Earticle

이 논문은 전형적인 소규모 개방경제라 할 수 있는 한국의 1977～2008년간 분기별 자료를 대상으로 최적화 모형에 기초해 자본이동성의 정도를 평가한다. 자본이동성을 검정하기 위한 추정식은 Shibata and Shintani(1998)와 이를 확장한 Cooray(2005)의 다기간 최적화 모형에서 유도되는 1계 조건을 활용하였다. 특히 완전 자본이동성이 성립하는 경우 순산출은 소비에 대한 설명력을 갖지 못한다는 조건을 활용하였으며 세계이자율의 가변성이 자본이동성에 영향을 미칠 수 있다는 소규모 개방경제의 특징을 감안하였다. 그랜져 인과관계와 1계조건 추정결과 자본이동성을 유의적으로 부정할 수 없음을 확인하였다. 아울러 자본이동성의 구조적 변화 시점은 1997년 1분기로 추정되었지만, 이러한 구조적 전환의 발생에 대한 통계적 유의성은 매우 작았다.

This paper examines the degree of international capital mobility for Korea. By applying the intertemporal optimizing model by Shibata and Shintani(1998) and Cooray (2005), we develop a theory-based estimation method under the assumption of a variable real interest rate to concentrate on the case of a small open economy. The empirical results based on Granger causality test and OLS estimation show that Korea is a relatively free capital mobile economy. Furthermore there did not appear any statistically significant structural break in capital mobility.

The policy which encourages people to use cars on the road has been based on the growth of economy in Korea. It has also caused the concentration and overcrowding in Seoul. That's because the increasing number of people possessing cars interconnects with the urban development. The transportation is a derived demand; so many scholars have recognized the importance of understanding the relationship between urban land use and transport. Considering such importance, this study theoretically compared the developed urban land use-transportation models each other and outlined the particular models briefly. Models were categorized by 2 types; optimizing model and predictive mode. Predictive model is also defined by static model, entropy based model, spatial-economic model, and activity model. After studying models, we investigated other major cities in America. This process is the pre-step for transport policy assessment. Through careful literature review, we can finally develop the integrated land-use transportation model in Seoul metropolitan area. In addition, we will be able to deal the changes of traffic demand pattern under U-Society. Consequently, the results of this study can be applied to ITS projects in the future.

As the Paris Convention on Climate Change takes effect as international law, Korea should reduce 37% greenhouse gases emission Business As Usual (BAU) by 2030. Because Forestry biomass is a carbon-neutral fuel and can be used for conventional thermal power plant, interest of forestry biomass have been increasing. But comparing with coal, its calorific value is low and it makes lots of impurities during combustion. So, in order to use this biomass more efficiently, torrefaction process would be suggested Torrefaction is the process which makes the energy density higher under low temperature (200℃~300℃), a relatively short time (10~60 minutes) and lean or without oxygen condition. After torrefaction process, biomass has the benefit as follows: increased calorific value of biomass, increased water resistance, and advantages of storing and transporting by mass reduction. But characteristics of biomass rely on process time and temperature and there is not enough analysis of process condition. In this study, a mass reduction and heating value prediction model during the torrefaction process was established and characteristics as fuel (hydrophobic property, calorific value) were analyzed. Using wood pellet, torrefaction experiments were performed at 200℃, 230℃, 270℃. After process, to prevent rapid reaction with oxygen, amount of mass reduction was measured after 30minutes cooling. From the experiments, 7.24~31.21% of mass reduction and 4880~5400kcal/kg of heating value were achieved under the different setting temeprtures and times. In simulation, mass reduction and heating value were observed in the range of 3.07~40.24% and 4748~6014kcal/kg respectively which were good agreement with the experimental results.

In this paper, separated and optimized pattern database model is proposed. In order to improve efficiency of Network-based IDS, pattern database is classified by proper basis. Classification basis is decided by the specific Intrusions validity on specific target. Using this model, IDS searches only valid patterns in pattern database on each captured packets. In result, IDS can reduce system resources for searching pattern database. So, IDS can analyze more packets on the network. In this paper, proper classification basis is proposed and pattern database classified by that basis is formed. And its performance is verified by experimental results.

연구목적: 본 연구는 전면전 위기 고조 시 효율적인 접경지역 주민 철수경로 및 일정수립에 최적화된 모형 제안을 목적으로 한다. 연구방법: 현실 반영을 위해 실제 도시의 지형, 인구, 도로 데이터를 기반으로 Kruscal’s Algorithm, Harmony Search, CCRP를 활용하여 행정구역(읍·면·동) 단위 네트워크를 생성 한 후, 최적의 주민철수로를 찾는 순서로 실험을 진행한다. 연구결과: 반복실험을 통해 최적의 주민철수 경로 및 스케줄을 산출하였고, 주민 철수시간 평균을 최소화하는 시나리오가 주민철수 계획수립에 효율적이라는 것을 확인하였다. 결론: 본 연구에서 제안하는 주민철수 모형을 활용하면, 주민철수 계획 수립 시 기존의 정 성적인 분석에 정량적 분석을 보완하여 보다 효율적인 계획 수립이 가능할 것으로 사료된다.

Purpose: This research proposes an optimization model for effective evacuation routing and scheduling of civilians near the border area when full-scale war threats heighten. Method: To reflect the reality, administrative unit network is created using Kruscal's Algorithm, Harmony Search, CCRP based on the geographical features, population, and traffic data of real cities, and then, optimal civilian evacuation routes are found. Results: Optimal evacuation routes and schedules are computed by repetitive experiments, and it is found that the scenario that minimizes the average civilian evacuation time is effective for the civilian evacuation plan. Conclusion: By using the civilian evacuation plan this research proposes, at the time of establishing the actual civilian evacuation plan, quantitative analysis is used for the effective plan making rather than only depending

Cloud 기술을 활용하여 정보자원을 필요한 만큼 즉시 활용할 수 있으며, 가상화 기술을 통해 다 양한 환경의 애플리케이션을 공동활용할 수 있다. HPC의 경우 고성능 컴퓨팅 환경을 지원하면서 정 보자원을 효율적으로 활용하기 위한 연구가 진행되고 있다. 본 연구는 Cloud 기술을 적용한 IaaS, HPC container, orchestration 모델의 아키텍처를 분석하고 HPC에서 활용할 수 있는 개선된 모델 을 제시한다. 개선된 모델은 고속의 컴퓨팅 및 네트워크 환경을 지원하면서 MPI 기반의 분산병렬 연 산을 처리한다. 다양한 사용자의 연산 요청처리를 위해 scheduler를 제공하며 정보자원이 부족한 경 우 다른 시스템과 연계하도록 federation을 제공한다. 이를 활용하여 기존 HPC 환경을 제공하면서 다른 애플리케이션도 실행가능한 환경을 제공하여 정보자원의 활용을 최적화 한다.

Information resources can be utilized immediately as needed by utilizing cloud technology, and applications in various environments can be utilized simultaneously through virtualization technology. In the case of HPC, research is being conducted to utilize information resources efficiently while supporting a high-performance computing environment. This study analyzes the architecture of IaaS, HPC container, and orchestration models to which Cloud technology is applied, and presents an improved model that can be utilized in HPC. The improved model handles MPI-based distributed parallel operation while supporting high-speed computing and network environments. A scheduler is provided to handle operation requests from various users, and federation is provided to link with other systems when information resources are insufficient. This study provides an environment in which other applications can be executed while providing the existing HPC environment to optimize the utilization of information resources.

산업기밀 유출 또는 사이버범죄 등의 사건 발생 시 범죄증명을 위하여 컴퓨터 포렌식 기법들이 사용되어져 왔다. 그러나 이러한 기법들은 단순 분석에 지나지 않아 삭제된 파일의 원인규명을 하지 못하여 신뢰성 있는 법정자료로서 제출되기에는 많은 문제점들을 내포하고 있었다. 파일 대한 연결성 원칙에 대한 연구는 아직까지 시도된바 없기에 연결성의 원칙을 중심으로 연구되었다. 본 논문에서는 사용자가 삭제하는 방식과 운영체제가 삭제하는 방식의 차이점을 체계적으로 분석하여 정형화된 탐지 모델을 개발 하였다. 탐지모델은 첫째, 사용자가 삭제한 경우의 탐지 모델, 둘째, 어플리케이션을 이용하여 삭제한 경우의 탐지 모델, 셋째, 운영체제가 삭제한 탐지 모델 세 가지이다. 탐지 모델은 현장에서 사용하기에 최적의 성능을 보장한다.

Computer forensics have been used for verify a crime when industry secret information or cyber crime occurred. However, these methods are simple analysis which cannot find the problem of deleted files. Therefore these cannot be a trusty evidence in a law court. We studied with focus on connectivity principle because it has never tried yet. In this paper, we developed optimizing detection model through systemized analysis between user-delete method and operating system-delete method. Detection model has 3 cases; Firstly, case of deleted by a user, secondly, case of deleted by application. Thirdly case of deleted by operating system. Detection model guarantees optimized performance because it is used in actual field.

With the development of the computer network technology and the electronic commerce, more and more firms establish the electronic sale channel and get great profits. The huge supply chain network is established through the new idea and technology. The node location selection is a planning process that chooses a location to set up a logistics node in an economy zone with multi demands. Hence, its location selection is a key link in the logistics system planning. In this paper, we consider the specific particularity of the e-commerce. Then, we constraints cost and delivery time of the logistics model. At last, we apply the heuristic algorithm to the location model. The results show that the algorithm can apply to the logistics distribution problem under the electronic commerce environment excellently.

Rapid technological advancements, especially in artificial intelligence and the Internet, have dramatically increased the demand for smart home systems, which promise enhanced convenience and efficiency. In this fast-growing market, Xiaomi Group has emerged as a prominent player. This paper utilizes a SWOT analysis to delve into Xiaomi’s smart home division’s competitive environment, pinpointing its strengths, weaknesses, opportunities, and threats. Through comprehensive analysis, an optimal marketing strategy is developed using the Analytic Hierarchy Process (AHP). The study proposes recommendations to bolster product intelligence and foster innovation, addressing the challenges unearthed. Additionally, it investigates Xiaomi’s worldwide marketing strategies for its smart home offerings, employing a hybrid AHP model that merges qualitative and quantitative insights. The result is a refined strategic marketing plan aimed at elevating Xiaomi’s smart home products’ global marketing effectiveness. This research not only provides valuable insights for strategic planning in the smart home sector but also offers practical guidelines to improve Xiaomi’s global competitive stance.

The purpose of this study is to analyze the operational efficiency of penny slots on the Strip and attempts to identify the optimal win percentage or hold for these slots. Game win refers to the amount retained by the casino operator from the total amount wagered. For a slot game, hold also referred to as win percentage is the ratio of what is actually won by the casino (game win) to coin-in, which is the total amount wagered by the player. Coin-in is a commonly used performance measure representing the total dollar amount of wagers accumulated by each machine. In this study, optimal win percentage refers to the rate at which the slot win is maximized. Based on the relationship between game win and its two determinants, coin-in and hold, this study developed a quadratic model for optimizing penny slot win revenue for Las Vegas Strip casinos. The model was empirically tested with aggregate monthly data of penny slots the most popular type of slots on the Strip. Using the quadratic function,this study found the optimal hold to be at 16.41%. Comparisons were made between the calculated optimal hold and the actual average holds. The findings of these comparisons indicate that the average holds of penny slots on the Strip were substantially lower than the optimal level. The resulting model and its coefficients suggest Strip casinos increase penny slots hold to maximize slot revenue. The study showed that adjusting the actual hold to the calculated optimal hold should improve the operational efficiency of slot games and optimize the game win from the casino’s existing customers.

Purpose: The objective of this study is to review researches regarding factors that potentially affect adolescent calcium (Ca) metabolism, and to suggest a potential modeling approach for optimizing gastrointestinal Ca absorption and peak bone mass. Background: Optimal gastrointestinal Ca absorption is a key to maximizing peak bone mass in adolescents. Urine Ca excretion in adolescents rises only after bone accretion is saturated, indicating that higher intestinal Ca absorption and bone retention is necessary to ensure maximum bone accretion. Hence, maximizing peak bone mass is possible by controlling the factors influencing gastrointestinal Ca absorption and bone accretion. However, a mechanism that explains the unique adolescent Ca metabolism has not yet been elucidated. Review: Dietary factors that enhance gastrointestinal Ca absorption may increase the available Ca pool usable for bone accretion, and a specific hormone may direct optimal Ca utilization to maximize peak bone mass. IGF-1 is an endocrine hormone whose levels peak during adolescence and increase fractional Ca absorption and bone Ca accretion. Prebiotics, generally obtained from dietary sources, have been reported to exert a beneficial effect on Ca absorption via microbiota activity. We selected and reviewed three candidates that could be used to propose a comprehensive Ca metabolic model for optimal Ca absorption and peak bone mass in adolescents. Modeling: Modeling has been used to investigate Ca metabolism and its regulators. Herein, we reviewed previous Ca modeling studies. Based on this review, we proposed a method for developing a comprehensive model that includes regulatory effectors of IGF-1 and prebiotics.

This study evaluates service quality in ski hotels across Jilin Province, China, an emerging winter-tourism hub anchored by Changbai Mountain. Using a convergent mixed-methods design, we combined SERVQUAL surveys (n = 300), Importance?Performance Analysis, and 30 stakeholder interviews to diagnose five service dimensions and map priorities. Empathy (β = 0.35) and responsiveness (β = 0.28) are the strongest drivers of satisfaction yet show the largest expectation?performance gaps. Equipment quality and response time fall into the “Concentrate Here” quadrant, signaling urgent remedial action. Qualitative themes?peak-season understaffing, limited cultural-sensitivity training, and under-investment in rental gear?explain these deficits and point to feasible interventions. We recommend deploying multilingual AI chatbots, instituting quarterly intercultural workshops, and reallocating 15% of annual capital budgets to modern equipment; scenario analysis suggests these steps could cut response times by 35%, raise empathy scores, and lift repeat visits by 10%. A projected incremental revenue of RMB 2 million per property underscores the economic payoff. An anonymized dataset and analysis code will be shared upon acceptance to foster replication and benchmarking. Findings enrich mixed-methods service research and offer a scalable blueprint for winter-sport destinations across Northeast Asia.

Objective Machine learning (ML) can assist in predicting suicide risk and identifying associated risk factors. Various resampling methods and algorithms must be applied to develop an ML prediction model with better performance. In this study, we developed an optimal Korean suicide prediction model by applying five ML algorithms, unsampled data, and two resampling methods.Methods In this study, data from the Korea National Health and Nutrition Examination Survey for 2017, 2019, and 2021 were integrated and analyzed to predict suicidal ideation in subjects aged ≥19 years. Logistic regression, random forest (RF), k-nearest neighbor, gradient boosting, and adaptive boosting were used as ML algorithms. Undersampling and oversampling are used as resampling methods to solve data imbalance problems.Results Among the study participants, 16,947 (95.14%) and 866 (4.86%) belonged to the control and suicidal ideation groups, respectively. Among the 15 ML models, the RF model exhibited excellent performance (sensitivity=0.781, area under the curve=0.870) in an algorithm trained with undersampled data.Conclusion Developing an optimized Korean suicide prediction model through additional validation based on the ML model developed in this study will help predict suicide risk factors caused by the interaction of individual, social, and environmental factors.

Optimizing advertising strategies has become increasingly critical for enhancing competitiveness within the marketplace. Consequently, performance-based advertising?which emphasizes measurable outcomes and user engagement?has become the preferred choice among advertisers seeking tangible results. This study explores the impact of integrating Herzberg’s Two-Factor Theory with the AIDA model to develop a more effective framework for advertising strategies. By employing Structural Equation Modeling (SEM) for empirical analysis, we examine the distinct roles that motivators and hygiene factors play in driving advertising effectiveness. Our findings indicate that motivators?such as creativity, content relevance, emotional resonance, and incentives?positively influence user engagement levels, specifically increasing attention, interest, and purchase intention. Concurrently, hygiene factors?including clarity, loading speed, information accuracy, and user experience?are found to significantly affect purchase intention and the likelihood of final action. Furthermore, the study highlights a notable synergistic interaction between motivators and hygiene factors, which amplifies overall advertising effectiveness. By integrating Herzberg’s Two-Factor Theory with the AIDA model, this study proposes a novel theoretical framework that provides empirical evidence for optimizing advertising strategies. Advertisers are encouraged to incorporate both motivators and hygiene factors into their ad design and deployment processes, thereby maximizing user engagement, enhancing satisfaction, and achieving superior campaign outcomes. This integrated approach underscores the importance of a holistic perspective in performance-based advertising.

Objective As the demand for community mental health services continues to grow, the need for well-equipped and organized services has become apparent. This study aimed to optimize the roles and infrastructure of mental health services, by establishing, among other initiatives, standardized operating models.Methods The study was conducted in multiple phases from May 12, 2021, to December 29, 2021. Stakeholders within South Korea and metropolitan mental health welfare centers were targeted, but addiction management support centers, including officials, patients, and their families, were integrated as well. A literature review and survey, focus group interviews, a Delphi survey, and expert consultation contributed to comprehensive revisions and improvements of the mental health service model.Results The proposed model for community mental health welfare centers emphasizes the expansion of personnel and infrastructure, with a focus on severe mental illnesses and suicide prevention. The model for metropolitan mental health welfare centers delineates essential tasks in areas such as project planning and establishment, community research, and education about severe mental illnesses. The establishment of a 24-hour emergency intervention center was a crucial feature. In the integrated addiction support center model, the need to promote addiction management is defined as an essential task and the establishment of national governance for addiction policies is recommended.Conclusion This study proposed standard operating models for three types of mental health service centers. To meet the increasing need for community care, robust mental health service delivery systems are of primary importance.

Purpose: Community-associated methicillin resistant Staphylococcus aureus (CAMRSA)infections are increasing. Although gentamicin (GEN) is usually susceptible against CA-MRSA, GEN is rarely considered for treatment as monotherapy. We employed an in vitro pharmacodynamic model (IVPDM) to compare efficacies of GEN against CA-MRSA with two dosing regimens [thrice-daily (TD), once-daily (OD)]. Materials and Methods: Using two strains of CA-MRSA, we adopted IVPDM comprised of two-compartments with a surface-to-volume ratio of 5.34 cm-1. GEN regimens were simulated with human pharmacokinetic data of TD and OD. Experiments were performed over 48 hours in triplicate for each strain and dosing regimen. Results: MICs of GEN for YSSA1 and YSSA15 were 1 and 2 mg/L,respectively. In OD, indices of peak/MIC were > 8.6 at least, in contrast to < 6.4 in TD. A ≥ 3-log10 reduction in CFU/mL was demonstrated prior to 4 hours in TD and OD, and continued until 8 hours for both strains. However, reductions in the colony counts at 24 and 48 hours were significantly larger for OD compared to TD in both strains (p < 0.001). During TD, resistance developed in YSSA1 and small colony variants (SCVs) were documented in YSSA15. No resistance or SCVs were observed during OD in both strains. Conclusion: TD and OD showed the same killing slopes until 8 hours. After the 24 hours of experiments, OD of GEN would be advantageous not only in having more reductions in colony counts, but also suppressing the development of resistance or SCVs for 48 hours.

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

최근 스마트폰과 같은 디지털 기기의 보급과 함께 개인화, 맞춤형 서비스의 수요가 늘어나면서 추천 서비스가 주목을 받고 있다. 세션 기반(Session based) 추천 시스템은 사용자의 아이템 선호에 따른 순서 정보를 고려한 학습 추천 모델로, 다양한 산업 분야에서 사용되고 있다. 세션 기반 추천 시스템 중 SASRec(Self-Attentive Sequential Recommendation) 모델은 MC/CNN/RNN 기반의 기존 여러 순차 모델들에 비하여 효율적인 성능을 보인다. 본 연구에서는 SASRec 모델의 하이퍼파라미터 중 배치 사이즈(Batch Size), 학습률 (Learning Rate), 히든 유닛(Hidden Unit)을 조정하여 실험함으로써 하이퍼파라미터에 의한 성능 변화를 분석하였다.

본 연구는 인지 기반 게임을 활용하여 인지적 부하를 정량적으로 산출할 수 있는 새로운 난이도 지수(ID) 모델을 제안하고, 이를 기존 대안 모델과 비교 검증하였다. 실험은 참가자가 0~9 사이의 숫자를 제시 속도에 맞추어 역순으로 입력하는 과제를 수행하는 방식으로 진행되었으며, 숫자의 개수와 표시 시간을 조합하여 총 60개의 난이도 조건을 설계하였다. 제안된 모델은 작업 기억 용량의 한계를 반영하여 정의되었으며, 이는 숫자 개수가 네 개를 초과할 때 난이도가 기하급수적으로 증가한다는 가정을 수학적으로 표현한 것이다. 비교 대상으로는 단순 선형 모델과 Fitts' Law를 변형한 로그 스케일 모델을 설정하였다. 회귀 분석 결과, 제안 모델은 가장 높은 설명력을 보여주었으며, 다른 두 모델에 비해 우수한 예측 성능을 입증하였다. 이러한 결과는 제안 모델이 난이도의 비선형적 증가를 효과적으로 반영하고, 실제 사용자 수행을 예측하는 데 있어 신뢰성 있는 지표로 활용될 수 있음을 시사한다. 나아가 본 연구는 작업 기억 연구의 이론적 토대와 실험적 검증을 결합함으로써, 교육 및 훈련 환경에서 도전성과 학습 효율성을 균형 있게 설계할 수 있는 실질적 도구를 제시한다는 점에서 학문적, 실천적 의의를 갖는다.

This study proposes a novel Difficulty Index (ID) model to quantitatively assess cognitive load using cognition-based games and validates it through comparison with existing alternative models. The experiment required participants to perform a task in which they entered digits from 0 to 9 in reverse order according to the presentation speed. A total of 60 difficulty conditions were designed by combining the number of digits and display time. The proposed model was defined to reflect the limitations of working memory capacity, mathematically expressing the assumption that difficulty increases exponentially when the number of digits exceeds four. For comparison, a simple linear model and a logarithmic-scale model adapted from Fitts' Law were employed. Regression analysis revealed that the proposed model exhibited the highest explanatory power and demonstrated superior predictive performance compared to the other two models. These findings suggest that the proposed model effectively captures the nonlinear increase in task difficulty and can serve as a reliable indicator for predicting actual user performance. Furthermore, by integrating theoretical foundations of working memory with experimental validation, this study provides a practical tool for designing educational and training environments that balance challenge and learning efficiency, thereby offering both academic and practical contributions.