• Journal of the Korea Academia-Industrial cooperation Society
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• 제13권1호
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• pp.343-347
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• 2012

Non-linear equalization techniques using decision feedback structure are highly demanded for cancellation of intersymbol interferences occurred in severe channel environments. In this paper decision feedback structure is applied to the linear blind equalizer algorithm that is based on information theoretic learning and a randomly generated symbol set. At the decision feedback equalizer (DFE) the random symbols are generated to have the same probability density function (PDF) as that of the transmitted symbols. By minimizing difference between the PDF of blind DFE output and that of randomly generated symbols, the proposed DFE algorithm produces equalized output signal. From the simulation results, the proposed method has shown enhanced convergence and error performance compared to its linear counterpart.

• Journal of the Korean Society for Aviation and Aeronautics
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• 제27권2호
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• pp.16-25
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• 2019

While various efforts are being made to ensure aviation safety, air accident rate induced by pilot human factors is still high worldwide. In particular, among pilot human factors, it would be the most important issue for pilots to anticipate and recognize flight environmental factors beyond their control and to make a positive decision making(ADM). In the Republic of Korea Air Force(ROKAF), there were many dizzying experiences induced by bird strike incidents and developed into dangerous moments such as damage to the aircraft and pilots' increased mental stress. It is a matter of serious concern in terms of safety management and human factors to dismiss bird strike incidents as inevitable misfortune due to environmental factors. In 2018, the ROKAF Aviation Safety Agency(ASA) conducted an experimental study to enhance pilots' ADM competence that can anticipate and avoid a bird strike. As the way of the study, 'Bird Strike Preventing Information' had been written and distributed every week by the ASA to flight units in the ROKAF during the period of the study. Through enhanced pilots' perceptual ADM competence, there was a noticeable number of reduction in bird strike incident compared to previous years of the experimental study.

• Journal of Institute of Control, Robotics and Systems
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• 제10권6호
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• pp.494-500
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• 2004

This paper presents a machine vision system, which inspects the measurement of the ALC block on a real-time basis in the production process. The automatic measurement system was established with a CCD camera, an image grabber, and a personal computer without using assembled measurement equipment. Images obtained by this system was processed by an algorithm, specially designed for an enhanced measurement accuracy. For the realization of the proposed algorithm, a preprocessing method that can be applied to overcome uneven lighting environment, boundary decision method, unit length decision method in uneven condition with rocking objects, and a projection of region using pixel summation are developed. From our experimental results, we could find that the required measurement accuracy specification is sufficiently satisfied by using the proposed method.

• Journal of Internet Computing and Services
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• 제25권4호
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• Journal of Satellite, Information and Communications
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• 제10권1호
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• pp.29-32
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• 2015

It has been a big trend of the convergence technologies about communication systems and vehicular industry to improve safety and convenience. To achieve a number of infotainment vehicular applications, vehicles should transmit information with high reliability. A robust and accurate channel estimation scheme is of great importance to achieve the goal. In this paper, we present a novel enhanced decision-directed channel estimation scheme called FADP (Frequency Averaging Data Pilot) for dynamic time-varying vehicular channels in IEEE 802.11p. We use linear averaging filtering in frequency domain, and utilize the correlation characteristic of the channels between the adjacent two data symbols, update the CR in time domain to get more accuracy. Finally, analysis and simulation results reveal that compared with exist schemes, the proposed scheme has a good performance in mean square error (MSE) and bit error rate (BER).

• The Transactions of the Korean Institute of Electrical Engineers A
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• 제54권4호
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• pp.177-184
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• 2005

This paper proposes a improved investment priority decision method of the facilities considering the reliability of distribution networks. The proposed method decides a investment order of the facilities combining, by fuzzy rules, the investment priority decision of KEPCO and the priority decision considering reliability evaluation indices. Where reliability evaluation indices are SAIFI(System Average Interruption Frequency Index) and SAIDI(System Average Interruption Duration Index), as referred to evaluation index for sustained interruption. The reliability analysis method of distribution networks applied in this paper utilizes analytic method, where the used reliability data is historical data of KEPCO. Particularly, we assumed that the failure rate increased as the equipment ages. To verify the performance of the proposed method, we applied it with the planned projects to reinforce the weak facility electrical facilities in KEPCO in 2004. The evaluation result showed that, under a limited budget, the reliability of the KEPCO in the Busan region using the proposed method can be enhanced than using the conventional KEPCO's method. Therefore, the results verify the proposed method can be efficiently used in the actual priorities method for investing the electrical facilities.

• Korean Journal of Artificial Intelligence
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• 제9권1호
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• pp.21-27
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• 2021

In this Paper, Since the 1990s, Korea's credit card industry has steadily developed. As a result, various problems have arisen, such as careless customer information management and loans to low-credit customers. This, in turn, had a high delinquency rate across the card industry and a negative impact on the economy. Therefore, in this paper, based on Azure, we analyze and predict the delinquency and delinquency periods of credit loans according to gender, own car, property, number of children, education level, marital status, and employment status through linear regression analysis and enhanced decision tree algorithm. These predictions can consequently reduce the likelihood of reckless credit lending and issuance of credit cards, reducing the number of bad creditors and reducing the risk of banks. In addition, after classifying and dividing the customer base based on the predicted result, it can be used as a basis for reducing the risk of credit loans by developing a credit product suitable for each customer. The predicted result through Azure showed that when predicting with Linear Regression and Boosted Decision Tree algorithm, the Boosted Decision Tree algorithm made more accurate prediction. In addition, we intend to increase the accuracy of the analysis by assigning a number to each data in the future and predicting again.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• Journal of applied mathematics & informatics
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• 제41권5호
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• pp.1129-1143
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• 2023

Social media platforms have become an integral part of our daily lives, and they generate vast amounts of data that can be analyzed for various purposes. However, the quality of the data obtained from social media is often questionable due to factors such as noise, bias, and incompleteness. Enhancing data quality is crucial to ensure the reliability and validity of the results obtained from such data. This paper proposes an enhanced decision-making framework based on Business Decision Management Systems (BDMS) that addresses these challenges by incorporating a data quality enhancement component. The framework includes a backtracking method to improve plan failures and risk-taking abilities and a steep optimized strategy to enhance training plan and resource management, all of which contribute to improving the quality of the data. We examine the efficacy of the proposed framework through research data, which provides evidence of its ability to increase the level of effectiveness and performance by enhancing data quality. Additionally, we demonstrate the reliability of the proposed framework through simulation analysis, which includes true positive analysis, performance analysis, error analysis, and accuracy analysis. This research contributes to the field of business intelligence by providing a framework that addresses critical data quality challenges faced by organizations in decision-making environments.

• Journal of Home Health Care Nursing
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• 제30권2호
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• pp.163-173
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• 2023

Purpose: The aim of this descriptive research study was to identify the factors affecting the ethical decision-making of nursing students. Methods: A convenience sample of 193 nursing students from three nursing colleges in D city who were engaged in clinical practice completed an online Google Forms questionnaire from September 9 to September 20, 2021. Using SPSS 23.0, data were analyzed with descriptive statistics, an independent t-test, a one-way ANOVA, Scheffé's test, Pearson's correlation coefficient, and a multiple regression analysis. Results: The influencing factors of ideal ethical decision-making were guilt (β=.38, p<.001), awareness of the nurses' Code of Ethics (β=.18, p=.023) and motivation for entering school, among general characteristics (β=-.18, p=.033). The explanatory power of the model was 22.2%. Further, the influencing factors of realistic ethical decision-making were ideal ethical decision-making (β=.26, p=.001) and grade (among general characteristics) (β=.15, p=.029); the explanatory power of the model was 17.9%. Conclusion: Various educational tools and programs pertaining to making ideal and ethical decisions must be enhanced to promote ethical choices in clinical areas and realistic ethical decision-making ability to actually make such choices. This focus may enable nurses to improve their nursing professionalism in the future.