• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.179-185
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• 2020

This study examines the relationship between the effectiveness of an audit committee and the amount of audit fees. The sample consists of 130 manufacturing companies listed on the Indonesia Stock Exchange (IDX) in 2016-2017. Data are obtained from the IDX and company's annual reports. The effectiveness of an audit committee is measured by independent audit committee size, frequency of audit committee meetings, and expertise of the audit committee. Results show first that the size of the independent audit committee has a positive effect on audit fees. This finding suggests that an increase in the number of independent audit committee members produces a higher quality of reporting, and so they tend to choose a reputable public accountant. Second, the frequency of its meetings has positive effects on audit fees. It suggests that the more effective the supervision of the committee to improve audit quality, the higher the audit fees to be paid. However, this study fails to provide evidence that the expertise of the audit committee affects audit fees. The result of this study suggests that the audit committee tends to adopt the demand approach based on the reputation of the public accounting firm accountant firm in determining the amount of audit fees.

• KIEE International Transactions on Power Engineering
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• 제5A권3호
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• pp.303-308
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• 2005

The restructure of the electrical power industry is accompanied by the extension of the electrical power exchange. One of the key pieces of information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). The traditional ATC deterministic approach is based on the severest case and it involves a complex procedure. Therefore, a novel approach for A TC calculation is proposed using cost optimization in this paper. The Jeju Island interconnected HVDC system has inland KEPCO (Korean Electric Power Corporation) systems, and its demand is increasing at the rate of about $\10[%]$ annually. To supply this increasing demand, the capability of the HVDC system must be enlarged. This paper proposes the optimal transfer capability of the HVDC system between Haenam in the inland and Jeju in Cheju Island through cost optimization. The cost optimization is based on generating cost in Jeju Island, transfer cost through Jeju-Haenam HVDC system and outage cost with one depth (N-1 contingency).

• 한국인구학
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• 제23권2호
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• pp.39-77
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• 2000

본 논문에서는 1860년대에서 1930년대까지 비농업직을 중심으로 하여 러시아 극동지역의 한인 이주민의 직업에 관하여 고찰한다. 먼저 이주민의 직업선택과 노동시장에서의 위치에 관한 이론들 즉 문화이론, 분절노동시종이론, 인적자본론 및 인종적 특수시장이론 등을 살펴보고 이들 이론이 러시아 극동지역 이주한인의 직업들 중 농업을 제외한 여러 가지 직종에 어떻게 적용되는지를 검토한다. 이러한 과정을 통해서 기존 이론들에서는 이주민의 직업에 관한 공급적 측면과 수요적 측면을 모두 분리하여 고찰하고 있다는 한계성을 지적한다. 이러한 한계성을 극복하기 위하여 본 논문에서는 러시아 극동지역 한인 이주민의 사례를 들어 이주민의 직업에 관한 두 가지 측면을 모두 통합해야 할 필요성을 설명하고 이를 위해 공급적·수요적 측면을 모두 고려할 수 있는 통합적 모델을 제시한 후 이러한 모델에 따라 러시아 극동지역 한인이주민의 직업에 영향을 키친 요인들을 광업, 어업, 자영업종, 서비스직종, 기타 단순 노무직 등으로 나누어 고찰한다. 마지막으로 이주의 통합적 모형에서 공급적 측면에 포함되는 것으로서는 한인의 농업중심적 성격, 이민직전의 조선에서의 사회경제적 모순 및 한인의 자급자족적 성격 등과 수요적 측면에서는 러시아의 이민정책과 러시아 극동의 다양한 지역적 상황이 고려되어야 함을 지적하였다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권7호
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• pp.478-488
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• 2009

태양 에너지를 이용한 무선 센서 네트워크에서는 공급되는 에너지의 변화가 크고 저장할 수 있는 배터리 용량이 제한적이기 때문에 이에 적응적으로 대처할 필요가 있다. 또한, 이렇게 변화하는 에너지 공급에 대처하기 위해 노드의 동작을 빈번히 변화시키는 것과는 달리, 일정한 수준 이상으로 안정되게 동작 하는 것을 필요로 하는 응용이 있을 수 있다. 따라서 태양 에너지 기반 센서 시스템에서 사용 가능한 에너지를 최대한 이용함과 동시에 일정 수준의 에너지를 안정적으로 제공하기 위해서는, 각 노드가 자신이 수집 할 수 있는 에너지의 양을 예측하고 이를 효율적으로 할당하는 기법이 필요하다. 본 논문에서는 시간 슬롯 단위의 수집 가능 에너지량에 대한 기댓값 모델을 기반으로, 각 시간 슬롯에 할당되는 에너지의 변화를 최소화함과 동시에, 주기적으로 수집되는 태양 에너지를 최대한 활용하기 위한 효율적인 에너지 할당 기법을 제안한다. 또한 이들의 유효성을 확인하기 위하여 테스트베드 구축하고, 우리의 기법을 평가하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.923-925
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• 1998

Electric arc furnaces (EAFs) are a main cause of voltage flicker due to the interaction of the high demand currents of the load with the supply system impedance. The stochastic models have described the physical phenomena of EAFs. An alternative approach is to include deterministic chaos in the characterization of the arc currents. In this paper, a chaotic approach to such modeling is described and justified. At the same time, a DLL (Dynamic Link Library) module, which is a FORTRAN interface with TACS (Transient Analysis of Control Systems), is developed to implement the chaotic load model in the Electromagnetic Transients Program (EMTP). The details of the module and the results of tests performed on the module to verify the model and to illustrate its capabilities are presented in this paper.

• 대한전기학회논문지:전력기술부문A
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• 제48권3호
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• pp.197-202
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• 1999

Electric arc furnaces (EAFs) use bulk electrical energy to create heat in metal refining industries. The electric arc process is a main cause of the degradation of the electric power quality such as voltage flicker due to the interaction of the high demand currents of the load with the supply system impedance. The stochastic models have described the aperiodic physical phenomena of EAFs. An alternative approach is to include deterministic chaos in the characterization of the arc currents. In this parer, a chaotic approach to such modeling is described and justified. At the same time, a DLL(Dynamic Link Library) module, which is a FORTRAN interface with TACS (Transient Analysis of Control Systems), is developed to implement the chaotic load model in the Electromagnetic Transients Program (EMTP). The details of the module and the results of tests performed on the module to verify the model and to illustrate its capabilities are presented in this paper.

• 한국수자원학회논문집
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• 제45권8호
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• pp.839-851
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• 2012

일반적으로 수계의 용수공급능력을 평가하기 위해서는 수요량을 단계적으로 증가시키며 용수부족 여부를 검토하고 그 결과에 따라 수요량을 조정하는 시행착오법을 사용한다. 이것은 시뮬레이션 모형 뿐 아니라 최적화모형의 경우에서도 마찬가지다. 그러나 이 같은 시행착오법은 수차례의 최적화를 반복하는 번거로움을 수반한다. 이에 본 연구에서는 물 부족이 발생하지 않은 최대 용수공급능력을 보다 쉽게 찾을 수 있도록 2단계의 목표계획기반 최적화 모형을 제시하였다. 즉, 제1단계에서는 제시된 모형을 유입량 정보가 정확하다는 가정아래 최대 용수공급능력을 산정한다. 그리고 제2단계에서는 실제 용수공급상황에서는 미래 유입량에 대한 정보가 없는 점을 고려하여 최대 갈수기간에 한해 실시간 모의운영을 통하여 용수공급능력을 산정한다. 이 방법을 다기능 보가 신설되어 기존 수문환경과 차이점을 보이는 금강수계에 적용하고, 완전한 정보가 있는 경우와 그렇지 않은 경우의 결과를 비교함으로써 유입량 정보가 용수공급능력에 미치는 영향을 이해하고 평가할 수 있었다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.187-192
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• 2006

In the absence of a clear command and control structure, a key challenge in supply chain management is the coordination and alignment of the supply chain members who pursue divergent and often conflicting goals. The newsvendor model is typically used as a framework to quantify the cost of misalignment and to assess the impact of coordination initiatives. This paper considers a fuzzy approach for the newsvendor problem which includes a single manufacturer and a single retailer. We use several fuzzy parameters in the model such as the demand, the wholesale price, and the market sales price. We apply a coordination policy, referred to as buyback, to solve the fuzzy newsvendor problem. Based on the buyback policy, the optimal order quantity of the retailer can be computed, and the possible profits of the members in the supply chain can be calculated with minimum sharing of private information. Focusing on the fuzzy model with buyback policy for the newsvendor problem, we illustrate exemplary fuzzy models. We also illustrate an integration model, which extends a single-manufacturer-single-retailer model to the single-manufacturer-multiple-retailer setting. In the extended model, we consider three coordination policies including quantity discount, profit sharing, and buyback, as well as non-coordination case.