• 한국정보통신학회논문지
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• 제18권9호
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• pp.2094-2100
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• 2014

수중에서의 음향 통신의 성능은 신호의 다중경로 전달과정에 의해 발생하는 지역 확산 현상으로 인하여 인접 심볼간 간섭의 영향을 받는다. 따라서 인접 심볼간 간섭을 제거하기 위하여 수중 통신에 적합한 등화기 기술, 채널 부호화 기술이 필요하다. 본 논문에서는 다중 경로 환경에서 원활한 통신과 함께 수신 신호의 성능을 향상시키기 위한 낮은 SNR에서 우수한 성능을 보이는 BCJR 복호기와 다중 경로로 인해 왜곡된 데이터를 보상하기 위한 기법인 결정 궤환 등화기가 결합된 반복기반 BCJR등화기 구조를 제안하고, 경북 문경 경천호에서의 실제 수중 실험을 통하여 제안한 구조의 성능이 반복횟수의 증가에 따라 향상됨을 알 수 있다.

• 한국항해항만학회지
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• 제35권6호
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• pp.501-507
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• 2011

본 논문은 컨테이너 운송을 위한 AGV(Automated Guided Vehicle) 배차 전략을 대상으로 한다. AGV 배차 문제는 안벽 크레인의 대기 시간과 AGV의 주행 거리를 최소화하도록 AGV에 작업을 할당하는 것이 목표이다. 터미널 환경의 동적인 특성으로 인해 계획 결과의 정확한 예측이 어렵고 수정이 빈번하기 때문에 실무에서는 의사결정 시간이 짧은 단순 규칙 기반 배차가 많이 쓰인다. 그러나 단순 규칙 기반 배차는 근시안적 특성으로 인해 배차의 다양한 성능 지표를 만족시키지 못하는 한계가 있으며 이를 극복하기 위해 본 논문에서는 복수 규칙 기반의 배차 전략을 제안한다. 복수 휴리스틱 기반 배차 전략은 여러 규칙의 가중합으로 구성되며 규칙 사이의 가중치를 최적화하기 위해 다목적 진화 알고리즘을 적용하였다. 시뮬레이션 실험을 통해 제안 방안이 기존 단일 규칙 기반 배차에 비해 더 좋은 성능을 보임을 확인하였다.

• 스마트미디어저널
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• 제6권3호
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• 융합정보논문지
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• 제9권11호
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• pp.234-240
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• 2019

본 연구는 노동집약적이고 관계지향형인 병원조직에서 인적자원관리의 효율성 방안 모색을 위하여 근접한 서비스 접점인 간호직 의료기사직 행정직 종사자들을 대상으로 조직태도와 개인성향간의 관계를 살펴보았다. 분석결과, 병원종사자들의 개인성향이 변덕형 수준이 낮을수록, 외향적 수준이 높을수록, 목표추구형 수준이 높을수록 조직태도가 우호적인 것이라는 것을 알 수 있었는데, 특히 변덕형 수준의 정도가 가장 큰 영향을 미쳤다. 따라서 병원조직은 조직구성원들이 적극적이고 논리적인 외향적 성향과 조심스러운 언행과 목표달성을 위한 감정조절 등의 목표추구형 성향의 수준을 강화시키고, 원만하지 않은 인관관계와 결정의 번복이나 미루는 행동 등의 변덕적 성향의 수준을 최소화 시킬 수 있는 방안에 대한 검토의 필요성이 제기된다.

• 산업경영시스템학회지
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• 제42권3호
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• 한국수자원학회논문집
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• 제52권8호
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• pp.565-573
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• 2019

본 연구에서는 지능형 정수장 운영시스템 개발 연구의 일환으로 선형계획법(Linear Programming, LP)을 이용한 정수장 취수계획 최적화 모형을 개발하였다. 개발된 최적화 모형은 원수의 정수처리비용의 최소화를 목적함수로 설정하였으며, 취수 후 정수처리에 소요되는 지연시간과 시간별 전력단가를 고려하여 취수가능량, 예측수요량, 정수지 운영수위 등의 제약조건을 만족하는 최적 취수계획을 제시하였다. 국내 H 정수장을 대상으로 경제적이고 안정적인 정수장 운영을 위해 세 가지 최적화 전략을 적용하고, 그 결과를 경제성과 안정성 측면에서 비교, 분석하였다. 개발 모형은 국내 정수장의 보다 효율적인 취수계획 수립을 위한 의사결정 지원시스템의 형태로 실무에서 활용이 가능할 것으로 기대된다.

• 보건행정학회지
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• 제32권1호
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• pp.73-93
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• 2022

Background: Appropriate physician workforce projection through reasonable discussions and decisions with a broad view on supply and demand of the workforce, thus, is very important for high-quality healthcare services. The study expects to provide preliminary research data on the workforce diagnosis standard model for Korean physician workforce policy decision through more flexible and objective physician workforce projection in reflection of diverse changes in healthcare policy and sociodemographic environments. Methods: A low flow rate through the causal map was developed, and an objective workforce demand projection from 2019 to 2040 was conducted. In addition, projections by scenarios under various situations were conducted with the low flow rate developed in the study. Lastly, the demand projection of the physician workforce by region of 17 cities and provinces was conducted. Results: First, demand of physicians in 2019 was 110,665, 113,450 in 2020, 129,496 in 2025, 146,837 in 2030, 163,719 in 2035, and 179,288 in 2040. Second, the scenario for the retirement of baby boomers led to a decrease in the growth rate due to time delay. Third, Seoul and Gyeonggi-do account for a high percentage of demand, a very high upward trend was identified in Gyeonggi-do, and as a result, the projection showed that the demand of the physician workforce in Gyeonggi-do would worsen over time. Conclusion: This study is meaningful in that rational and collective physician workforce supply and demand and its imbalance in workforce distribution were verified through various projections by scenarios and regions of Korea with System Dynamics.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권12호
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• pp.437-444
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• 2022

최근 무선 네트워크에서 발생하는 계산 집약적이고 지연시간에 민감한 태스크를 처리하기 위해 모바일 엣지 서비스에 대한 연구가 진행되고 있다. 하지만 지상에 고정되어 있는 MEC는 출퇴근 시간과 같이 태스크 처리 요청이 일시적으로 급증하는 상황에 대해 유연하게 대처할 수 없다. 이를 해결하기 위해 UAV(Unmanned Aerial Vehicle)를 추가로 이용해 모바일 엣지 서비스를 제공하는 기술이 등장하였다. UAV는 지상 MEC 서버와 달리 배터리 용량이 제한되어 있어 UAV MEC 서버 간 로드 밸런싱을 통해 에너지 효율성을 최적화 하는 것이 필요하다. 따라서 본 논문에서는 UAV의 에너지 상태와 차량의 이동성을 고려하며 유전 알고리즘 기반의 태스크 오프로딩과 Q-learning 기반의 태스크 마이그레이션을 통한 로드 밸런싱 기법을 제안한다. 제안 시스템의 성능을 평가하기 위해 차량 속도와 수에 따른 실험을 진행하고, 로드 분산, 에너지 사용량, 통신 오버헤드, 지연 시간 만족도 측면에서 성능을 분석하였다.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.725-731
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• 2009

Construction documents are recognized as an essential component for making a decision and supporting on construction processes. In construction, the management of project document is a complex process due to different factors such as document types, stakeholder involvement, document flow, and document flow processes. Therefore, inappropriate management of project documents can cause several impacts on construction work processes such as delay or poor quality of work. Several information and communication technologies (ICT) were proposed to overcome problems concerning document management practice in construction projects. However, the adoption of ICT may have some limitation on the compatibility of specific document workflow. Lack of understanding on designing document system may cause many problems during the use and implementation phase. Thus, this paper proposes the framework that integrates Soft System Methodology (SSM) concept and Integrated Definition Modeling Technique (IDEF) for analyzing document management system in construction project. Research methodology is classified as the case study. Five main construction building projects are selected as case studies. The qualitative data related to problems and processes are collected by interviewing construction project participants such as main contractors, owners, consultants, and designers. The findings from case study show the benefits of using SSM and IDEF. The use of SSM can help identify the problems in managing construction document in rich picture view whereas IDEF can illustrate the document flow in construction project in details. In addition, the idea of integrating these two concepts can be used to identify the root causes of process problems at the information level. As the results, this idea can be applied to analyze and design web-based document management system in the future.