• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• Information Systems Review
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• v.25 no.3
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• pp.27-41
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• 2023

Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.177-184
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• 2024

In the situation of increasing data at fast speed, we use all kinds of complex ensemble and deep learning algorithms to get the highest accuracy. It's sometimes questionable how these models predict, classify, recognize, and track unknown data. Accomplishing this technique and more has been and would be the goal of intensive research and development in the data science community. A variety of reasons, such as lack of data, imbalanced data, biased data can impact the decision rendered by the learning models. Many models are gaining traction for such interpretations. Now, LIME and SHAP are commonly used, in which are two state of the art open source explainable techniques. However, their outputs represent some different results. In this context, this study introduces a coupling technique of LIME and Shap, and demonstrates analysis possibilities on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence on the IEEE CIS dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.87-94
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• 2024

We investigated the feasibility of utilizing Large Language Models (LLMs) to perform text-based games without training on game data in advance. We adopted ChatGPT-3.5 and its state-of-the-art, ChatGPT-4, as the systems that implemented LLM. In addition, we added the persistent memory feature proposed in this paper to ChatGPT-4 to create three game player agents. We used Zork, one of the most famous text-based games, to see if the agents could navigate through complex locations, gather information, and solve puzzles. The results showed that the agent with persistent memory had the widest range of exploration and the best score among the three agents. However, all three agents were limited in solving puzzles, indicating that LLM is vulnerable to problems that require multi-level reasoning. Nevertheless, the proposed agent was still able to visit 37.3% of the total locations and collect all the items in the locations it visited, demonstrating the potential of LLM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.34-42
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• 2006

As art analytic method to uncover interesting patterns hidden under a large volume of data, data mining research has been actively done so far in various fields. However, current state-of-the-arts in data mining research have several challenging problems such as being too ad-hoc. The existing techniques are mostly the ones designed for individual problems, so there is no unifying theory applicable for more general data mining problems. In this paper, we address the problem of classification, which is one of significant data mining tasks. Specifically, our objective is to evaluate radial basis function (RBF) model for classification tasks and investigate its usefulness. For evaluation, we analyze the popular Monk's problems which are well-known datasets in data mining research. First, we develop RBF models by using the representational capacity based learning algorithm, and then perform a comparative assessment of the results with other models generated by the existing techniques. Through a variety of experiments, it is empirically shown that the RBF model has not only the superior performance on the Monk's problems but also its modeling process can be controlled in a systematic way, so the RBF model with RC-based algorithm might be a good candidate to handle the current ad-hoc problem.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.229-243
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• 2018

The purpose of this study is to develop a personality education program that can enhance the virtues of elementary school students, self-esteem, Consideration communication, self-control, honesty courage by using coaching methodologically, To improve the personality virtue. The subjects of this study were 31 students from 4th grade of U elementary school in S Gu, Seoul. The experimental group was divided into 16 sessions, weekly from March 5, 2018 to June 30, 2018, 1 hour (40 minutes). The program consisted of items such as self-esteem, Consideration communication, self-control, honesty courage among the personality virtues of KEDI personality test, Each activity used a tough coaching model developed by combining the GROW model with the Empowering model and considering the developmental level of elementary school students. As a result, all four virtues of personality were significantly improved, model with the Empowering model and considering the developmental level of elementary school students. We compared pre and post test result with paired t-test. As a results, the experimental group showed improvement in all four virtues of personality at 0.05 significance level, whereas the control group did not. This suggests that the program can be usefully used as a tool to improve four virtues of personality of elementary school students. For further research, we expect that the program would be integrated with state-of-art technology such as online program or CBI(Computer-Based Instruction).

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.337-342
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• 2014

This paper is aims to build the advancement structural model of GICOMS through identification of required system and improvement for implementation of e-Navigation. We derived nine improvement subject for model of advanced GICOMS through the analysis of problems for GICOMS and brainstorming with expert in the maritime safety. And we analyzed the structure of nine improvement subject using by FSM(Fuzzy Structural Modeling) method, and proposed a structural model that to grasp the correlation between elements. As a result, we found out that "advancement of GICOMS" is the final goal, and "improvement a system of information production", "improvement a scheme of information providing", "linkage between GICOMS and VTS" and "building global networks for safety cooperation" are located lowest level. Especially, "advancement of GICOMS" is influenced by "advancement function of VMS" and "Activation of usage" on middle level. We suggested that utilizing state-of-the-art IT facilities, equipment and expertise to improve and enhance the user-centered transition such as maritime workers for advancement of GICOMS based on proposed structure model.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.24-32
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• 2021

Personal Air Vehicle (PAV), Cargo UAS (Cargo UAS), and existing manned and unmanned aircraft are key vehicles for urban air mobility (UAM), and should demonstrate compatibility for the design of aircraft systems. The safety assessment required by for certification to ensure safety and reliability should be systematically performed throughout the entire cycle from the beginning of the aircraft development process. However, with the increasing complexity of safety critical aviation systems and the application of state-of-the-art systems, conventional experience-based and procedural-based safety evaluation methods make ir difficult to objectively assess safety requirements and system safety. Therefore, Model-Based Safety Assessment (MBSA) using modeling and simulation techniques is actively being studied at domestic and foreign countries to address these problems. In this paper, we propose a Model-Based Safety Evaluation framework utilizing modeling and simulation-based integrated flight simulators. Our case studies on the Traffic Collision Availability System (TCAS) and Wheel Brake System (WBS) confirmed that they are practical for future safety assessments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.741-747
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• 2008

Nowadays, the accuracy of the geoid model has been improved through development of the combination model which was composed of traditional gravimetric geoid and geometric geoid by the GPS/leveling data in USA and Japan. It is a state of the art method in geoid modeling field that what so called hybrid geoid. In this paper, as a basic study to develop Korean hybrid geoid model, we studied gravimetric geoid solutions using three gravity reduction methods (Helmert's condensation method, RTM method and Airy-isostatic method) and evaluated the usefulness of each method in context of precise geoid. The gravimetric geoid model were determined by restoring the gravity anomalies (included TC) and the indirect effects were made from various reduction methods on the EIGEN-CG03C reference field. The results are compared with respect to the geometric geoid undulation determined from 498 GPS/leveling after LSC fitting. The results showed that hybrid geoid with RTM (Residual terrain model) reduction method was most accurate method and the value of the difference compared to geometric geoid was $0.001{\pm}0.053m$.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.71-79
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• 2024

ChatGPT, an interactive artificial intelligence (AI) chatbot developed by Open AI in the U.S., gaining popularity with great repercussions around the world. Some academia are concerned that ChatGPT can be used by students for plagiarism, but ChatGPT is also widely used in a positive direction, such as being used to write marketing phrases or website phrases. There is also an opinion that ChatGPT could be a new future for "search," and some analysts say that the focus should be on fostering rather than excessive regulation. This study analyzed consciousness about ChatGPT for college students through a survey of their perception of ChatGPT. And, plagiarism inspection systems were prepared to establish an education support model using ChatGPT and ChatGPT. Based on this, a university education support model using ChatGPT was constructed. The education model using ChatGPT established an education model based on text, digital, and art, and then composed of detailed strategies necessary for the era of the 4th industrial revolution below it. In addition, it was configured to guide students to use ChatGPT within the permitted range by using the ChatGPT detection function provided by the plagiarism inspection system, after the instructor of the class determined the allowable range of content generated by ChatGPT according to the learning goal. By linking and utilizing ChatGPT and the plagiarism inspection system in this way, it is expected to prevent situations in which ChatGPT's excellent ability is abused in education.