As millennials are continuously growing; hence they are considered today's fine jewelry business treasure. Understanding why they buy fine jewelry using the means-end theory is the key objective of this study. Brand, function and beauty are means that lead to happiness and self-confidence, the end value of millennials mindset

The amount of waste that contains or is contaminated with radionuclides is increasing gradually due to the use of radioactive material in various fields including the operation and decommissioning of nuclear facilities. Such radioactive waste should be safely managed until its disposal to protect public health and the environment. Predisposal management of radioactive waste covers all the steps in the management of radioactive waste from its generation up to disposal, including processing (pretreatment, treatment, and conditioning), storage, and transport. There could be a lot of strategies for the predisposal management of radioactive waste. In order to comply with safety requirements including Waste Acceptance Criteria (WAC) at the radioactive waste repository however, the optimal scenario must be derived. The type and form of waste, the radiation dose of workers and the public, the technical options, and the costs would be taken into account to determine the optimal one. The time required for each process affects the radiation dose and respective cost as well as those for the following procedures. In particular, the time of storing radioactive waste would have the highest impact because of the longest period which decreases the concentrations of radionuclides but increases the cost. There have been little studies reported on optimization reflecting variations of radiation dose and cost in predisposal management scenarios for radioactive waste. In this study, the optimal storage time of radioactive waste was estimated for several scenarios. In terms of the radiation dose, the cumulative collective dose was used as the parameter for each process. The cost was calculated considering the inflation rate and interest rate. Since the radiation dose and the cost should be interconvertible for optimization, the collective dose was converted into monetary value using the value so-called “alpha value” or “monetary value of Person-Sv”.

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

During the shift from gasoline vehicles to electric ones, auto parts manufacturing companies have realized the importance of improvement in the manufacturing process that does not require any layout changes nor extra investments, while maintaining their current production rate. Due to these reasons, for the auto part manufacturing company, I-company, this study has developed the simulation model of the PUSH system to conduct a process analysis in terms of production rate, WIP level, and logistics work’s utilization rate. In addition, this study compares the PUSH system with other three manufacturing systems -KANBAN, DBR, and CONWIP- to compare the performance of these production systems, while satisfying the company’s target production rate. With respect to lead-time, the simulation results show that the improvement of 77.90% for the KANBAN system, 40.39% for the CONWIP system, and 69.81% for the DBR system compared to the PUSH system. In addition, with respect to WIP level, the experimental results demonstrate that the improvement of 77.91% for the KANBAN system, 40.41% for the CONWIP system, and 69.82% for the DBR system compared to the PUSH system. Since the KANBAN system has the largest impacts on the reduction of the lead-time and WIP level compared to other production systems, this study recommends the KANBAN system as the proper manufacturing system of the target company. This study also shows that the proper size of moving units is four and the priority allocation of bottleneck process methods improves the target company’s WIP and lead-time. Based on the results of this study, the adoption of the KANBAN system will significantly improve the production process of the target company in terms of lead-time and WIP level.

The single vendor single buyer integrated production inventory problem with lead time proportional to lot size and space restriction is studied. Demand is assumed to be stochastic and the continuous review inventory policy is used for the buyer. If the buyer places an order with lots of products, then the vendor will produce lots of products and the products will be transferred to the buyer with equal shipments many times. Mathematical model for this problem is defined and a Lagrangian relaxation approach is developed.

불연속 갤러킨 정식화에 기초를 둔 시간적분법에 대하여 시간을 변수로 한 유한요소적 접근법을 시도하였다. 단일 형상함수와 두 형상함수 정식화에 대해 각각 선형, 이차 형상함수를 적용하여 모두 네 종류의 시간적분법을 유도하였으며, 각 방법에 대하여 시간시텝의 증가에 따른 변위와 속도의 관계를 나타내는 증폭행렬을 계산하였다. 유도된 방법들의 성능을 평가하기 위하여 부하가 갑자기 변화는 진동 문제를 해석하고 변위의 오차를 비교하였다. 네 가지의 방법에 대하여 국부 오차 추정치를 개발하였으며, 오차 추정치의 정확도를 수치예를 이용하여 평가하였다. 단일 형상함수 정식화에서 이차 형상함수를 이용한 오차 추정치가 실제 국부오차를 잘 나타내었으며 유도된 오차 추정치는 시간간격제어 기법에서 시간간격의 크기를 결정하는 척도로 이용 가능하다.

시계열 예측은 효과적인 기업 경영에 반드시 필요한 활동이지만, 이와 관련 인간의 인지처리 과정에 대한 연구는 아직까지 미비하다. 본 연구는 인간 감성이 시계열 예측에 미치는 영향을 탐색하였다. 본 실혐에서는 반복을 통한 2(감성) x 2(횟수) 팩토리얼 설계를 채택하였다. 감성은 청각, 시각, 후각자극을 통해 환경을 조성하고 준비된 시나리오를 연상케하여 유발시켰다. 12명의 대학, 대학원생이 실험에 참여하였으며, 감성의 영향을 탐색하기 위해 뇌파(EEG)와 피부저항(GSR)이 후두엽(Oz)과 전두엽(Fz)에서 측정되었다. 그 결과 인간의 감성은 예측에 유의적인 영향을 보였다. 즉, 피험자가 부정적인 감성을 갖을 때 긍정적인 감성에 비해 예측의 정확성이 높은 경향이 있었다. 그 이유는 부정적 감성일 경우 전두엽에서 베타가 많이 출현하였고, 이는 시계열 예측의 정확도를 향상시키는 역할을 하였다.

초, 고등학생들이 작성한 대몽항쟁 서사는 유사하지만 세부내용에서 차이가 있다는 점에 착안하여 세부내용의 차이를 만드는 시간개념을 언어적으로 어떻게 표현하는지를 살펴보았다. 초등학생과 고등학생이 주로 사용하는 시간개념 유형이 달랐다. 구체적으로 보면, 초등학생과 고등학생 모두 시간 단계화하기를 많이 활용하였다. 학생들은 대몽항쟁의 시작, 과정, 결과를 순서대로 서술하였다. 초등학생은 전쟁의 패배로 서술을 끝냈지만 고등학생들은 전쟁이 끝난 이후 공민왕과 권문세족의 등장으로 시간을 이어나갔다. 시간 설정하기에 서 학생들은 일반적 시간표현과 대강의 시간표현으로 시간을 설정하였다. 초등학생이 일반적 시간 표현을 주로 사용했다면 고등학생들은 세기까지 고려한 대강의 시간범주까지 사용하였고 시간의 범위를 오늘날까지로 확장하였다. 시간 분할하기는 초, 고등학생간 차이가 많이 나타났다. 원간섭기, 대몽항쟁기와 같은 시간을 포함한 역사용어가 고등학생에게 주로 나타났다. 시간을 통해 조직 하기는 초, 고등학생 모두 ‘이후’라는 언어장치를 사용하여 앞 사건과 뒤 사건을 순서대로 연결하려고 하였다. 고등학생은 순서를 제시하는 언어장치를 활용하여 앞으로 다룰 내용을 전체적으로 개괄한 후 순서에 따라 내용을 제시하였다. 시간을 표현하는 장치이면서도 텍스트 구조 표지어 역할도 하였다. 초, 고등학생이 활용한 시간개념을 볼 때, 역사의 시간이 단절된 것으로 파악하는 초등학생에게 역사의 계속성과 함께, 시간을 포함한 역사용어와 같이 특수한 역사용어를 가르쳐야 한다는 점을 알 수 있다. 예컨대, 일반적 시간표현보다 대강의 시간표현을, 대몽항쟁과 같은 특수한 역사용어를 익혀나가는 과정을 경험하게 하는 것이다. 이는 일상언어가 역사언어로 전환하는 과정이다.

The present study aimed at investigating the effects of collaborative work between Korean EFL university students and international foreign students on Intercultural Communicative Competence. Twenty four students (14 Korean students and 10 international students) participated in this research. Chen and Starosta’s (2000) intercultural sensitivity scale was implemented with Paradigm Software to measure the participants’ resolution latency time while they were responding to the survey. The results demonstrated that two groups showed significant differences in the areas of respect for cultural difference and interaction confidence. Also, apart from the response value, the analysis of resolution latency time showed other aspects of participants’ cognitive level of intercultural sensitivity. Thus, this study indicates that a multi-round analysis can give a more in-depth insight beyond the mean value of the survey’s response.

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

Cereal seeds, sorghum, foxtail millet, hog millet, adlay, and corn are traditionally used as health assistant as well as energy supplying food in Korea. While beneficial phytochemicals to human have revealed in cereals, the information on peptides from cereals is far less accumulated than major reserve protein. Here, we analyzed peptide profiles using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) in cereal seeds for construction of peptide information and attempted to develop peptide biomarkers for cereal identification. To optimize the analysis condition of SELDI-TOF MS, the effect of dilution factor on binding affinity to protein chips was tested using CM10 and Q10 arrays. Peptide clusters were significantly different at the level of 0.01 p-value. Peak spectra were the most stable in 1:50 of dilution factor in both chip arrays. Numbers of detected peak of 5 cereal seeds were 131 in CM10 and 74 in Q10 array. Each cereal was grouped as a cluster and well discriminated into different cluster in the level of 0.01 p-value. Numbers of potentially identified peptide biomarkers are 11, 13, 9, 5 and 12 in sorghum, foxtail millet, hog millet, adlay and corn, respectively. This study demonstrates that each cereal seed have own distinguishable specific peptides although their function are not identified yet in this study. In addition, the proteomic profiling using SELDI-TOF MS techniques could be a useful and powerful tool to discover peptide biomarker for discrimination and assess crop species, especially under 20 kDa.

본 논문에서는 큰 변형이 일어나는 얇은 쉘을 실시간에 시뮬레이션하는 기법을 제안한다. 쉘이란 나뭇잎이나 종이와 같이 이차원 구조라 할 수 있는 얇은 물체이다. 얇은 쉘의 시각적으로 사실적인 애니메이션을 실시간에 생성하는 것은 컴퓨터 그래픽스 분야에서 오랫동안 주요한 도전 과제였다. 본 논문에서는 연속체 역학에 있어서 가장 복잡한 쉘 이론에 의존하는 대신 Grinspun 등이 제안한 이산 쉘 에너지 함수를 채용하고, 지배방정식의 실시간 적분을 위해서는 쉘 구조를 위한 모달 와핑 기법을 개발한다. 이와 같은 새로운 시뮬레이션 기법은 삼차원 솔리드를 위해 개발된 종전의 모달 와핑 기법을 확장한 것이다. 본 논문에서 제안한 방법은 매우 많은 정점으로 이루어진 메쉬 구조의 큰 휨과 큰 꼬임 변형도 실시간에 사실적으로 생성할 수 있다.