Earticle

Ball stud parts are manufactured by a cold forging process, and fastening with other parts is secured through a head part cutting process. In order to improve process quality, stabilization of the forging quality of the head is given priority. To this end, in this study, a predictive model was developed for the purpose of improving forging quality. The prediction accuracy of the model based on 450 data sets acquired from the manufacturing site was low. As a result of gradually multiplying the data set based on FE simulation, it was expected that it would be possible to develop a predictive model with an accuracy of about 95%. It is essential to build automated labeling of forging load and dimensional data at manufacturing sites, and to apply a refinement algorithm for filtering data sets. Finally, in order to optimize the ball stud manufacturing process, it is necessary to develop a quality prediction model linked to the forging and cutting processes.

In this study, as part of the paradigm shift for manufacturing innovation, data from the multi-stage cold forging process was collected and based on this, a big data analysis technique was introduced to examine the possibility of quality prediction. In order for the analysis algorithm to be applied, the data collection infrastructure corresponding to the independent variable affecting the quality was built first. Similarly, an infrastructure for collecting data corresponding to the dependent variable was also built. In addition, a data set was created in the form of an independent variable-dependent variable, and the prediction accuracy of the quality prediction model according to the traditional statistical analysis and the tree-based regression model corresponding to the big data analysis technique was compared and analyzed. Lastly, the necessity of changing the manufacturing environment for the use of big data analysis in the manufacturing process was added.

Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 121.7% higher than the selfish algorithm.

본 연구는 객관적인 데이터 기반 방법을 통해 인간 중심 디자인 과정을 효과적으로 보완하는 디자인 프로세스를 제시한다. 즉, 주관적 방법에 의한 인간 중심 디자인 프로세스에서 결여되는 객관성이 데이터 기반 접근에 의해 보완되 어 숨겨진 사용자의 니즈를 효과적으로 발견하는 프로세스로 발전될 수 있다. 이에 본 연구에서는 설문조사 데이터 마이닝 분석 과정과 공동 디자인 프로세스가 접목된 인간 중심 디자인 프로세스를 제시하며, 스마트 난방복 사례연구를 통해 이를 검증한다. 설문조사 데이터 마이닝 분석 과정에서는 클러스터링과 의사결정 나무의 두 가지 분석 방법이 사용된다. 클러스터링은 타겟 그룹을 선정하는 기준이 되는 페르소나의 초안을 제시하며, 의사결정 나무는 제품 구매에 중요한 사용자 인식 속성 파악과 사용자 가치 체계를 일차적으로 제안한다. 이후 데이터 분석을 통해 얻어진 광범위한 관점에 대하여 타겟 그룹을 대표하는 사용자가 직접 참여하는 공동 디자인 프로세스가 수행되며 맞춤형 워크북을 이용 하여 신제품에 대한 사용자의 여정맵, 니즈, 아이디어, 가치 체계 등을 체계적으로 도출한다. 본 논문에서 수행한 스마 트 난방복 사례 연구는 제안된 방법론의 적용성을 보여주고 있다.

This research suggests a design process that effectively complements the human-centered design through an objective data-driven approach. The subjective human-centered design process can often lack objectivity and can be supplemented by the data-driven approaches to effectively discover hidden user needs. This research combines the data mining analysis with co-design process and verifies its applicability through the case study on the smart heating jacket. In the data mining process, the clustering can group the users which is the basis for selecting the target groups and the decision tree analysis primarily identifies the important user perception attributes and values. The broad point of view based on the data analysis is modified through the co-design process which is the deeper human-centered design process by using the developed workbook. In the co-design process, the journey maps, needs and pain points, ideas, values for the target user groups are identified and finalized. They can become the basis for starting new product development.

본 논문은 조사 연구와 빅데이터 분석을 바탕으로 여성 가방에 대한 단계별디자인 프로세스를 구축하였다. 이 디자인 프로세스는 조사 연구, 개념 도출, 단계별 디자인, 디테일 개선 등 네 가지 단계로 구분되어 있다. 여성 가방 디자인에 영향을 미치는 요소의 비중에 근거하여 핵심 요소 데이터를 분석하고, 신제품 디자인과 관련된 요소를 추출 하여 개념을 조합한 뒤 단계별로 나누어 디자인 실험을 진행하였다. 그로써 디자인에 적용 가능한 비중을 가늠하고, 최종적으로 신제품 디자인에 적용하였다. 이 프로세스는 디자이너에게 새로운 디자인 각도를 제공해 패션 디자인의 실효성과 실용성을 높일 수 있다는 것이 실험으로 입증됐다.

Based on survey and data analysis, this paper established a separate and hierarchical design process for lady bags. The design process is divided into four parts: survey, concept extraction, separation and hierarchy design and detail improvement. In light of the influence value grade of lady bag design elements, the data of key elements of lady bags were analyzed, and elements related to new product design were extracted to form conceptual elements, and integrated into design experiments at different levels. Then, their usable proportion in design was measured and applied in design to complete the design of new products. Through the experiment, this design process can provide designers with a new design perspective and improves the timeliness and practicability of fashion design.

This study examines how the Korean government should prepare for changes in the ICT and data markets. The government is emphasizing openness and innovation as well as improving efficiency and effectiveness. It insists that the public sector, public policy, and public service need more open and innovative. An open government is an open governance system that observes and considers how the roles and behaviors of other actors in the state and society are changing, not just the situations that occur within the public sector. Because it is difficult to accurately define the social problems facing the government alone, it is necessary to cooperate with other entities outside the government by enhancing openness, transparency, and participation based on ICT. Standards are required to strengthen universality in technical aspects such as interoperability, and supporting standard service quality. These can be used in other countries for processes such as procurement, planning, and decision- making. By examining the historical evolution and preparation process of open government from e-government, this study tries to derive the essential elements for building a successful platform government in the future.

This paper presents how Fuzzy Cognitive Map (FCM) technique can be used for open data analytics for a policy decision maker to support the policy impact evaluation using the example of Policy Compass, an EU research project. The practical usage example on drug policy shows the potential of FCMs as a policy impact modelling and decision support tool that can utilise the open data. Through the provision of a more intuitive and easier means of using open data based on FCM techniques, the Policy Compass project can play a critical role for both policy maker and lay public to evaluate the policy impact and prepare for future policy making.

In the process of analyzing the case of whether the insurance company shall have the obligation to pay the insurance benefits for the insured’s death resulted from his or her drunk driving, the following sequence is followed. Firstly, carry out the academic theory promotion. Secondly, connect the process of academic theory promotion and judicial promotion. Then, make the judicial function-oriented big data observation. Finally, choose the litigation strategy and judicial judgment path. Judging from the connection degree of proof, evidence and fact, Article 45 of the Insurance Law of the People’s Republic of China (hereinafter referred to as the “Insurance Law”) only needs proving that the insured deliberately commits a crime, and there is no need to procedurally certify whether the insurer’s obligation to expressly explain those clauses that exempt the insurer from liability in the insurance contract is performed or not. Article 45 of the Insurance Law stipulates that where the insured deliberately commits crimes or resists the criminal compulsory measures adopted in accordance with applicable laws that cause him/her injured, disabled or dead, the insured shall not be liable for paying the insurance benefits. If the premium has been paid for more than two full years, the insurer shall return the cash value of the insurance policy as agreed in the contract. The core fact of the case “the insured dies due to drunk driving” is applied to this article, which can be intercepted as if the insured’s death results from him or her intentionally committing a crime, the insurer shall have no obligation to pay the insurance benefits. The death of the insured resulting from him or her intentionally committing a crime includes two possibilities. Firstly, the insured dies before the criminal judgment is made on the insured’s intentional crime. Secondly, the insured dies after the criminal judgment is made on the insured’s intentional crime. If the standard of proof of criminal crime is strictly followed, in the case of “the insured dies before the criminal judgment is made on the insured’s intentional crime”, it will fall into the following paradox: the insured cannot be prosecuted after his/her death, and the insured’s intentional crime cannot be confirmed without a lawsuit. The standard of proof of the “the insured’s intentional crime” should accord with the characteristic of the proof standard of civil ruling—— high probability. There exists a contradiction between the interpretation of the criminal proof standard in Article 22 of the Interpretation of the Supreme People’s Court on Several Issues concerning the Application of the Insurance Law of the People’s Republic of China (III) and the civil proof standard implied in Article 45 of the Insurance Law. Under the premise of the lack of effective legal documents of criminal investigation organs, prosecutorial organs and judicial organs (hereafter referred to as the “three organs”), the understanding and scope of other conclusive opinions of the three organs has become the key to resolving the aforementioned contradiction. From the perspective of semantics, the content connected by the word “or” in judicial interpretation is juxtaposed. Other conclusive opinion documents of the three organs must be juxtaposed with the effective legal documents of the three organs. Other conclusive opinions in practice include judicial expertise documents affirmed by the three organs and traffic accident responsibility confirmations (judicial expertise documents themselves are not conclusive opinions because the three organs are not the subject of appraisal institution. Therefore, they belong to other conclusive opinions only when the judicial expertise documents are adopted and affirmed by the three organs.) Understanding the basic functions of big data in legal research requires resorting to psychology, behavioral psychology and social psychology. This is true for both the analysis of the main elements that produce big data and that of the bodies that use big data because the usefulness of big data to the methodology of jurisprudence is actually that for legal researchers and legal practitioners. In the process of giving concluding opinions on cases, the application sequence and value category of judicial big data should be scientifically and rationally configured, and combined with the psychological motivation of big data users to observe. The application of judicial big data should follow the following logical sequence. First, the acquisition and use of judicial big data should be based on the fact that concluding opinions have been made on the case and various possible solutions of the case have been prejudged. Second, judicial big data has limited error correction function. Finally, judicial big data has certain complementary functions. The above-mentioned process is only the primary function of judicial big data. The deeper function is to conduct further case tracking on the judgments’ and case facts’ differences between the case prejudging solution and judicial big data. The process also includes an in-depth comparison and study of facts, evidence and judgments between the cases to further clarify, confirm, deny and choose the case prejudging solution. Judicial big data is likely a high-definition camera. The judgment results of similar cases are clear at a glance. Under the premise that the parties and legal professionals are in the radiation range of this high-definition camera, the judicial function-oriented decision at least affects the selection result of the case-solving solution. The reasons are as follows: (1) The convergence psychology of judges and lawyers often leads them to choose the case handling solutions that account for a large proportion in judicial big data. (2) The parties are more likely to question or reject the judgment that accounts for a small proportion in judicial big data. If the judgment that accounts for a small proportion is based on, the appeal rate will increase. Combined with judicial big data, it is concluded that the case can be judged in accordance with Article 45 of the Insurance Law.

빅데이터 기술의 발달로 4차 산업혁명이 시작되면서 스마트 팩토리에 대한 관심이 증가하고 있 다. 제조업에서는 여러 종류의 데이터들이 기하급수적으로 증가하고 있지만 관리하기가 어렵고, 데이 터가 수집되어도 중요한 데이터를 찾기 힘들뿐더러 어떠한 데이터를 어떻게 사용하여야 할지도 알 수 없다. 또한, 기존의 공정에서는 공정물품에 대해 양품과 불량품만을 구분하여 불량품에 대해 작업자가 직접 눈으로 가성불량품과 불량품을 구별하였다. 이 경우 시간이 오래 걸릴뿐더러 작업자의 상태에 따 라 생산성이 낮아지는 현상이 발생한다. 본 논문에서는 이러한 문제점을 해결하기 위해 딥러닝을 이 용한 제조 공정 영상을 분류하는 기법을 제안한다. 제안하는 방법은 CNN(Convolutional Nueral Network)를 이용하여 화상검사 공정에서 결과로 나오는 2588*1940 크기의 영상에 대해 양품, 불량 품, 가성불량(조명, 퓨즈, 뒤틀림(왜곡))에 대해 학습시켜 분류하고 테스트한다. 그 결과로 양품과 진성 불량품에 대해 98% 정확도를 확인하였고, 가성불량에 대해서는 93%의 정확도를 확인할 수 있었다.

Interest in smart factory is increasing with the growth of 4th industrial revolution due to the development of big data technology. Diverse kinds of data in the manufacturing industry are growing exponentially, but it is difficult to manage, and even if the data is collected, it is hard to find important data and find the appropriate way to use the data. In addition, the worker sorted out defective products with the pseudo-defective products only through his/her direct eyes in the original manufacturing process. This takes long time and also is influenced a lot by the individual workers’ capability. In this paper, we propose a manufacturing process image classification method using deep learning to solve these problems. The proposed method uses CNN(Convolutional Neural Network) to learn the well-made, defective, and pseudo-defective (lights, fuse, distortion) products with the 2588*1940 size image that comes out as a result in the image inspection process. The outcomes show 98% accuracy in well-made and defective products, and 93% accuracy in pseudodefective products.

현대 사회에서 안전 서비스 및 시스템 환경을 구축하고 활용하는 것은 매우 중요하고 관심이 큰 분야이다. 특히, 어린이, 고령자, 여성, 장애인 및 외국인 등 사회적 취약 계층에 대한 안전 서비스 제공은 사회적 이슈가 되고 있다. 그러나, 대부분의 안전 서비스 및 시스템은 일반인을 대상으로 적용되고 있기 때문에 사회적 취약 계층을 위한 시스템 개발이 필요하다. 따라서, 본 논문에서는 실시간으로 상황을 인식하고, 신속한 대응을 할 수 있도록 데이터를 처리, 전송 하는 시스템 모듈을 개발하였다. 이를 위해, 실시간 상황 인식에 필요한 다양한 센서를 통합 모듈로 설계하고, 이를 통해 수집된 데이터를 분석하여 처리 결과를 전송하는 안전 시스템 모듈을 개발하였다.

In modern society, developing and utilizing safety service and system environments is a very important and great interest. In particular, the provision of safety services to socially vulnerable groups such as children, the elderly, women, the disabled and foreigners has become a social issue. However, since most safety services and systems are applied to the general public, it is necessary to develop systems for socially vulnerable groups. Therefore, in this paper, we developed a system module that processes and transmits data to recognize the situation in real time and respond quickly. To this end, various sensors for real-time situation recognition were designed as integrated modules, and a safety system module was developed to analyze the collected data and transmit the processing results.

최근 기업의 각 업무가 정보화되면서 부문별, 업무별 정보시스템의 데이터 간에 심각한 중복성과 불일치성의 문제가 대두되면서 데이터 품질관리에 관심이 집중되고 있다. 본 연구는 실제로 데이터 품질 관리 프로세스 개선을 통해 데이터 품질이 향상된 기업의 사례를 통하여 프로젝트 수행 과정에서 의 주요 이슈와 위험요인을 살펴보고 그 해결방안을 제시함으로써 데이터 품질 향상을 위해 노력하는 타 기업들에게 도움을 주고자 하였다. 또한, 개선된 데이터 품질 관리 프로세스에 대한 다차원적인 평가로서 데이터 품질, 생산성, 고객만족도, 조직 및 문화의 측면에서 정성적이고 정량적인 지표를 통한 개선효과를 살펴보고 평가함으로써 제안된 프로세스에 의해 품질수준이 향상되었음을 검증하였고 평가 분석을 통한 시사점을 도출하였다.

In this study, we proposed a simulator for the development of a digital multi-process welding machine and a welding process monitoring system. The simulator, which mimics the data generation process of the welding machine, is composed of process control circuit, peripheral device circuit, and wireless communication circuit. Utilizing this simulator, we aimed to develop a welding process monitoring system that can monitor the welding situations of four multi-process welding machines and three processes each, with data transmission through wireless communication. Through the operation of the proposed simulator, sequential digital processing of multi-process welding data and wireless communication were achieved. The welding process monitoring system enabled real-time monitoring and accumulation of the process data. The selection of upper and lower limits for process variables was carried out using a deep neural network based on allowable changes in bead shape, enabling the management of welding quality by applying a process control technique based on the trend of received data.

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.