Anomaly detection technique for the Unmanned Aerial Vehicles (UAVs) is one of the important techniques for ensuring airframe stability. There have been many researches on anomaly detection techniques using deep learning. However, most of research on the anomaly detection techniques are not consider the limited computational processing power and available energy of UAVs. Deep learning model convert to the model compression has significant advantages in terms of computational and energy efficiency for machine learning and deep learning. Therefore, this paper suggests a real-time anomaly detection model for the UAVs, achieved through model compression. The suggested anomaly detection model has three main layers which are a convolutional neural network (CNN) layer, a long short-term memory model (LSTM) layer, and an autoencoder (AE) layer. The suggested anomaly detection model undergoes model compression to increase computational efficiency. The model compression has same level of accuracy to that of the original model while reducing computational processing time of the UAVs. The proposed model can increase the stability of UAVs from a software perspective and is expected to contribute to improving UAVs efficiency through increased available computational capacity from a hardware perspective.

Republic of Korea is building a multi-layered missile defense system against North Korea’s growing ballistic missile threat. To maximize the intercept performance of a multi-layered missile defense system, it is important to develop an efficient engagement plan that considers the interceptable time/space of each interceptor system for ballistic missiles. To do so, it is necessary to predict the flight trajectory of the ballistic missile, which must be done within a short time considering the short battlefield environment and the speed of the ballistic missile. This study presents a model for rapid trajectory prediction of ballistic missiles using the kinetic characteristics of each flight phase(thrust phase, midcourse phase, and re-entry phase) of ballistic missiles, a method for estimating kinetic information from ballistic missile observation data(time and position), and a mathematical analysis of the equations of motion of ballistic missiles.

Business model(BM) innovation is widely known as a differentiated strategy and strategic framework for companies to secure a sustainable competitive advantage in an uncertain environment. While prior research has studied new business models in accordance with changes in manufacturing trends such as digitalization and servitization, empirical understanding of the dynamic processes of BM innovation is still lacking. This study addresses this gap by proposing an analytical framework of the BM innovation matrix that classifies companies' BM innovation cases into four types according to the degree of BM change and the influential level of the industry/market outcome through a critical literature review on business models and dynamics. Drawing on this framework, we conduct longitudinal case studies of leading global 3D printing firms to examine the dynamic processes and external environmental factors that shape the evolution of BM innovation. Our findings reveal previously underexplored patterns of co-evolution between firms’ business models and their broader industrial and market environments. This study has the significance of constructing a framework for dynamically analyzing BM innovation based on longitudinal case studies of emerging 3D printing companies. We presented implications for companies seeking successful commercialization of emerging technologies, such as the strategic usefulness of the BM innovation framework and the importance of co-evolution with industrial structure and environmental factors in the process of change.

일 의미감은 조직구성원의 주요 심리적 및 행동적 결과와 밀접하게 연결되는 핵심 변수로 주목받고 있다. 특히 일 의미감은 단순히 주관적인 평가를 넘어서, 개인의 가치, 관계, 목적의식 등 다양한 심리적 요소와 연계되는 다차원적 구성개념으로 인식되고 있다. 본 연구에서는 일 의미감을 일반요인과 세부요인으로 구성된 bifactor 구조로 모형화하고, 각각이 대표적인 조직관련 변인들인 조직몰입, 이직의도, 번아 웃과 어떠한 관계를 가지는지를 분석하였다. 연구를 위해 국내 다양한 산업 분야에 종사하는 풀타임 근로 자 407명을 대상으로 설문조사를 실시하였으며 구조방정식모형(SEM)을 활용하여 분석을 실시하였다. 분 석 결과, bifactor 구조는 적절한 모형 적합도를 나타냈고, 일 의미감의 일반요인은 정서적 조직몰입 증가, 이직의도 감소, 번아웃 완화 등 모든 조직결과변인에 대해 안정적인 예측력을 보였다. 반면, 세부요인들은 각 결과변인에 대해 상이한 방향성과 유의미한 추가 설명력을 보여주었으며, 일부 요인은 오히려 부정적 인 효과를 나타내는 등 복합적인 양상이 확인되었다. 이러한 결과는 일 의미감이 단일한 정서적 경험이 아닌 다양한 심리적 자원들이 조합되어 형성되는 복합적 구조임을 시사하며, 각 하위요인이 구성원 태도 와 행동에 미치는 영향은 상황과 맥락에 따라 달라질 수 있음을 보여준다. 본 연구는 일 의미감에 대한 이론적 이해를 확장함과 동시에, 조직 내에서 보다 정교하고 실질적인 의미감 기반의 인사전략 수립을 위한 실증적 기초자료를 제공한다는 점에서 학문적 및 실천적 의의를 가진다.

As conventional road traffic noise prediction models are designed to estimate long-term representative noise levels, capturing fine-scale noise fluctuations caused by real-world traffic dynamics is challenging. A previous study proposed a microscopic road traffic noise model (MTN) can calculate time-series noise levels with a resolution of 1 s using the concept of a moving noise source. In this study, two experiments were conducted to verify the accuracy of the noise prediction of the model. First, by comparing the calculated noise levels of two conventional road traffic noise models and the MTN in a simple road simulation environment, it was confirmed that the calculation error was within 3 dB(A) when calculating the 1-h equivalent noise level. Second, an experiment was conducted to verify the noise prediction error of the MTN on six actual roads. A comparison of the calculated noise level using the MTN based on traffic data collected from actual roads with the measured noise level on real roads showed that the calculated noise level achieved a mean absolute error (MAE) of 1.88 dB(A) from the equivalent noise level and 1.28 dB(A) from the maximum noise level. This was similar to the MAE of the foreign road traffic noise models. However, when the location of the receiver is within 10 m of the road, an error of more than 3 dB(A) occurs because of the simplicity of the MTN propagation model, which remains a problem that must be solved in the future. This study proved that the noise level calculation using the MTN is similar to the noise of an actual road environment. Additionally, the continuous development of the MTN is expected to make it an effective alternative for the management of road noise.

With the rapid expansion of personal mobility (PM) devices as urban transport alternatives, the associated safety risks have increased significantly. Although previous studies have offered insights into user behavior and accident traits, more integrated approaches that consider spatial and administrative contexts are required to better understand the factors affecting accident severity. This study investigated the factors influencing accident severity involving PM devices in Seoul, South Korea by employing a cross-classified multilevel model (CCMM) to account for both police jurisdiction and regional characteristics. Analyzing the 2021 data from the Traffic Accident Analysis System (TAAS), the model showed strong validity (ICC: 15.8%, DIC: 697.2), outperforming the logistic and hierarchical models. Key predictors of higher severity included crashes in non-standard areas (e.g., other than single roads or intersections), helmet non-use, and older age of victims and perpetrators. Violations, such as exceeding passenger capacity, were negatively associated with severity. Industrial areas and high subway station densities reduced the severity, reflecting the benefits of pedestrian-friendly infrastructure. Larger areas covered by police officers significantly increased the severity, revealing enforcement limitations. The 2021 Road Traffic Act revision has had no statistically significant impact. These results highlight the need for integrated policies that combine infrastructure improvements, enhanced enforcement, and behavioral changes to reduce the severity of PM-related accidents in urban environments.

임파워먼트에 대한 관심이 꾸준히 증가하고 있는 추세 속에 많은 기업의 관리자들이 구조적 임파워먼 트의 긍정적인 측면만을 바라보고 실제 자신들의 기업에 적용하고 있다. 이는 구조적 관점에서 의사결정 의 권한을 하위 부서로 이양하는 것이 불확실한 상황 속에서 혁신적인 결과물을 탐색하는 데 매우 효과적 일 것이라는 가정에서 비롯되었다. 그러나 조직이 처한 환경 속에서 이러한 구조적 임파워먼트가 실제로 효과적일까에 대한 연구는 거의 이루어지지 않고 있으며, 몇몇 연구에서조차 조직이 처한 다양한 상황적 요인들에 대해서는 전혀 고려되지 않고 있다. 그러므로 본 연구는 NK 모델을 통해 조직의 과업 상호의존 성과 부서의 업무역량에 따라 구조적 임파워먼트가 조직의 창의적 성과 탐색에 미치는 영향을 확인하고, 이에 따른 이론적/실무적 시사점을 제공하기 위해 실시하였다. 분석 결과 첫째, 부서 간 과업의 상호의존 성이 매우 높은 상황에서는 부서의 업무역량에 관계없이 구조적 임파워먼트가 창의적 조직 성과 탐색에 부정적으로 작용하였으며, 조직 차원의 의사결정에 대한 개입이 있을 시 성과가 개선되었다. 또한, 부서의 업무역량이 높을수록 일정 수준 이상의 조직의 개입(조직 수준의 의사결정)이 발생했을 때, 다시 창의적 조직 성과 탐색이 낮아짐을 확인하였다. 둘째, 과업의 상호의존성이 낮은 상황에서는 부서의 업무역량이 높을수록 구조적 임파워먼트의 창의적 조직 성과 탐색에 대한 효과성이 높은 것으로 나타났다. 또한, 구조 적 임파워먼트가 낮아질수록 부서의 업무역량에 관계없이 창의적 조직 성과 탐색도 같이 낮아지는 것을 확인하였다. 이러한 결과는 구조적 임파워먼트가 반드시 창의적 조직 성과 탐색에 긍정적인 영향을 미치 는 것이 아니라 조직이 처한 상황적 요인을 확인하여 실행할 필요가 있음을 시사하며, 구조적 임파워먼트 가 긍정적인 효과를 가져오기 위해서는 조직 차원의 의사결정 개입이 필요할 수 있음을 시사한다.

Written examination for driver’s license certification plays a critical role in promoting road safety by assessing the applicants' understanding of traffic laws and safe driving practices. However, concerns have emerged regarding structural biases in multiple-choice question (MCQ) formats, such as disproportionate answer placement and leading linguistic cues, which may allow test-takers to guess the correct answers without substantive legal knowledge. To address these problems, this paper proposes a prompt-driven evaluation framework that integrates structural item analysis with response simulations using a large language model (LLM). First, we conducted a quantitative analysis of 1,000 items to assess formal biases in the answer positions and option lengths. Subsequently, GPT-based simulations were performed under four distinct prompt conditions: (1) safety-oriented reasoning without access to legal knowledge, (2) safety-oriented reasoning with random choices for knowledge-based questions, (3) performance-oriented reasoning using all available knowledge, and (4) a random-guessing baseline model to simulate non-inferential choice behavior. The results revealed notable variations in item difficulty and prompt sensitivity, particularly when safety-related keywords influence answer selection, irrespective of legal accuracy. The proposed framework enables a pretest diagnosis of potential biases in the MCQ design and provides a practical tool for enhancing the fairness and validity of traffic law assessments. By improving the quality control of item banks, this approach contributes to the development of more reliable knowledge-based testing systems that better support public road safety.