인공지능은 우리가 매일 들고 다니는 휴대폰을 비롯한 생활 전반뿐만 아니라 모든 사회 영역에까지 침투하고 있다. 특히 최근에 대두된 인공지능의 학습방법으로서의 머신러닝 기법의 발달로 이제 인공지능은 엄청난 양의 데이터를 학습하면서 성장해나가는 형태를 기본으로 하고 있다. 사법절차를 비롯한 분쟁해결영역도 인공지능이 넘보는 분야에서 더 이상 예외가 아니다. 언뜻 생각하면 법체계는 언어, 논리, 그리고 개념 간의 관련성이 지배하는 영역이어서, 전자적⋅기계적인 분석방법이 적극적으로 도입될 수 있는 분야로 비추어지나, 실제로 아직까지는 법 영역에서 자연어처리나 머신러닝 기법은 기대만큼의 성과를 내지는 못하고 있다. 최근 판결의 결과를 예측하는 시스템을 개발하는 연구들이 활발한데, 현재까지 공표된 대부분의 판결예측 연구들은 일단 사실관계는 확정을 해둔 채, 해당 사실관계의 특징적인 요소들이 담긴 기존 사례들을 분석하여 인간이 하는 것과 유사한 결론을 도출하는 과정으로 이루어진다. 그에 비하여 날 것 그대로의 증거들을 분석하여 허위의 주장이나 증거들을 걸러내고 진실한 사실관계를 재구성하는 정도에 이른 연구들은 찾아보기 힘들다. 특히 우리나라의 경우에는 인공지능이 학습에 필요한 충분한 판례 데이터를 확보하지 못하기가 어렵다는 점이 많이 지적되고 있는 현실이다. 다만 판결문을 훈련데이터로 활용하게 되더라도, 이 유기재가 생략된 판결문도 많다는 점이나 판사 개인이 판결 이유를 기재하는 방식이 훈련데이터의 분석에 영향을 끼칠 수도 있다는 점 등을 유념 해야 한다. 또한 판결문 등 기존 훈련데이터에 편향성이나 오류가 내재되어 있다면, 인공지능의 학습도 당연히 영향을 받을 수밖에 없다. 나아가 인공지능이 추상적인 불확정개념의 해석이나 적용 까지도 할 수 있을지는 아직 의문이다. 인공지능 판사의 출현은 적어도 단시일에 이루어지지는 않을 것이고, 단기적으로는 인공지능을 활용하여 판사의 업무를 보조하는 역할을 더 기대해볼 수 있겠다. 인공지능이 사법분야 내에서 활동할 수 있는 영역을 구체적ㆍ세부적으로 연구 하는 작업은, 인간 법관이 주도권을 잡은 채로 인공지능이 보조적인 역할을 하는 시대에서 인공지능을 활용할 수 있는 영역을 효과적으로 짚어내는 데에 도움이 될 수 있다. 다만 점차적으로 판사를 보조하는 지위에서 어느새 조금씩 인간 판사의 판단을 잠식할 위험성은 항시 경계해야 한다. 특히 인공지능 시스템이 개선될수록 판사가 인공지능이 작성한 보고서나 판결문 초안에 과도하게 의지하여 기계적, 통계적인 판단만을 내리게 될 위험을 조심해야 한다.

개(Canis lupus familiaris)는 인간의 소외 현상을 개선하고, 공동체 생활 의식 향상에 기여하는 반려동물이다. 반려견 품종을 명확히 관리하는 것은 유전병을 감소시키거나, 형질 개량, 종 다양성 유지 등을 위해 중요하다. 본 연구에서는 고밀도 SNP 칩 유전자형 데이터와 기계학습 기술을 이용하여, 유전자형 데이터에 기반한 품종 식별이 가능한지, 가능하다면 최소 몇 개의 유전마커로 품종 식별을 유의하게 수행할 수 있는지 확인하기 위하여, 반려견 11 품종 226두의 23K SNP 칩 데이터를 분석하였다. 9종의 기계학습 다중범주 분류 알고리즘과 2종의 특징 선택 방법의 성능을 비교하여, 선형 서포트 벡터 머신 분류기와 주성분 분석 특징 기여도를 이용한 특징 선택 방법을 이용했을 때, 11종의 반려견 품종을 90% 이상 정확도로 식별하였으며, 이 때 40개의 유전마커가 필요함을 확인하였다. 최종 선발 된 40개의 반려견 품종 식별 유전마커는 타 질병 예측 마커와 결합하여 유전자 검사 키트로 제작될 수 있으며, 반려견 품종 관리 및 질병 관리 기술로 유용하게 활용될 수 있을 것이다.

In this research, we evaluate on the disassemblability of recycling process for vehicle front door using the symbolic chart method and machine-learning algorithm. It is applied to the front door of 1600cc class vehicle, and then the conventional steel door and CFRP door were compared. Based on the principle symbolic chart method, the number of processes can be different according to decomposer proficiency of suitability of recycling process, so the evaluation method is required to supply this issue. The machine learning algorithm, and artificial intelligence method were applied and the applicable tools for each experiment were used to compensate the variations in the number of processes according to different proficiencies. Because CFRP front door has integrated components compare to steel door, so its disassemblability processes were decreased to 80 from 103 of the conventional steel door’s. It can be confirmed that the disassemblability was increased from the suitability of recycling equation. In case of the steel, disassemblability was approximately 60.6, in case of the CFRP is approximately 72 for car front door. Therefore, it can be concluded that the disassemblability of CFRP was better in the evaluation of suitability of recycling.

It is known that the growth and development of the mosquito are greatly affected by the change of the meteorological factors. In particular, temperature and precipitation are closely related to the life cycle of the mosquito, and their effects have different characteristics for each species of mosquito. Therefore, to develop a mosquito activity index based on mosquito density, it is essential to develop a prediction model based on weather data. In this study, we developed a functional formula that can estimate the change of mosquito density according to the change of meteorological factors using the mosquito classification data of Incheon region collected from 2011 to 2017. Also, using the data of the digital mosquito monitoring system (DMS) from April to October 2018, mosquito activity index according to characteristics of space in city was developed. In order to reflect the temporal characteristics of the mosquito life-cycle, we used data of temperature and precipitation prior to 1-2 weeks, and used land cover data to reflect the spatial characteristics of mosquito density. Density of Culex pipiens collected in the Incheon area were gradually increased when the average temperature increased two weeks ago after adjusting the precipitation. However, when the average temperature reached 22°C, the density of Culex pipiens marked a peak, and above the 22°C, the density was decreased. The predicted mosquito activity index calculated by applying the machine learning method to the DMS data of the Incheon area is designed to calculate from 1 to 10 grades. The accuracy of the mosquito activity index was 87% when the one grade error was allowed.

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.

PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method. METHODS: In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models. RESULTS: The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with R2: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with R2: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model’s statistical significance. CONCLUSIONS: Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

Marketing becomes more and more data driven and hence enables machine learning to empower instruments to foster the interaction between firms and consumers to a new level of customization. Replacement and redirection of workforce through machine learning powered devices is not anymore a mere myth (Huang and Rust 2018). Adaption of machine learning has remained low in the recent years even though disposability was given. Nevertheless the acceptance and the implementation of machine learning based marketing efforts experience currently an exponential increase (Syam and Sharma 2018). In this article, the authors aim to develop a stronger understanding of machine learning in the context of marketing as well as to provide an overview about already established usage and implementation of machine learning in the interactions between firms and customers. To achieve this objective, the authors discuss and study machine learning in marketing from both the management and the consumer perspective. This is supported by survey data retrieved from managers out of varies industries as well as consumers, which reveal great variety in usage of machine learning not only among different marketing activities but also among industries. The authors examine the roots of machine learning in marketing and evaluate inferential state-of-the-art instruments. Predictions of what can and will evolve in the marketing context with the help of machine learning in the near future are also connected with concerns and safety issues related to the increasingly transparent consumer-firm-relationship. To conclude, the article the authors present a summary of state-of-the-art mechanisms in machine learning in marketing and propose a research agenda for upcoming research.

The road surface condition in winter is important for road maintenance and safety. To estimate the road surface condition in winter, the RWIS(Road Weather Information System) is used. However RWIS is not measured the continuous road surface information but measured the locational road surface information. To overcome the current RWIS limitation, the thermal mapping sensor which can collect the road surface condition employed in some countries. Although the thermal mapping sensor can collect the continuous road surface information, it is difficult to collect vast data due to apply few probe car. This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors and collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. To find out the correlation between road surface and ambient temperature which may affect patterns of road surface temperature variation, the various weather and topographical conditions along with the test route were considered. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

요약; 최근에는 기계 학습, 특히 심층 학습에 많은 연구가 진행되고 있다. 구글, 페이스 북과 같은 대기업이 인공 지능과 기계 학습에 관심을 가지고 있기 때문에, 이러한 연구는 날마다 발전하고있다. 기계 학습은 의학, 번역 및 IT와 같은 다양한 산업에서 사용될 것으로 기대됩니다. 게임 부문은 기계학습 기술적용의 효과가 예상되는 영역 중 하나라고 간주됩니다. 본 논문에서는 MMORPG-Tera의 게임 콘텐츠에서 몬스터의 승패를 예측하는 신경망을 Tensorflow를 통해 설계하였다. 이 모델은 1 개의 입력 레이어, 2 개의 숨겨진 레이어 및 1 개의 출력 레이어를 가지고 있다. 입력 레이어에는 8 개의 노드가 있고 각 숨겨진 레이어에는 16 개의 노드가 있으며 출력 레이어에는 1 개의 노드가 있다. 더 나은 결과를 위해 우리는 그라디언트 디센트, 시그 모이드 (Sigmoid) 함수 및 Relu 함수 (Activate 함수)에 Adam을 사용한다. 준비된 데이터 세트의 마지막 부분은 테스 트 데이터 용으로 사용되고 나머지는 학습 모델 용으로 사용되었다. 이 모델은 5 ~ 10 % 오차 이내의 확률을 예측할 수 있다. 데이터 세트의 부족은 만족스럽지 않은 점으로 남아 있으며, 충분한 데이터가 수집되고 더 개선 된 모델이 준비되면 오류를 더 줄일 수 있다. 그리고 제안된 모델은 앞으로 다른 게임이나 스포츠 게임에도 적용될 것이다.