• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.79-94
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• 2013

Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point, ${\alpha}$and ${\beta}$. Dynamic State Synchronization was meaningful when the movement value was over critical point ${\beta}$, while Static State Synchronization was effective under critical point ${\alpha}$. ${\beta}$ is made by audience' clapping movement of 10 teams in stead of using average number of movement. After checking the reactive movement of audience, the percentage(%) ratio was calculated from the division of "people having reaction" by "total people". Total 37 teams were made in "2012 Seoul DMC Culture Open" and they involved the experiments. First, they followed induction to clap by staff. Second, basic scene for neutralize emotion of audience. Third, flow scene was displayed to audience. Forth, the reversal scene was introduced. And then 24 teams of them were provided with amuse and creepy scenes. And the other 10 teams were exposed with the sad scene. There were clapping and laughing action of audience on the amuse scene with shaking their head or hid with closing eyes. And also the sad or touching scene made them silent. If the results were over about 80%, the group could be judged as the synchronization and the flow were achieved. As a result, the audience showed similar reactions about similar stimulation at same time and place. Once we get an additional normalization and experiment, we can obtain find the flow factor through the synchronization on a much bigger group and this should be useful for planning contents.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Hospice and Palliative Care
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• v.9 no.1
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• pp.17-29
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• 2006

Purpose: This research aimed to investigate the actual condition and satisfaction of volunteer activity in Australian hospital. Methods: Data was collected by self reported questionnaire from 101 volunteers and analyzed by frequency and percentage, t-test, ANOVA and Sheffe and Pearson's correlation coefficients using SPSS 12.0. Results: 1. Years involved in volunteer work were $5{\sim}10$ years (32.7%), above 10 years (30.7%), $2{\sim}3$ years (11.9%) and $3{\sim}5$ years (10.9%). Types of volunteer work were physical care (32.7%), physical and emotional care (14.9%), and others (18.8%). Types of allocation of tasks were by volunteer coordination (55.7%), and by volunteer preference and consent between volunteer and coordinator (both respectively, 20.5%). Main reasons for volunteer work were to help sick people (61.4%) and to make good use of leisure time (22.8%). Routes to start volunteer work were from his (her) own inquiries (43.4%), from hearing from other volunteers (30.7%) and from mass media (13.1%). 80.2% of volunteers had received some kinds of training or preparation for volunteer work. Suitability of volunteer's skill and ability to voluntary work were 'very well' (74.0%) and 'mostly well' (18.0%). Reimbursements or benefits received for volunteer work were token or lunch or group outing (31.7%), and token and lunch or group outing (19.8%). Evaluation frequency for volunteer work was occasionally (372%), frequently (30.9%), always (17.0%) and never (14.9%). Relationship with volunteer work coordinator was very good (85.0%). The relationship with other volunteers was very good (81.2%). The relationship with hospital staffs was very good (69.7%) and mostly good (21.2%). Family and friend's support for volunteer work was very good (83.2%). 2 The mean score of satisfaction for the hospital volunteer activity was $3.09{\pm}0.49\;(range:\;1{\sim}4)$. The highest score domain was 'social contact', $3.48{\pm}0.61$, and the lowest was 'social exchange', $1.65{\pm}0.63$. An item of the highest score was 'I have an opportunity to help other people' ($3.83{\pm}0.40$), and the lowest score item was 'I will receive compensation for volunteer work I have done ($1.10{\pm}0.78$).' 3. The satisfaction from hospital volunteer activity was shown by significant difference according to sex (t=2.038, P=0.044), marital status (F=3.806, P=0.013), years involved in volunteer work (F=3.326), nam reason to do volunteer work (F=2.707, P=0.035), receive any training or preparation for volunteer work (t=-1.982, 0=0.050), frequency of evaluation for volunteer work (F=7.877, P=0.000), suitability of volunteer's skill and ability to voluntary work (t=2.712, P=0.049), relationship with volunteer work coordinators (F=-2.517, P=0.013), relation with hospital staffs (F=5.202, P=0.007), and support of their volunteer work by their family and friends (t=-3.394, P=0.001). Conclusion: The satisfaction of hospice volunteer activity was moderate. The satisfaction for hospice volunteer activity was shown by significant difference according to sex (t=2.038, P=0.044), marital status (F=3.806, P=0.013), years involved in volunteer work (F=3.326), main reason to do volunteer work (F=2.707, P=0.035), receive any training or preparation for volunteer work (t=-1.982, 0=0.050), frequency of evaluation for volunteer work (F=7.877, P=0.000), suitability of volunteer's skill and ability to voluntary work (t=2.712, P=0.049), relationship with volunteer work coordinator (F=-2.517, P=0.013), relation with hospital staffs (F=5.202, P=0.007), and family and friend's support for volunteer work (t=-3.394, P=0.001). Therefore, it is necessary to consider various factors to improve the satisfaction of voluntary work.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• The Korean Journal of Nuclear Medicine
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• v.4 no.1
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• pp.19-26
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• 1970

1. Sixteen normal healthy subjects free from occult blood in the stool were selected and administered with their $^{51}Cr$ labeled own blood via duodenal tube and the recovery rate of radioactivity in feces and urine was measured. The average fecal recovery rate was 90.7 per cent ($85.7{\sim}97.7%$) of the administered radioactivity, and the average urinary excretion rate was 0.8 per cent ($0.5{\sim}1.5%$) 2. There was a close correlation between the amount of blood administered and the recovery rate from the feces; the more the blood administered, the higher the recovery rate was. It was also found that the administration of the tagged blood in the amount exceeding 15ml was suitable for measuring the radioactivity in the stools. 3. In five normal healthy subjects whose circulating erythrocytes had been tagged with $^{51}Cr$, there was little fecal excretion of radioactivity (average 0.9 ml of blood per day). This excretion is not related to hemorrhage and the main route of excretion of such an negligible radioactivity was postulated as gastric juice and bile. 4. A comparison of the radioactivity in the blood and feces of the patients with $^{51}Cr$ labeled erythrocytes seems to be a valid way of estimating intestinal blood loss.

• Journal of Information Management
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• v.6 no.5
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• pp.131-134
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• 1973

본고(本稿)시리이즈의 제1보(第一報)에서 우리는 물리(物理), 사회과학(社會科學) 및 공학분야(工學分野)의 12,442명(名)의 과학자(科學者)와 기술자(技術者)에 대한 정보교환활동(情報交換活動)의 78례(例)에 있어서 일반과정(一般過程)과 몇 가지 결과(結果)를 기술(記述)한 바 있다. 4년반(年半) 이상(以上)의 기간(其間)($1966{\sim}1971$)에서 수행(遂行)된 이 연구(硏究)는 현재(現在)의 과학지식(科學知識)의 집성체(集成體)로 과학자(科學者)들이 연구(硏究)를 시작(始作)한 때부터 기록상(記錄上)으로 연구결과(硏究結果)가 취합(聚合)될 때까지 각종(各種) 정형(定形), 비정형(非定形) 매체(媒體)를 통한 유통정보(流通情報)의 전파(傳播)와 동화(同化)에 대한 포괄적(包括的)인 도식(圖式)으로 표시(表示)할 수 있도록 설정(設定)하고 또 시행(施行)되었다. 2보(二報), 3보(三報), 4보(四報)에서는 데이터 뱅크에 수집(蒐集) 및 축적(蓄積)된 데이터의 일반적(一般的)인 기술(記述)을 적시(摘示)하였다. (1) 과학(科學)과 기술(技術)의 정보유통(情報流通)에 있어서 국가적(國家的) 회합(會合)의 역할(役割)(Garvey; 4보(報)) 국가적(國家的) 회합(會合)은 투고(投稿)와 이로 인한 잡지중(雜誌中) 게재간(揭載間)의 상대적(相對的)인 오랜 기간(期間)동안 이러한 연구(硏究)가 공개매체(公開媒體)로 인하여 일시적(一時的)이나마 게재여부(揭載如否)의 불명료성(不明瞭性)을 초래(招來)하기 전(前)에 과학연구(科學硏究)의 초기전파(初期傳播)를 위하여 먼저 행한 주요(主要) 사례(事例)와 마지막의 비정형매체(非定形媒體)의 양자(兩者)를 항상 조직화(組織化)하여 주는 전체적(全體的)인 유통과정(流通過程)에 있어서 명확(明確)하고도 중요(重要)한 기능(機能)을 갖는다는 것을 알 수 있었다. (2) 잡지(雜誌)에 게재(揭載)된 정보(情報)의 생산(生産)과 관련(關聯)되는 정보(情報)의 전파과정(傳播過程)(Garvey; 1보(報)). 이 연구(硏究)를 위해서 우리는 정보유통과정(情報流通過程)을 따라 많은 노력(努力)을 하였는데, 여기서 유통과정(流通過程)의 인상적(印象的)인 면목(面目)은 특별(特別)히 연구(硏究)로부터의 정보(情報)는 잡지(雜誌)에 게재(揭載)되기까지 진정으로는 공개적(公開的)이 못된다는 것과 이러한 사실(事實)은 선진연구(先進硏究)가 자주 시대(時代)에 뒤떨어지게 된다는 것을 발견할 수 있었다. 경험(經驗)이 많은 정보(情報)의 수요자(需要者)는 이러한 폐물화(廢物化)에 매우 민감(敏感)하며 자기(自己) 연구(硏究)에 당면한, 진행중(進行中)이거나 최근(最近) 완성(完成)된 연구(硏究)에 대하여 정보(情報)를 얻기 위한 모든 수단(手段)을 발견(發見)코자 하였다. 예를 들어, 이들은 잡지(雜誌)에 보문(報文)을 발표(發表)하기 전(前)에 발생(發生)하는 정보전파과정(情報傳播過程)을 통하여 유루(遺漏)될지도 모르는 정보(情報)를 얻기 위하여 한 잡지(雜誌)나 2차자료(二次資料) 또는 전형적(典型的)으로 이용(利用)되는 다른 잡지류중(雜誌類中)에서 당해정보(當該情報)가 발견(發見)되기를 기다리지 않는다는 것이다. (3) "정보생산 과학자(情報生産 科學者)"에 의한 정보전파(情報傳播)의 계속성(繼續性)(이 연구(硏究) 시리이즈의 결과(結果)는 본고(本稿)의 주내용(主內容)으로 되어 있다.) 1968/1969년(年)부터 1970/1971년(年)의 이년기간(二年期間)동안 보문(報文)을 낸 과학자(科學者)(1968/1969년(年) 잡지중(雜誌中)에 "질이 높은" 보문(報文)을 발표(發表)한)의 약 2/3는 1968/1969의 보문(報文)과 동일(同一)한 대상영역(對象領域)의 연구(硏究)를 계속(繼續) 수행(遂行)하였다. 그래서 우리는 본연구(本硏究)에 오른 대부분(大部分)의 저자(著者)가 정상적(正常的)인 과학(科學), 즉 연구수행중(硏究遂行中) 의문(疑問)에 대한 완전(完全)한 해답(解答)을 얻게 되는 가장 중요(重要)한 추구(追求)로서 Kuhn(제5보(第5報))에 의하여 기술(技術)된 방법(방법)으로 과학(연구)(科學(硏究))을 실행(實行)하였음을 알았다. 최근(最近)에 연구(硏究)를 마치고 그 결과(結果)를 보문(報文)으로서 발표(發表)한 이들 과학자(科學者)들은 다음 단계(段階)로 해야 할 사항(事項)에 대하여 선행(先行)된 동일견해(同一見解)를 가진 다른 연구자(硏究자)들의 연구(硏究)와 대상(對象)에 밀접(密接)하게 관련(關聯)되고 있다. 이 계속성(繼續性)의 효과(效果)에 대한 지표(指標)는 보문(報文)과 동일(同一)한 영역(領域)에서 연구(硏究)를 계속(繼續)한 저자(著者)들의 약 3/4은 선행(先行) 보문(報文)에 기술(技術)된 연구결과(硏究結果)에서 직접적(直接的)으로 새로운 연구(硏究)가 유도(誘導)되었음을 보고(報告)한 사항(事項)에 반영(反映)되어 있다. 그렇지만 우리들의 데이터는 다음 영역(領域)으로 기대(期待)하지 않은 전환(轉換)을 일으킬 수도 있음을 보여주고 있다. 동일(同一) 대상(對象)에서 연구(硏究)를 속행(續行)하였던 저자(著者)들의 1/5 이상(以上)은 뒤에 새로운 영역(領域)으로 연구(硏究)를 전환(轉換)하였고 또한 이 영역(領域)에서 연구(硏究)를 계속(繼續)하였다. 연구영역(硏究領域)의 이러한 변화(變化)는 연구자(硏究者)의 일반(一般) 정보유통(情報流通) 패턴에 크게 변화(變化)를 보이지는 않는다. 즉 새로운 지적(知的) 문제(問題)에 대한 변화(變化)에서 야기(惹起)되는 패턴에 있어서 저자(著者)들은 오래된 문제(問題)의 방법(方法)과 기술(技術)을 새로운 문제(問題)로 맞추려 한다. 과학사(科學史)의 최근(最近) 해석(解釋)(Hanson: 6보(報))에서 예기(豫期)되었던 바와 같이 정상적(正常的)인 과학(科學)의 계속성(繼續性)은 항상 절대적(絶對的)이 아니며 "과학지식(科學知識)"의 첫발자욱은 예전 연구영역(硏究領域)의 대상(對象)에 관계(關係)없이 나타나는 다른 영역(領域)으로 내딛게 될지도 모른다. 우리들의 연구(硏究)에서 저자(著者)의 1/3은 동일(同一) 영역(領域)의 대상(對象)에서 속계적(續繼的)인 연구(硏究)를 수행(遂行)치 않고 새로운 영역(領域)으로 옮아갔다. 우리는 이와 같은 데이터를 (a) 저자(著者)가 각개과학자(各個科學者)의 활동(活動)을 통하여 집중적(集中的)인 과학적(科學的) 노력(努力)을 시험(試驗)할 때 각자(各自)의 연구(硏究)에 대한 많은 양(量)의 계속성(繼續性)이 어떤 진보중(進步中)의 과학분야(科學分野)에서도 나타난다는 것과 (b) 이 계속성(繼續性)은 과학(科學)에 대한 집중적(集中的) 진보(進步)의 필요적(必要的) 특질(特質)이라는 것을 의미한다. 또한 우리는 이 계속성(繼續性)과 관련(關聯)되는 유통문제(流通問題)라는 새로운 대상영역(對象領域)으로 전환(轉換)할 때 연구(硏究)의 각단계(各段階)의 진보(進步)와 새로운 목적(目的)으로 전환시(轉換時) 양자(兩者)가 다 필요(必要)로 하는 각개(各個) 과학자(科學者)의 정보수요(情報需要)를 위한 시간(時間) 소비(消費)라는 것을 탐지(探知)할 수 있다. 이러한 관찰(觀察)은 정보(情報)의 선택제공(選擇提供)시스팀이 현재(現在) 필요(必要)로 하는 정보(情報)의 만족(滿足)을 위하여는 효과적(效果的)으로 매우 융통성(融通性)을 띠어야 한다는 것을 암시(暗示)하는 것이다. 본고(本稿)의 시리이즈에 기술(記述)된 전정보유통(全情報流通) 과정(過程)의 재검토(再檢討) 결과(結果)는 과학자(科學者)들이 항상 그들의 요구(要求)를 조화(調和)시키는 신축성(伸縮性)있는 유통체제(流通體制)를 발전(發展)시켜 왔다는 것을 시사(示唆)해 주고 있다. 이 시스팀은 정보전파(情報傳播) 사항(事項)을 중심(中心)으로 이루어 지며 또한 이 사항(事項)의 대부분(大部分)의 참여자(參與者)는 자기자신(自己自身)이 과학정보(科學情報) 전파자(傳播者)라는 기본적(基本的)인 정보전파체제(情報傳播體制)인 것이다. 그러나 이 과정(過程)의 유통행위(流通行爲)에서 살펴본 바와 같이 우리는 대부분(大部分)의 정보전파자(情報傳播者)가 역시 정보(情報)의 동화자(同化者)-다시 말해서 과학정보(科學情報)의 생산자(生産者)는 정보(情報)의 이용자(利用者)라는 것을 알 수 있다. 이 연구(硏究)에서 전형적(典型的)인 과학자((科學者)는 과학정보(科學情報)의 생산(生産)이나 전파(傳播)의 양자(兩者)에 연속적(連續的)으로 관계(關係)하고 있음을 보았다. 만일(萬一) 연구자(硏究者)가 한 편(編)의 연구(硏究)를 완료(完了)한다면 이 연구자(硏究者)는 다음에 무엇을 할 것이냐 하는 관념(觀念)을 갖게 되고 따라서 "완료(完了)된" 연구(硏究)에 관한 정보(情報)를 이용(利用)하여 동시(同時)에 새로운 일을 시작(始作)하게 된다. 예를 들어, 한 과학자(科學者)가 동일(同一) 영역(領域)의 다른 동료연구자(同僚硏究者)에게 완전(完全)하며 이의(異議)에 방어(防禦)할 수 있는 보고서(報告書)를 제공(提供)할 수 있는 단계(段階)에 도달(到達)하였다면 우리는 이 과학자(科學者)가 정보유통과정(情報流通過程)에서 많은 역할(役割)을 해낼 수 있다는 것을 알 것이다. 즉 이 과학자(科學者)는 다른 과학자(科學者)들에게 최신(最新)의 과학적(科學的) 결과(結果)를 제공(提供)할 때 하나의 과학정보(科學情報) 전파자(傳播者)가 되며, 이 연구(硏究)의 의의(意義)와 타당성(妥當性)에 관한 논평(論評)이나 비평(批評)을 동료(同僚)로부터 구(求)하는 관점(觀點)에서 보면 이 과학자(科學者)는 하나의 정보탐색자(情報探索者)가 된다. 또한 장래(將來)의 이용(利用)을 위하여 증정(贈呈)이나 동화(同化)한 이 정보(情報)로부터 피이드백을 받아 드렸을 때의 범주(範疇)에서 보면 (잡지(雜誌)에 투고(投稿)하기 위하여 원고(原稿)를 작성(作成)하는 경우에 있어서와 같이) 과학자(科學者)는 하나의 정보이용자(情報利用者)가 되고 이러한 모든 가능성(可能性)에서 정보생산자(情報生産者)는 다음 정보생산(情報生産)에 이미 들어가 있다고 볼 수 있다(저자(著者)들의 2/3는 보문(報文)이 게재(揭載)되기 전(前)에 이미 새로운 연구(硏究)를 시작(始作)하였다). 과학자(科學者)가 자기연구(自己硏究)를 마치고 예비보고서(豫備報告書)를 만든 후(後) 자기연구(自己硏究)에 관한 정보(情報)의 전파(傳播)를 계속하게 되는데 이와 관계(關係)되는 일반적(一般的)인 패턴을 보면 소수(少數)의 동료(同僚)그룹에 출석(出席)하는 경우 (예로 지역집담회)(地域集談會))와 대중(大衆) 앞에서 행(行)하는 경우(예로 국가적 회합(國家的 會合)) 등이 있다. 그러는 동안에 다양성(多樣性) 있는 성문보고서(成文報告書)가 이루어진다. 그러나 과학자(科學者)들이 자기연구(自己硏究)를 위한 주정보전파목표(主情報傳播目標)는 과학잡지중(科學雜誌中)에 게재(揭載)되는 보문(報文)이라는 것이 명확(明確)한 사실(事實)인 것이다. 이러한 목표(目標)에 도달(到達)할 때까지의 각(各) 정보전파단계(情報傳播段階)에서 과학자(科學者)들은 목표달성(目標達成)을 위하여 청중(聽衆), 자기동화(自己同化)된 정보(情報) 및 이미 이용(利用)된 정보(情報)로부터 피이드백을 탐색(探索)하게 된다. 우리가 본고(本稿)의 시리이즈중(中)에 표현(表現)하려 했던 바와 같이 이러한 활동(活動)은 조사수임자(調査受任者)의 의견(意見)이 원고(原稿)에 반영(反映)되고 또 그 원고(原稿)가 잡지게재(雜誌揭載)를 위해 수리(受理)될 때까지 계속적(繼續的)으로 정보(情報)를 탐색(探索)하는 과학자(科學者)나 기타(其他)사람들에게 효과적(效果的)이었다. 원고(原稿)가 수리(受理)되면 그 원고(原稿)의 저자(著者)들은 그 보문(報文)의 주내용(主內容)에 대하여 적극적(積極的)인 정보전파자(情報傳播者)로서의 역할(役割)을 종종 중지(中止)하는 일이 있는데 이때에는 저자(著者)들의 역할(役割)이 변화(變化)하는 것을 볼 수 있었다. 즉 이 저자(著者)들은 일시적(一時的)이긴 하나 새로운 일을 착수(着手)하기 위하여 정보(情報)의 동화자(同化者)를 찾게 된다. 또한 전(前)에 행한 일에 대한 의견(意見)이나 비평(批評)이 새로운 일에 영향(影響)을 끼치게 된다. 동시(同時)에 새로운 과학정보생산(科學情報生産) 과정(過程)에 들어가게 되고 현재(現在) 진행중(進行中)이거나 최근(最近) 완료(完了)한 연구(硏究)에 대한 정보(情報)를 항상 찾게 된다. 활발(活潑)한 연구(硏究)를 하는 과학자(科學者)들에게는, 동화자(同化者)로서의 역할(役割)과 전파자(傳播者)로서의 역할(役割)을 분리(分離)시킨다는 것은 실제적(實際的)은 못된다. 즉 후자(後者)를 완성(完成)하기 위해서는 전자(前者)를 이용(利用)하게 된다는 것이다. 과학자(科學者)들은 한 단계(段階)에서 한 전파자(傳播者)로서의 역할(役割)이 뚜렷하나 다른 단계(段階)에서는 정보교환(情報交換)이 기본적(基本的)으로 정보동화(情報同化)에 직결(直結)되고 있는 것이다. 정보전파자(情報傳播者)와 정보동화자간(情報同化者間)의 상호관계(相互關係)(또는 정보생산자(情報生産者)와 정보이용자간(情報利用者間))는 과학(科學)에 있어서 하나의 필수양상(必修樣相)이다. 과학(科學)의 유통구조(流通構造)가 전파자(傳播者)(이용자(利用者)로서의 역할(役割)보다는)의 필요성(必要性)에서 볼 때 복잡(複雜)하고 다이나믹한 시스팀으로 구성(構成)된다는 사실(事實)은 과학(科學)의 발전과정(發展過程)에서 필연적(必然的)으로 나타난다. 이와 같은 사실(事實)은 과학정보(科學情報)의 전파요원(傳播要員)이 국가적 회합(國家的 會合)에서 자기연구(自己硏究)에 대한 정보(情報)의 전파기회(傳播機會)를 거절(拒絶)하고 따라서 전파정보(電波情報)를 판단(判斷)하고 선별(選別)하는 것을 감소(減少)시키며 결과적(結果的)으로 잡지(雜誌)나 단행본(單行本)에서 비평(批評)을 하고 추고(推敲)하는 것이 배제(排除)될 때는 유형적(有形的) 과학(科學)은 급속(急速)히 비과학성(非科學性)을 띠게 된다는 것을 Lysenko의 생애(生涯)에 대한 Medvedev의 기술중(記述中)[7]에 지적(指摘)한 것과 관계(關係)되고 있다.