• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.211-227
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• 2023

Personal service robots, a type of social robot that has emerged with the aging population and technological advancements, are undergoing a transformation centered around technologies that can extend independent living for older adults in their homes. For older adults to accept and use social robot innovations in their daily lives on a long-term basis, it is crucial to have a deeper understanding of user perspectives, contexts, and emotions. This research aims to comprehensively understand older adults by utilizing a mixed-method approach that integrates quantitative and qualitative data. Specifically, we employ the Van Kaam phenomenological methodology to group conversations into nine categories based on emotional cues and conversation participants as key variables, using voice conversation records between older adults and social robots. We then personalize the conversations based on frequency and weight, allowing for user segmentation. Additionally, we conduct profiling analysis using demographic data and health indicators obtained from pre-survey questionnaires. Furthermore, based on the analysis of conversations, we perform K-means cluster analysis to classify older adults into three groups and examine their respective characteristics. The proposed model in this study is expected to contribute to the growth of businesses related to understanding users and deriving insights by providing a methodology for segmenting older adult s, which is essential for the future provision of social robots with caregiving functions in everyday life.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• Journal of Korean Port Research
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• v.11 no.2
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• pp.321-337
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• 1997

Rapid change in the technological environment of marine transportation and the development of the ocean shipping industry have fostered a revolution in the port system. This in turn has caused major changes in the function and use of port in Korea. Aside from this. Mokpo Port, however continues to decline, because the existing port facilities and related subsystem are already obsolete with no chance of regaining operational effectiveness and treatment for proper implementation. Although a few studies have been done on the Mokpo Port, has not been found, any reseach for the analytical approach to the transportation system of it. This paper aims to make an extensive analysis of the physical distribution system in Mokpo Port focusing on the coordination of subsystems such as navigational aids system, quay handling and transfer system, storage system and inland transport system. The base of introduced simulation tool here is the queueing theory. The overall findings are as follows; 1. Among those vessels called at Mokpo Port in 1994, the average size of oceangoing vessels is 4,922.1 G/T, and the domestic is 317.8 G/T. The average arrival interval and service time of the domestic vessels are 6.0 hours and 24.1 hours respectively marking the berth occupation rate over 100%. Those for oceangoing vessels are 34.5 hours, 120.0 hours and 37.2%. In order to maintainin the berth occupation rate to 70% the capacity considering the 1994 of domestic piers must be extended to 145% and oceangoing vessels must be increased to 165% year called. 2. The capacity of approaching channel is enough to handle the total traffic volume. 3. Tugs are sufficiently being provided to handle all ships requiring their services 4. The capacity of storage and inland transportation systems are sufficient to handle the throughput and the yard stroage utilization rate of No.1 $\cdots$ No.5 is 4.5% and No.6 1S 30% of 1993's. 5. The utilization rate of LLc(Level Looping Crane) and PNT(PNeumaTic) are 2.7% and 18.8%, respectively. Practical solution and proposal for improvement of Transportation System in Mokpo Port are as follows; 1. To avoid the congestion in domestic pier introduction of a new port operation system is necessary allowing the domestic vessel to use the oceangoing pier. 2. To establish the port management information system to improve the efficiency of port operation. 3. To build a new storage system for high valued cargos including modernization of the present storage and handling system. 4. To insure the safety of navigation in approaching channel, The Vessel Traffic System including separation scheme is introduced. 5. To interest enormously on public relation to ship owner's association, shippers and consignees by showing that they can save cost and ship turnaround time in order to promote the call to Mokpo Port. At last, to be strategically change the function of Mokpo Port to the Leisure, Fishing & Ferry as well as Maritime port.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.61-69
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• 2023

In recent years, due to heightened environmental awareness, Electric Power Steering (EPS) has been increasingly adopted as the steering control unit in manufactured vehicles. This has had numerous benefits, such as improved steering power, elimination of hydraulic hose leaks and reduced fuel consumption. However, for EPS systems to respond to actions, sensors must be employed; this means that the consistency of the sensor's linear variation is integral to the stability of the steering response. To ensure quality control, a reliable method for detecting defects and assessing linearity is required to assess the sensitivity of the EPS sensor to changes in the internal design characters. This paper proposes a data-driven defect and linearity assessment monitoring system, which can be used to analyze EPS component defects and linearity based on vehicle speed interval division. The approach is validated experimentally using data collected from an EPS test jig and is further enhanced by the inclusion of a Graphical User Interface (GUI). Based on the design, the developed system effectively performs defect detection with an accuracy of 0.99 percent and obtains a linearity assessment score at varying vehicle speeds.

• Journal of Daesoon Thought and the Religions of East Asia
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• v.2 no.2
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• pp.33-50
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• 2023

'Fasting of the Mind (心齋, ch. xīn zhāi)' is arguably the most important concept within the practical approach to the spiritual cultivation formulated by the Daoist philosopher, Zhuangzi (莊子). Most scholars have interpreted 'Fasting of the Mind' as an apophatic practice centered around the aim of the 'Dissolution of the Self (喪我, ch. sàng wŎ).' The Korean new religious movement, Daesoon Jinrihoe (大巡眞理會), can be shown to instead consistently utilize cataphatic descriptions of spiritual cultivation based on the 'quieting of the mind (安心, kr. anshim)' and 'quieting of the body (安身, kr. anshin)' with the highest attainable state referred to as the 'Perfected State of Unification with the Dao (道通眞境, kr. Dotong-jingyeong).' While the language used by Zhuangzi and Daesoon Jinrihoe appears quite different on a superficial level, a deeper examination shows that these rhetorical framings are likely negativistic and positivistic descriptions of the same, or at least reasonably similar, phenomena. Zhuangzi, who focused primarily on the body, mind, and internal energy, cautioned practitioners that 'mere listening stops with the ears (聽止於耳, ch. tīng zhǐ yú ěr)' and 'mere recognition stops with the mind (心止於符, ch. xīn zhǐ yú fú).' He therefore encouraged cultivators of the Dao to 'listen with the spirit (聽之以氣 ch. tīng zhī yǐ qì).' The main scripture of Daesoon Jinrihoe states that "The mind is a pivot, gate, and gateway for gods; They, who turn the pivot, open, and close the gate, and go back and forth through the gateway, can be either good or evil (心也者, 鬼神之樞機也, 門戶也,道路也)," and the Supreme God of the Ninth Heaven (九天上帝, kr. Gucheon Sangje) even promises to visit anyone who possesses a 'singularly-focused mind (一心, kr. il-shim).' In both these approaches, there is a sense of what must be kept out of the mind (e.g., external disturbances, strong emotions, malevolent entities) and what the mind should connect with to attain spiritual progress (e.g., spirit, singular focus, the Supreme God). The observations above serve as the main basis for a comparison between the apophatic descriptions of cultivation found in Zhuangzi and their cataphatic counterparts in Daesoon Thought. However, the culmination of this nuanced comparative exploration reveals that while the leanings of Zhuangzi and Daesoon Thought generally hold true, ultimately, both systems of cultivation transcend the categories of apophatic and cataphatic.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.55-65
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• 2006

This paper presents the closing analysis of a symmetric steel cable-stayed bridge erected by a free cantilever method. Two independent structural systems are formed before the closing procedure of a bridge is performed, and thus the compatibility conditions for vertical displacement and rotational angle are not satisfied at the closing section without the application of proper sectional forces. Since, however, it is usually impossible to apply sectional forces at the closing section, the compatibility conditions should be satisfied by proper external forces that can be actually applicable to a bridge. Unstrained lengths of selected cables and the pull-up force of a derrick crane are adjusted to satisfy nonlinear compatibility conditions, which are solved iteratively by the Newton-Raphson method. Cable members are modeled by the elastic catenary cable elements, and towers and main girders are discretized by linear 3-D frame elements. The sensitivities of displacement with respect to the unstrained lengths of selected cables and the pull-up force of the derrick crane are evaluated by the direct differentiation of the equilibrium equation. A Monte-Carlo simulation approach is proposed to estimate expected construction errors for a given confidence level. The proposed method is applied to the second Jindo Grand Bridge to demonstrate its validity and effectiveness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of the Korea Knowledge Information Technology Society
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• v.13 no.5
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• pp.607-614
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• 2018

Oryeong-san is a prescription widely used for diseases where water is stagnant because it has the effect of circulating the water in the body and releasing it into the urine. In order to investigate the mechanisms of oryeong-san, we in this paper construct and analysis the compound-target network of medicinal materials constituting oryeong-san based on a systems pharmacology approach. First, the targets related to the 475 chemical compounds of oryeong-san were searched in the STITCH database, and the search results for the interactions between compounds and targets were downloaded as XML files. The compound-target network of oryeong-san is visualized and explored using Gephi 0.8.2, which is an open-source software for graphs and networks. In the network, nodes are compounds and targets, and edges are interactions between the nodes. The edge is weighted according to the reliability of the interaction. In order to analysis the compound-target network, it is clustered using MCL algorithm, which is able to cluster the weighted network. A total of 130 clusters were created, and the number of nodes in the cluster with the largest number of nodes was 32. In the clustered network, it was revealed that the active compounds of medicinal materials were associated with the targets for regulating the blood pressure in the kidney. In the future, we will clarify the mechanisms of oryeong-san by linking the information on disease databases and the network of this research.