• 한국통신학회논문지
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• 제29권9A호
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• pp.991-997
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• 2004

본 논문은 HomePNA 2.0 모뎀 칩을 위한 모뎀 수신부의 구조를 제안한다. HomePNA 2.0 전송 채널은 브릿지 탭과 HAM 대역의 영향 등으로 매우 열악하다. 이러한 채널을 통해 전송을 가능하게 하기 위해 HomePNA 2.0 은 훈련신호를 사용하여 매 프레임 마다 채널을 등화하고 FD-QAM 전송 방식을 선택적으로 사용한다. 따라서 모뎀 수신부는 일반적 QAM 방식 신호의 북조 기능과 함께 이러한 전송 방식의 특정을 최대한 상려 모뎀 수신 성 능을 극대화 히는 구조가 필요하다 연구 결과 모뎀 수신부의 가능을 송수신 상태에 따라 정상 수신 모드와 충돌 감지 오드의 2 가지로 정의 하였다 본 논문은 특히, 모뎀 수신부를 구성하는 핵심 블록인 등화기와 위상 동기부, 프레임 동기부에 대해서 사용된 알고리즘을 밝혔으며, 버스트 방식 모뎀의 채널 등화 성능을 높이고 안정적으로 동작 시키기 위한 구조를 제얀 하였다 마지막으로 제안된 모뎀 수신부의 성능을 분석하기 위해서 SPW 모델을 사용하여 채널 별 전송 가능 속도를 예측 하였다.

• 한국통신학회논문지
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• 제25권1A호
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• pp.167-177
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• 2000

본 논문에서는 DMT(discrete multitone)방식의 $\chi$DSL(digital subscriber line)시스템에 사용되는 시간영역 등화기 설계를 위한 새로운 알고리듬을 제안한다. 제안된 알고리듬은 DMT 시스템의 등화기 설계기 사용되는 ARMA(autoregressive moving average) 모델에서 DMT시스템의 성능에 영향을 주지 않는 항을 삭제 시킴으로써 최소의 계산량을 갖는다. 제안된 방식은 matrix inverse 방식, fast algorithm방식, iterative 방식, inverse power 방식과 같은 기존의 시간영역 등화 알고리듬들과 비교할 때 매우 적은 계산량을 사용하나, 성능면에서는 기존의 방식과 비슷하거나 우수한 결과를 보인다. 또한 제안된 방식에서는 수신된 신호만 사용하므로 채널의 임펄스 응답을 추정하거나 훈련신호를 사용할 필요가 없다는 장점이 있다. 또한 bridged tap 유무에 대한 정보를 이용하였다. 즉, bridged tap이 포함되지 않는 채널의 경우 시간영역 등화기 계수의 개수를 반으로 줄일 수 있음을 보인다. ADSL(asymmertrical digital subscriber line)서비스 환경에서 제안된 시간영역 등화기 알고리듬과 기존 시간영역 등화기 알고리듬의 성능을 비교한다.

• 한국물환경학회지
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• 제24권1호
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• 대한인간공학회지
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• 제25권4호
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• pp.81-90
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• 2006

Despite the government promoting women's participation in the engineering field, some statistics show that it has yet to be achieved. Potential reasons for this phenomenon include lower level of applications by women, or inherent gender gap in the professional field. Therefore, this study attempted to find impact of gender on college admission from the Lens Model perspective and Signal Detection Theory. This study consisted of three phases: identifying the necessary cues used in the admission process, analyzing existing data, and conducting two experiments to identify the effect of gender on admission decisions. Although the college application consisted of many cues, only five cues, school ranking, GPA, SAT score, resident status, and gender, were used to capture the officers' judgment policies for engineering admissions. Two experiments were conducted to investigate the impact of the gender factor in college admission. The enrollment officers first were presented with the existing data without the gender and asked to make dichotomous judgments. Secondly, the officers were asked to perform the judgment task with the gender cue present. Results showed that the gender did not play an important role in the judgments as expected. However, ideographical analyses on judgment strategies revealed that there were significant differences between the admission officers. Possible training implications are discussed.

• 한국정보통신학회논문지
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• 제21권9호
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• pp.1674-1680
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• 2017

본 논문에서는 고차원 국부이진패턴과 결합베이시안 알고리즘을 이용한 얼굴인증 임베디드 시스템을 제안한다. 또한, 제안된 알고리즘에 대한 임베디드 시스템을 라즈베리파이 3을 이용하여 구현한 결과를 제시한다. 제안된 얼굴인증 알고리즘에 대한 평가는 500명의 얼굴 데이터가 저장된 데이터베이스를 이용하여 수행하였다. 여기서 각각의 얼굴 데이터는 학습용과 테스트용 이미지로 구성하였다. 성능평가를 위한 척도로는 주성분분석법의 차원에 따른 스코어 분포와 얼굴인증 시간을 이용하였다. 그 결과, 최적화된 임베디드 환경에서 우수한 얼굴인증 성능을 가지는 임베디드 시스템을 상대적으로 저렴한 비용으로 구현할 수 있음을 확인하였다.

• 한국통신학회논문지
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• 제39C권9호
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• 대한안전경영과학회지
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• 제13권1호
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• pp.99-105
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• 2011

Location-Based Service(LBS) is a service that provides a variety of convenience in life using location information that can be obtained by mobile communication network or satellite signal. In order to provide LBS precisely and efficiently, we have to need technologies such as location determination technology, platform technology and server technology first. In this study, we studied on how we can reduce the error on location determination of objects such people and things. Fingerprint location determination method was applied to this study because it can be used at current wireless communication infrastructure and less influenced by a variety of noisy environment than other location determination methods. We used the time of arrival(ToA) method in fingerprint location determination method. In order to confirm the performance of suggested method, we developed location determination test program with LAbVIEW 2010 and performed the test. According to indoor test results, the suggested method reduced the distance error by 24%, 34% and 19% respectively at indoor environment compared with deterministic kWNN and Rice Gaussian fingerprint methods.

• 대한안전경영과학회지
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• 제14권1호
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• pp.217-224
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• 2012

Location-Based Services(LBS) is a service that provide location information by using communication network or satellite signal. In order to provide LBS precisely and efficiently, we studied how we can reduce the error on location determination of objects such people and things. We focus on using the least square method and triangulation positioning method to improves the accuracy of the existing location determination method. Above two methods is useful if the distance between the AP and the tags can be find. Though there are a variety of ways to find the distance between the AP and tags, least squares and triangulation positioning method are wildely used. In this thesis, positioning method is composed of preprocessing and calculation of location coordinate and detail of methodology in each stage is explained. The distance between tag and AP is adjusted in the preprocessing stage then we utilize least square method and triangulation positioning method to calculate tag coordinate. In order to confirm the performance of suggested method, we developed the test program for location determination with Labview2010. According to test result, triangulation positioning method showed up loss error than least square method by 38% and also error reduction was obtained through adjustment process and filtering process. It is necessary to study how to reduce error by using additional filtering method and sensor addition in the future and also how to improve the accuracy of location determination at the boundary location between indoor and outdoor and mobile tag.

• 한국전기전자재료학회논문지
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• 제24권9호
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• pp.701-709
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• 2011

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권1호
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• pp.24-32
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• 2000

인간의 생각만으로 기계를 작동할 수 있게 하는 인터페이스 시스템에 관한 연구 분야인 BCI (Brain-Computer Interface)에서는 피험자의 두피로부터 EEG(Electroencephalograph)를 측정하고 인식하여 뇌 상태를 알아내고 그 결과를 기계의 조종에 응용하는 방법을 사용한다. 본 연구에서는 각 개인으로부터 고유의 뇌파인 EEG를 얻고 신호처리하여 인식하는 인식모델을 제안하였다. 제안된 모델은 특정 작업을 수행하고 있을 때의 EEG 신호로부터 인식에 중요한 영향을 미치는 특징들을 추출해 내고, 이를 인식에 이용한다. 제안된 모델은 인식할 EEG 패턴들을 두개씩 분류하여 각각을 인식한 후, 그 결과를 종합하여 최종적인 인식결과를 얻도록 하였다. 본 연구의 실험에서는 피험자가 4가지의 작업을 수행하는 동안 얻어지는 4가지 EEG 패턴을 인식하였다. 제안된 모델은 90%이상의 높은 인식율을 보였고, 각 피험자에게 독특하게 존재하는 특징들을 인식 결과로서 제공하였다. 제안된 모델의 높은 인식율과 빠른 처리속도는 실시간 BCI 시스템에 응용될 수 있는 가능성을 보여주고 있다.