• 디지털융복합연구
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• 제13권9호
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• pp.243-250
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• 2015

모바일 학습 콘텐츠 구현은 IT와 관광을 융합하는 IT관광 융합 학문이다. 학습의 효과를 증대하기 위해서 모바일 학습 콘텐츠를 학습 모듈별로 분류한다. 본 논문은 u-스마트 관광정보2.0 시스템을 제안한다. 모바일 학습 콘텐츠 구현은 IT 관광 융합인 u-스마트 관광정보2.0 시스템을 이용한다. 이것은 디자인 패턴과 XML를 융합하여 학습자에게 재미와 흥미를 준다. 이것은 국가 직무 능력 표준구조로 학습자들의 디자인 패턴으로 나뉘어 학습 모듈별로 수업진행이 최대 장점이다. 그 결과 학습자 출석률이 향상되며 더욱 좋은 학습효과가 나왔다. 다른 장점은 관광정보 콘텐츠 정보품질에 맞는 모바일 학습 콘텐츠를 생성하고 관광정보콘텐츠를 실시간으로 학습 할 수 있다. 또한 모바일 학습 콘텐츠 구현은 향후 국가 직무 능력 표준 학습에 많은 도움을 줄 것으로 기대된다.

• 터널과지하공간
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• 제6권4호
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• pp.316-325
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• 1996

A new accelerated neural network adopting modified sigmoid function was developed and applied to estimate engineering properties of rock from insufficient geological data. Developed network was tested on the well-known XOR and character recognition problems to verify the validity of the algorithms. Both learning speed and recognition rate were improved. Test learn on the Lee and Sterling's problems showed that learning time was reduced from tens of hours to a few minutes, while the output pattern was almost the same as other studies. Application to the various case studies showed exact coincidence with original data or measured results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.42.6-42
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• 2002

Kohonen's self organizing feature map (SOFM) converts arbitrary dimensional patterns into one or two dimensional arrays of nodes. Among the many competitive learning algorithms, SOFM proposed by Kohonen is considered to be powerful in the sense that it not only clusters the input pattern adaptively but also organize the output node topologically. SOFM is usually used for a preprocessor or cluster. It can perform dimensional reduction of input patterns and obtain a topology-preserving map that preserves neighborhood relations of the input patterns. The traditional SOFM algorithm[1] is a competitive learning neural network that maps inputs to discrete points that are called nodes on a lattice...

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.466-471
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• 1994

To make a dynamic system a given desired motion trajectory, a new feedback error learning scheme is proposed which is based on the repeatability of dynamic system motion. This method is composed of feedforward and feedback control laws. A benefit of this control scheme is that the input pattern that generates the desired motion can be formed without estimating the physical parameters of system dynamics. The numerical simulations show the good performance of the proposed scheme

• 한국수학사학회지
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• 제14권2호
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• pp.125-148
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• 2001

For this purpose, most of all, we thought over the theoretical background for the application of Web-resources to the teaching and learning program at the school mathematics. Second, we looked into the applied class and the class pattern with the internet. And then, we arranged the cases using the internet web materials. The last, we mentioned what the matters of learning based on the web are and what the teacher's roles are.

• 대한의용생체공학회:의공학회지
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• 제12권3호
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• Soft Computing and Machine Intelligence
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• 제1권1호
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• pp.24-30
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• 2021

The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

• 디지털산업정보학회논문지
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• 제9권2호
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• pp.111-119
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• 2013

Face recognition has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous applications, such as access control, surveillance, security, credit-card verification, and criminal identification. In this paper, we propose a simple descriptor called an ECSP(Extended Center-Symmetric Pattern) for illumination-robust face recognition. The ECSP operator encodes the texture information of a local face region by emphasizing diagonal components of a previous CS-LBP(Center-Symmetric Local Binary Pattern). Here, the diagonal components are emphasized because facial textures along the diagonal direction contain much more information than those of other directions. The facial texture information of the ECSP operator is then used as the input image of an image covariance-based feature extraction algorithm such as 2D-PCA(Two-Dimensional Principal Component Analysis). Performance evaluation of the proposed approach was carried out using various binary pattern operators and recognition algorithms on the Yale B database. The experimental results demonstrated that the proposed approach achieved better recognition accuracy than other approaches, and we confirmed that the proposed approach is effective against illumination variation.

• Journal of Communications and Networks
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• 제17권6호
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• 한국생물환경조절학회:학술대회논문집
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• 한국생물환경조절학회 1999년도 정기총회 및 학술논문발표요지
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• pp.24-29
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• 1999

포도 수확기를 개발하기 위해서는 포도 형상과 위치를 정확하게 파악하는 것이 필요하다. 신경회로망(Neural network)의 연상메모리(Associative memory)를 이용하여 포도 형상 정보를 인식하고자 한다. 신경회로망을 이용한 연상메모리는 학습 패턴(Learning pattern)을 학습한 후에 입력 패턴(Input pattern)으로부터 출력패턴을 얻는다. (중략)