기후 변화에 의해 해수면 온도 상승, 태풍의 최고 강도 북상, 태풍 강도 증가가 나타나고 있으며, 미래의 태풍 강도 변화가 더 심화될 것으로 예상하고 있다. 본 논문에서는 기후 변화 시나리오에 의해서 발생할 수 있는 한반도 부근의 태풍 강도를 예측하기 위하여 딥러닝 기반 태풍 강도 예측 모델을 개발하였다. 기후 예측정보를 이용하여 미래 기후 변화 환경장 변화에 따른 태풍의 강도를 예측할 수 있도록 과거 환경장을 학습 자료로 사용하였다. 학습자료는 1980년에서 2022년까지의 태풍 발생 빈도가 높은 6~10월의 기상 및 해양 재분 석 월평균 자료와 Best Track 태풍 241개를 입력자료로 사용하였다. 환경장 변화에 따른 태풍 강도 예측을 위해 자료의 공간적인 특징과 시간적인 특징을 함께 고려하는 딥러닝 모델인 ConvLSTM 기반으로 모델을 개발하였다. 태풍 트랙 시퀀스의 각 이동 경로에 대한 월평균 환경장 자료를 모델에 학습하여 태풍의 중심 기압을 예측하였다. 태풍의 공간적 특성을 반영할 수 있도록 범위를 설정하여 입력자료로 학습하였으며, 5°⨉ 5°의 범위일 때 가장 좋은 결과를 보였다. 몬테카를로 방법을 이용한 민감도 실험을 통해 모델 예측에 가장 큰 영향을 미치는 변수는 SST로 확인되었다.

The damage to structures during an earthquake can be varied depending on the frequency characteristics of seismic waves and the geological properties of the ground. Therefore, considering such attributes in the design ground motions is crucial. The Korean seismic design standard (KDS 17 10 00) provides design response spectra for various ground classifications. If required for time-domain analysis, ground motion time series can be either selected and adjusted from motions recorded at rock sites in intraplate regions or artificially synthesized. Ground motion time series at soil sites should be obtained from site response analysis. However, in practice, selecting suitable ground motion records is challenging due to the overall lack of large earthquakes in intraplate regions, and artificially synthesized time series often leads to unrealistic responses of structures. As an alternative approach, this study provides a case study of generating ground motion time series based on the hybrid broadband ground motion simulation of selected scenario earthquakes at sites in the Nakdonggang delta region. This research is significant as it provides a novel method for generating ground motion time series that can be used in seismic design and response analysis. For large-magnitude earthquake scenarios close to the epicenter, the simulated response spectra surpassed the 1000-year design response spectra in some specific frequency ranges. Subsequently, the acceleration time series at each location were used as input motions to perform nonlinear 1D site response analysis through the PySeismoSoil Package to account for the site response characteristics at each location. The results of the study revealed a tendency to amplify ground motion in the mid to long-period range in most places within the study area. Additionally, significant amplification in the short-period range was observed in some locations characterized by a thin soil layer and relatively high shear wave velocity soil near the upper bedrock.

In order to solve the rapidly increasing domestic delivery volume and various problems in the recent metropolitan area, domestic researchers are conducting research on the development of “Urban Logistics System Using Underground Space” using existing urban railway facilities in the city. Safety analysis and scenario analysis should be performed for the safe system design of the new concept logistics system, but the scenario analysis techniques performed in previous studies so far do not have standards and are defined differently depending on the domain, subject, or purpose. In addition, it is necessary to improve the difficulty of clearly defining the control structure and the omission of UCA in the existing STPA safety analysis. In this study, an improved scenario table is proposed for the AGV horizontal transport device, which is a key equipment of an urban logistics system using underground space, and a process model is proposed by linking systematic STPA safety analysis and scenario analysis, and UCA and Control Structure Guidelines are provided to create a safety analysis.

The serendipitous uncovering of carbon dot (CQDs) as budding candidate of carbonaceous nanomaterial has become now one of the hot topics in the research of material science and technology. The unique features of CQDs such as photo-physical properties, excellent biocompatibility, ease of synthesis, good aqueous dispersity, high chemical stability, and accessible functional groups for further modification make them one of the promising competitors in biological, photonic and energyrelated applications. Although some review articles on CQDs have been published, they typically cover all areas of CQDs applications, and no particular evaluation on the advancement of doped CQDs (D-CQDs) has been reported so far. In this review, we demonstrated characteristic features of D-CQDs focusing on doping strategies, discussion on recently adopted various synthesis processes, its applications and its qualitative comparison with each other. The recently developed concept on understanding the structure and optical properties of D-CQDs are also briefly described followed by their application on various fields primarily concentrated on bio-imaging and sensing applications. We also speculate its use in a variety of intriguing fields and its perspectives in near future.

최근 급격한 변화를 겪고 있는 자율주행 자동차 분야의 미래 기술 및 시장 전 망 예측에 대한 요구와 관심이 집중되고 있다. 자동차 산업의 특성상, 복합적 요인의 상관관 계가 미치는 영향력이 크고 요인 간의 복잡도가 높으므로, 체계적인 미래 예측 방법론 적용 을 통한 미래 전망분석 및 전략 수립이 시급하다. 본 연구에서는 자동차 분야에 적합한 미래 예측 방법론 중 필드 변칙 완화기법(Field Anomaly Relaxation)과 다중관점 개념 기법 (Multiple Perspective Concept)을 복합적으로 적용하여, 자율주행 자동차 분야의 핵심기술 및 산업 동향에 관한 미래 시나리오들을 개발하여 실증하였다. 도출된 3개의 시나리오는 전 문가 평가 체크리스트를 통하여 타당성을 검증하였다. 본 연구 결과는 자율주행 자동차 산업 과 같은 다양한 변동성이 존재하는 분야의 미래 예측 방법 중 한 가지로 적용될 수 있다는 점에 의의가 있다.

In this study, the Euro NCAP-based AEB system evaluation simulation was conducted by applying the calculated corrected TTC by road condition estimation of scenarios (CPFA, CPNC, CPLA) of the V2P situation scenario using PC-Crash, a program used for traffic accident analysis. The scenario was evaluated in consideration of the two road conditions. the low-speed conditions among every scenarios avoided collision, but in the medium and high-speed conditions has been collided with pedestrian. It was confirmed that the time point at recognizing pedestrian was lower than set TTC at which AEB system was operated, even though the AEB was operated immediately, a collision occurred due to insufficient braking distance. As in this study, if studies such as V2V, V2P, and V2B considering road friction are actively conducted, it is expected to be useful data for automobile accident prevention and accident analysis.

In this study, the AEB system evaluation simulation was conducted by applying a corrected TTC from 0s to 3s obtained by estimating the road-friction factor and slope applying two scenarios(CCRs and CCRm) that may occur in V2V situation using PC-CrashⓇ, a program used for traffic accident analysis. As a result of two scenarios : At 0.8 of road-friction factor and 0° slope which adapted default model of TTC had longer braking distance so that it crashed. At 0.2 of road-friction factor with corrected TTC slidingly crashed due to slope and braking condition. CCRs and CCRm scenario showed that the maximum slipped distance of the collision avoidance situation was 31.7m, 87.7m. And the collision speed was 35.7km/h at 50km/h and 66.1km/h at 80km/h.

Recently, most of the information system use environment is changing to GUI environment based on windows and web. Most of the development tools for building such a GUI-based information system support object-oriented and event-driven programming concepts. However, there is still a lack of a development methodology that systematically supports event-based information system construction. From a business perspective, an information system is one that supports business processes efficiently and effectively to improve business performance. These business processes are composed of business activities which involve a series of business events. A business event is executed according to a business scenario. Therefore, it is necessary to grasp these events in the requirements analysis stage and to apply it on the system development methodology. However, information systems development methodology which systematically reflect the event processing concept still is insufficient. From this viewpoint, this paper proposes an event-driven scenario-based development methodology that can meet the recent development environment of information systems, and applies the proposed methodology to a small scale information system development case.

From simple niche models to machine learning methods, there have been intensive efforts to understand the potentialdistribution of species in last two decades. Especially in the agricultural sector, recent SDM, Species Distribution Models,studies highly enthused to predict the potential distribution of invasive species under Climate Change. Beyond the distribution,efforts are needed to assess potential risk caused by the target pest. The Shared Socio-Economic Pathways (SSPs) are scenariosfor climate change impacts and adaptation measures. We used MaxEnt model to predict potential distribution of melonthrips with two RCPs (4.5, 8.5) and three SSPs (SSP1, SSP2, SSP3) scenarios. In agricultural land, the potential distributionof melon thrips increases under climate change, but the impact is reduced with the development-oriented scenario, SSP3.

While companies in the field of e-commerce have long engaged in the collection of large amounts of customer data and consider them one of their most important assets, insurance companies have only recently started to collect customer data on a large scale (Smith, Dinev & Xu, 2011). Recently, insurance companies have developed tariffs which adjust premiums based on collected data about the insurant’s behavior (e.g. steps/day, visits to the gym etc.). Benefits like fitness courses or lower insurance rates are provided to encourage a healthy lifestyle and attract healthy customers. However, this model can only succeed, if customers are willing to disclose data. As many customers fear an intrusion of their privacy by companies and consider personal health data to be especially sensitive, this disclosure cannot be taken for granted (Anderson & Agarwal, 2011). The paper evaluates two main influencing factors for the willingness to disclose private health data (benefit offered to customers and sensitivity of data requested). It analyzes their effect by conducting an online scenario-based quasiexperiment with 408 participants. Participants are presented with six hypothetical offers by a health insurance (financial and non-financial benefits; low, medium, high data sensitivity) and indicate how they would respond to these offers in terms of data disclosure. We control for individual heterogeneity by including privacy concerns and trust as between-subject factors (Malhotra, Kim, & Agarwal, 2004). Our results indicate that the willingness to disclose health data can be increased by financial rewards at low and medium sensitivity levels. If information is highly sensitive, the willingness to provide data decreases and cannot be compensated by a tariff reduction. Health care providers should therefore carefully consider which data points they choose as mandatory to participate in personalized insurance tariffs, as they could easily scare off potential customers. In our study non-financial benefits (prevention courses) are not able to increase the willingness to disclose data as much as financial benefits. This could be due to a general preference for financial rewards or to the unknown quality of the courses offered.