• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.3-13
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• 2009

When promoting BTL(Build Transfer Lease; below BTL) project, negotiation is a stage of examining observation and reflection of RFP(Request for Proposal below RFP) in terms with facilities, operating and financing. It keeps an important position in whole process. However, there is no consistent guideline or model which helps evaluating the result of negotiation. It is difficult to apprise the quantitative outcome after executing negotiation. Thus, this study presented the Value Engineering -based process and model of estimating the outcome of negotiation for the purpose of estimating and verifying the result of negotiation objectively, Evaluating factors of negotiation were classified into 6 fields, 38 divisions and 135 items, focused on cultural facilities on BTL project. Weight of every factor was estimated, and quantitative checklist was established. This study presented the model which could measure the outcome of negotiation. This result would be a critical checklist before negotiation on BTL project, an index of feedback during negotiation, and also a standard of estimating the outcome after negotiation.

• Korea journal of population studies
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• v.9 no.1
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• pp.89-99
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• 1986

이 연구는 우리나라에 있어서의 도시로의 인구이동과 도시매력도와의 관계를 <인지-행태>론적인 관점에서 설명하려데 그 목적이 있다. Julian Wolpert (1965)의 "장소효용접근방법"으로 체계화된 이 이론의 요지는 사람들이 이주대상지를 결정함에 있어서 그 대상지의 객과적인 매력도 보다는 자신들이 갖고 있는 정보를 토대로 주관적으로 판단한 이른바 "인지된 매력도"에 좌우된다는 점이다. 이 연구에서는 우리나라의 자료를 토대로 Wolpert의 가설을 검증하여 보았다. 인구이동의 관점에서 본 도시매력도의 구성요소를 (1) 소득수준, (2) 취업기회, (3) 교육기회, (4) 주거사정, (5) 도시시설의 질, (6) 이주시 도움을 받을 수 있는 친지의 유무, (7) 사회적 성장기회 등 7개 항목으로 분류하여 우리나라의 도시들을 객관적인 매력도와 주관적인 매력도로 계량화하였다. 객관적인 매력도는 기존의 통계자료를 지표화하여 측정하였고, 주관적인 매력도는 충청북도 주민들을 대상으로 1983년 현재 인구 10만 이상의 36개 도시에 대한 매력도 순위 설문조사를 통해 계량화하였다. 이들 매력도를 독립변수로 하고 충북으로부터 각 도시로 전출된 인구를 종속변수로 하여 통계적 분석을 한 결과 객관적인 매력도는 인구이동 현상을 55-58% 설명하였으나, 주관적인 매력도는 약 95%정도 설명하는 것으로 나타나 인구이동 의사결정이 주민들에게 인지된 주관적 매력도에 크게 의존하고 있음이 판명되었다. 따라서 학교교육이나 대중매체를 이용한 장기적인 <인포메이션 프로그램>을 개발하여 농촌생활이나 중소도시에서의 생활의 장점을 널리 계몽하여 도시의 주관적 매력도와 객관적 매력도간의 간격을 좁혀주는 정책도 매우 유용한 대도시 인구분석정책대안의 하나가 될수 있을 것이다.정책대안의 하나가 될수 있을 것이다.다. 고로 본고에서는 주사제의 처방설계및 제조(방법, 공정)에 관하여 개괄적으로 논하고자 한다.약화되어 저적면적빈도분포가 정상분포 단계에 도달되기 전에 바로 platykurtic분포로 되는 것이 아니고 leptokurtic 분포적 단계를 거친다고 본다때 시간의 경과를 따라 생성되어지는 Cyclodextrin의 함량의 변화를 추적하여 4시간전후에서 최고량이 되는 것을 볼 수 있으며 동시에 포위화합물을 형성시킬수 있을때는 그 생성률이 큰 영향을 이르킬수 있는 것을 지적할 수 있다.한 특성을 보여 식품제조, 식육연화 등 식품산업 분야에서의 활용가능성이 높을 것으로 보이며, 나아가 단백질이 갖는 식품학적 기능성을 높이는 데에도 사용할 수 있을 것으로 판단된다.를 한 후 저온 냉장차를 이용하여 유통한다면 관행 유통 구조보다 고품질의 포도를 유통시킬 수 있는 것으로 사료되며 앞으로는 완숙된 고 당도(12.0~15.0Bx)$^{\circ}$ 포도를 수확 한 즉시 예냉 처리하고 저온 유통한다면 보다 신선한 과일을 소비자에게 전달 할 수 있을 것이다.갈변물질이 생성되었다. 이와 같은 결과로 볼 때, BAAG의 처리는 BAAC의 경우보다 가격은 저렴하면서도 항균력은 우수한 천연 항균복합제재로써 농산물 식품원료에 적용하여 선도유지 기간을 연장할 수 있는 효과를 기대할 수 있었다. 과일 등의 포장제로서 이용할 가능성을 확인하였다.로 [-wh] 겹의문사는 복수 의미를 지닐 수 없 다. 그러면 단수 의미는 어떻게 생성되는가\ulcorner 본 논문에서는 표면적 형태에도 불구하고 [-wh]의미의 겹의문사는 병렬적 관계의 합성어가 아니라 내부구조를 지니지 않은 단순한 단어(minimal $X^{0}$ elements)로 가정한다. 즉, [+wh] 의미의 겹의문사는

• Korean Journal of Construction Engineering and Management
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• v.9 no.5
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• pp.159-167
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• 2008

This study provides a effective approach to the construction management problem with the limited number or amount of available resources using the analytic hierarchy process. Construction management is a series of decision making processes for planning and controling of cost, time and quality as main objectives in construction works. When several activities need the limited same resource at the same time, it is very hard to decide the priority of the activities in the real situations. For that the scientific decision making method and procedure for resource allocation are required. This study solves the resource allocation problem by dealing with the decision making problem which the activities are distributed to multiple projects and under multiple criteria. The analytic hierarchy process is a method devised to solve complex multi-criteria decision problems. The result shows that this study can be effectively used to make decisions in situations involving multiple objectives by evaluating the prioritized ranking and degree of the activity alternatives based on the overall preferences.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.