• Title/Summary/Keyword: 개인정보침해

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A Mixed Method of Gap-jil Behavior in Educational Institutions : Focusing on abuse of authority (통합연구방법을 활용한 교육기관 내 갑질 행태에 관한 연구 : 권한남용을 중심으로)

  • Choi, Sung-Kwang;Choi, Ye-Na;Kim, Ok-Hee
    • Journal of Korea Entertainment Industry Association
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    • v.15 no.4
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    • pp.243-254
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    • 2021
  • This study analyzed the abuse of authority among the types of power abuse in educational institutions in order to create an educational climate in which democracy and equality are respected and to create a better education and an equal society. First, we analyzed the concept and cause of power abuse through literature research, and then explored the cases of members of educational institutions according to the type of abuse of authority through qualitative research to derive implications. As a result, abuse of authority within educational institutions were found as follows: additional work without consultation, transfer of duties, coercive and unilateral instructions using status, instructions violating laws and guidelines, private instructions for personal convenience, specific institutions, personal rights, and privacy. Based on this analysis, a policy was proposed. First, an agreed standard for abuse of authority, an institutional mechanism to mediate conflicts and complaints over abuse of authority, mandatory installation and legislation of the best decision body, active and transparent disclosure of information, and a shift to open and listening administration are needed. Second, analyzing and seeking ways to reduce overuse of authority in educational institutions will be the cornerstone for leading education's democracy and equality by creating a culture of mutual respect and communication among members of the organization. Hope that follow-up studies will be carried out and that the Gap-jil in educational institutions will be reduced to create a better educational environment.

A Study on the Reliability and Validity of the Collection of the Ethnography Method of Service Experience Data - Focusing on I know You_AI Service - (서비스경험데이터의 에스노그라피 방식 수집에 대한신뢰성과 타당성 연구 - I know you_AI 서비스를 중심으로 -)

  • Ahn, Jinho;Lee, Jeungsun
    • Journal of Service Research and Studies
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    • v.10 no.4
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    • pp.43-55
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    • 2020
  • Recently, as the importance of experience data increases, there are many attempts to deal with experience data from a data science perspective. In the case of approaching as a collection method of a quantitative survey method that seeks to quantify numerically such as big data, it is difficult to interpret the value of experience in a wide range, and it is relatively expensive and time consuming, and personal information infringement There is a limit to the analysis due to the risk of However, since ethnography, a procedure for collecting experience data based on qualitative research, is mainly carried out in the natural real environment of future customers from the perspective of users, it is possible to confirm the nature that customers face with a small sample. In addition, it is also easy to interpret the relational dimension of the empirical data. Although the ethnography method of collecting experiential data is economical and efficient, it is important to reduce errors in the collection process because the lack of scientific procedures for the data collection process can be a problem. It is important to secure the validity of whether the correct measurement tool is used for ethnography-based experiential data collection and to secure the reliability of the use of a valid measurement tool and method by accurately selecting the measurement target. From this point of view, it is necessary to verify the reliability of the research method that clearly selects the measurement target and secures the validity for the development of the correct measurement method and tool for the collection of ethnography experience data. Therefore, in this study, a verification study was conducted on the data and methodology cases of the'I know you_AI' service that analyzes the customer experience of self-employed based on the ethnography method of collecting experience data..

A Study on People Counting in Public Metro Service using Hybrid CNN-LSTM Algorithm (Hybrid CNN-LSTM 알고리즘을 활용한 도시철도 내 피플 카운팅 연구)

  • Choi, Ji-Hye;Kim, Min-Seung;Lee, Chan-Ho;Choi, Jung-Hwan;Lee, Jeong-Hee;Sung, Tae-Eung
    • Journal of Intelligence and Information Systems
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    • v.26 no.2
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    • pp.131-145
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    • 2020
  • In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data (스마트폰 다종 데이터를 활용한 딥러닝 기반의 사용자 동행 상태 인식)

  • Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.163-177
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    • 2019
  • As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

A study on security independent behavior in social game using expanded health belief model (건강신념모델을 확장한 소셜게임(Social Game) 보안의지행동에 관한 연구)

  • Ahn, Ho-Jeong;Kim, Sung-Jun;Kwon, Do-Soon
    • Management & Information Systems Review
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    • v.35 no.2
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    • pp.99-118
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    • 2016
  • With the development of Internet and popularization of smartphones over recent years, social network services are experiencing rapid growth. On top of this, smartphone gaming market is showing a rapid growth and the use of mobile social games is on the significant rise. The occurrence of game data manipulation targeting these services and personal information leakage is highlighting the importance of social gaming security. This study is intended to propose development plans effective and efficient in social game services by figuring out factors putting effects on security dependent behavior of social game users in Korea and carrying out a practical study on the casual relationship between factors influencing security dependent behavior through recognized behavioral control and attitudes for privacy infringement of these factors. To do this, proposed was a study model in which the HBM(Health Belief Model) allowing the social game user to influence security dependent behavior was expanded and applied as a major variable. To verify the study model of this study practically, a survey was conducted among university students in Seoul-based K University and S University who had experienced using social game services. According to the study findings, firstly, the perceived seriousness turned out to provide positive influence to trust. But, the perceived seriousness turned out not to put positive effects on self-efficacy. Secondly, the perceived probability turned out not to put positive effects on self-efficacy and trust. Thirdly, the perceived gain turned out to put positive effects on self-efficacy and trust. Fourthly, the perceived disorder turned out not to put positive effects on self-efficacy and trust. Fifthly, self-efficacy turned out to put positive effects on trust. But, self-efficacy turned out not to put positive effects on security dependent behavior. Sixthly, trust turned out not to put positive effects on security dependent behavior. This study is intended to make a strategic proposal so that social game users can raise awareness of their level of security perception and security willingness through this.

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Analysis of Social Trends for Electric Scooters Using Dynamic Topic Modeling and Sentiment Analysis (동적 토픽 모델링과 감성 분석을 활용한 전동킥보드에 대한 사회적 동향 분석)

  • Kyoungok, Kim;Yerang, Shin
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.1
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    • pp.19-30
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    • 2023
  • An electric scooter(e-scooter), one popularized micro-mobility vehicle has shown rapidly increasing use in many cities. In South Korea, the use of e-scooters has greatly increased, as some companies have launched e-scooter sharing services in a few large cities, starting with Seoul in 2018. However, the use of e-scooters is still controversial because of issues such as parking and safety. Since the perception toward the means of transportation affects the mode choice, it is necessary to track the trends for electric scooters to make the use of e-scooters more active. Hence, this study aimed to analyze the trends related to e-scooters. For this purpose, we analyzed news articles related to e-scooters published from 2014 to 2020 using dynamic topic modeling to extract issues and sentiment analysis to investigate how the degree of positive and negative opinions in news articles had changed. As a result of topic modeling, it was possible to extract three different topics related to micro-mobility technologies, shared e-scooter services, and regulations for micro-mobility, and the proportion of the topic for regulations for micro-mobility increased as shared e-scooter services increased in recent years. In addition, the top positive words included quick, enjoyable, and easy, whereas the top negative words included threat, complaint, and ilegal, which implies that people satisfied with the convenience of e-scooter or e-scooter sharing services, but safety and parking issues should be addressed for micro-mobility services to become more active. In conclusion, this study was able to understand how issues and social trends related to e-scooters have changed, and to determine the issues that need to be addressed. Moreover, it is expected that the research framework using dynamic topic modeling and sentiment analysis will be helpful in determining social trends on various areas.

Problems of Environmental Pollution (환경오염의 세계적인 경향)

  • 송인현
    • Proceedings of the KOR-BRONCHOESO Conference
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    • 1972.03a
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    • pp.3.4-5
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    • 1972
  • 생활수준이 낮은 단계에 있어서는 우선 식량에 대한 수요가 강하다. 인간의 욕구가 만족스럽게 먹는다는 것에 대하여 제일 강하게 발동하는 것이다 그러나 점차 과학기술과 산업과 경제가 발전하여 성장과정에 오르게 되고 소득수준도 향상하게 되면 시장기구를 통해서 구입 할 수 있는 개인의 물적 소비재에 대해서는 점차 충족하게 되며 식량이외에도 의복, 전기기구 및 일용생활용품, 자동차 등에 이르기까지 더욱 고차원의 소비재가 보급하게 되는 것이다. 이렇게 되며는 사람의 욕구는 사적 재물이나 물적 수요에서 점진적으로 공공재나 또는 질적 수요(주택, 생활환경 등)의 방향으로 움직이게 되는 것으로써 여기에 환경오염 또는 공해문제에 대하여 의식하게 된다. 그러나 여기에서 더욱이 문제점이 되는 것은 소득 수준의 향상 과정이란 그 자체가 환경오염의 커다란 요인이라는 점이며 자동차의 급격한 보급과 생활의 편의성을 구하여 집중되는 도시인구의 집적, 높은 소득을 보장하기 위한 생산성 높은 중화학공업의 발전 등등은 그 자체가 환경권이란 사람이 요구하는 고차원의 권리를 침해하는 직접적인 요인이 된다는 것이다. 이와 같은 환경오염이나 공해문제에 대한 세계적인 논의는 이미 시작된 지 오래이지만 현재는 우리의 건강보호를 위해서나 생활환경의 보전을 위해서라는 점에서는 그치는 것이 아니고, 더욱 넓혀서 자연의 보호, 자원의 보호라는 견지로 확대되고 있다. 이와 같은 세계적인 확대된 이해와 이에 대한 대책강구의 제안은 1968년 국제연합의 경제사회이사회에서 스웨덴 정부대표에 의하여 제시되었으며 1969년의 우- 탄트 사무총장의 인간환경에 관한 보고서, 1970년 Nixon 미대통령의 연두일반교서 그리고 1972년 5월 6일 스웨덴의 스톡홀롬에서 개최되는 인간환경회의의 주제 등을 통해서 알 수 있고, 종래의 공해나 생활환경의 오염문제라는 좁은 개념에서가 아니고 인간환경전체의 문제로 다루고 있는 것이다. 즉 환경개발(도시, 산업, 지역개발에 수반된 문제), 환경오염(인위적 행위에 의하여 환경의 대인간조건이 악화하는 문제) 자연ㆍ자원의 보호관리(지하, 해양자원, 동식물, 풍경경치의 문제)란 3개 측면에서 다루고 있는 것이다. 환경오염이란 문제를 중 심하여 보면 환경을 구성하는 기본적인 요소로서 대기, 물, 토지 또는 지각. 그리고 공간의 사대요소로 집약하여 생각할 수 있음으로 이 4요소의 오염이 문제가 되는 것이다. 대기의 오염은 환경의 오염중 가장 널리 알려진, 또 가장 오랜 역사를 가진 오염의 문제로써 이에 속하는 오염인자는 분진, 매연, 유해가스(유황산화물, 불화수소, 염화수소, 질소산화물, 일산 화염소 등) 등 대기의 1차 오염과 1차 존재한 물질이 자외선의 작용으로 변화발생 하는 오존, PAN등 광화학물질이 형성되는 2차적인 오염을 들 수 있다. 기외 카도미움, 연등 유해중금속이나 방사선물질이 대기로부터 토지를 오염시켜서 토지에 서식하는 생물의 오염을 야기케 한다는 점등이 명백하여지고 있으며 대기의 오염은 이런 오염물질이 대기중에서 이동하여 강우에 의한 침강물질의 변화를 일으키게 되며 소위 광역오염문제를 발생케하며 동시에 토지의 토질저하등을 가져오게 한다. 물의 오염은 크게 내육수의 오염과 해양의 오염의 양면으로 나누어 볼 수 있다. 하천의 오염을 방지하고 하천을 보호하기 위한 움직임 역시 환경오염의 역사상 오래된 문제이며 시초에는 인분뇨와의 연결에서 오는 세균에 의한 오염이나 양수 기타 일반하수와의 연결에서 오는 오염에 대비하는 것부터 시작하였지만 근래에는 산업공장폐수에 의한 각종 화학적유해물질과 염료 그리고 석유화학의 발달에 의한 폐유등으로 인한 수질오탁문제가 점차 크게 대두되고 있다. 이것은 측 오염이란 시초에 우리에게 주는 불쾌감이 크므로 이것을 피하자는 것부터 시작하여 인간의 건강을 지키고 각종 사용수를 보존하자는 용수보존으로 그리고 이제는 건강과 용수보존뿐만 아니라 이것이 농림 수산물에 대한 큰 피해를 주게됨으로써 오는 자연환경의 생태계보전의 문제로 확대전환하고 있는 것이다. ?간 특히 해양오염에 대한 문제는 국지적인 것에만 끝이는 것이 아니고 전세계의 해양에 곧 연결되는 것이므로 세계각국의 공통관심사로 등장케 되었으며 이것은 특히 폐유가 유류수송 도중에 해양에 투기되는 유류에 의한 해양의 유막성형에서 오는 기상의 변화와 물피해등이 막심함으로 심각화 되고 있다. 각국이 자국의 해안과 해양을 보호하기 위하여 조치를 서두르고 있는 현시점에서 볼 때에는 이는 국제문제화하고 있으며 세계적인 국제적 협력과 협조의 필요성이 강조되는 좋은 예라 하겠다. 토양의 오염에 있어서는 대기나 수질의 오염이 구국적으로 토양과 관련되고 토양으로 환원되는 것이지만 근래에 많이 보급사용되는 농약과 화학비료의 문제는 토양자체의 오염에만 그치는 것이 아니고 농작물을 식품으로 하여 섭취함으로써 발생되는 인체나 기타생물체의 피해를 고려할 때 더욱 중요한 것이며, 또 토질의 저하를 가져오게 하여 농림생산에 미치는 영향이 적지 않을 것이다. 지반강하는 지각 에 주는 인공적 영향의 대표적인 것으로써 지하수나 지하 천연가스를 채취이용하기 위하여 파들어 감으로써 지반이 침하 하는 것이며 건축물에 대한 영향 특히 풍수해시의 재해를 크게 할 우려가 있는 것이다. 공간에 있어서의 환경오염에는 소음, 진동, 광선, 악취 등이 있다. 이들은 특수한 작업환경의 경우를 제외하고는 건강에 직접적인 큰 피해를 준다고 생각할 수 없으나 소음, 진동, 관선, 악취 등은 일반 일상시민생활에 불쾌나 불안을 줌으로써 안정된 생활을 방해하는 요인이 되는 것이다. 공간의 오염물로써 새로운 주목을 끌게된 것은 도시산업폐기물로써 이들은 대기나 물 또는 토지를 오염시킬 뿐만 아니라 공간을 점령함으로써 도시의 미관이나 기능을 손상케 하는 것이다. 즉 노배폐차의 잔해, 냉장고등고형폐기물등의 재생불가능한 것이나 비니루등 합성물질로 된 용기나 포장 등으로 연소분해 되지 않은 내구소비재가 이에 해당하는 것으로 이는 maker의 양식에 호소하여 그 책임 하에 해결되어야 할 문제로 본다. 이렇듯 환경오염은 각양각색으로 그 오염물질의 주요 발생원인 산업장이나 기타 기관에서의 발생요인을 살펴보며는 다음과 같은 것으로 요약할 수 있다. A. 제도적 요인 1. 관리체재의 미비 2. 관리법규의 미비 3. 책임소재의 불명확 B. 자재적 요인 1. 사용자재의 선택부적 2. 개량대책급 연구의 미흡 C. 기술적 요인 1. 시설의 설계불량, 공정의 결함 2. 시설의 점검, 보전의 불충분 3. 도출물의 취급에 대한 검사부족 4. 발생방지 시설의 미설치, 결함 D. 교육적 요인 1. 오염물질 방제지식의 결여 2. 법규의 오해, 미숙지 E. 경제적 요인 1. 자금부족 2. 융자상의 문제 3. 경제성의 문제 F. 정신적 요인 1. 사회적 도의심의 결여(이기주의) 2. 태만 3. 무지, 무관심 등이다. 따라서 환경오염의 방지란 상기한 문제의 해결에 기대하지 않을 수 없으나 이를 해결하기 위하여는 국내적 국제적 상호협조에 의한 사회각층의 총력적 대책이 시급한 것이다. 이와 같은 환경오염이 단속된다 하며는 미구에 인류의 건강은 물론 그 존립마저 기대하기 어려울 것이며, 현재는 점진적으로 급성피해에 대하여는 그 흥미가 집중되어 그 대비책도 많이 논의되고 있지만 미량의 단속접촉에 의한 만성축적에 관한 문제나 이와 같은 환경오염이 앞으로 태어날 신생률에 대한 영향이나 유전정보에 관한 연구는 장차에 대비하는 문제로써 중요한 것이라 생각된다. 기외에 우려되는 점은 오염방지책을 적극 추진함으로써 올 수 있는 파생적인 문제이다. 즉 오염을 방지하기 위하여 생산기업체가 투자를 하게 되며는 그만큼 생산원가가 상승할 것이며 소비가격도 오를 것이다. 반면 이런 시책에 뒤떨어진 후진국의 값싼 생산국은 자연 수입이 억제 당할 것이며, 이렇게되면 후진국은 무역경쟁에서 큰 상처를 입게될 것이고 뿐만 아니라 선진국에 필요한 오염물질의 발생이 높은 생산기기를 자연후진국에 양도하게 될 것임으로 후진국의 환경오염은 배가할 우려가 있는 것이다. 또 해양오염을 방지할 목적에서와 같이 자국의 해안보호를 위하여 마련된 법의 규제는 타국의 선박운항에 많은 제약을 가하게 될 것이며 이것 역시 시설이 미약한 약소후진국의 선박에 크게 영향을 미치게 될 것임으로 교통, 해운, 무역등을 통한 약소후진국의 경제성장에 제동을 거는 것이 될 것이다. 이렇듯 환경오염의 문제는 환경자체에 대해서만 아니라 부산물적으로 특히 후진국에는 의외 문제를 던져주게 되는 것임으로 환경오염에 대해서는 물론, 전술한 바와 같이 인간환경전체의 문제로써 Nixon 대통령이 말한 결의와 창의와 그리고 자금을 가지고 과감하게 대처해 나가야 할 것이다.

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