• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Korea Journal of Hospital Management
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• v.23 no.3
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• pp.28-38
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• 2018

Purpose: To evaluate the medical expenditures for prostate cancer patients, including out-of-pocket costs, and compared the costs between androgen deprivation therapy and radical prostatectomy treatment. Methodology: This study combined clinical data from 357 prostate cancer patients from the Smart Prostate Cancer Database and the medical expenditure data from the claims and cost databases. We used the independent two-sample t-tests to compare androgen deprivation therapy and radical prostatectomy. Multivariable logistic regression analysis was conducted to identify determining factors for androgen deprivation therapy and radical prostatectomy treatments. Findings: The medical costs of androgen deprivation therapy treatment were much lower than radical prostatectomy treatment at the one year and remained lower until the fourth-year. However, after four years, the accumulated medical expenditures of androgen deprivation therapy become significantly higher than radical prostatectomy treatment. Patients with a higher cancer stage and older age had higher chances of being treated using androgen deprivation therapy treatment than radical prostatectomy treatment. Practical Implications: Our results show that early detection of cancer reduces the treatment cost for both patients and insurance payers. It also demonstrates that cost comparisons should be conducted over long periods of time in order to most accurately assess the costs.

• Proceedings of the KWS Conference
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• 2010.05a
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• pp.49-49
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• 2010

용접은 산업계의 기계 조립 및 접합을 위한 공정의 주요한 작업으로 조선, 중공업, 건설 등 산업현장에서 사람에 의한 수동적인 작업으로 대부분 수행된다. 이러한 용접 작업을 수행하는 용접 기술자는 산업 현장 훈련원과 직업 교육 학교에서 양성되지만 용접 훈련 과정은 실습 초보자에게 위험하고, 장시간 교육하기에 어려울 뿐 아니라 재료 낭비, 의사 소통의 한계, 즉석 결과 평가의 한계, 공간부족 등 다양한 문제가 있다. 그러므로, 안전하고 반복적인 실습 환경 제공하고 장시간 및 다수 교육참여 지원 등이 가능한 시스템을 구축하여 숙련된 우수 인력 조기 확보와 훈련 비용을 절감할 필요가 있다. 본 논문에서는 실제와 동일한 상호작용을 제공할 뿐만 아니라 고품질로 훈련 환경을 가시화하여 용접 상황을 동일하게 모사하는 가상 현실 기반 용접 훈련 시뮬레이터를 제시한다. 이 시스템은 용접의 형상과 환경의 고품질 가시화, 경험 DB를 통한 용접의 비드 형상 데이터 획득, 용접 토치를 이용하는 사용자 상호작용, 용접 훈련 결과 평가 및 최적 작업 가이드, 용접 콘텐츠 저작, 다양한 용접 훈련을 가시화하는 하드웨어 플랫폼으로 구성된다. 고품질 가상 용접 가시화는 경험 DB 기반 비드 형상 데이터와 신경회로망을 이용한 비드 형상 예측을 통해 실시간 비드 표현이 이루어지며 쉐이더 기반 고품질 모재 및 비드 표현, 아크 불꽃 효과 표현을 포함한다. 사용자 상호작용은 현장 작업 도구와 일치된 토치 인터페이스와 위치추적을 이용하여 토치의 작업각, 진행각, 속도, 거리 등을 반영할 수 있으며 진동과 소리 등 용접 훈련의 사실적 상호작용도 재현하였다. 용접 훈련 평가 및 최적 작업 가이드는 훈련자의 용접속도, 거리, 각도 등의 사용자 작업 결과를 그래픽으로 표현하고, 애니메이션을 통한 훈련 자세를 추후 분석할 수 있도록 하였고, 가상토치, 기준선, 수치계기 등을 이용한 최적 작업 훈련 가이드 제시하였다. 훈련 콘텐츠 저작은 메뉴UI 기반으로 용접의 전류, 전압 등의 조건과 상황을 선택하도록 제시하였고, 하드웨어 플랫폼은 워크벤치형 입체 디스플레이 방식으로 용접 환경을 가시화하였고, 위, 정면, 아래보기 등 다양한 용접 자세 변경을 지원 할 수 있도록 구축하였다. 이러한 가상현실 기반 훈련 시뮬레이터는 아크열 발생에 따른 장시간 훈련의 어려움을 극복할 수 있고, 다양한 실습 환경을 바꾸어 가며 반복적인 훈련이 가능하고, 실 재료를 사용하지 않아 재료의 낭비를 줄일 수 있는 환경 친화적인 안전하고 효율적인 훈련 실습 환경을 제공할 수 있다.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.1-14
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• 2014

Both researchers and practitioners are showing an increased interested in interactive exhibition services. Interactive exhibition services are designed to directly respond to visitor responses in real time, so as to fully engage visitors' interest and enhance their satisfaction. In order to install an effective interactive exhibition service, it is essential to adopt intelligent technologies that enable accurate estimation of a visitor's emotional state from responses to exhibited stimulus. Studies undertaken so far have attempted to estimate the human emotional state, most of them doing so by gauging either facial expressions or audio responses. However, the most recent research suggests that, a multimodal approach that uses people's multiple responses simultaneously may lead to better estimation. Given this context, we propose a new multimodal emotional state estimation model that uses various responses including facial expressions, gestures, and movements measured by the Microsoft Kinect Sensor. In order to effectively handle a large amount of sensory data, we propose to use stratified sampling-based MRA (multiple regression analysis) as our estimation method. To validate the usefulness of the proposed model, we collected 602,599 responses and emotional state data with 274 variables from 15 people. When we applied our model to the data set, we found that our model estimated the levels of valence and arousal in the 10~15% error range. Since our proposed model is simple and stable, we expect that it will be applied not only in intelligent exhibition services, but also in other areas such as e-learning and personalized advertising.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.871-874
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• 2010

Needs for Smart Grid development are increasing all over the world as a solution to its problem according to depletion of energy resources, climatic and environmental rapidly change and growing demand for electrical power. Especially decentralized power is attracting world's attention. In this mood a new era for a unit scale of decentralized power environment is on its way in building. However there is a problem to have to be solved in the uniformity of power quality because the amount of power generated from renewable energy resources such as wind power and solar light is very sensitive to climate fluctuation. And thus this paper tries to suggest an energy management method on basis of real time monitoring for meteorological data. In the current situation of lacking in USN-based killer application in Smart Grid field, this paper proposes the USN-based DER management system which collects the meteorological data and control power system througout utilizing wireless sensor network technique this business. This communication technique is regarded to be efficient in aspects of installation cost and tits maintenance cost. The proposed EMS model embodies the method for predicting the power generation by monitoring and analyzing the climatic data and controling the efficient power distribution between the renewable energy and the existing power. The ultimate goal of this paper is to provide the technological basis for achieving zero-energy house.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.318-329
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• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.1-10
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• 2008

Ubiquitous health care system, which is one of the developing solution technologies of IT, BT and NT, could give us new medical environments in future. Implementing health information systems can be complex, expensive and frustrating. Healthcare professionals seeking to acquire or upgrade systems do not have a convenient, reliable way of specifying a level of adherence to communication standards sufficient to achieve truly efficient interoperability. Great progress has been made in establishing such standards-DICOM, IHE and HL7, notably, are now highly advanced. IHE has defined a common framework to deliver the basic interoperability needed for local and regional health information networks. It has developed a foundational set of standards-based integration profiles for information exchange with three interrelated efforts. HL7 is one of several ANSI-accredited Standards Developing Organizations operating in the healthcare arena. Most SDOs produce standards (protocols) for a particular healthcare domain such as pharmacy, medical devices, imaging or insurance transactions. HL7's domain is clinical and administrative data. HL7 is an international community of healthcare subject matter experts and information scientists collaborating to create standards for the exchange, management and integration of electronic healthcare information. The ASTM specification for Continuity of Care Record was developed by subcommittee E31.28 on electronic health records, which includes clinicians, provider institutions, administrators, patient advocates, vendors, and health industry. In this paper, there are suggestions that provide a test bed, demonstration and specification of how standards such a IHE, HL7, ASTM can be used to provide an integrated environment.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1095-1106
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• 2014

Symbolic data analysis extends the data mining and exploratory data analysis to the knowledge mining, we can suggest the SDA tree model on clinical and genomic data with new knowledge mining SDA approach. Using SDA application for huge genomic SNP data, we can get the correlation the availability of understanding of hidden structure of HCC data could be proved. We can confirm validity of application of SDA to the tree structured progression model and to quantify the clinical lab data and SNP data for early diagnosis of HCC. Our proposed model constructs the representative model for HCC survival time and causal association with their SNP gene data. To fit the simple and easy interpretation tree structured survival model which could reduced from huge clinical and genomic data under the new statistical theory of knowledge mining with SDA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.116-126
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• 2021

Ground Component Command (GCC) has been developing operational planning and execution systems to implement "Decisive Integrated Operations", which is the concept of ground operations execution, and achieved remarkable results. In particular, "Simultaneous Offense-Defense Integrated Operations" is developed mainly to neutralize enemies in deep areas and develop favorable conditions for the allies early by simultaneously attacking and defending from the beginning of the war. On the other hand, it is limited to providing scientific and reasonable support for the commander's decision-making process because analyzing the effects of the deep operation with existing M&S systems is impossible. This study developed a model for analyzing the effects of deep operations that can be used in the KJCCS. Previous research was conducted on the effects of surveillance, physical strike, and non-physical strike, which are components of deep operations to find the characteristics and limitations and suggest a research direction. A methodology for analyzing the effects of deep operations reflecting the interactions of components using data was then developed by the GCC, and input data for each field was calculated through combat experiments and a literature review. Finally, the Deep operations Effect CAlculating Model(DECAM) was developed and distributed to the GCC and Corps battle staff during the ROK-US Combined Exercise. Through this study, the effectiveness of the methodology and the developed model were confirmed and contribute to the development of the GCC and Corps' abilities to perform deep operations.