Earticle

본 연구는 조선 산업의 선박 건조 공사 수행 과정에서 불규칙적으로 발생하는 재작업문제에 대한 분석 및 개선 방안을 제시하고자 하였다. SPP조선의 SAP(ERP) 외주공사 재작업 이벤트 로그를 수집하여 정제하고, 프로세스 마이닝 방법 중 퍼지 마이닝, 패턴 분석, 보틀넥 분석으로 프로세스 발견 및 분석을 실시하였고 결과 및 시사점을 도출하였다. SPP조선의 재작업은 크게 선주 요구사항, 설계 개선 사항, 현장 작업 도 미숙의 원인이 되어 생산 재작업, 설계 변경 재작업이 발생함을 발견하였다. 또한 재작업 프 로세스에서 가장 많이 발생하는 패턴인 PB(선체도장)-2A(선주요구)-P1(생산 재작업)공사의 사 전 방지를 위해 사례분석 결과의 방법을 수행해야한다. 시사점으로는 먼저 조선 산업에서 본 공사 후 재작업 공사 프로세스 모델 도출 및 원인 분석 으로 재작업 예방 및 최소화, 생산성 향상, 비용감소에 기여하였다. 둘째, 프로세스 마이닝 방법 론을 국내 조선기업에 대한 재작업 사례를 분석함으로서 추후 연구의 가이드라인을 제시하였다. 셋째, 기업 관리자들에게 프로세스 모델, 문제점 및 원인들을 체계적으로 보여주어 업무 능력 향상에 기여하였다는 점에서 의의를 가진다.

The purpose of this study is to analyze and remedy the irregular rework problems in shipbuilding industry. We collected and refined the rework event log of the SAP (ERP) outsourcing company of SPP shipbuilding. The process discovery and analysis were performed by fuzzy mining, pattern analysis and bottle neck analysis among process mining methods. The reworking of SPP shipbuilding was found to cause production rework and design change rework as ship owner 's requirement, design improvement, and field work were also causes of immaturity. In addition, method of case analysis result should be carried out in order to prevent PB(ship painting)-2A(ship owner request)-P1(re-work of production) which is the most common pattern in rework process. First, in the shipbuilding industry, the reworking process model and the analysis of the cause of rework contributed to prevention and minimization of rework, productivity improvement and cost reduction. Second, the process mining methodology is analyzed and the guidelines for future research are presented. Finally, it has a significance in that it contributes to improvement of business ability by systematically showing process model, problems and causes to enterprise managers.

Mobile devices are the most important equipment for accessing various kinds of services. These services are accessed using wireless signals, the same used for mobile calls. Today mobile services provide a fast and excellent way to access all kinds of information via mobile phones. Mobile service providers are interested to know the access behavior pattern of the users from different locations at different timings. In this paper, we have introduced an associated tree for analyzing user behavior patterns while moving from one location to another. We have used four different parameters, namely user, location, dwell time, and services. These parameters provide stronger frequent accessing patterns by matching joins. These generated patterns are valuable for improving web services, recommending new services, and predicting useful services for individuals or groups of users. In addition, an experimental evaluation has been conducted on simulated data. Finally, performance of the proposed approach has been measured in terms of efficiency and scalability. The proposed approach produces excellent results.

Abnormal data in the manufacturing process makes it difficult to find useful information that can be applied in data management for the manufacturing industry. It causes various problems in the daily process of production. An issue from the abnormal data can be handled by our method that uses big data and visualization. Visualization is a new technology that transforms data representation into a two-dimensional representation. Nowadays, many newly developed technologies provide data analysis, algorithm, optimization, and high efficiency, and they meet user requirements. We propose combined production of the data visualization approach that uses integrative visualization of sources of abnormal pattern analysis results. The perceived idea of the proposed approach can solve the problem as it also works for big data. It can also improve the performance and understanding by using visualization and solving issues that occur in the manufacturing process with a calendar heat map.

This study depicts a Scientometric technique of Bifurcation Research output in India for around five years during 2016-2020. It is an active field of analysis in which the bulk of investigation is being done. Scopus is an advanced research platform, assisting in finding, probing and yielding information in the sciences, social sciences, arts, and humanities. The contemporary study discusses the "Bifurcation" as reflected in SCOPUS for 2016-2020. This study examines the highly prolific authors, document type etc. The study exhibited that 2017 shown maximum research papers 1700 (20.44%), the lowermost maturity rate was deciphered in 2018 (19.65%), the standard value of the period of collaboration is 0.18. The time series examines for single-authored, multi-authored, and collaborative papers are 137.8, 1564 and 1701.8 respectively.

In this paper, the Self-Organizing Feature Maps (SOFM) neural network is applied to analyse the multi dimensional process data, and to diagnose the inter-relationship of the process variables in a real municipal sewage treatment plant. The data set had been collected from a sewer system in the Gwangyang city, Korea. The data had been measured in the period of 1st January, 2004 and 31th December, 2006. The data set contains daily averaged values for each of the twenty three variables. Through the component planes visualization, it is evident that the effluent is related to rainfall, flow rate, temperature, MLSS, SRT, RAS and DO. Especially, rainfall, flow rate and temperature are the most important driving force to increase in effluent levels in the Gwangyang municipal sewage treatment plant. It is concluded that the SOFM technique provides an effective analyzing and diagnosing tool to understand the system behavior and to extract knowledge contained in multi-dimensional data of a large-scale sewage treatment plant.

The global population is increasing rapidly because of increasing urbanization and such increasing urbanization directs the up-growing need of urban safety and preventions. This urbanization is also responsible for two things that is increased job opportunities and increased the crime rates. In this era technology has gone far more forward in a positive way. By making use of these technologies such as machine learning, artificial intelligence and big data we presented an approach through which crime pattern analysis is done. We have used apache spark (scala-programming) and machine learning algorithm for predictive crime pattern analysis. The data that we have used is a real-world data set based on Chicago city of United State of America. Our main goal of work is to define a predictive crime analysis which shows top crime patterns related to the top community areas of Chicago city.

The coat and muzzled colors in mammals are determined by the relative distribution of pheomelanin and eumelanin. In melanocytes, the expression of these two pigments is controlled by the melanocortin 1 receptor (MC1R) as well as by agouti locus alleles. For example, high expression of the AC (Adenylate cyclase 1) family genes, which interact with MC1R may affect the expression of CREB. leading to the over-expression of melanogenesis-associated genes such as PRKACB and CAMK2α. Further, increased expression of genes involved in tyrosine metabolism, such as TYR, is likely to affect the synthesis of eumelanin in the dark-muzzle cows. Our previous result, that the found intriguing distinction between the dark and light muzzle with respect to the expression of genes involved in the MAPK vs. Wnt signaling pathways. The aim of the present study was to identify melanogenesis-associated genes that are differentially expressed in the yellow and brindle coat of Korean native cattles. The first results, The expressed evaluation of MC1R protein were mostly highly between KBC to KDC, and there level at ST of KDC was higher than we expected, and decreased expressed in large sweat gland of epidermis zone. The RT-PCR analysis found that MC1R expressed at the KBC showed the same results like it was localization analyzed. Especially, levels of MC1R mRNA were elevated in HC compared to another group, while that the expression mostly higher in KDC. Next secondly results, was showed that the associated- genes of Want and MARK pathway were differential expression between KBC and KHC. Especially, in the KBC of malanogenesis major genes expression was highly, but the Want and minor genes were highly expression from KHC. In addition, analysis result of DNA methylation in Bos taurus genomes to survey the coat color–related epigenetic markers (differential methylated CpGs, DM CpGs) by RRBS was the appears that KBC has the biological functions of the DM CpG-associated genes. Regarding the increasing interest in the genetic diversity of cattle stocks, genes we identified for differential expression of melanogenesis and DM CpG-associated genes in the KBC vs. KHC may serve as novel markers for genetic diversity among cattle based on the coat color phenotype.

The application of the information and communication technologies (ICTs) towards sound solid waste management (SWM) is a challenging research area. This study is aimed at identifying evolving technological trends, competitor’s distribution and technological convergence pattern between ICTs and SWM technology. A total of 1041 patents’ applications that were submitted to Korean Intellectual Property Office (KIPO) from 1996 to 2016 were investigated as a dataset. Convergence patterns were obtained by applying the association rules of the International Patent Classification (IPC) codes. The results show some related technological fields where convergence technologies are mostly adopted and can contribute to establishing the development strategies of the ICTs - SWM technology in Korea.

Spatial point-patterns analyses were conducted to provide insight into the ecological process behind competition and mortality in two lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) stands, one in the Lower Foothills, and the other in the Upper Foothills natural subregions in the boreal forest of Alberta, Canada. Spatial statistical tests were applied to live and dead trees and included Clark-Evans nearest neighbor statistic (R), nearest neighbor distribution function (G(r)), and a variant of Ripley’s K function (L(r)). In both lodgepole pine plots, the results indicated that there was significant regularity in the spatial point-pattern of the surviving trees which indicates that competition has been a key driver of mortality and forest dynamics in these plots. Dead trees generally showed a clumping pattern in higher density patches. There were also significant bivariate relationships between live and dead trees, but the relationships differed by natural subregion. In the Lower Foothills plot there was significant attraction between live and dead tees which suggests mainly one-sided competition for light. In contrast, in the Upper Foothills plot, there was significant repulsion between live and dead trees which suggests two-sided competition for soil nutrients and soil moisture.

The purpose of this study was to comparatively analyze normal gait on the plains by gender for old people reference data for the normal gait pattern for the old people. Participants were selected according to the Korean standard body type provided by the Ministry of Health and Wel-fare and used a three-dimensional motion analysis system. Cortex, Or-thotrak, and Excel were used as the software for analyzing the extract-ed data, and IBM SPSS Statistics ver. 24.0 was used for statistical anal-ysis. When data standardization was performed using the dimension-less numbers conversion, the step length and stride length of the lower extremities, which had differences between genders before dimension-less numbers conversion, showed no difference after dimensionless numbers conversion. Cadence, step time, and single support time of the left lower extremity, which had no difference between genders before dimensionless numbers conversion, were found to have significant dif-ferences after dimensionless numbers conversion. In addition, as a re-sult of analyzing the coefficient of variation value to find out the degree of change in data due to dimensionless numbers conversion, there were increase and decrease in the coefficient of variation value rang-ing from -8.11% to 6.67% before and after dimensionless numbers con-version, which means dimensionless numbers conversion can affect the statistical test.

The game of Go represents a major challenge for artificial as well as human intelligences due to its profound complexity. Although computer programs capable of playing Go have existed for decades, the period from 2015 to 2025 has marked a turning point, with these systems achieving—and even exceeding—professional human performance. A notable shift in gameplay strategy emerged around 2016, driven by the adoption of unconventional yet effective moves demonstrated by AI programs, challenging previous conventions of preferred moves. Nowadays, the process of learning Go has evolved from traditional knowledge transfer, based on centuries of established conventions regarding strategy, positional judgement and move value to modern trends developed by AI playing style, lacking the explanatory heuristics inherent in both human style of playing and oral tradition. Facing this new challenge, we rely on our curiosity to understand our opponents’ moves beyond technological and language barriers as we continue to explore the mysterious depths of this timeless game.

The proliferation of social network services affects individual’ lifestyle. Users get a lot of information through social network services. Accordingly, the social network services are useful tools, as means of promotion and marketing. In this research, we analyze the information exchange network using social network analysis in a specific-purpose trade show. That is, social features of interested parties (organizer, exhibitors and visitors) can be analyzed in information exchange network of the trade show. The aim of this research is that how the social network services influence the behavior of interested parties. We expected that the analysis can utilize the social features of interested parties to design better trade shows for promotion and be a role as an exploratory analysis for discovering a useful communication channel.

Mobile applications (apps) have become central to the digital economy, yet the proliferation of Dark Patterns—deceptive interface designs hindering user autonomy—increasingly undermines user experience (UX) and trust, posing a significant challenge. While this issue is internationally recognized (e.g., by the OECD), systematic analysis leveraging large-scale app store review data, which captures authentic user voices, to investigate the prevalence of dark patterns and compare their characteristics across service types remains limited. This study addresses this gap by applying text mining techniques to user reviews from major South Korean mobile apps, focusing on leading e-commerce and OTT streaming platforms. The primary objective is to exploratorily identify core complaint themes, keywords, and specific user experience narratives related to Dark Patterns within this dataset. Furthermore, the research aims to deepen the multifaceted understanding of the issue by comparatively analyzing whether distinct patterns of Dark Pattern-related complaints emerge across these different service types. Methodologically, the study involves collecting review data, preprocessing it using Natural Language Processing (NLP), applying LDA topic modeling and keyword analysis to uncover complaint patterns, and qualitatively examining review texts for contextual insights. The findings are expected to provide empirical evidence on the real-world landscape of Dark Pattern issues in the Korean app ecosystem, demonstrating the utility of text mining for this purpose. This research will contribute to the academic discourse on Information Systems (IS) design and UX, while offering practical insights for businesses towards ethical interface improvements and informing future consumer protection policies and regulatory considerations.

Recently, microorganism studies increases have provided potential applications and challenges for all organisms. Understanding how the microorganisms' diverse metabolisms have an essential effect on other organisms is crucial. Isolated bacteria from cold environments and have the potential to play various roles at low temperatures. The genetic pattern analysis of carbohydrateactive enzymes (CAZymes) in isolated bacteria from cold environments can help to determine how they adapt and survive in extreme environments. In a genome sequence-based, this study provides insights into the diversity of redundant patterns of CAZyme for finding new compounds and active enzymes that can endure the cold environment of microbial. Genome-wide comparisons confirmed that AA3, AA4, CE1, CE4, CE9, CE14, GH3, GH13, GH23, GT2, GT4, GT51, and CBM48 families of CAZyme were highlighted from isolated bacteria genomes from the cold environment. The CAZyme pattern of cold environment microbial can infer that energy is obtained from polysaccharides found in crustaceans, algae, and grasses in cold environments, such as glycogen, cellulose, hemicellulose, and chitin. These overall results explained the genome sequence based CAZyme pattern analysis of cold environment microbial. The most visible patterns were groups of enzymes that could degrade the main components of a fungal cell wall or the shell components of crustaceans. Thus, In this study provides a way for isolated bacteria from a cold environment to utilize polysaccharide or carbohydrate degradation as an energy source to generate energy in cold environments and provides a foundation for understanding survival adaptation mechanisms under cold adaption.

Recently, microorganism studies increases have provided potential applications and challenges for all organisms. Understanding how the microorganisms' diverse metabolisms have an essential effect on other organisms is crucial. Isolated bacteria from cold environments and has the potential to play various roles at low temperatures. The genetic pattern analysis of carbohydrate-active enzymes (CAZymes) in isolated bacteria from cold environments can help to determine how they adapt and survive in extreme environments. In a genome sequence-based, this study provides insights into the diversity of redundant patterns of CAZyme for finding new compounds and active enzymes that can endure the cold environment of microbial. Genome-wide comparisons confirmed that AA3, AA4, CE1, CE4, CE9, CE14, GH3, GH13, GH23, GT2, GT4, GT51, and CBM48 families of CAZyme were highlighted from isolated bacteria genomes from the cold environment. The CAZyme pattern of cold environment microbial can infer that energy is obtained from polysaccharides found in crustaceans, algae, and grasses in cold environments, such as glycogen, cellulose, hemicellulose, and chitin. These overall results explained the genome sequence based CAZyme pattern analysis of cold environment microbial. The most visible patterns were groups of enzymes that could degrade the main components of a fungal cell wall or the shell components of crustaceans. Thus, in this study provides a way for isolated bacteria from a cold environment to utilize polysaccharide or carbohydrate degradation as an energy source to generate energy in cold environments and provides a foundation for understanding survival adaptation mechanisms under cold adaption.

바둑은 적어도 2,500년 이상의 역사를 지녔고 그동안 인간 고유의 게임 영역으로 여겨왔으나, 2016년 컴퓨터 바둑인 알파고에 의해 제압된 마지막 보드게임이 되었다. 바둑에서의 사활문제는 컴퓨터 바둑을 구축 시 반 드시 해결해야 되는 기본 문제 영역이 된다. 연역적 추론은 이미 알고 있는 판단을 근거로 새로운 사실을 추 론하는 논리학의 용어이다. 본 논문에서는 연역적 추론을 위해 제약 충족 방법을 활용하여 사활문제와 직결 되는 3궁, 4궁, 5궁, 6궁의 원형 안형을 표현하는 4-튜플의 형식을 찾고자 했다. 이후 생성된 4-튜플의 형식 을 갖고 점 패턴 매칭을 활용하여 각 궁도의 원형 안형의 갯수를 파악하고자 했다. 실험 결과에 따른 4-튜플 형식의 갯수는 3궁 1개, 4궁 3개, 5궁 4개, 6궁 8개가 있음을 알 수 있었다. 또한 각 궁도의 원형 안형의 갯 수는 3궁 2개, 4궁 5개, 5궁 12개, 6궁 35개가 존재함을 찾아냈다. 마지막으로 컴퓨터 바둑에서의 사활문제 해결을 위해 원형 안형들을 4-튜플 형식의 변형인 5-튜플 형식으로도 제시하였다.

Go has a history of at least 2,500 years and has been regarded as a unique game field for humans, but in 2016 it became the last board game to be overpowered by alphaGo, a computer Go. The life-and-death problem in Go becomes a fundamental problem domain that must be solved when implementing a computer Go. Deductive reasoning is a term in logic that infers new facts based on known judgements. In this paper, we tried to find the 4-tuple forms representing the prototypical eye shapes of 3-eyes, 4-eyes, 5-eyes, and 6-eyes, which are directly related to the life-and-death problems, by using the constraint satisfaction method for deductive reasoning. Afterwards, we tried to figure out the number of prototypical eye shapes of each eyes by using point pattern matching with the generated 4-tuple form. According to the experimental results, we can see that the 4-tuple forms have 1 for 3-eyes, 3 for 4-eyes, 4 for 5-eyes, and 8 for 6-eyes. We also found that the numbers of prototypical eye shapes for each eyes are 2 for 3-eyes, 5 for 4-eyes, 12 for 5-eyes, and 35 for 6-eyes. Lastly, for solving the life-and-death problem in computer Go, we also presented prototypical eye shapes as the 5-tuple forms which are variants of 4-tuple forms.