• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.396-404
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• 2023

The blockage rate for three kinds of nets commonly used in agricultural facilities was assessed by using the image acquisition and its relevant processing. By using both empirical relations presented by Idel'chik and Richards and Robinson, and the blockage rate obtained from the image processing, the pressure drop through the nets was predicted and also compared with wind tunnel experiment results. The results of the study showed that the blockage rate of the net was discriminated according to such factors as the magnitude of nets, the existence of inside threads, the thickness and number of threads. In addition, the blockage rate for the incident angle of 0° when the wind blew at the front had the range of 0.22-0.29 (0.22-0.32 when considering whole incident angles from 0° to 45° by 15°). For the nets with the blockage rate of about 30% or below, the prediction by the empirical relations of by Idel'chik and Richards and Robinson showed a little higher pressure drop overall than that of the wind tunnel test, but the use of the empirical relations and the blockage rate could be thought of as providing effectively meaningful guidelines for the safe design of agricultural facilities including nets because the wind tunnel test has been tedious and expensive. Further research and potential application on the prediction technique of the pressure drop, regarding both a subtle deformation by the wind and manufacturing methods with regard to the level of knots and the existence of inside threads, needs to be done for the nets with higher blockage rate.

• The Korean Journal of Air & Space Law and Policy
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• v.27 no.2
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• pp.211-241
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• 2012

The Draft International Code of Conduct for Outer Space Activities officially proposed by the European Union on the occasion of the 55th Session of the United Nations Peaceful Uses of the Outer Space last June 2012 in Vienna, Austria is to fill the lacunae of the relevant norms to be applied to the human activities in the outer space and thus has the merit our attention. The missing elements of the norms span from the prohibition of an arms race, safety and security of the space objects including the measures to reduce the space debris to the exchange of information of space activities among space-faring nations. The EU's initiatives, when implemented, cover or will eventually prepare for the forum to deal with such issues of interests of the international community. The EU's initiatives begun at the end of 2008 included the unofficial contacts with major space powers including in particular the USA of which position is believed to have been reflected in the Draft with the aim to have it adopted in 2013. Although the Code is made up of soft law rather than hard law for the subscribing countries, the USA seems to be afraid of the eventuality whereby its strategic advantages in the outer space will be affected by the prohibiting norms, possibly to be pursued by the Code from its current non-binding character, of placing weapons in the outer space. It is with this trepidation that the USA has been opposing to the adoption of the United Nations Assembly Resolutions on the prevention of an arms race in the outer space (PAROS) and in the same context to the setting-up of a working group on the arms race in the outer space in the frame of the Conference on Disarmament. China and Russia who together put forward a draft Treaty on Prevention of the Placement of Weapons in Outer Space and of the Threat or Use of Force against Outer Space Objects (PPWT) in 2008 would not feel comfortable either because the EU initiatives will steal the lime light. Consequently their reactions are understandably passive towards the Draft Code while the reaction of the USA to the PPWT was a clear cut "No". With the above background, the future of the EU Code is uncertain. Nevertheless, the purpose of the Code to reduce the space debris, to allow exchange of the information on the space activities, and to protect the space objects through safety and security, all to maximize the principle of the peaceful use and exploration of the outer space is the laudable efforts on the part of EU. When the detailed negotiations will be held, some problems including the cost to be incurred by setting up an office for the clerical works could be discussed for both efficient and economic mechanism. For example, the new clerical works envisaged in the Draft Code could be discharged by the current UN OOSA (Office for Outer Space Affairs) with minimal additional resources. The EU's initiatives are another meaningful contribution following one due to it in adopting the Kyoto Protocol of 1997 to the UNFCCC (UN Framework Convention on the Climate Change) and deserve the praise from the thoughtful international community.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Progress in Medical Physics
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• v.24 no.2
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• pp.92-98
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• 2013

The experimental verification of treatment planning on the treatment spot is the ultimate method to assure quality of radiotherapy, so in-vivo skin dose measurement is the essential procedure to confirm treatment dose. In this study, glass rod dosimeter (GRD), which is a kind of photo-luminescent based dosimeters, was studied to produce a guideline to use GRDs in vivo dosimetry for quality assurance of radiotherapy. The pre-processing procedure is essential to use GRDs. This is a heating operation for stabilization. Two kinds of pre-processing methods are recommended by manufacturer: a heating method (70 degree, 30 minutes) and a waiting method (room temperature, 24 hours). We equally irradiated 1.0 Gy to 20 GRD elements, and then different preprocessing were performed to 10 GRDs each. In heating method, reading deviation of GRDs at same time were relatively high, but the deviation was very low as time went on. In waiting method, the deviation among GRDs was low, but the deviation was relatively high as time went on. The meaningful difference was found between mean reading values of two pre-processing methods. Both methods present mean dose deviation under 5%, but the relatively high effect by reading time was observed in waiting method. Finally, GRD is best to perform in-vivo dosimetry in the viewpoint of accuracy and efficiency, and the understanding of how pre-processing affect the accuracy is asked to perform most accurate in-vivo dosimetry. The further study is asked to acquire more stable accuracy in spite of different irradiation conditions for GRD usage.

• Progress in Medical Physics
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• v.24 no.2
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• pp.85-91
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• 2013

At present, megavoltage computed tomography (MVCT) is the only method used to correct the position of tomotherapy patients. MVCT produces extra radiation, in addition to the radiation used for treatment, and repositioning also takes up much of the total treatment time. To address these issues, we suggest the use of a video image-guided setup (VIGS) system for correcting the position of tomotherapy patients. We developed an in-house program to correct the exact position of patients using two orthogonal images obtained from two video cameras installed at $90^{\circ}$ and fastened inside the tomotherapy gantry. The system is programmed to make automatic registration possible with the use of edge detection of the user-defined region of interest (ROI). A head-and-neck patient is then simulated using a humanoid phantom. After taking the computed tomography (CT) image, tomotherapy planning is performed. To mimic a clinical treatment course, we used an immobilization device to position the phantom on the tomotherapy couch and, using MVCT, corrected its position to match the one captured when the treatment was planned. Video images of the corrected position were used as reference images for the VIGS system. First, the position was repeatedly corrected 10 times using MVCT, and based on the saved reference video image, the patient position was then corrected 10 times using the VIGS method. Thereafter, the results of the two correction methods were compared. The results demonstrated that patient positioning using a video-imaging method ($41.7{\pm}11.2$ seconds) significantly reduces the overall time of the MVCT method ($420{\pm}6$ seconds) (p<0.05). However, there was no meaningful difference in accuracy between the two methods (x=0.11 mm, y=0.27 mm, z=0.58 mm, p>0.05). Because VIGS provides a more accurate result and reduces the required time, compared with the MVCT method, it is expected to manage the overall tomotherapy treatment process more efficiently.

• Hwankyungkyoyuk
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• v.7 no.1
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• pp.46-87
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• 1994

Our country has been consuming a huge amount of energy in the course of industrialization and its demand is expected to increase enormously in the future. However, the deposits of energy resources are so limited that the settlement of energy problem comes up the essential subject. To solve the energy problem, it is requested that new resources to gain energy stably should be developed and also energy should be economized and used effectively. The effective use of energy and an the wisdom of economy in energy are requested to everybody and these things should be habitualized from very young age through education. Nevertheless, almost every school in our country hasn’t been concerned about energy education. Even though they have a concern, they are very short of the energy education materials and the quality of the materials is not so good. Therefore it is very meaningful to the settlement of energy problem of the country to make the students who will lead our country to make the students who will lead our country in the future realize the seriousness of energy problem and to provide them the necessary knowledge and methods to solve this problem so that they practice those things in everyday life. Having these necessities, this research, supported by The Korea Energy Management Corporation(KEMCO), was performed for 8 months from April 17, 1994 to December 17, 1994. Many peoples participated in this study such as 30 staffs of researchers and authors, 5 staffs of photographers and illustrators, and 3 VCR program producers developing an energy education material set for middle school students that includes a printed material for student, a diskette for computer simulation, a teacher's guidebook, VCR material and its guidebook. The following main development direction was established : First, the material for student should be consisted of units that let students know the seriousness of energy problem. Second, the focus should be put on the necessary method and practice to economize energy actually in real life based on the basic knowledge learned in elementary school. Third, material for student should be consisted of modules to be student activity-oriented teaching-learning rather than lecture-oriented one. The activity, to maximize student's interests, should be presented in various forms such as experiments, investigation, play, data interpretation, computer simulation, visits, expression and appreciation, etc. To develop the energy education materials for middle school students, a research plan was made first. After literature review about domestic and foreign energy education materials, several research trips home and abroad, and discussion meetings, the basic theory of energy education such as the principle, objective, contents, teaching-learning method, and evaluation method was established. Material for student was developed through the following procedures : The activities in the existing energy education materials were analysed and were divided into four categories related to energy using places of home, school, community, and country, and which were again divided into three categories related to time of past, present, and future, Considering these division, nine modules which are structure units of material for student were chosen, Each module comprises 2-4 activities. Totally 31 activities were designed in this way. The syllabi were made out for each activity and writing was asked for to experts related to each activity after several discussions and revision. To complement the draft, another several discussions and revision were also made on it and then pictures and illustrations were asked for. All these procedures complete the material for student, titled ; Energy Inquiry of Middle School Students', which totals 129 pages and is all in color. As the manuscript of material for student was fixed, writing for teacher's guidebook was asked for to the same writers. The draft of teacher's guidebook was also complemented through the several concentrated works and discussions. Teacher's guidebook focused on the teaching-learning principle and methods of energy education and on the concrete instruction cases for effective instruction of material for student. It is organized with two parts : the one is 'general outline' which introduces theoretical contents and the other is 'details' which are practically helpful to teaching-learning. It is totally 131 pages including both 'general outline' and 'details'. The VCR material and its guidebook consist of contents that cultivate the good attitude trying to economize energy and raise student's interests with a purpose of strong motivation to recognize the necessity of economy and practice it. After establishing development direction of VCR material through discussion meetings and research trips, its script was made by relevant experts. Then the script was also reviewed two times. The drafted VCR material made by a video material developing expert was examined and modified by previews twice. After completion of VCR material, the VCR guidebook was made. All these procedures led to the development of VCR material which runs 20 minutes in VHS type. The VCR guidebook shows a production purpose of the program, structure of contents, evaluation methods, and contents of the program in detail to give help to instructors when they use this VCR material, When these energy education materials are used, it is desirable that the VCR material should be presented first to induce student's motive, and then material for student is introduced Since the material for student is composed of activity-oriented modules and each module is independent one another in general, and each activity is, too. the necessary module or activity can be chosen and utilized in any order according to school or class conditions. This energy education materials will contribute to the development of student's ability to solve energy problem in everyday life and teacher's ability to teach the fundamental knowledge and method in solving energy problem.