• Journal of Sensor Science and Technology
• /
• v.10 no.6
• /
• pp.286-294
• /
• 2001

In this study, 3-D underwater object recognition using the self-made 3-3 type composite ultrasonic transducer and modified SOFM(Self Organizing Feature Map) neural network are investigated. Properties of the self-made 3-3 type composite specimens are satisfied considerably with requirements as an underwater ultrasonic transducer's materials. 3-D underwater all object's recognition rates obtained from both the training data and testing data in different objects, such as a rectangular block, regular triangular block, square block and cylinderical block, were 100% and 94.0%, respectively. All object's recognition rates are obtained by utilizing the self-made 3-3 type composite transducer and SOFM neural network. From the object recognition rates, it could be seen that an ultrasonic transducer fabricated with the self-made 3-3 type composite resonator will be able to have application for the underwater object recognition.

• Journal of Aerospace System Engineering
• /
• v.14 no.3
• /
• pp.32-41
• /
• 2020

The International Civil Aviation Organization (ICAO) in 2015 proposed a road-map for the global recognition system of the Approved Maintenance Organization (AMO) fto mitigate the redundant work and regulatory burdens of the aviation industry and authorities on the certification and oversight activities of the State of Registry. Since then, the ICAO standards and guidelines have been revised accordingly with the goal of implementing the system in 2024. Korea should actively prepare for this AMO global recognition system to cope with the ICAO road-map appropriately as well as to develop the Maintenance Repair Overhaul (MRO) industry. Thus, this paper focused on the ratings and limitations system, a key element of the AMO, and proposes the improvement of domestic regulatory/administrative rules necessary for the global recognition system, through the review of newly established ICAO standards/guidelines and the comparative analysis of leading aviation countries' and Korean system/requirements.

• IEIE Transactions on Smart Processing and Computing
• /
• v.2 no.5
• /
• pp.277-281
• /
• 2013

Modern speaker verification systems based on support vector machines (SVMs) use Gaussian mixture model (GMM) supervectors as their input feature vectors, and the maximum a posteriori (MAP) adaptation is a conventional method for generating speaker-dependent GMMs by adapting a universal background model (UBM). MAP adaptation requires the appropriate amount of input utterance due to the number of model parameters to be estimated. On the other hand, with limited utterances, unreliable MAP adaptation can be performed, which causes adaptation noise even though the Bayesian priors used in the MAP adaptation smooth the movements between the UBM and speaker dependent GMMs. This paper proposes a sparse MAP adaptation method, which is known to perform well in the automatic speech recognition area. By introducing sparse MAP adaptation to the GMM-SVM-based speaker verification system, the adaptation noise can be mitigated effectively. The proposed method utilizes the L0 norm as a regularizer to induce sparsity. The experimental results on the TIMIT database showed that the sparse MAP-based GMM-SVM speaker verification system yields a 42.6% relative reduction in the equal error rate with few additional computations.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.29-35
• /
• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.9-18
• /
• 2005

In order to systematically and visually understand well-known but qualitative and rotatively complicated relationships between synoptic fields in the BAIU season and heavy rainfall events in Japan, these synoptic fields were classified using the Self-Organizing Map (SOM) algorithm. This algorithm can convert complex nonlinear features into simple two-dimensional relationships, and was followed by the application of the clustering techniques of the U-matrix and the K-means. It was assumed that the meteorological field patterns be simply expressed by the spatial distribution of wind components at the 850 hPa level and Precipitable Water (PW) in the southwestern area including Kyushu in Japan. Consequently, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial feature represented by high PW accompanied by strong wind components known as Low-Level Jet (LLJ). The features of this cluster indicate a typical meteorological field pattern that frequently causes disastrous heavy rainfall in Kyushu in the rainy season. From these results, the SOM technique may be an effective tool for the classification of complicated non-linear synoptic fields.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1479-1484
• /
• 2005

This paper represents an intelligent sensor controller of mobile robot for object recognition in an indoor hon environment. A range finder sensor module has been developed by using optic PSD (Position Sensitive Detector) sensor way at a low Vice. While PSD sensor is cost effective and light weighting, it has switching noise and white noise. To remove these noises, we propose a heuristic filter. For line-based map building. also we prorosed advanced Hough transformation and navigation algorithm. Some experiments were illustrated for the validity of the developed system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1887-1891
• /
• 2005

Despite significant advances in multimedia transferring technologies in various fields of robotics, it is sometimes quite difficult for the operator to fully understand the context of 3D remote environments from 2D image feedback. Particularly, in the remote control of mobile robots, the recognition of the object associated with the task is very important, because the operator has to control the robot safely in various situations not through trial and error. Therefore, it is necessary to provide the operator with 3D volumetric models of the object and object-related information as well such as locations, shape, size, material properties, and so on. Thus, in this paper, we propose a vision-based human interface system that provides an interactive, information-rich map through network-based information brokering. The system consists of an object recognition part, a 3D map building part, a networked knowledge base part, and a control part of the mobile robot.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.2
• /
• pp.19-26
• /
• 1999

The geographic information system is needed in the image recognition field. This study recommends an efficient method to construct the GIS from the feature extraction of roads through scanning of a normal or hand-made maps. Many algorithms have been presented for such image information recognition. However, such algorithm processes have limitations due to their complexity. To efficiently extract road information from scanning map images. a $3{\times}3$ directional form is applied - structuring element, erosion and dilation, and opening and closing. This method allows for efficient evaluation of the featured road extracts from the map image and from the character sets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.191-194
• /
• 2005

This paper represents an intelligent sensor controller of mobile robot for object recognition in an indoor known environment. A range finder sensor module has been developed by using optic PSD (Position Sensitive Detector) sensor array at a low price. While PSD sensor is cost effective and light weighting, it has switching noise and while noise. To remove these noises, we propose a heuristic filter. For line-based map building, also we proposed advanced Hough transformation and navigation algorism. Some experiments were illustrated for the validity of the developed system.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.245-250
• /
• 2008

최근 동작 및 행동 인식에 대한 연구가 활발하다. 특히, 센서가 소형화되고 저렴해지면서 그 활용을 위한 관심이 증가하고 있다. 기존의 많은 행동 인식 연구에서 사용되어 온 정적 분류 기술 기반 동작 인식 방법은 연속적인 데이터 분류 기술에 비해 유연성 및 활용성이 부족할 수 있다. 본 논문에서는 연속적인 데이터의 패턴 분류 및 인식에 효과적인 확률적 추론 기법인 은닉 마르코프 모델(Hidden Markov Model)과 사전 지식 없이도 자동 학습이 가능하며 의미 깊은 궤적 패턴을 클러스터링하고 효과적인 양자화가 가능한 자기구성지도(Self Organizing Map)를 이용한 동작 인식 기술을 소개한다. 또한, 그 유용성을 입증하기 위해 실제 가속도 센서를 이용하여 다양한 동작에 대한 데이터를 수집하고 분류 성능을 분석 및 평가한다. 실험에서는 실제 가속도 센서를 통해 수집된 숫자를 그리는 동작의 성능 평가 결과를 보이고, 행동 인식기 별 성능과 전체 인식기별 성능을 비교한다.