• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.3
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• pp.39-54
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• 2009

As of July, 2009, Chungnam Province get DI (Direct Investment) of 2,502 corporations as the amount of 31 billion US dollars only for 3 years. Especially, Chungnam provincial governor make a excessive performance of 2.5 times comparing to the target number, 1,000 of inviting capital, that is public promise in the election. Now, the amount of inviting foreign capital is 1.2 billion dollars, at the end of this year it might be 1.4 billion dollars just in case of making a success on going negotiations. This outstanding performance comes from governor's leadership and aggressive strategies of well-trained subordinates. Chungnam Province has nation-wide multiple targets focused on interdisciplinary industries including strategic industries of display, auto-parts, steel, and oil-chemistry. Also, it has organic network system based on the very descriptive and accurate informations managing the task force team consisted of 35 competent members. In conclusion, the core competence of inviting foreign capital in a regional government is governor's strong leadership, activated organization consisted of specially well trained subordinates, and predominant differentiated strategies in details.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.201-219
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• 2006

The key to developing software with the lowest cost and highest quality is to implement or fit the software development process into a given environment. Generally, applying commercial or standard software development processes on a specific project can cause too much overhead if there is no effort to customize the given generic processes. Even though the customizing activities are done before starting the project, these activities are thoroughly dependent on the process engineers who have abundant experience and knowledge with tailoring processes. Owing to this dependence on human knowledge, it has been very difficult to explain the rationale for the results of process tailoring and it takes a long time to get the customized process that is applicable. Hence, we suggest a process tailoring method which adopts the artificial neural network based teaming theory to reduce the time consumed by process tailoring. Furthermore, we suggest the feedback loop mechanism to get higher accuracy in the neural network designed for the process tailoring. It can be done by reusing the process tailoring data results and determining its appropriateness level as sample data to the neural network. We proved the effectiveness of our process tailoring method through case studies using real historical data, which yielded abundant process tailoring results as sample data.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.31-43
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• 2015

For battery based real-time embedded systems, high performance to meet their real-time constraints and energy efficiency to extend battery life are both essential. Real-Time Dynamic Voltage Scaling (RT-DVS) has been a key technique to satisfy both requirements. In this paper, we present an efficient RT-DVS algorithm called EccEDF that is designed based on ccEDF. The proposed algorithm can precisely calculate the maximum unused utilization with consideration of the elapsed time while keeping the structural simplicity of ccEDF, which overlooked the time needed to run the task in calculating the available slack. The maximum unused utilization can be calculated by dividing remaining execution time($C_i-cc_i$) by remaining time($P_i-E_i$) on completion of the task and it is proved using Fluid scheduling model. We also show that the algorithm outperforms ccEDF in practical applications which is modelled using a PXA250 and a 0.28V-to-1.2V wide-operating-range IA-32 processor model.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.102-111
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• 2023

Automatic Music Transcription (AMT) is a task that detects and recognizes musical note events from a given audio recording. In this paper, we focus on reducing the latency of real-time AMT systems on piano music. Although neural AMT models have been adapted for real-time piano transcription, they suffer from high latency, which hinders their usefulness in interactive scenarios. To tackle this issue, we explore several techniques for reducing the intrinsic latency of a neural network for piano transcription, including reducing window and hop sizes of Fast Fourier Transformation (FFT), modifying convolutional layer's kernel size, and shifting the label in the time-axis to train the model to predict onset earlier. Our experiments demonstrate that combining these approaches can lower latency while maintaining high transcription accuracy. Specifically, our modified model achieved note F1 scores of 92.67 % and 90.51 % with latencies of 96 ms and 64 ms, respectively, compared to the baseline model's note F1 score of 93.43 % with a latency of 160 ms. This methodology has potential for training AMT models for various interactive scenarios, including providing real-time feedback for piano education.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.563-574
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• 2024

While the integration of chatbot and the simplification of financial terminology in Financial services' apps are increasingly common, automotive finance apps often show lower user satisfaction for complex terminol- ogy and rigid content. This study investigates the effects of chatbot interaction modes and the simplification of financial terminology on user experience in automotive finance apps. We developed prototypes for car lease tasks under different conditions: the type of user interaction channel (chatbot vs menu-based), and the usage of financial terminology. A 2 x 2 experimental survey was conducted to measure perceptions of friendliness, read- ability, trust, and accuracy. The findings revealed that chatbot interactions significantly enhance friendliness more than menu-based interactions, and simplifying terminology significantly improves readability and friendliness. However, no significant differences were observed in trust and accuracy between the conditions. Furthermore, nosignificant interaction effects were found between the two conditions across all variables. This study contributes by quantitatively assessing the impacts of chatbot consultation modes and terminology sim- plification on customer experience in financial services.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.320-323
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• 2007

웹 서비스와 같은 분산된 환경에서, 특정 서비스를 수행하기 위해서는 원격의 컴퓨터나 사이트상에서 다중 에이전트들의 협업을 통해 이루어진다. 이때 서비스는 여러 에이전트들의 복잡한 행위들에 의해 구성된다. 또한 지능적인 서비스를 위해서는 에이전트들의 상태정보, 목적정보, 그리고 계획정보 등을 이용한다. 특히 계획정보는 에이전트들이 일련의 행위들로 구성된다. 하지만 계획수립을 위한, 기존 연구들 대부분은 정적으로 기술된 서비스 명세와 초기상태 정보를 이용하여 특정 목표를 만족시키는 단일 계획 생성 방법을 연구해왔다. 따라서 계획수립이 실행 도중에 기대하지 않은 네트워크 장애나 방해 등으로 서비스 수행을 실패하는 경우, 그 계획은 무효가 되고 다시 계획을 생성 해야만 한다. 그러나 다시 계획을 생성하기 위해서는 많은 시간을 소비하게 될 뿐만 아니라 태스크 중복이 불가피하므로 매우 비효율적이다. 이 논문에서는 강건한 계획수립과 그 계획을 실행하기 위한 효과적인 방법을 제안한다. 즉, 계획수립의 재생성을 피하기 위한 방법으로 단일 계획수립 대신에 실행 가능한 다중 계획들로 표현된 강건한 계획을 생성하는 것이다. 강건한 계획의 행위들이 실행되는 동안, 각 단계마다 실행 가능한 행위를 선택한 후, 그 행위를 실행한다. 그러나 선택된 행위가 실행결과를 낼 수 없을 경우, 대체 가능한 서브 계획 경로를 선택하여 실행한다. 강건한 계획을 표현하기 위해 페트리 넷 기반의 방법을 제안한다. 강건한 계획 생성 방법에서는 이용 가능한 모든 계획들을 입력으로 사용한다. 그 계획수립 방법은 HTN 계획수립기로 잘 알려진 JSHOP2 계획수립기내에 구현하였다. 계획 실행 방법으로는 주어진 강건한 계획에 대하여 행위들이 직접 실행하수 있도록 한다.며 용량에 의존하는 양상을 보였다. $H_2O_2$에 의해 유발(誘發)된 DNA의 손상은 catalase와 deferoxamine에 의해 억제되었지만 DPPD는 억제시키지 못했다. 배기음(排氣飮)은 $H_2O_2$에 의해 유발(誘發)된 ATP의 소실을 회복시켰다. 이러한 실험결과 $H_2O_2$에 의해 유발(誘發)된 세포(細胞)의 손상(損傷)은 지질(脂質)의 과산화(過酸化)와는 다른 독립적인 기전에 의해 일어남을 나타낸다. 결론 : 이러한 결과들로 볼 때 Caco-2 세포(細胞)에서 배기음(排氣飮)이 항산화작용(亢酸化作用)보다는 다른 기전을 통하여 Caco-2 세포안에서 산화제(酸化劑)에 의해 유발(誘發)된 세포(細胞)의 사망(死亡)와 DNA의 손상(損傷)을 방지할 수 있다는 것을 가리킨다. 따라서 본 연구(硏究)는 배기음(排氣飮)이 반응성산소기(反應性酸素基)에 의해 매개된 인체(人體) 위장관질환(胃腸管疾患)의 치료(治療)에 사용할 수 있을 가능성(可能性)이 있음을 제시하고 있다.에 이를 이용하여 유가배양시 기질을 공급하는 공정변수로 사용하였다 [8]. 생물학적인 폐수처리장치인 활성 슬러지법에서 미생물의 활성을 측정하는 방법은 아직 그다지 개발되어있지 않다. 본 연구에서는 슬러지의 주 구성원이 미생물인 점에 착안하여 침전시 슬러지층과 상등액의 온도차를 측정하여 대사열량의 발생량을 측정하고 슬러지의 활성을 측정할 수 있는 방법을 개발하였다.enin과 Rhaponticin의 작용(作用)에 의(依)한 것이며, 이는 한의학(韓醫學) 방제(方劑) 원리(原理)인 군신좌사(君臣佐使) 이론(理論)에서 군약(君藥)이 주증(主症)에 주(主)로 작용(作用)하는 약물(藥物)이라는 것을 밝혀주는