• Journal of Korean Society for Quality Management
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• v.30 no.3
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• pp.79-93
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• 2002

Many companies are struggling with the issue of performance measurement because a generally accepted framework does not exist. The Balanced Scorecard is designed to help firms that have historically overemphasized short-term financial performance. In this article, we discuss the interrelation between supply relationship and BSC. The Balanced Scorecard to measure quality performance with respect to supply relationship is suggested. Based on the survey of 84 venture firms, the validity of the measurement tool is tested statistically. Consequently, it was found that the suggested items have validity to measure supply relationship quality performance

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.97-100
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• 2003

This paper addresses a method of convolutive source separation that based on SEONS (Second Order Nonstationary Source Separation) [1] that was originally developed for blind separation of instantaneous mixtures using nonstationarity. In order to tackle this problem, we transform the convolutive BSS problem into multiple short-term instantaneous problems in the frequency domain and separated the instantaneous mixtures in every frequency bin. Moreover, we also employ a H infinity filtering technique in order to reduce the sensor noise effect. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach and compare its performances with existing methods.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.604-614
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• 2013

In this study, a short-term activity program during vacation mainly consisted of creative drama and mindfulness was held in order to increase elementary school students' positive psychology and its effects were verified. Effects of the program were verified by means of quantitative and qualitative analysis and the results were as follows; First, most students participated in this program because their parents wanted them to instead of them wanting it voluntarily. Second, it was appeared that their self-esteem and happiness level increased significantly and their stress and depression level decreased after participating in the program. Third, approximately 65~70% students stated that it was fun, helpful, educative, and would like to recommend to other friends. Also, they reported on the perception of positive emotions and acquisition of empirical knowledge. Based on these results, implications and suggestions were discussed in this study.

• Hip & pelvis
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• v.34 no.1
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• pp.45-55
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• 2022

Purpose: The aim of this study was to evaluate the short-term outcome of physiotherapy in patients with acetabular labral tears and to assess the effectiveness of physiotherapy according to the severity of the labral tear. Materials and Methods: Thirty-five patients who underwent physiotherapy for treatment of symptomatic acetabular labral tears were enrolled. We evaluated the severity of the acetabular labral tears, which were classified based on the Czerny classification system using 3-T MRI. Clinical findings of microinstability and extra-articular pathologies of the hip joint were also examined. The International Hip Outcome Tool 12 (iHOT12) was use for evaluation of outcome scores pre- and post-intervention. Results: The mean iHOT12 score showed significant improvement from 44.0 to 73.6 in 4.7 months. Compared with pre-intervention scores, significantly higher post-intervention iHOT12 scores were observed for Czerny stages I and II tears (all P<0.01). However, no significant difference was observed between pre-intervention and post-intervention iHOT12 scores for stage III tears (P=0.061). In addition, seven patients (20.0%) had positive microinstability findings and 22 patients (62.9%) had findings of extra-articular pathologies. Of the 35 patients, eight patients (22.9%) underwent surgical treatment after failure of conservative management; four of these patients had Czerny stage III tears. Conclusion: The iHOT12 score of patients with acetabular labral tears was significantly improved by physiotherapy in the short-term period. Improvement of the clinical score by physiotherapy may be poor in patients with severe acetabular labral tears. Determining the severity of acetabular labral tears can be useful in determining treatment strategies.

• Korean Journal of Clinical Pharmacy
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• v.3 no.1
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• pp.45-53
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• 1993

Biphenyl Dimethyl dicarboxylate(DDB) has been regarded as a safe, effective drug for decreasing serum aminotransferase levels from elevated serum aminotransferase levels, which cause acute or chronic hepatitis and chronic liver diseases. This study was designed to low dose(22.5mg/day) & short-term therapy effectiveness for 4 weeks of DDB in 30 chronic hepatitis patients with elevated serum aminotransferases. The following results were observed. 1. Serum alanine aminotransferase(ALT) levels significantly decresed from 173. $97\pm130.62(U/L)$ of pretreatment level to $32.23\pm19.22(U/L)$ after treatment for 4 weeks(p<0.00l) and normalized patients by $73\%$ 2. Serum aspartate (AST) aminotransferase levels significantly decreased from $94.90\pm49.17(U/L)$ of pretreatment level to $45.30\pm23.25(U/L)4 after treatment(p0<0.01). 3. However, no significant effects in the serum AST & ALT changes by which cause hepatitis and hepatitis duration (p>0.05). 4. No significant adverse effects were observed except for mild epigastric discomfort in one patient during DDB treatment It is suggested that DDB small dosage administration can result effectively decreasing serum aminotransferase levels from chronic hepatitis patients with elevated serum aminotransferase levels.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.831-840
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• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• Clinical Pain
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• v.18 no.2
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• pp.121-125
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• 2019

Arthritis of hip joints deteriorates the quality of life in ankylosing spondylitis (AS) patients. Secondary to the articular inflammatory process, the shortened hip-girdle muscles contribute to the decreased joint mobility which may lead to the functional impairment. As the limitation of range of motion (ROM) usually progress slowly, clinicians regard it as a chronic condition and prescribe long-term therapy. However, by short-term intensive multimodal treatment, a 20-year-old man diagnosed as AS with severely limited hip joint ROM who relied on crutches doubled the joint angle and could walk independently only within 2 weeks. The combination included intra-articular steroid injection, electrical twitch obtaining intramuscular stimulation, extracorporeal shock wave therapy, heat, manual therapy, and stretching exercises. The management focused on the relaxation of hip-girdle muscles as well as the direct control of intra-articular inflammation. Hereby, we emphasize the effectiveness of intensive multimodal treatment in improving the function even within a short period.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.