Earticle

This study proposes a collaboration system framework on the web service in a supply chain to implement an efficient virtual supply chain and improve an ability to fulfill received orders over a supply chain. The system based on the framework proposed in this paper plays an important role for automatic order negotiation and placing/receiving an order noticed in web service by the main system. Furthermore, it can evaluate the fulfillment of received orders over a supply chain by using the transaction results from hierarchically related vendors and establish efficient manufacturing plans. In a word, this system is the automated system for creating manufacturing plans, placing and receiving orders. A little more important main function of this system is that it has a dynamic evaluation capability about fulfillment with received orders over a supply chain, and improves the evaluation method of fulfillment ability with received orders with related to direct dealing vendors, which is a main problem in existing system. As a result, this system is able to reduce the work load of the sales and purchasing materials, production control division, to manage accurate inventory promptly, to maintain the optimal inventory level by analyzing the information of fulfillment ability with received orders, and to enhance the level of service for customers.

본 연구는 우리나라에서 상대적으로 적용사례가 적은 비시장-가치평가기법인 개인별 여행비용접근법(Individual Travel Cost Method, TCM)를 사용하여, 국책사업으로 추진 중인 새만금 간척사업지와 인접해 있는 변산반도 국립공원의 방문수요함수를 추정하고 이를 바탕으로 관련 자연환경자원의 사용가치를 측정하고자 한다. 전통적인 TCM모형을 확장하여, 화폐소득과 시간소득의 두 예산제약 조건 하에서 효용극대화과정을 전개함으로써 자연환경자원의 방문수요함수를 완전가격(full price)과 완전소득(full income)의 함수로 도출하였다. 나아가서 방문수요함수의 종속변수인 방문빈도가 비음의 정수(non-negative integers)인 점을 감안하여 방문빈도를 연속함수로 보는 전통적인 회귀분석에서 포아송(Poisson)이나 음이항(Negative Binomial) 모형 등의 카운트자료(count data) 모형으로 확장하였다. 또한 비방문자들을 수요함수 추정에서 제외함으로써 발생할 수 있는 표본선택편의(sample selection bias)를 완화하기 위하여 방문여부함수와 방문빈도함수를 결합추정하였다. 전국에서 무작위로 추출된 1,303명의 일반가구 소비자들로 구성된 표본을 사용하여 실증분석을 수행한 결과, 종속변수를 연속변수로 취급하느냐 아니면 카운트 자료모형으로 취급하느냐에 따라 가격변수의 계수추정치에 차이가 있었고, 이는 소비자들의 관련 지점에 대한 사용가치를 반영하는 접근가치의 측정에도 반영이 되었다. 그리고 여가시간의 기회비용을 가격과 소득에 포함시키느냐의 여부 역시 계수추정치와 편익추정치에 영향을 미쳤다. 그러나 방문여부에 따른 표본선택편의의 가능성은 존재하기는 하였지만, 방문수요함수의 계수추정치나 편익추정치에 미치는 영향은 비교적 적은 편이었다.

This paper extends the standard individual travel cost method (TCM) by incorporating values of leisure time in both price and income variables, and the data generating process (treatment of trip frequency and sample selection effects) into recreation demand models for a national park. Recognizing the important role of leisure time in recreation demand, full prices and full incomes were derived from two-constraint (money and time budgets) models, in which the marginal value of leisure time was assumed to be a fraction of market wage rates. Count data models such as Poisson or Negative Binomial models, compared with the Tobit model, were reviewed to reflect the non-negative integer nature of the dependent variable (numbers of trips taken over the year) in recreation demand models. Equally important, Probit-tobit and Probit-poisson selection models were developed to take into account effects of no visitors at a recreational site in a general household survey. Empirical results from a survey of 1,303 households randomly selected throughout the nation suggest that the inclusion of values of leisure time and treatments of dependent variables affected estimates of price and income coefficients, and therefore estimates of the value of access to the site as a welfare measure. However, the selection effects alone were less definitive both in demand parameter and welfare measure estimates.

Business processes are often of long duration, and include internal worker's decision making, which makes business processes to be exposed to many exceptional situations. These properties of business processes makes it difficult to guarantee successful termination of business processes at the design phase. The behavioral properties of business processes mainly depends on the data aspects of business processes. To formalize the data aspect of process modeling, this paper proposes a graph-based model, called Data Dependency Graph (DDG), constructed from dependency relationships specified between business data. The paper also defines a mechanism of describing a set of mapping rules that generates a process model semantically equivalent to a DDG, which is accomplished by allocating data dependencies to component activities.

연구데이터를 서술하는 데이터논문은 데이터를 생산한 연구자에게 연구논문과 같은 학술적 성과로 인정받을 수 있게 하고 다른 연구자가 데이터논문에서 공유한 데이터를 활용하여 기존 연구를 재현하거나 새로운 연구를 시작할 수 있도록 도움을 준다. 이러한 장점으로 데이터논문의 동료심사, 출판, 인용은 지속적으로 증가하고 있다. 데이터논문을 출판하고자 하는 학술단체는 연구논문과 다른 데이터논문의 핵심 개념, 데이터논문 심사 절차, 데이터논문 출판과 관계된 정보시스템 등을 이해하고 데이터논문에 포함할 구성요소 정의, 동료심사 절차 수립, 그리고 데이터저장소와 연계 등 세부 사항을 결정하는 것이 필요하다. 하지만, 데이터논문의 동료심사와 관련하여 다루어야 할 정보가 방대하고, 데이터논문 출판에 관한 연구 및 체계적인 정보가 부족하여 국내 학술단체는 데이터논문의 동료심사 업무에 어려움을 겪고 있다. 이러한 문제를 해결하는 데 도움이 될 수 있도록, 본 논문은 국내외 다섯 종 데이터학술지의 데이터논문 양식, 동료심사 사례를 조사․분석하여 데이터논문의 핵심 구성요소를 포함한 데이터논문 동료심사의 핵심 개념을 제안하고 프로세스를 모델링하였다. 데이터논문을 신규로 출판하거나, 기존의 연구논문과 함께 데이터논문을 심사하여 출판하고자 하는 학술단체는 본 논문에서 제시한 데이터논문의 핵심 개념과 동료심사 프로세스를 활용하여 데이터논문의 양식 개발, 체계적인 동료심사 프로세스 정립, 출판 정책 개발 등의 학술지 업무에 신속하고 효과적으로 적용할 수 있을 것으로 기대된다.

A data paper describing research data helps credit researchers producing the data while helping other researchers verify previous research and start new research by reusing the data. Publishing a data paper and depositing data to a public data repository are increasing with these benefits. A domestic academic society that plans to publish data papers faces challenges, including timely acquiring tremendous knowledge concerning data paper structures and templates, peer review policy and process, and trustworthy data repositories, as a data paper has different characteristics, unlike a research paper. However, the need for more research and information concerning the critical elements of data paper and the peer-review process makes it difficult to operate for data paper review and publication. To address these issues, we propose essential concepts of the data paper and the data paper peer-review, including the process model of the peer-review with in-depth analysis of five data journals’ data paper templates, articles, and other guides worldwide. Academic societies intending to publish or add data papers as a new type of paper may establish policies and define a peer-review process by adopting the proposed conceptual models, effectively streamlining the preparation of data paper publication.

Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling architecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System (MIS).

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

In this paper, we propose a data-driven service and model which trades the data products generated by the big data analytics and perception process. Existing global data brokers have traded the data products in categorized and listed. Data has been traditionally managed in closed form or limited to specific industries and eco-systems. However, we are faced on the fourth Industrial Revolution, where diverse data is based on utilization, data is becoming a core competence for change and the future. Finding the value in Big Data has been generalized and responded to the change. Also, there is a great demand for revitalization of data processing and utilization of data for industrial application. To support these requirements, the data must be traded in a way that can be easily utilized according to the data analytics and perception process. This is a key technology in the data broker industry, which will enhance data-driven industries.

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

역전파 신경망 (BPNN)은 반도체 공정 모델링에 효과적으로 응용되고 있다. 뉴런의 활성화 함수는 동일한 값을 가지며, 이로 인해 예측정확도를 증진하는 데에는 한계가 있었다. 본 연구에서는 난수발생기(Random generator-RG)를 이용하여 뉴런 경사들이 다중값을 가지도록 최적화하였다. 본 기법은 은닉충의 뉴런수의 함수로 고찰하였으며, 종래의 고정된 경사를 갖는 모델과 그 성능을 비교 평가하였다. 평가에 이용된 데이터는 플라즈마 식각 공정데이터이며, 모델에 이용된 응답은 식각률과 프로파일 각이다. 비교결과 종래의 모델에 비해 예측정확도가, 식각률의 경우 19%-43%, 프로파일의 경우 10%-56% 정도 향상하였으며, 이는 제안된 기법이 모델개발에 매우 효과적으로 적용될 수 있음을 보여준다.

비대칭적 문항반응모형은 능력검사 문항 내에 존재하는 인지적 단계를 설명하는 중요한 도구로 간주되었다(Lee & Bolt, 2018). 비대칭 모형은 프로세스 데이터 형태로 기록된 문항응답 데이터에 적용해 봄으로써 그 유용성을 확인할 수 있다. 프로세스 데이터(process data)는 피험자가 시험을 치르는 동안 문항에 반응하는 모든 행위(action)를 연속적으로 기록한 데이터로서, 정답에 이르기 위해 피험자가 이용한 계획, 전략, 또는 조작에 관한 정보를 풍부하게 제공함으로써, 응답 결과만을 담고 있는 기존의 문항반응 데이터와 차별성을 가진다. 본 논문에서는 대표적인 비대칭적 문항반응모형 두 가지를 소개하고 PISA 2012의 프로세스 데이터에 적용하여 어떻게 두 모형의 비대칭적 문항모수가 문항 내에 존재하는 인지단계의 수준(level)과 연관되어 있는지 연구하였다. 두 모형의 프로세스 데이터로의 적용은 (1) 비대칭모수의 크기는 프로세스 데이터의 응답시간과 행동의 횟수를 통해 정의된 문항의 인지단계 수준을 잘 나타내고, (2) 문항특성곡선에도 인지단계의 수준에 따라 다른 형태의 비대칭성을 부여하는 것으로 나타났다. 이를 통해, 비대칭적 문항반응모형이 문항복잡성(item complexity)의 수준을 타당화하는 도구로 사용될 수 있음을 실제 데이터를 통해 확인하였고 향후 검사문항 개발 시 유용성이 있음을 시사한다.

Asymmetric IRT models have been considered as a useful tool for capturing the various number of cognitive subprocesses underlying educational test items (Lee and Bolt, 2018). One useful practical application of such models is toward the items recorded as a format of process data. Under the process data, all the generated actions of test??taker during the item response process are recorded as a unique sequence number/letter for each individual, and thus the data can provide information about test??taker’s plan, strategy, and operation to achieve the correct answer of problem. This paper demonstrates the applicability of Samejima’s logistic positive exponent (LPE) model and Molenaar’s residual heteroscedastic latent trait (RH) model to process data from PISA 2012 including problem??solving, mathematics, and reading subjects, and discusses how asymmetry parameters in the two models function in relation to the provided information about cognitive steps in each item. Also, this study shows its advantages relative to a difficulty parameter from two??parameter logistic (2PL) model in addressing such effects. Application of the LPE/RH in the context of process data demonstrated (1) asymmetry parameter estimates well captures the level of item complexity defined by process data and (2) yield distinct difference in the form of asymmetry in item characteristic curves. The results support the potential use of the asymmetric IRT model as a basis for item property validation tool so as to account for the level of cognitive subprocess in item.

본 논문에서는 관리자가시스템 운영과 감사 측면에서 침해사고에 대응하고 사고 발생 즉시 포렌식 데이터를 수집 분석 및 복구할 수 있는 포렌식 데이터의 실시간 수집 절차 모델을 제안한다. 제안한 모델은 기능 요소별로 구별된 7단계 절차를 가지며 추상적이고 관리적인 기존의 포렌식 절차와는 달리, 관리자가 시스템 운영과 감사 측면에서 침해사고에 대응하고, 사고 발생시 포렌식 데이터를 수집 분석 및 복구할수 있는 절차들이 포함되어 있다. 또한 즉각적인 대응이 어려운 경우 기존의 절차와 마찬가지로 종합적이고 조직적인 대응이 가능하도록 대응 전략 체계화 단계를 통한 포렌식 데이터 수집 단계로의 피드백 절차를 둔다.

This study proposes a model of collection process for real-time forensic data, in which the manager was to respond to infringement incidents in terms of system operation and inspection and to collect, analyze and restore forensic data immediately after an incident took place. The suggested model was modeled in seven processes according to functional elements. Unlike the old and managerial forensic processes, the model allowed the manager to react to infringement incidents in the aspects of system operation and inspection and to follow the processes of collecting, analyzing, and restoring forensic data in case of an incident. There also was a feedback process designed towards the step of gathering forensic data through the stages of systematizing reaction strategies in order that he or she be able to bring about comprehensive and structural responses to an incident like the former processes in which it is difficult to bring about instant responses.

연구데이터를 서술하는 데이터논문은 데이터를 생산한 연구자에게 연구논문과 같은 학술적 성과로 인정받을 수 있게 하고 다른 연구자가 데이터논문에서 공유한 데이터를 활용하여 기존 연구를 재현하거나 새로운 연구를 시작할 수 있도록 도움을 준다. 이러한 장점으로 데이터논문의 동료심사, 출판, 인용은 지속적으로 증가하고 있다. 데이터논문을 출판하고자 하는 학술단체는 연구논문과 다른 데이터논문의 핵심 개념, 데이터논문 심사 절차, 데이터논문 출판과 관계된 정보시스템 등을 이해하고 데이터논문에 포함할 구성요소 정의, 동료심사 절차 수립, 그리고 데이터저장소와 연계 등 세부 사항을 결정하는 것이 필요하다. 하지만, 데이터논문의 동료심사와 관련하여 다루어야 할 정보가 방대하고, 데이터논문 출판에 관한 연구 및 체계적인 정보가 부족하여 국내 학술단체는 데이터논문의 동료심사 업무에 어려움을 겪고 있다. 이러한 문제를 해결하는 데 도움이 될 수 있도록, 본 논문은 국내외 다섯 종 데이터학술지의 데이터논문 양식, 동료심사 사례를 조사·분석하여 데이터논문의 핵심 구성요소를 포함한 데이터논문 동료심사의 핵심 개념을 제안하고 프로세스를 모델링하였다. 데이터논문을 신규로 출판하거나, 기존의 연구논문과 함께 데이터논문을 심사하여 출판하고자 하는 학술단체는 본 논문에서 제시한 데이터논문의 핵심 개념과 동료심사 프로세스를 활용하여 데이터논문의 양식 개발, 체계적인 동료심사 프로세스 정립, 출판 정책 개발 등의 학술지업무에 신속하고 효과적으로 적용할 수 있을 것으로 기대된다.

A data paper describing research data helps credit researchers producing the data while helping other researchers verify previous research and start new research by reusing the data. Publishing a data paper and depositing data to a public data repository are increasing with these benefits. A domestic academic society that plans to publish data papers faces challenges, including timely acquiring tremendous knowledge concerning data paper structures and templates, peer review policy and process, and trustworthy data repositories, as a data paper has different characteristics, unlike a research paper. However, the need for more research and information concerning the critical elements of data paper and the peer-review process makes it difficult to operate for data paper review and publication. To address these issues, we propose essential concepts of the data paper and the data paper peer-review, including the process model of the peer-review with in-depth analysis of five data journals' data paper templates, articles, and other guides worldwide. Academic societies intending to publish or add data papers as a new type of paper may establish policies and define a peer-review process by adopting the proposed conceptual models, effectively streamlining the preparation of data paper publication.

Proposed in this paper is a standard-compliant business process modeling tool which is based on the UML(Unified Modeling Language) activity diagram and produces an XPDL(XML Process Definition Language) file as an output. The XPDL is a standard process definition exchange format by WfMC(Workflow Management Coalition). To develop an UML/XPDL-based modeling tool, the mapping of modeling elements between activity diagram and XPDL format is conducted after the detailed analysis of each modeling specification. As a result of this mapping, it is revealed that modeling elements of each activity diagram and XPDL must be expanded. So new modeling elements are identified and added to each specification. Based on this mapping, the prototype system is developed, and the usefulness of the developed system is shown through the case study.

In this paper, we investigate the statistical correlation of the time series for temperature measured at the heat box in the automobile drying process. We show, in terms of the sample variance, that a significant non-linear correlation exists in the time series that consist of absolute temperature changes. To investigate further the non-linear correlation, we utilize the volatility, an important concept in the financial market, and induce volatility time series from absolute temperature changes. We analyze the time series of volatilities in terms of the de-trended fluctuation analysis (DFA), a method especially suitable for testing the long-range correlation of non-stationary data, from the correlation perspective. We uncover that the volatility exhibits a long-range correlation regardless of the window size. We also analyze the cross correlation between two (inlet and outlet) volatility time series to characterize any correlation between the two, and disclose the dependence of the correlation strength on the time lag. These results can contribute as important factors to the modeling of forecasting and management of the heat box's temperature.