• 문화기술의 융합
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• 제8권2호
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• pp.1-6
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• 2022

라이프로그는 다양한 디지털 센서로부터 수집되는 개인의 디지털 기록으로, 활동량, 수면 정보, 체중 변화, 체질량, 근육량, 지방량 등이 포함된다. 최근, 웨어러블 디바이스가 보편화되면서 양질의 라이프로그 데이터가 많이 생산되고 있다. 라이프로그 데이터는 개인의 신체의 상태를 보여주는 데이터로, 개개인의 건강관리 뿐만 아니라, 질병의 원인 및 치료에도 활용될 수 있다. 그러나, 현재는, AI/ML 기반의 상관관계 분석 및 개인화를 반영하지 못하고 있다. 단순 기록이나 단편적인 통계치를 제시하는 수준에 그치고 있다. 이에 본 논문에서는, 라이프로그 데이터와 질병과의 연관성 및 AI/ML 기술의 라이프로그 데이터의 적용 사례를 살펴보고, 더 나아가, AI/ML을 활용한 라이프로그 데이터 분석 프로세스를 제안하고, 실제 갤럭시워치에서 수집된 데이터를 사용하여, 분석 프로세스를 실증한다. 더불어, 미래의 헬스케어 서비스인, 라이프로그 데이터와 식단, 건강정보, 질병정보와의 융복합 서비스 로드맵을 제안한다.

• 센서학회지
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• 제30권1호
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• pp.1-9
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• 2021

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Nuclear Engineering and Technology
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• 제53권11호
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• pp.3563-3579
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• 2021

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

• Journal of Medicine and Life Science
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• 제20권4호
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.

• ETRI Journal
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• 제26권4호
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• pp.360-366
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• 2004

Synchronization Markup Language (SyncML) is a specification of a common data synchronization framework for synchronizing data on networked devices. SyncML is designed for use between mobile devices that are intermittently connected to a network and network services that are continuously available on the network. We have designed and developed a data synchronization system based on the SyncML protocol and evaluated the throughput of the system using the stochastic Petri nets package (SPNP) and analyzed the relationship between the arrival rate and the system resources. Using this model, we evaluate various performance measures in different situations, and we estimate the relationship between the arrival rate and the system resources. From the results, we can estimate the optimal amount of resources due to the arrival rate before deploying the developed system.

• Toxicological Research
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• 제19권4호
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• pp.297-301
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• 2003

To investigate biomarker-based exposure to phthalates and related factors with demographics, 100 subjects who had participated in comprehensive health check-up were selected. We collected demographics through questionnaires and analyzed urine samples for 5 phthalates. Statistical likelihoods and regression methods were applied for data analysis using censored data. The highest levels of urine phthalates were 216$\mu\textrm{g}$/ml in di-isodecyl phthalate, 29.0$\mu\textrm{g}$/ml in di-butyl phthalate, 5.78$\mu\textrm{g}$/ml in di-(2-ethylhexyl) phthalate. The median values of di-(2-ethylhexyl) phthalate were 0.2340 $\mu\textrm{g}$/ml for male smokers, 0.0399 $\mu\textrm{g}$/ml for male non-smokers and 0.0085 $\mu\textrm{g}$/ml for female non-smokers, respectively. Di-(2-ethylhexyl) phthalate, benzyl butyl phthalate and di-isodecyl phthalate were higher in males than in females. In addition, mono-2-ethylhexyl phthalate was decreased with age. Our findings suggest that there might be significant demographic variations in exposure and/or metabolism of phthalates, and that health-risk assessment for phthalate exposure in humans should consider different potential risk groups.

• Communications for Statistical Applications and Methods
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• 제26권4호
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• 융합정보논문지
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• 제11권6호
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• pp.49-54
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• 2021

최근 스마트 헬스케어 기술의 발전에 따라 일상적인 질환에 대한 관심이 증가하고 있다. 이에 따라 헬스케어 데이터를 통해 예측 모델로 질병을 분석하거나 예측하는 연구들이 증가하고 있다. 그러나 헬스케어 데이터에는 양성 데이터와 음성 데이터의 불균형이 존재한다. 이는 특정 질환을 가진 환자에 비하여 상대적으로 환자가 아닌 사람이 많아 데이터 수집에 어려움이 있어 발생하는 현상이다. 데이터 불균형은 질병 예측 및 탐지 시 진행하는 모델의 성능에 영향을 끼치기 때문에 이를 제거할 필요가 있다. 따라서 본 연구에서는 오버샘플링과 결측값 대치를 통해서 데이터 불균형을 해소한다. AutoML을 기반으로 여러 모델의 성능을 파악하고 모델 중 상위 3개의 모델을 앙상블한다.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.