• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 하계종합학술대회 논문집
• /
• pp.452-455
• /
• 1999

We introduce an evolutionary approach to on-line learning for mobile robot control using reconfigurable hardware. We use genetic programming as an evolutionary engine. Control programs are encoded in tree structure. Genetic operators, such as node mutation, adapt the program trees based on a set of training cases. This paper discusses the advantages and constraints of the evolvable hardware approach to robot learning and describes a FPGA implementation of the presented genetic programming method.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2007년도 춘계종합학술대회
• /
• pp.153-156
• /
• 2007

In this paper, we propose a fast face detection approach using PCA and SVM. In our detection system, first we filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, we use SVM classifier to detect whether there are faces present in the test image. Support Vector Machine (SVM) has great performance in classification task. PCA is used for dimension reduction of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database.

• 전자공학회논문지B
• /
• 제33B권9호
• /
• pp.88-96
• /
• 1996

Computer vision-based gesture recognition systems consist of image segmentation, object tracking and decision. However, it is difficult to segment an object from image for gesture in computer systems because of vaious illuminations and backgrounds. In this paper, we describe a method to learn features for segmentation, which improves the performance of computer vision-based hand-gesture recognition systems. Systems interact with a user to acquire exact training data and segment information according to a predefined plan. System provides some models to the user, takes pictures of the user's response and then analyzes the pictures with models and a prior knowledge. The system sends messages to the user and operates learning module to extract information with the analyzed result.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
• /
• pp.387-388
• /
• 2022

본 연구에서는 비 정보과 교사들의 효과적인 SW 교육 교수효능감 함양을 위하여 팀 프로젝트 기반 융합 교육 프로그램을 개발하였다. 개발된 교육 내용은 융합교육 및 팀 프로젝트에 대한 이해를 바탕으로 수학, 과학, 정보 등이 융합된 다양한 프로젝트를 실습한 뒤, 직접 문제 해결을 위한 프로젝트의 설계 및 개발과 발표, 동료 평가 및 피드백의 과정으로 구성되어 있다. 이는 10주간 비 정보과 교사들에게 처치되었고, 사전-사후 t-검정 결과, 통계적으로 유의한 향상을 나타내었다. 하지만 본 연구의 실험은 단일집단을 대상으로 하였기 때문에 추후 통제집단과의 비교를 통하여 향상에 대한 통계적 비교가 필요로 된다.

• 한국컴퓨터정보학회논문지
• /
• 제24권9호
• /
• pp.21-27
• /
• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• Journal of Information Processing Systems
• /
• 제15권5호
• /
• pp.1231-1242
• /
• 2019

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

• 대한음성학회지:말소리
• /
• 제68권
• /
• pp.49-63
• /
• 2008

This paper describes the development and assessment of a hidden Markov model (HMM) based Tagalog speech synthesis system, where Tagalog is the most widely spoken indigenous language of the Philippines. Several aspects of the design process are discussed here. In order to build the synthesizer a speech database is recorded and phonetically segmented. The constructed speech corpus contains approximately 89 minutes of Tagalog speech organized in 596 spoken utterances. Furthermore, contextual information is determined. The quality of the synthesized speech is assessed by subjective tests employing 25 native Tagalog speakers as respondents. Experimental results show that the new system is able to obtain a 3.29 MOS which indicates that the developed system is able to produce highly intelligible neutral Tagalog speech with stable quality even when a small amount of speech data is used for HMM training.

• 한국시뮬레이션학회:학술대회논문집
• /
• 한국시뮬레이션학회 2003년도 춘계학술대회논문집
• /
• pp.25-29
• /
• 2003

본 논문에서는 울진 표준형원전 시뮬레이터의 강의실용 교육훈련 시스템의 일원으로 개발된 CBT (Computer Based Training) 및 WBT (Web Based Training)에 대해 다루고 있다. CBT는 발전소 시뮬레이션, 노심 다이나믹스, 중대사고, 비상발령 및 증기발생기 열변환 과정으로 구성되어 있다. Simulator Operation 기능을 이용하면 강사는 강사조작 메뉴를 통하여 시뮬레이터를 조작을 할 수 있고, Sim-Diagram 등 각종 화면을 보여줄 수 있다. 중대사고는 모의사고에 의해 구축된 데이터를 근거로 하여 개발되었으며, 방사선 비상등급에 따라 백색비상, 청색비상, 적색비상으로 구성된 비상발령은 각 발령의 발령상황 등을 Open Window를 통하여 볼 수 있도록 하였다. 한편 WBT는 강사와 교육생이 강의실 이외의 장소에서 시간과 공간의 제약을 벗어나서 원격교육이 가능하도록 구축한 웹서버 환경이다. 현재는 기존에 구축된 강사들의 홈페이지를 Intra-Net환경에서 접근이 가능하도록 링크된 상태에 있다. 향후에는 일부내용에 대해서는 원격으로 강의가 가능하도록 다양한 컨텐츠를 개발할 예정이며, 현재는 발전소 운전과 관련한 교육자료, 각종 동영상 및 이미지, 각종교재 등에 대한 DB 구축을 준비중에 있다

• 한국산학기술학회:학술대회논문집
• /
• 한국산학기술학회 2007년도 추계학술발표논문집
• /
• pp.81-84
• /
• 2007

In this thesis, we have designed the indirect adaptive controller using Dynamic Neural Units(DNU) for unknown nonlinear systems. Proposed indirect adaptive controller using Dynamic Neural Unit based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• Journal of Information Processing Systems
• /
• 제16권4호
• /
• pp.795-808
• /
• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.