http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 주호정 (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Enforcement Credibility and Frequency of Negotiations in Civil Wars
주호정,황태희 한국국제정치학회 2019 The Korean Journal of International Studies Vol.17 No.2
This article explores the effect of enforcement credibility on the number of negotiations during civil war peace processes. While the use of negotiations during civil wars has increased since the Cold War, there has been a lack of attention to the obstacles faced by conflict parties once negotiations begin. We argue that conflict resolution should be evaluated as a stepwise process, in which factors that increase prospects for agreement enforcement may impact the onset or frequency of negotiations differently. We rely on the international cooperation literature which addresses the relationship between bargaining and enforcement problems. As enforcement becomes more credible, parties engage in bargaining more rigorously in order to make sure the distributive terms are satisfactory. We argue that while the presence of third-party mediators and a negotiating partner with a strong internal support base might increase the likelihood of agreement enforcement, they may also make conflict parties more careful when drafting the agreement, since the cost of revising or breaking the agreement also rises with enforcement credibility. We test the effect of third parties and internal support on the number of negotiations using a zero-inflated negative binomial regression model. We find that the presence of mediators and the presence of a rebel group with a strong support base increase the frequency of negotiations. We also find that factors such as rebel territorial control and Cold War have distinct effects on negotiation onset and not on negotiation frequency, emphasizing the importance of evaluating conflict resolution as a multilevel process.
-
한국 남부 해역 SST의 계절 및 경년 변동이 단기 딥러닝 모델의 SST 예측에 미치는 영향
주호정,채정엽,이은주,김영택,박재훈,JU, HO-JEONG,CHAE, JEONG-YEOB,LEE, EUN-JOO,KIM, YOUNG-TAEG,PARK, JAE-HUN 한국해양학회 2022 바다 Vol.27 No.2
Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.
-
비주석 재귀신경망 앙상블 모델을 기반으로 한 조위관측소 해수위의 준실시간 이상값 탐지
이은주,김영택,김송학,주호정,박재훈,LEE, EUN-JOO,KIM, YOUNG-TAEG,KIM, SONG-HAK,JU, HO-JEONG,PARK, JAE-HUN 한국해양학회 2021 바다 Vol.26 No.4
상시 관측되는 조위관측소 해수위 자료는 결측값과 오측값을 포함하고 있으며, 그 중 오측 값은 이상값으로 분류되는 전처리 대상이다. 이러한 오측을 제거하기 위해 대표적으로 3𝜎 (three standard deviations) 규칙이 적용되어왔으나, 기상이변 등에 의한 극값이 존재하거나 3𝜎 범위 안에서도 오측이 존재하는 해수위 자료에는 그 적용이 어렵다. 본 연구에서 설계된 모델은 오측에 대한 사전 정보가 필요하지 않은 비주석 학습으로 구성되며, 재귀신경망과 앙상블 기법을 이용함으로써 실시간으로 수집되는 해수위 자료가 오측일 가능성을 발생한지 20분 이내로 제시한다. 검증이 완료된 모델은 평시 및 기상이변시의 정상값과 오측값을 잘 분리하며, 학습이 이뤄지지 않은 연도의 해수위 자료에서도 이상값 탐지가 가능함을 확인하였다. 본 연구의 관측 이상치 탐지 알고리즘은 조위관측소 해수위에 국한되지 않고 다양한 해양 및 대기자료의 이상치 탐지 인공신경망 모델에 확장 적용할 수 있다. Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
