types. In order to evaluate the performance of research collaboration, Data Envelopment Analysis (DEA) is employed for measuring the efficiency of GRIs in this paper. DEA is a non-parametric approach to measuring the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. The empirical results represent that the performance of collaboration with industry is generally superior to other collaboration types. These findings from this paper are expected to provide basic information for national collaboration strategy making.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1071-1082
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• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.13-24
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• 2009

This paper introduces various kinds of applications of the scenario-based seismic risk assessment in Taiwan. Seismic scenario simulation (SSS) is a GIS-based technique to assess distribution of ground shaking intensity, soil liquefaction probability, building damages and associated casualties, interruption of lifeline systems, economic losses, etc. given source parameters of an earthquake. The SSS may integrate with rapid earthquake information release system to obtain valuable information and to assist in decision-making processes to dispatch rescue and medical resources efficiently. The SSS may also integrate with probabilistic seismic hazard analysis to evaluate various kinds of risk estimates, such as average annual loss and probable maximum loss in one event, in a probabilistic sense and to help proposing feasible countermeasures.