• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.37 no.3
• /
• pp.1-8
• /
• 2000

In this paper, we formulate an optimal control problem to obtain the optimal signal strength and detection threshold for tracking with NN filter, First, we predict the tracking performance of NN filter by using the HYCA method. Based on this method, the predicted tracking performance is represented with respect to signal strength and detection threshold. Using this relation, we find the optimal parameters for following three examples： 1) the sequence of optimal detection threshold which minimizes sum of position estimation error； 2) the sequence of optimal detection threshold which minimizes sum of validation gate volume； and 3) the sequence of optimal signal strength and detection threshold which minimizes sum of signal strength.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.1647-1650
• /
• 2010

다양한 옥내 측위 방법이 연구 발표되었다. 그 중에서 무선근거리통신망을 이용하는 방법은 측위를 위한 별도의 특수 장비를 요구하지 않기 때문에 실용적이다. 무선근거리통신망 기반 옥내 측위에서는 지문방식과 신호세기를 거리로 환산하여 사용하는 방법이 가장 흔히 사용되는 두 가지 방법이다. 지문방식은 시간과 노력이 많이 소요되는 준비단계가 필요하지만 신호세기를 거리로 환산하여 사용하는 방법보다 정확한 반면, 신호세기를 거리로 환산하여 사용하는 방법은 구현하기가 용이하지만 오차가 심하다. 때때로 지문방식 조차도 실제 응용에 적용되기에 부적절할 만큼 오차가 커, [1]은 일종의 지문방식인 K-NN (K nearest neighbors) 방법의 오차를 개선하기 위한 파티클 필터를 소개하였다. 칼만 필터도 역시 K-NN 옥내 측위의 정확도 개선을 위하여 사용된 바 있다. 본 논문은 K-NN 방법의 정확도 개선에 있어 칼만 필터와 파티클 필터의 성능을 비교하는 실험 결과를 소개한다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.2
• /
• pp.30-36
• /
• 2002

This paper describes a nonlinear adaptive noise canceler(ANC) using neural networks(NN) based on filter to make up for the drawback of the conventional ANC with the linear adaptive filter. The proposed ANC was tested its noise rejection performance using broadband time-varying noise signal and compared with the ANC of TDL linear filter. Experimental results show that in cases of nonlinear correlations between the noise of primary input and reference input, the neural network based ANC outperforms the linear ANC with respect to mean square error It is also verified that the recurrent NN adaptive filter is superior to the feedforward NN filter. Thus, we identify that the NN adaptive filter is more effective than the linear adaptive filter for rejection of broadband time-varying noise in the ANC.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1334-1337
• /
• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1034-1038
• /
• 2005

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

• International journal of advanced smart convergence
• /
• v.12 no.1
• /
• pp.1-8
• /
• 2023

Smart IoT over high speed network and high performance smart devices explodes the ubiquitous services and applications. Nearest Neighbor(NN) query is one of the important type of queries that have to be supported for ubiquitous information services. In order to process efficiently NN queries in the wireless broadcast environment, it is important that the clients determine quickly the search space and filter out NN from the candidates containing the search space. In this paper, we propose a hybrid index of Voronoi and grid partition to provide quick search space decision and rapid filtering out NN from the candidates. Grid partition plays the role of helping quick search space decision and Voronoi partition providing the rapid filtering. We show the effectiveness of the proposed index by comparing the existing indexing schemes in the access time and tuning time. The evaluation shows the proposed index scheme makes the two performance parameters improved than the existing schemes.

• Smart Media Journal
• /
• v.3 no.2
• /
• pp.14-19
• /
• 2014

This paper presents a novel approach to recognize human gestures using k-NN and DTW based on Con tactless Electronic Potential Sensor(CEPS) in the smart devices such as smart TV and smart-phone in the proposed method, we used a Kalman filter to remove noise on gesture signal from CEPS and a PCA algorithm is utilized for reducing the dimensionality of gesture signal without data losses. And then in order to categorize gesture signals, k-NN classifier with DTW distance measure is considered. In the experimental result, we evaluate recognition performance with CEPS gesutres signal form the above two types of smart devices, and we can successfully identify five different gestures with more than 90% of recognition accuracy.

• Proceedings of the IEEK Conference
• /
• 2002.07b
• /
• pp.818-821
• /
• 2002

In this paper, we propose Neuro-Fuzzy discrimination method of the new and old bill using bill money acoustic data. The concept of the histogram is introduced to improve the processing time into the proposal system. The adaptative filter is used in order to remove the motor sound from an observed bill money acoustic data. The output signal of this adaptive digital filter is converted into not only a spectrum but also a histogram. It became easy that features of the paper money sound were extracted from the bill money acoustic data. The spectral data and the histogram is obtained like this, and it become an input pattern of the neural network(NN). Then, the discrimination result of the NN is finally judged by the fuzzy inferece in the new bill or the exhaustion bill.

• Journal of Information Technology Applications and Management
• /
• v.15 no.2
• /
• pp.51-65
• /
• 2008

The purpose of this paper is introducing WiFi based EKF(Extended Kalman Filter) method for indoor positioning. The advantages of our EKF method include: 1) Any special equipment dedicated for positioning is not required. 2) implementation of EKF does not require off-line phase of fingerprinting methods. 3) The EKF effectively minimizes squared deviation of the trilateration method. In order to experimentally prove the advantages of our method, we implemented indoor positioning systems making use of the K-NN(K Nearest Neighbors), Bayesian, decision tree, trilateration, and our EKF methods. Our experimental results show that the average-errors of K-NN, Bayesian and decision tree methods are all close to 2.4 meters whereas the average errors of trilateration and EKF are 4.07 meters and 3.528 meters, respectively. That is, the accuracy of our EKF is a bit inferior to those of fingerprinting methods. Even so, our EKF is accurate enough to be used for practical indoor LBS systems. Moreover, our EKF is easier to implement than fingerprinting methods because it does not require off-line phase.

• Proceedings of the KIPE Conference
• /
• 1999.07a
• /
• pp.526-530
• /
• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.