• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Hospice and Palliative Care
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• v.19 no.1
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• pp.26-33
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• 2016

Purpose: This study was conducted to identify clinical characteristics of oncologic patients at a point when they signed their do-not-resuscitate (DNR) orders. Methods: From January through December 2014, we retrospectively analyzed the records of 197 patients who passed away after agreeing to a DNR order in the hemato-oncology department of a tertiary hospital. Results: Of all, 121 patients (61.4%) were male and 76 (38.6%) were female, and their average age was 58.7 years. Ninety-four patients (47.7%) had gastrointestinal cancer. The ECOG performance status at admission was grade 3 in 76 patients (36.5%) and grade 4 in 11 (5.6%). The patients' mean hospital stay was 20 days. The mean duration from the admission to DNR decision was 13 days, and the mean duration from DNR decision to death was seven days. Conclusion: Study results indicate that a decision on signing or refusing a DNR order was made by medical staff mostly based on the opinions of patients' guardians rather than the patients themselves. This suggests that patients' own wishes are not well respected. Thus, it is urgent to establish institutional devices to enhance cancer patients' autonomy regarding DNR and to define an adequate timing for withdrawal of treatments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.129-135
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• 2011

The alternate adaptation algorithm (AAA) is proposed to improve the convergence characteristics and steady-state performance of the constant modulus algorithm (CMA). The alternate adaptation algorithm is a new equalization method which adapts an equalizer alternately by the algorithm with excellent blind convergence characteristics or the algorithm with better steady-state error performance. In this paper, it is introduced that the alternate adaptation equalization of the vsCMA (variable step-size CMA) and the decision-directed (DD) algorithm. We, first, designed the vsCMA with variable step-size to improve the steady-state error performance of the CMA, and combined it with the DD by alternate adaptation. As a result, it was mitigated that the sensitivity of performance fluctuation due to switching timing in CMA-DD switching method, and it was improved that the convergence speed and steady-state error performance of the CMA. Through computer simulations, under multipath channel condition, the usefulness of the proposed method was confirmed for 16-QAM.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.1
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• pp.83-91
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• 2003

High-speed QPSK demodulator has been in important design objective of any wireless communication systems, especially those offering broadband multimedia service. This paper describes all-digital QPSK demodulator for high-speed wireless communications, and its hardware structures are discussed. All-digital QPSK demodulator is mainly composed of symbol time circuit and carrier recovery circuit to estimate timing and phase-offsets. There are various schemes. Among them, we use Gardner algorithm and Decision-Directed carrier recovery algorithm which is most efficient scheme to warrant the fast acquisition and tacking to fabricate FPGA chip. The testing results of the implemented onto CPLD-EPF10K100GC 503-4 chip show demodulation speed is reached up to 2.6[Mbps]. If it is implemented a CPLD chip with speed grade 1, the demodulation speed can be faster by about 5 times. Actually in case of designing by ASIC, its speed my be faster than CPLD by 5 times. Therefore, it is possible to fabricate the all-digital QPSK demodulator chipset with speed of 50[Mbps].

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.12
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• pp.1248-1255
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• 2003

High-speed QPSK demodulator has been one important design objective of any wireless communication systems, especially those offering broadband multimedia service. This paper describes Zero-Crossing IF-level(ZCIF) QPSK demodulator for high-speed wireless communications, and its hardware structures are discussed. ZCIF QPSK demodulator is mainly composed of symbol time circuit and carrier recovery circuit to estimate timing and phase-offsets. There are various schemes. Among them, we use Gardner algorithm and Decision-Directed carrier recovery algorithm which is most efficient scheme to warrant the fast acquisition and tracking to fabricate FPGA chip. The testing results of the implemented onto CPLD-FLEX10K chip show demodulation speed is reached up to 2.6［Mbps］. Actually in case of designing by ASIC, its speed may be faster than CPLD by 5 times. Therefore, it is possible to fabricate the ZCIF QPSK demodulator with speed of 10 Mbps.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.4
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• pp.18-26
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• 2001

In this paper, we propose an ATC(Automatic Transfer mode Controller) algorithm and an average-mode-change method for use in Gear shift PLL which can automatically change loop gain. The proposed ATC algorithm accurately detects proper timing or the mode change and has a very simpler structure - than the conventional lock detector algorithm often used in QPSK. And the proposed average mode change method can obtain low errors of estimated frequency offset by averaging the loop filter output of frequency component in shift register. These algorithms are also useful in designing ASIC, since these algorithms occupy small circuit area and are adaptable for high speed digital processing. We also present phase tracking performance of proposed Gear-shift PLL, which is composed of polarity decision PD, ATC and average mode change circuit, and analyze the results by examining constellation at each mode.

• International Journal of Highway Engineering
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• v.15 no.5
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• pp.91-99
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• 2013

PURPOSES : This study aims at proposing the methodology for benefit evaluations in pavement maintenance methods and timings using KoPMS(Korean Pavement Management System) software which was developed for efficient pavement management. METHODS : This study classified pavement sections into 4 clusters considering AADT(Annual Average Daily Traffic) and ESAL(Equivalent Single-Axle Load) using cluster analysis and used the deterioration models in each cluster. Increased user costs due to pavement deterioration as time goes by and agent costs for maintenance were estimated. Based on deterioration model and KoPMS software, Methodology for benefit evaluation was proposed in pavement maintenance methods and with/without implementation using real pavement section data. RESULTS : This study verified that considering agent costs only would be constrained to decide pavement maintenance methods and timings, and ascertained that decision making with agent and user costs would be effective. In addition, this study revealed that pavement maintenance methods and timings can be affected by AADT and ESAL and frequent pavement maintenances can be more efficient for benefits in pavement sections with more AADT and ESAL. Also this study found that user costs would be more affected to decision making than agent costs. Moreover, Delay of conducting pavement maintenance caused increased vehicle operating costs and environmental costs because of poor conditions of pavements. CONCLUSIONS : This study proposed LCCA and benefit estimation methodology of pavement with considering agent and user costs. The results of this study can be used for baseline data of efficient pavement asset management.

• Journal of Preventive Medicine and Public Health
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• v.35 no.4
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• pp.287-294
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• 2002

Objectives : To examine whether cumulative chronic stress influences the immune status, and to verify the effect of social support on the relationship between these two dimensions in male manufacturing workers. Methods : A total of 39 workers were recruited for this study. A structured-questionnaire was used to assess general characteristics, job characteristics (work demand and decision latitude), psychosocial distress, and social support. The serum levels of CD4 and CD8 were measured as immune markers, and were collected between 8:00 and 10:00am in order to standardize the markers. Nonparametric statistics were used to estimate the differences between job characteristics and the immune markers. Results : General characteristics, and health-related behaviors, were not associated with CD4, CD8 or CD4/CD8. No relationships were found between job characteristics and the mean levels of immune reactivity. These results were consistent, even after controlling for social support. Social support failed to modify the relationship toward work demand, decision latitude or psychosocial distress to CD4, CD8, and CD4/CD8. Conclusion : Cumulative chronic life stress might not influence the immune status, and the effects of social support on the immune function under chronic stress, may not play a crucial role in modifying the relationships. This implication supports that the effect of stress on the immune function may be determined by the characteristics of that stress. further research should effectively considers the type, magnitude and timing of a stress event, and modifiable factors, such as personality traits, coping style, and hormone excretion levels, on the alteration of immune status.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.81-88
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• 2021

Convertible bonds are financial products that contain the nature of both bonds and shares, which are generally issued by companies with lower credit ratings to increase liquidity. Conversion bonds rely on qualitative judgment in the past, although decision-making on whether and when to exercise the right to convert is the most important issue. Therefore, this paper proposes to apply artificial neural network techniques to scientifically determine the exercise of conversion rights. We distinguish between a total of 1,800 learning data published in the past and 200 predictive experimental data and build an artificial neural network learning model. As a result, the parity performance in most groups was excellent, achieving an average excess of about 10% or more. In particular, groups 3-6 recorded an average excess of about 20% and group 6 recorded an average excess of about 37%. This paper is meaningful in that it focused on solving decision problems by converging and applying machine learning techniques, a representative technology of the fourth industry, to the financial sector.

• Smart Media Journal
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• v.11 no.2
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• pp.9-17
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• 2022

The purpose of this study is to implement a predictive model to support decision-making to replace a starting pitcher before a crisis situation in a baseball game. To this end, using the Major League Statcast data provided by Baseball Savant, we implement a predictive model that preemptively replaces starting pitchers before a crisis situation. To this end, first, the crisis situation that the starting pitcher faces in the game was derived through data exploration. Second, if the starting pitcher was replaced before the end of the inning, learning was carried out by composing a label with a replacement in the previous inning. As a result of comparing the trained models, the model based on the ensemble method showed the highest predictive performance with an F1-Score of 65%. The practical significance of this study is that the proposed model can contribute to increasing the team's winning probability by replacing the starting pitcher before a crisis situation, and the coach will be able to receive data-based strategic decision-making support during the game.