• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.269-274
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• 2004

In this paper, proposed is the dual-mode algorithm of blind decision feedback equalizer (DFE) for digital terrestrial television signals. According to channel impairments, the proposed dual-mode algorithm for blind DFE operates in decision-directed mode or in blind mode of operation. The error signals being used in tap update of the equalizer are generated in the best mode of operations, so that the confidence of equalizer tap coefficient update is more accurate. As a result, it is possible to track the channel characteristics variations by automatic switching over between two modes of operations. For 8-level vestigial sideband modulated digital television signals, the mean square errors and symbol error rates of the proposed algorithm are compared with those of conventional methods. And the usability of the proposed scheme is assessed by computer simulations under various static and dynamic multipath channel environments.

• Journal of IKEEE
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• v.19 no.3
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• pp.400-406
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• 2015

In this paper, we designed a decision feedback based diversity modem hardware architecture for IEEE802.11p WAVE and tested the modem on the road with car attached shark antenna. One of the dual channel modem and the diversity single modem with maximum ratio combining algorithm can be selected on the designed architecture. The designed modem have been implemented on the Xillinx Kintex7 FPGA. We tested the modem performance on the smart highway experience road. As experimental results, we can verify the performance of the diversity modem on real road and the enlarged communication range by more than 100%.

• The Journal of Information Systems
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• v.17 no.3
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• pp.205-233
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• 2008

This study suggests the dynamic value chain model, that will be able to not only show changing processes to organization's significant capital by integrating an individual, implicit, and explicit knowledge which affect organizational decision making, but also distinguish the key driver for raising organizational competitive power because it makes possible to analyze sensitivity of performance along with decision making alternatives and policy changes from dynamic view by connecting knowledge management capability, knowledge management activity, and relations with organizational performance with specific strategic map. Recently, a lot of organizations show interest in measuring and evaluating their performance synthetically. In organizations taking knowledge management, they introduce effective value chain model like a dynamic balanced scorecard (DBSC), and therefore they can reflect their knowledge management condition as well as show their changes by checking performance of established vision and strategy periodically. Furthermore, they can ask for their inner members' understanding and participation by communicating with and inspiring their members with awareness that members are one of their group, present a base of benchmarking, and offer significant information for later decision making. The BSC has been a successful framework for measuring an organization's performance in various perspectives through translating an organization's vision and strategy into an interrelated set of key performance indicators and specific actions. The BSC, while having significant strengths over traditional performance measurement methods, however, has its own limitations, due to its static nature, such as overlooking two-way causation between performance indicators and neglecting the impact of delayed feedback flowing from the adoption of new strategies or policy changes. To overcome these limitations, this study employs SD, a methodology for understanding complex systems where dynamic feedback among the interrelated system components significantly impact on the system outcomes. The SD simulation model in the form of DBSC would serve as a useful strategic teaming tool for facilitating an organization's communication process through various scenario analyses as well as predicting the dynamic behavior pattern of their key performance measures over a future time frame. For the demonstration purpose, this study applied the DBSC model to Prototype of Korea manufacturing and service firm.

• Journal of Internet Computing and Services
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• v.25 no.4
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.278-282
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• 2001

The discovery of tacit knowledge from domain experts is one of the most exciting challenges in today\`s knowledge management. The nature of decision knowledge in determining the quality a firm\`s short-term liquidity is full of abstraction, ambiguity, and incompleteness, and presents a typical tacit knowledge extraction problem. In dealing with knowledge discovery of this nature, we propose a scheme that integrates both knowledge elicitation and knowledge discovery in the knowledge engineering processes. The knowledge elicitation component applies the Verbal Protocol Analysis to establish industrial cases as the basic knowledge data set. The knowledge discovery component then applies fuzzy clustering to the data set to build a fuzzy knowledge based system, which consists of a set of fuzzy rules representing the decision knowledge, and membership functions of each decision factor for verifying linguistic expression in the rules. The experimental results confirm that the proposed scheme can effectively discover the expert\`s tacit knowledge, and works as a feedback mechanism for human experts to fine-tune the conversion processes of converting tacit knowledge into implicit knowledge.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.211-229
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• 2004

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

• Perspectives in Nursing Science
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• v.5 no.1
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• pp.17-31
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• 2008

Purpose: The purpose of this study was to evaluate the usability of a prototype personal digital assistant (PDA)-based decision support system for the management of obesity through usability testing with end-users (Advanced Practice Nurses [APNs]) prior to its implementation in clinical settings. Methods: This descriptive study used observational and think aloud techniques to address the research question: what usability problems are perceived by end-users? Five APNs were provided with the scenarios and the list of tasks to evaluate the application. Their verbalizations were recorded through Morae usabil ity software. Data analysis was based on the data captured through Morae, transcriptions, notes, and the end-user survey. Results: End-users completed all the required tasks without encountering a severe usability problem, and agreed that the system was easy to use. clear, concise, and useful. Usability issues that were unrecognized by the developer or usability experts were identified by APNs. The usability problems were categorized according to positive characteristics, negative characteristics, and recommendations. The usability issues were discussed with the project team members, and solutions were suggested to improve the user interface of the PDA-based decision support system before the final implementation. Conclusions: This approach had an important impact on making the system easier to use and more useful from the perspective of design and content. The results of this evaluation provided iterative feedback regarding the design and implementation of the PDA-based decision support system for the management of obesity.

• Journal of Energy Engineering
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• v.8 no.4
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• pp.612-620
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• 1999

Environmental impact assessment in the step of the Life Cycle Assessment (LCA) measures relative values of importance or weight of the environmental load characterized in the inventory analysis. The weight measurements are used to evaluate the environmental load or the effect of the industrial product or technology. In this paper the Analytic Network Prpcess (ANP) is introduced to calculate a relative weighting of the environmental impact. The ANP is considered as one of the useful decision making framework and allow for more complex interrelationships, feedback, and inner/outer dependence among the decision level and factors. The weighting from the ANP may applied to obtain the overall evaluation value of environmental load.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.139-148
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• 2010

This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.

• Korean System Dynamics Review
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• v.4 no.1
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• pp.69-91
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• 2003

Using qualitative methods hinged on urban dynamics models, the paper addresses major issues concerned with new administrative capital construction. It tries to summarize the existing debates on new administrative capital construction and reinterpret diverse interacting factors in terms of reinforcing or balancing feedback structure. The paper suggests that understanding up on the dynamic mechanism imbedded in circular causal loop diagrams is the key to set up appropriate proposals and action plans for the new administrative capital, as they would reveal complicated linkages between the Capital Region and the rest, in addition to the urban dynamic of new administrative capital. In the same context, the paper can confirm similar features reflected in the relocation of capital functions at Canberra, Australia and Berlin, Germany. It has paid special attention to the fact that both Australian and German governments altogether stress the positive feedback loops as they have overcome unprecedented political confrontation among rival cities: Basically, they have encouraged gives-and-takes among major stake-holders. These research findings indicate that the future of new administrative capital construction depends on consensus buildings that can accommodate socio-economic and territorial changes between pros and cons. Although further researches and validations are needed, the system approach presented in this paper could assist Korean decision-makers in developing robust and responsive policy initiatives under uncertainties.