• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1996년도 추계학술대회 논문집
• /
• pp.115-119
• /
• 1996

An estimation of surface roughness value is a very important and difficult issue in grinding process. The definition of the D.A.R.F(Dimensionless Average Roughness Factor) has been made including the absolute average and tile standard deviation that are the parameters of the AE(Acoustic Emission) sign. The theoretical equation of the surface roughness applying the D.A.R.F has been derived from the regressive analysis and specified with respect to the availability through the experimental approach on the machine.

• 한국산업융합학회 논문집
• /
• 제2권2호
• /
• pp.147-153
• /
• 1999

An in-process extraction method of the ground surface roughness is a bottle-neck and essential field in conventional machining process. We define the D.A.R.F(Dimensionless Average Roughness Factor) that has a roughness characteristic of ground surface. D.A.R.F include the absolute average and the standard deviation values which are the analytic parameters of the AE(Acoustic Emission) signal generated during the grinding operation. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• 한국수의병리학회지
• /
• 제2권2호
• /
• pp.127-138
• /
• 1998

Transmissible venereal tumor(TVT) is a naturally occurring contagious neoplasm which can be transmitted by mechanical contact during mating in dogs and transplanted as intact viable cells to dogs and other members of canine family such as coyotes, jackals, wolves, and foxes. The incidence of this tumors tends to increase in Korean native Jindo dogs. This is probably due to the high density and unrestrained management system. With time, TVT reaches the maximum size and then tends to regress spontaneously unless individuals are immunologically compromised. It consists of different types of cells depending on the stage. In this study, 10 tumors were selected from Jindo dogs. These were histologically calssified into three stages; progressive, steady-state, and regressive. Mitotic figures were counted, and their histological appearance at each stage is compared with their DNA ploidy. Histologically, 5 tumor cases were calssed as the progressors, 3 cases as the steady-state tumors, and 2 cases as regressors. Progressors were composed of round cells with large nuclei containing conspicuous nucleoli and frequent mitotic figures. A few spindle-shaped cells and inflammatory cells including mainly lymphocytes, a few neutrophils and macrophages were also seen. In the steady-state tumors, there was an increased number of spindle shaped cells and mitotic figures were rare. Six tumors were diploid and four were aneuploid with the variation coefficient of 7.02. Two of five progressive tumors were aneuploid. Two of three steady-state tumors were aneuploid while both tumors at the regressive stage were diploid. Progressive and steady-state tumors had a much larger S/G2M fraction and a higher mitotic index than regressive tumors. Two tumors which persisted for more than one year were aneuploid. These results suggest that the progressive and steady-state tumors had more active cell division than the regressive neoplasms.

• 말소리와 음성과학
• /
• 제7권2호
• /
• pp.19-28
• /
• 2015

This study investigated final consonant error characteristics at word-medial position in children with functional articulation disorder. Data was collected from 11 children with functional articulation and 11 normal children, ages 4 to 5. The speech samples were collected from a naming test. Seventy-five words with every possible bi-consonants matrix at the word-medial position were used. The results of this study were as follows : First, percentage of correct word-medial final consonants of functional articulation disorder was lower than normal children. Second, there were significant differences between two groups in omission, substitution and assimilation error. Children with functional articulation disorder showed a high frequency of omission and regressive assimilation error, especially alveolarization in regressive assimilation error most. However, normal children showed a high frequency of regressive assimilation error, especially bilabialization in regressive assimilation error most. Finally, the results of error analysis according to articulation manner, articulation place and phonation type of consonants of initial consonant at word-medial, both functional articulation disorder and normal children showed a high error rate in stop sound-stop sound condition. The error rate of final consonant at word-medial position was high when initial consonant at word-medial position was alveolar sound and alveopalatal sound. Futhermore, when initial sounds were fortis and aspirated sounds, more errors occurred than linis sound was initial sound. The results of this study provided practical error characteristics of final consonant at word-medial position in children with speech sound disorder.

• Smart Structures and Systems
• /
• 제17권6호
• /
• pp.1031-1053
• /
• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.342-345
• /
• 2005

This paper describes dynamics analysis of a small training boat and a new type of ship's autopilot not only to keep her course but also to reduce her roll motion. Firstly, statistical analysis through multi-variate auto regressive model is carried out using the real data collected from the sea trial on an actual small training boat Sazanami after the navigational system of the boat was upgraded. It is shown that the roll motion is strongly influenced by the rudder motion and it is suggested that there is a possibility of reducing the roll motion by controlling the rudder order properly. Based on this observation, a new type of ship's autopilot that takes the roll motion into account is designed using the muti-variate modern control theory. Lastly, digital simulations by white noise are carried out in order to evaluate the proposed system and a typical result is demonstrated. As results of simulations, the proposed autopilot had good performance compared with the original data.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2010년도 정기 학술대회
• /
• pp.145-148
• /
• 2010

항만 구조물의 건전성 평가 기술의 개발을 위한 기초 연구로서, 강제진동해석을 통하여 케이슨 구조-지반 경계부의 손상에 대한 진동응답을 분석하고자 한다. 이를 위해 세 단계의 연구를 수행하였다. 첫째, 케이슨 구조물의 진동특성 분석을 위해 시간영역기반의 AR(auto-regressive)모델을 선정하였다. 둘째, 모형 케이슨 구조물을 대상으로 진동응답 계측실험을 수행하였으며, AR-모델을 통해 진동특징을 실험적으로 분석하였다. 셋째, 대상 케이슨 시스템의 유한요소모델을 구성하고, 구조-지반 경계부의 손상에 따른 동적응답 특성의 변화를 수치적으로 분석하였다. 이를 위해 강제진동을 모사 하였으며, 구조-지반 경계부의 강성변화에 따른 케이슨 구조물의 진동응답의 변화를 분석하였다.

• Communications for Statistical Applications and Methods
• /
• 제17권3호
• /
• pp.413-421
• /
• 2010

본 연구에서는 5대 범죄중 사람의 생명과 신체에 심각한 위해를 가하는 강력범죄인 살인과 강도 범죄의 이변량 가산자료에 대해 이변량조건부자기회귀모형을 사용하여 공간상관성을 반영한 강력범죄모형을 제안하였다. 범죄자료와 같은 가산자료에 대한 과대산포 검정을 위해 우도비 검정 실시하였으며, 그 결과 과대산포가 유의하지 않음에 따라 공간포아송모형을 이용하였다. 실증예제로 2007년 서울시에서 제공하는 25개 자치구별 강력범죄자료를 지리정보시스템을 이용하여 강력범죄 발생실태를 시각화하였으며 강력범죄에 영향을 주는 다양한 요인들에 대하여 분석을 실시하였다.

• 융합정보논문지
• /
• 제9권12호
• /
• pp.54-61
• /
• 2019

최근 들어 IITS는 스마트 시티관련 산업계에서 중요한 주제로 떠오르고 있다. IITS의 주요 목적인 교통체증 (차량 사고에 기인한) 예방책들이 발전된 센서 및 통신 기술의 도움을 받아 다양하게 시도되었다. 관련 연구들에서는 자동차 사고와 사고 위치적 특성, 날씨, 운전자 행동, 시간 등 다양한 요인들과 상관 관계가 있음을 보여주고 있다. 우리 연구는 자동차 사고와 사고 발생 시간 사이의 상관관계에 주제를 집중했다. 본 논문에서는 ARIMA (Auto-Regressive Integrated Moving Average) 자동 회귀, 정상 및 지연 순서를 결정하는 세 가지 요소를 확인하기 위해 ADF (Augmented Dickey-Fuller)를 포함한 ARIMA 테스트를 수행했다. 본 연구 결과로서 시간 별 자동차 충돌 수 예측에 대한 요약을 제시하며, 한국 내 자동차 사고 데이터는 ARIMA 모델에 적용될 수 있음을 보여주었고, 국내 자동차 사고는 하루를 기준으로 일정한 주기가 존재하는 성격을 가지고 있다는 것을 제시했다.

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