Sentiment analysis is a method used to comprehend feelings, opinions, and attitudes in text, and it is essential for evaluating consumer feedback and social media posts. However, creating sentiment dictionaries, which are necessary for this analysis, is complex and time-consuming because people express their emotions differently depending on the context and domain. In this study, we propose a new method for simplifying this procedure. We utilize syntax analysis of the Korean language to identify and extract sentiment words based on the Reason-Sentiment Pattern, which distinguishes between words expressing feelings and words explaining why those feelings are expressed, making it applicable in various contexts and domains. We also define sentiment words as those with clear polarity, even when used independently and exclude words whose polarity varies with context and domain. This approach enables the extraction of explicit sentiment expressions, enhancing the accuracy of sentiment analysis at the attribute level. Our methodology, validated using Korean cosmetics review datasets from Korean online shopping malls, demonstrates how a sentiment dictionary focused solely on clear polarity words can provide valuable insights for product planners. Understanding the polarity and reasons behind specific attributes enables improvement of product weaknesses and emphasis on strengths. This approach not only reduces dependency on extensive sentiment dictionaries but also offers high accuracy and applicability across various domains.

This study aimed to suggest a suitable collar pattern by visually evaluating the appearance of the amount of collar drape by the starting position of the lapel line of a double-breasted tailored jacket using a 3d virtual fitting program. It created an avatar based on the mean size of women in their 20s (the 8th Size Korea) using clo network (double fastening: 10cm, collar width: 4.5cm, collar stand: 3cm, and lapel width: 8.5cm). The starting of the lapel twist line was waistline level, the 1/2 level of bustline and waistline, or bustline level, and collar laying amount was 4.5, 5.5, 6.5, or 7.5cm. It was evaluated by garment construction experts using 5, 6, and 4 items on the front, sides, and back, respectively. Descriptive statistics, F-test, Duncantest, and reliability analysis were conducted using SPSS 22. When collar laying amount was 6.5cm, it was best rated regardless of the starting point. Under waist line, when collar laying amount was 6.5cm, it was best rated regardless of the starting point. When collar laying amount was large, the collar’s outline length increased, resulting in unnecessary wrinkles from the neckline to the lapel, affecting the overall collar appearance. When collar laying amount was the smallest, the collar was lifted and the width was narrowed, exposing the seam connecting the collar and neckline. The length of the collar’s outline varied depending on collar laying amount, which was important to make the outline sit comfortably on the body.

This study analyzes consumer fashion purchase patterns from a big data perspective. Transaction data from 1 million transactions at two Korean fashion brands were collected. To analyze the data, R, Python, the SPADE algorithm, and network analysis were used. Various consumer purchase patterns, including overall purchase patterns, seasonal purchase patterns, and age-specific purchase patterns, were analyzed. Overall pattern analysis found that a continuous purchase pattern was formed around the brands’ popular items such as t-shirts and blouses. Network analysis also showed that t-shirts and blouses were highly centralized items. This suggests that there are items that make consumers loyal to a brand rather than the cachet of the brand name itself. These results help us better understand the process of brand equity construction. Additionally, buying patterns varied by season, and more items were purchased in a single shopping trip during the spring season compared to other seasons. Consumer age also affected purchase patterns; findings showed an increase in purchasing the same item repeatedly as age increased. This likely reflects the difference in purchasing power according to age, and it suggests that the decision-making process for purchasing products simplifies as age increases. These findings offer insight for fashion companies’ establishment of item-specific marketing strategies.

Cnidium officinale M. is an important crop that is widely used as a raw material for health functional foods. However, it is experiencing cultivation difficulties due to climate change and abnormally high temperatures. In response to this problem, the characteristics and main causes of the high-temperature damage occurring in C. officinale M. cultivation fields were analyzed. A survey of five farmhouse fields in Jecheon and Bonghwa, major C. officinale M. cultivation areas in Korea in 2018, indicated that about 5% to 37% of the cultivation fields in Jecheon and 5% to 15% of the fields in Bonghwa died from wilting. The high-temperature damage of the C. officinale M. fields is divided into two categories: upper leaves drying due to solar radiation and temperature, and lower leaves dying serially to the radiant heat of the vinyl mulch. Damage caused by radiant heat was typically greater. This is due to the greenhouse effect that occurs in the small space between the black vinyl mulching and the soil. The heat radiated to the surface of the ridge creating an environmental condition that greatly exceeded the atmospheric temperature especially on hot days. As a result, short plants with underground parts, such as C. officinale M., can suffer more high-temperature damage than other plants, so it is considered that it is necessary to develop related technologies such as mulching materials that can reduce pavement temperature in the future.

In order to optimising the sea traffic network efficiency, improving the safety of shipping and the protection of the environment, it is useful to model the sea network and its spatio-temporal characteristics of the ship patterns. These maritime patterns could also be an a-priori set of knowledge for the upcoming Maritime Autonomous Surface Ships (MASS) which are starting to navigate our seas with or without remote human controls. The above concepts are crucial and essential elements for defining and understanding the Maritime Situational Awareness (MSA). Nowadays the applied methodologies for modelling the maritime traffic use large scale of database for extracting the patterns. The Knowledge Discovery from Data (KDD), strictly connected with Data Mining (DM) is growing significantly to modelling the behaviour of the vessels in relations to their surroundings. This is just one example that confirms the growing up of the cloud computing usage for maritime applications too. Besides these applications there are also a continuous and fast evolution of the IT services, which more often than not means data centre scale-ups with consequent improve of power consumptions. This paper is a case study based on real world data assessing a multi-objective energy consumption analysis. It is based on the comparison between the traditional air conditioning structures known as Heating, Ventilation and Air Conditioning (HVAC) and the Free Cooling Technique (FCT) in order to reduce the data centre power consumption keeping the same number of computational calculations performed.

이 연구는 전염병의 잠재적 확산 가능성이 높은 지역의 탐색을 목적으로, 코로나19 전후의 버스 네트워크 클러스터의 시공간적 변화를 분석한다. 분석방법으로는 Getis와 Ord의  통계를 공간 네트워크로 확장 및 적용한  통계 값을 사용하였다. 이 과정은 서울시 전체 버스 네트워크의 개별 흐름에 대해 각각 적용되기 때문에 대규모 연산을 위해 병렬컴퓨팅 방식을 적용한 슈퍼컴퓨터를 사용하였다. 연구 결과, 첫째, 코로나19 이후 버스 네트워크가 일부 흐름으로 집중된 경향을 보였다. 둘째, 코로나19이 후의 버스 흐름은 주거지, 농업지로의 이동은 증가하고 상업지역, 교통지역으로의 이동은 감소했음을 확인하였다. 셋째, 중심업무 지구 중 여의도 방면의 클러스터, 구로디지털단지역 방면의 클러스터와 달리, 강남일대는 코로나19 전후의 유의미한 변화가 나타나 지 않았다. 이 연구는 국내에서 처음으로 코로나19전후의 버스 네트워크 클러스터를 확인하고 변화 특징을 제시한다는 의미가 있다.

This study is a follow-up of Lee (2018) providing a quantitative variationist analysis on the variation of English loanword expressions for ‘smartphone application’ in Korean: ayp, ephul, ayphullikheyisyen, and ephullikheyisyen. Two different data sets including search term frequency ratio from Naver Data Lab and sociolinguistic survey responses from 335 participants regarding the usage of the four loanword variants were examined to identify the usage pattern of the lexical variable. Both search term frequency data and survey responses confirmed that the usage of clipped variants, ayp and ephul, were clearly preferred to their full-formed variants. Logistic regression analyses on the survey data reported that survey takers with higher educational background and more experience in English speaking countries favored using ayp and ayphullikheyisyen. This study argues that Korean speakers with higher education background and more exposure to English favored ay- variants because they considered those variants as more appropriately generated loanwords than evariants.

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.

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

인간의 편의 향상과 경제성을 앞세운 중국은 수십 년 전부터 도시개발에 대한 환경오염, 도시 외곽지역 재개발 등의 문제가 끊임없이 논의되어 왔다. 이러한 녹지단절이나 종 다양성의 문제를 방지하기 위해 광역적 생태 네트워크가 요구되어지며 공간변동의 파악이 필요하다고 사료된다. 따라서 본 연구에서는 도시 확산이 빠르게 진행되고 있는 중국 중남부지역 ‘녹심’이라는 대도시권 녹지공간을 중심으로 토지피복도 및 경관 생태지수를 이용하여 시공간적 패턴변화와 경관다양성을 분석하고자 하였다. 1978-2019년의 Landsat 위성사진을 이용하여 ENVI 5.3과 ArcGIS 10.2를 통해 시계열의 경관요소를 유형화하였다. 경관 구조적·기능적 측면에서 객관적인 정량화하는 방법으로 FRAGSTATS를 통하여 경관지수를 정량화하여 산출하였다. 연구결과 1)이 지역의 경관은 1978년부터 오랜기간 토지변화에 따라 자연스럽게 형성되었고, 2)1989-2009년에는 택지개발이나 도로건설에 의해 주로 산림패턴에서 심각하게 파편화를 규명하였으며, 3)1999년 이후 시가지역의 경관지수에 따라 더욱 단순해지고 안정된 상태를 보였다. 4)수역이나 나지의 패치고립도가 높게 나타나고, 경관다양성에 부정적인 영향을 초래하였다. 5)2009년 이후 녹심계획과 함께 경관패턴은 전반적으로 규칙성이 있고 정형화되었다. 이와 같은 생태경관의 가치를 회복하는 것이 지속가능한 도시권 환경을 보전하는 데 가장 바람직할 것이며, 본 연구는 향후 녹심지역에 지속적으로 발전할 수 있는 매우 중요한 기초자료로 기대된다.

중국산과 국내산 홍삼 농축액의 혼합비율에 따는 원산지 판별 가능성을 검토하기 위해 전자코를 이용하여 향기 패턴을 분석하였다. 중국산 홍삼 농축액과 국내산 홍삼 농축액의 원산지 판별이 가능하였고 중국산 홍삼농축액의 혼합비율이 증가할수록 검출되어지는 향기 성분의 패턴은 감소하는 경향을 나타내었다. Frequency pattern, derivative pattern을 Vapor printTM 으로 도형화 하여 비교한 결과 서로 다 른 패턴을 보여주어 중국산 홍삼 농축액 첨가비율에 따른 차이는 물론 원산지의 차이도 뚜렷하게 나타났다.

The aim of this study to provide basic reference data for the development of video contents used in pattern drafting education and to explore the possibility of utilizing YouTube videos in such education. Subject videos were selected using the number of views. A total of 596 videos and 28 channels were analyzed for the period July to September 2019 and the results are as follows. With regard to content, there were 27 pattern drafting items, the majority being dress, pants, skirt, blouse and sleeve drafting, although high-level content such as cowl, bustier, corset patterns were also available. Therefore, there is a high likelihood that YouTube videos could be used as educational material, especially as supplementary references to provide specific examples and easy explanations for difficult concepts or method, for students majoring in this field. However, as most videos currently focus on a few items, expanding video content to features a wider variety of clothing items at different levels is necessary. With regard to video length, it mostly ranged from 10 to 15 minutes. It is not advisable to create lengthy lecture-style videos expounding on different principles or variations in pattern drafting when developing educational video material.

미국선녀벌레(Metcalfa pruinosa)는 2009년 국내에 최초로 보고된 이후 남부지역 단감 과원을 중심으로 다양한 농작물에 경제적 피해를 야기한다. 미국선녀벌레는 기주 범위가 넓고 다양하여 농경지뿐 아니라 산림지역에도 존재하여 과수원 인근 산림에서 농경지로 유입되는 개체로 인하여 효율적인 방제를 위한 시간적, 공간적 범위를 결정하는데 어려움이 많다. 본 연구에서는 투명 점착트랩을 이용하여 단감원에서 미국선녀벌 레의 발생 양상과 유입 및 유출 경향을 조사하였고, SADIE(Spatial Analysis by Distance Indices)를 사용하여 공간분포를 조사하였다. 또한 조사 시점간 공간분포의 상관관계를 분석하여, 분포의 패턴의 변화하는 시점들을 확인하였다. 단감원에서 미국선녀벌레의 발생 최성기는 5월 중 순과 8월 중순으로 5월에는 약충이, 8월에는 성충이 발견되었다. 10월 이후에는 발견되지 않았다. 5월 중하순경 부화한 약충은 임의분포 하였으나 포장 외부로 분산한 후 집중 분포하는 패턴을 보였다. 성충은 다시 포장으로 유입된 후 임의분포하였다. 트랩 높이별 채집 정도는 약충기에는 상대적으로 아래쪽 트랩에서, 성충기에는 위쪽 트랩에서 많이 채집되었다. 이러한 경향성은 포장 내부와 주변부의 밀도 변화에서도 확인할 수 있 었고, 포장 내 단감에서의 미국선녀벌레 실제 밀도와도 매우 유사한 경향을 보였다.

비래해충 개체군은 중국 남쪽에서 제트기류를 타고 한국으로 유입되는 해충이다. 애멸구(Laodelphax striatellus), 벼멸구(Nilaparvata lugens), 흰등멸구(Sogatella furcifera), 멸강나방(Mythimna separata), 혹명나방(Cnaphalocrocis medinalis)은 주요 비래해충 5종으로 주요 작물인 벼에 피해를 주기에 중요하다. 이 연구는 2016년 7월 하순에서 9월 상순, 2017년 7월에서 8월까지 전라도의 벼논에서 하였다. 멸강나방과 혹명나방은 페로몬 트랩을 사용하여 채집하였고, 벼멸구, 흰등멸구, 애멸구는 육안 조사, 포충망 조사, 끈끈이 트랩을 사용하여 채집하였다. 비래해충을 분석 하기 위해 공간통계학 중 SADIE (Spatial Analysis by Distance IndicEs)를 사용하였다. SADIE는 사용하여 공간분포 및 집중지수 Ia를 분석 하였고, 클러스터지수 Vi, Vj를 사용하여 공간분포를 조사하였다. 또한, 클러스터지수는 red-blue plot를 사용하여 지도 위에 나타내었다. 멸강나 방과 혹명나방은 SADIE 공간 집중 분석, red-blue plot 분석에서 다른 분포를 보였다. 애멸구와 다른 멸구의 초기 공간분포는 표본 추출 위치와 시간이 다르게 나타났다.