• Archives of design research
• /
• v.18 no.2 s.60
• /
• pp.155-164
• /
• 2005

Home-service robots need to have sufficient spatial information about the surroundings for interacting with human intelligently and performing services efficiently. It is very important to investigate the efficient interaction style that supports map building task through human-robot collaboration. We first analyzed map building task with a cleaning robot and drew 4 design factors and tentative solutions, including map building procedure (task-preferred procedure/space- preferred procedure), LCD display installation (robot/robot+remote control), navigation method (push type/pull type), feedback modality(GUI/GUI+TTS). The design factors and tentative solutions were defined as independent variables and levels. This research investigated how those variables affect to the human task performance and behavior in map building tast. 8 kinds of experiment prototypes were built and usability test among 16 house wives was conducted for acquiring empirical data. As the experiment result, in terms of map building procedure, space-preferred procedure indicated better task performance than task-proffered procedure as we expected. For the LCD display installation factor, remote control with LCD display indicated higher task performance and subjective satisfaction. In robot navigation method, it was very difficult to find a significant difference between push type and pull type which contrary to our expectation. In fact, push type indicated higher subjective satisfaction. Also in feedback modality, we have acquired negative feedback an additional TTS operation guidance. It seems that robot's autonomy before achieving spatial information is rudiment condition which means users are just interacting with a mobile appliance. Thus they prefer remote-control-based interaction style in robot map building process as they used in traditional appliance control.

• Journal of Technology Innovation
• /
• v.2 no.1
• /
• pp.1-57
• /
• 1994

본 고는 지속적 개발 론에 입각한 적극적인 에너지수요 관리정책을 추진한다는 전제하에 2001년과 2006년의 우리 나라 가정부문 전력수요를 전망하고자 한다. 본 고는 지속적 개발 시나리오를 추정함에 있어서 기존의 계량모형보다 일종의 공학적 모형인 공정분석(process analysis)을 선호한다. 계량모형이 주로 과거 수요의 소득 및 가격 탄성 치를 바탕으로 미래의 수요를 예측하는데 비하여 공정분석모형은 기술발전에 따른 미래의 효율변화(향상)를 비교적 잘 반영할 수 있기 때문이다. 본 고는 덴마크공과대학교 Norgard 교수팀이 개발한 모형을 도입하여 분석모형(수식 (6))을 전력수요 = 기기 수 $\times$ 전력서비스$\times$ 전력집약도와 같이 설정하고 이를 사용하여 냉장고, 텔레비전, 조명 기기, 난방기기 등과 같은 전력사용 기기 별로 2001년과 2006년이 전력수요를 전망하였다. 본 고는 전력수요를 전력사용 기기의 사용용량(300리터 용량의 냉장고 등)과 사용시간을 나타내는 전력서비스와 전력 서비스당 필요 전력사용량을 나타내는 전력집약도로 나누어 구분하고 있는 모형을 이용함으로써 소득향상효과와 함께 기술발전에 따른 효율개선효과를 분석할 수 있다. 1) 생활수준 향상에 따라 전력서비스는 지금과 같이 증가한다, 2) 현실적으로 가능한 범위 내에서 전력사용 기기에 대한 최저 에너지 효율 제를 실시한다, 3) 현재 사용중인 기기 들은 원칙적으로 수명이 다한 후 고효율 기기 들로 자연 교체한다, 4) 최저 에너지 효율 제를 제외한 다른 제도 및 정책개선, 사용자의 에너지소비형태 개선에 따른 절전 잠재 량을 고려하지 않는다 등의 가정 하에 전력수요를 추정한 결과 1992년에 796 GWh(100)이었던 우리 나라 가정부문 전력수요는 2001년과 2006년에 29,237 GWh(134)와 33,118 GWh(152)로 각각 34%와 52%증가할 것으로 나타났다. 이 경우 1992년부터 2006년까지 가정용 전력수요 증가율은 연평균 3%로 추정된다. 기기의 서비스(가구수$\times$기기의 보급 율$\times$기기의 전력서비스)가 소득향상에 따라 증가하는데도 불구하고 전력수요의 증가율이 GDP(같은 기간 동안 연평균 증가율 5.7%)보다 매우 낮은 것은 기기의 대형화와 기기의 보급을 증가에 따른 전력의 추가수요가 기기의 에너지효율 개선으로 대부분 상쇄될 것이기 때문이다. 향후 10년 내에 기기에 따라 전력사용량을 25%~50%정도까지 줄일 수 있을 것으로 분석된다. 기술발전에 따른 기기의 에너지효율 개선효과는 본 고의 2006년도 가정용 전력수요의 전망치 33,118 GWh가 기존방식에 의한 한전의 전망치 61,155 GWh의 54%수준밖에 되지 않는데 서도 잘 나타나고 있다. 한편 본 고는 경제성장과 환경보존을 동시에 달성할 수 있는 지속적 개발의 실천방안으로서 에너지 수요관리를 논하고자 한다. 고효율 기기의 개발과 조기도입을 촉진시키는 에너지 수요관리 통하여 우리는 에너지효율을 대폭 개선시키며 대기오염 배출량도 대폭 줄일 수 있다. 본 고는 에너지 공급관리(공급확충)위주에서 에너지 수요관리위주로서의 에너지정책 전환은 불가피하다고 판단한다. 에너지 공급시스템보다 에너지 수요시스템위주로 전체 에너지시스템을 획기적으로 개선시키기 위해서는 최저 에너지효율제의 광범위한 실시와 함께 고효율 기기의 개발과 보급에 필요한 유인책의 도입, 고효율 기기와 에너지의 효율적 이용에 대한 정보 등이 필요시 되고 있다. 우리 나라의 경우 현재의 산업구조와 기술수준을 고려하여 에너지 효율의 기준을 미국보다 다소 낮게 설정한다면 최저 에너지효율제의 도입이 문제가 되지 않을 것으로 판단된다. 본 고는 고효율 기기의 개발과 조기도입을 지원하기 위한 가칭 대기환경보존 및 에너지 수요관리기금의 창설을 제안한다. 전력부문의 경우 기금은 1. 탄소세, 2. 전력소비에 대한 수요 관리 세의 도입 혹은 3. 한국전력공사 전력판매수입의 일정 분으로 조성될 수 있을 것으로 본다. 예를 들어 선진국들이 탄소세를 예정대로 도입한다는 전제하에 우리 나라가 2000년을 기준으로 탄소 톤당 8달러(석유 배럴 당 85센트)의 탄소세를 도입한다면 연간 7억 2,000만 달러(약5,760억 원)규모의 기금을 조성할 수 있다. 이 중 연간 2,000억 원 정도를 고효율 기기의 개발과 조기도입에 지원한다면 우리 나라 에너지 시스템 효율은 대폭 개선될 수 있을 것으로 예상된다.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.63-83
• /
• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.95-112
• /
• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.