• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.237-242
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• 2023

Research on high-performance walking robots is being actively conducted, and quadruped walking robots are receiving a lot of attention due to their excellent mobility and adaptability on uneven terrain, but they are difficult to introduce and utilize due to high cost. In this paper, to increase utilization by applying intelligent functions to a low-cost quadruped robot, we present a method of improving uneven terrain overcoming ability by mounting IMU and reinforcement learning on embedded board and automatically detecting objects using camera and deep learning. The robot consists of the legs of a quadruped mammal, and each leg has three degrees of freedom. We train complex terrain in simulation environments with designed 3D model and apply it to real robot. Through the application of this research method, it was confirmed that there was no significant difference in walking ability between flat and non-flat terrain, and the behavior of performing person detection in real time under limited experimental conditions was confirmed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3A
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• pp.172-178
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• 2012

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.51-54
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• 2016

In computer vision especially in image processing, it has become popular to apply deep convolutional networks for supervised learning. Convolutional networks have shown a state of the art results in classification, object recognition, detection as well as semantic segmentation. However, supervised learning has two major disadvantages. One is it requires huge amount of labeled data to get high accuracy, the second one is to train so much data takes quite a bit long time. On the other hand, unsupervised learning can handle these problems more cheaper way. In this paper we show efficient way to learn features for classification in an unsupervised way. The network trained layer-wise, used backpropagation and our network learns features from unlabeled data. Our approach shows better results on Caltech-256 and STL-10 dataset.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.471-473
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• 2004

현재 세계적인 열차제어의 추세는 궤도회로에 의한 고정폐색방식을 이용한 열차 운행이 아닌 발리스 또는 무선통신에 의한 이동권한을 이용한 열차제어방식을 적용하고 있으며, 국내에서도 이에 맞추어 철도청에서는 ATP 사업을 통하여 발리스에 의한 이동권한을 이용한 열차제어시스템을 적용하고 있다. 기존의 궤도회로를 이용한 열차제어시스템에서는 열차를 궤도회로에 의해 검지하였으나 발리스 또는 무선통신에 의한 이동권한을 이용한 열차제어시스템에서는 궤도회로를 사용하지 않기 때문에 이에 패한 새로운 접근이 요구된다. 본 논문에서는 특히 발리스를 이용하였을 때 열차 검지 및 속도 검지를 위한 방안에 대해 연구하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.399-403
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• 1998

In this paper, we proposed a new fuzzy supervised learning algorithm. We construct, and train, a new type fuzzy neural net to model the linear activation function. Properties of our fuzzy neural net include : (1) a proposed linear activation function ; and (2) a modified delta rule for learning algorithm. We applied this proposed learning algorithm to exclusive OR,3 bit parity using benchmark in neural network and pattern recognition problems, a kind of image recognition.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.111-117
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• 2011

Vibration monitoring is required for reliable thermosonic testing to decide whether sufficient vibration is achieved in each test for the detection of cracks. From a practical point of view, a cheaper and convenient monitoring method is better for the application to real tests. Therefore, the performance of different sensors for vibration monitoring was investigated and compared in this study to find a convenient and acceptable measurement method for thermosonics. Velocity measured by a laser vibrometer and strain provide an equivalent HI when measured at the same position. The microphone can provide a cheaper vibration monitoring device than the laser and the heating index calculated by a microphone signal shows similar characteristics to that calculated from velocity measured by the laser vibrometer. The microphone frequency response shows that it underestimates high frequency components but it is applicable to practical tests because it gives a conservative value of HI.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.42-45
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• 2010

This paper proposes an intelligent video surveillance system for human object tracking. The proposed system integrates the object extraction, human object recognition, face detection, and camera control. First, the object in the video signals is extracted using the background subtraction. Then, the object region is examined whether it is human or not. For this recognition, the region-based shape descriptor, angular radial transform (ART) in MPEG-7, is used to learn and train the shapes of human bodies. When it is decided that the object is human or something to be investigated, the face region is detected. Finally, the face or object region is tracked in the video, and the pan/tilt/zoom (PTZ) controllable camera tracks the moving object with the motion information of the object. This paper performs the simulation with the real CCTV cameras and their communication protocol. According to the experiments, the proposed system is able to track the moving object(human) automatically not only in the image domain but also in the real 3-D space. The proposed system reduces the human supervisors and improves the surveillance efficiency with the computer vision techniques.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.11a
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• pp.264-267
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• 2005

Impact damages are very important in the perspective of residual strength of composite structures such as aircrafts, ships, and trains because those damages are sometimes not visible on the surface of the point of impact and the impact resistance of laminated composites is usually not so high. Thus, the impact characteristics of laminated composites should he investigated for the safety of composite structures. This paper investigates the low-velocity impact and damage detection conducted on woven carbon/epoxy laminates. Experimental results show that the type of damage is dependent on the impact energy level and the delamination area becomes larger as the impact energy increases.

• Smart Structures and Systems
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• v.9 no.1
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• pp.21-33
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• 2012

In this study, a Lamb-wave based damage detection approach is proposed for damage localization in plate. A sensor network consisting of three PZT wafer type actuators/sensors is used to generate and detect Lamb waves. To minimize the complication resulted from the multimode and dispersive characteristics of Lamb waves, the fundamental symmetric Lamb mode, $S_0$ is selectively generated through designing the excitation frequency of the narrowband input signal. A damage localization algorithm based upon the configuration of the PZT sensor network is developed. Time-frequency analysis method is applied to purify the raw signal and extract damage features. Experimental result obtained from aluminum plate verified the proposed damage localization approach.