Earticle

IT 기술이 발달함에 따라 센서･모바일을 기반으로 사물에 정보를 담아 네트워크로 상호연계되는 유비쿼터스 정보기술이 발달하고 있다. 그러나 서버에 저장되어 있는 데이터를 손쉽게 사용하기 위한 보안 해결책이 미미한 상태이다. 본 논문에서는 빅 데이터 서비스에서 제공되고 있는 대용량 데이터를 사용자가 안전하게 처리하기 위해서 빅 데이터 서비스에 사용되는 데이터에 다중의 속성을 해쉬 체인 기법에 적용한 데이터 관리 기법을 제안한다. 제안 기법은 빅 데이터 서비스에 사용한 데이터의 종류, 기능, 특성에 따라 데이터의 속성을 분류하여 분류된 속성 정보를 해쉬 체인으로 묶어 데이터의 안전성을 향상시켰다. 또한, 제안 기법은 여러 지역에 분산된 데이터를 손쉽게 접근하기 위해서 데이터 속성 정보를 해쉬 체인의 연결 정보로 활용하여 빅 데이터의 접근 제어를 분산 처리하였다.

Put your information in the object-based sensors and mobile networks has been developed that correlate with ubiquitous information technology as the development of IT technology. However, a security solution is to have the data stored in the server, what minimal conditions. In this paper, we propose a data management method is applied to a hash chain of the properties of the multiple techniques to the data used by the big user and the data services to ensure safe handling large amounts of data being provided in the big data services. Improves the safety of the data tied to the hash chain for the classification to classify the attributes of the data attribute information according to the type of data used for the big data services, functions and characteristics of the proposed method. Also, the distributed processing of big data by utilizing the access control information of the hash chain to connect the data attribute information to a geographically dispersed data easily accessible techniques are proposed.

소셜 네트워크와 스마트폰의 대중화로 인하여 서버에 저장되어 있는 데이터를 손쉽게 사용할 수 있는 빅데이터 서비스가 증가하고 있다. 빅 데이터 처리기술은 빅 데이터 서비스에서 가장 중요한 기술 중 하나지만 보안에 대한 해결책이 미미한 상태이다. 본 논문에서는 빅 데이터 서비스에서 제공되는 분산된 대용량 데이터를 이중 해쉬를 이용하여 사용자가 손쉽게 데이터에 접근할 수 있는 다중 해쉬 체인 기반의 데이터 분산 처리 기법을 제안한다. 제안 기법은 빅 데이터를 데이터의 종류, 기능, 특성에 따라 해쉬 체인으로 묶어 데이터에 높은 처리량을 지원한다. 또한, 토큰 및 데이터 노드가 공격자에게 노출되었을 때 발생하는 보안 취약점을 해결하기 위해서 데이터의 속성 정보를 해쉬 체인의 연결 정보로 활용하여 빅 데이터의 접근 제어를 분산 처리한다.

Social network server due to the popularity of smart phones, and data stored in a big usable access data services are increasing. Big Data Big Data processing technology is one of the most important technologies in the service, but a solution to this minor security state. In this paper, the data services provided by the big -sized data is distributed using a double hash user to easily access to data of multiple distributed hash chain based data processing technique is proposed. The proposed method is a kind of big data data, a function, characteristics of the hash chain tied to a high-throughput data are supported. Further, the token and the data node to an eavesdropper that occurs when the security vulnerability to the data attribute information to the connection information by utilizing hash chain of big data access control in a distributed processing.

South Korea has allowed parallel importation since 1995. Parallel importation causes competition among importers in the logistics process allowing, consumers to purchase foreign brand products at low prices. Most parallel importers base product pricing on subjective judgements. Fashion products in particular, have different sales rates depending on trends and seasons, so sales performance varies greatly depending on selling price timing and policy. The merchandiser (MD) set the price on parallel importation products by aggregating information on imported products and pricing goods. However, this customized process is very time consuming for the MD. This is because the logistics process of parallel importation's customs clearance procedures and repair works is complicated and takes a significant amount of time. In this paper, we propose an improved parallel importation logistics process based on big data, which automatically sets the price of parallel importation products.

The premise of this research is the recent advancement of Building Information Modeling(BIM) Technology and Laser Scanning Technology(3D Scanning). The purpose of the paper is to amplify the potential offered by the combination of BIM and Point Cloud Data (PCD) for structural analysis. Today, enormous amounts of construction site data can be potentially categorized and quantified through BIM software. One of the extraordinary strengths of BIM software comes from its collaborative feature, which can combine different sources of data and knowledge. There are vastly different ways to obtain multiple construction site data, and 3D scanning is one of the effective ways to collect close-to-reality construction site data. The objective of this paper is to emphasize the prospects of pre-scanning and post-scanning automation algorithms. The research aims to stimulate the recent development of 3D scanning and BIM technology to develop Scan-to-BIM. The paper will review the current issues of Scan-to-BIM tasks to achieve As-Built BIM and suggest how it can be improved. This paper will propose a method of coordinating and utilizing PCD for construction and structural analysis during construction.

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.

The yield and quality of a micromanufacturing process are important management factors. In real-world situations, it is difficult to achieve a high yield from a manufacturing process because the products are produced through multiple nanoscale manufacturing processes. Therefore, it is necessary to identify the processes and equipment that lead to low yields. This paper proposes an analytical method to identify the processes and equipment that cause a defect in the plastic ball grid array (PBGA) during the manufacturing process using logistic regression and stepwise variable selection. The proposed method was tested with the lot trace records of a real work site. The records included the sequence of equipment that the lot had passed through and the number of faults of each type in the lot. We demonstrated that the test results reflect the real situation in a PBGA manufacturing process, and the major equipment parameters were then controlled to confirm the improvement in yield; the yield improved by approximately 20%.

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.

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.

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.

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.

제조 산업은 국가 경제 성장의 원동력으로 그 중요성이 부각되고 있다. 이에 따라 제조 공정상에서 생성되는 제조 데이터 분석의 중요성 또한 조명 받고 있다. 본 논문에서는 PCB(Printed Circuit Board) 제조 공정에서 발생한 로그 데이터를 분석하여 PCB 상에서 빈번하게 발생하는 고장 영역에 대해서 작업자가 고장 영역을 직접 눈으로 볼 수 있도록 시각화하는 방법을 제안한다. 우선 고장 영역을 파악하기 위해서 PCB 공정 데이터 집합에 K-means, DB-SCAN 클러스터링 알고리즘을 적용하여 군집화 하였고, 두 알고리즘 중 더 정확한 고장 영역을 도출하는지 비교하였다. 또한 MVC(Model-View-Controller) 구조 시스템을 개발하여 실제 PCB 이미지 상에 클러스터링 결과를 출력하는 것으로 실제 고장영역을 눈으로 확인할 수 있도록 시각화하였다.

Recently, there is an increasing interest in analysis of big data that is coming from manufacturing industry. In this paper, we use PCB (Printed Circuit Board) manufacturing data to provide manufacturers with information on areas with high PCB defect rates, and to visualize them to facilitate production and quality control. We use the K-means and DBSCAN clustering algorithms to derive the high fraction of PCB defects, and compare which of the two algorithms provides more accurate results. Finally, we develop a system of MVC structure to visualize the information about bad clusters obtained through clustering, and visualize the defected areas on actual PCB images.

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.