• Smart Media Journal
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• v.9 no.1
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• pp.23-29
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• 2020

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.1-11
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• 2012

When the microarray experiment developed, main interest was limited to detect differentially expressed genes associated with a phenotype of interest. However, as human diseases are thought to occur through the interactions of multiple genes within a same functional category, the unit of analysis of the microarray experiment expanded to the set of genes. For the phenotype of censored survival time, Gene Set Enrichment Analysis(GSEA), Global test and Wald type test are widely used. In this paper, we modified the Wald type test by adopting normal score transformation of gene expression values and developed a parametric test which requires much less computation than others. The proposed method is compared with other methods using a real data set of ovarian cancer and a simulation data set.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.587-591
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• 2001

This paper deals with inspect algorithm using visual system. One of the major problems that arise during polymer production is the estimation of the noise of the color product.(bad pallets) An erroneous output can cause a lot of losses (production and financial losses). Therefore new methods for real-time inspection of the noise are demanded. For this reason, we have presented a development of vision system algorithm for the defect inspection of PE color pallets. First of all, in order to detect the edge of object, the differential filter is used. And we apply to the labelling algorithm for feature extraction. This algorithm is designed for the defect inspection of pallets. The labelling algorithm permits to separate all of the connected components appearing on the pallets. Labelling the connected regions of a image is a fundamental computation in image analysis and machine vision, with a large number of application. Also, we suggested vision processing program in window environment. Simulations and experimental results demonstrate the performance of the proposal algorithm.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1864-1873
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• 2018

Energy shifting is an innovative method used to obtain the highest profit from the operation of energy storage systems (ESS) by controlling the charge and discharge schedules according to the electricity prices in a given period. Therefore, in this study, we propose an optimal charge and discharge scheduling method that performs energy shift operations derived from an ESS efficiency model. The efficiency model reflects the construction of power conversion systems (PCSs) and lithium battery systems (LBSs) according to the rated discharge time of a MWh-scale ESS. The PCS model was based on measurement data from a real system, whereas for the LBS, we used a circuit model that is appropriate for the MWh scale. In addition, this paper presents the application of a genetic algorithm to obtain the optimal charge and discharge schedules. This development represents a novel evolutionary computation method and aims to find an optimal solution that does not modify the total energy volume for the scheduling process. This optimal charge and discharge scheduling method was verified by various case studies, while the model was used to realize a higher profit than that realized using other scheduling methods.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.423-427
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• 2002

Contrary to the traditional text-based information, 4S(GIS,GNSS,SIIS,ITS) information can contribute to the citizen's welfare in upcoming era. Recently, GSIS(Geo-Spatial Information System) has been applied and stressed out in various fields. As analyzed the data from GSIS arena, the position information of objects and targets is crucial and critical. Therefore, several methods of getting and knowing position are proposed and developed. From this perspective, Position collection and processing are the heart of 4S technology. We develop 4S-Van that enables real-time acquisition of position and attribute information and accurate image data in remote site. In this study, the configuration of 4S-Van equipped with GPS, INS, CCD and eye-safe laser scanner is shown and the merits of DGPS/INS integration approach for geo-referencing is briefly discussed. The algorithm of DGPS/INS integration fur determination of six parameters of motion is eccential in the 4S-Van to avoid or simplify the complicated computation such as photogrammetric triangulation. 4S-Van has the application of Laser-Mobile Mapping System for three-dimensional data acquisition that merges the texture information from CCD camera. The technique is also applied in the fields of virtual reality, car navigation, computer games, planning and management, city transportation, mobile communication, etc.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.71-83
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• 2020

In this paper, we propose a system that can detect the shape of a hand at high speed using an FPGA. The hand-shape detection system is designed using Verilog HDL, a hardware language that can process in parallel instead of sequentially running C++ because real-time processing is important. There are several methods for hand gesture recognition, but the image processing method is used. Since the human eye is sensitive to brightness, the YCbCr color model was selected among various color expression methods to obtain a result that is less affected by lighting. For the CbCr elements, only the components corresponding to the skin color are filtered out from the input image by utilizing the restriction conditions. In order to increase the speed of object recognition, a median filter that removes noise present in the input image is used, and this filter is designed to allow comparison of values and extraction of intermediate values at the same time to reduce the amount of computation. For parallel processing, it is designed to locate the centerline of the hand during scanning and sorting the stored data. The line with the highest count is selected as the center line of the hand, and the size of the hand is determined based on the count, and the hand and arm parts are separated. The designed hardware circuit satisfied the target operating frequency and the number of gates.

• The KIPS Transactions:PartA
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• v.15A no.1
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• pp.1-8
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• 2008

An occlusion culling method, one of visibility culling methods, excludes invisible objects or triangles which are covered by other objects. As it reduces computation quantity, occlusion culling is an effective method to handle complex scenes in real-time. But an existing common occlusion culling method, such as hardware occlusion query method, sends objects' data twice to GPU and this causes processing overheads once for occlusion culling test and the other is for rendering. And another existing hardware occlusion culling method, VCBP, can test objects' visibility quickly, but it neither test bounding volume nor return test result to application stage. In this paper, we propose a single pass occlusion culling method which uses temporal and spatial coherency, with effective occlusion culling hardware architecture. In our approach, the hardware performs occlusion culling test rapidly with cache on the rasterization stage where triangles are transformed into fragments. At the same time, hardware sends each primitive's visibility information to application stage. As a result, the application stage reduces data transmission quantity by excluding covered objects using the visibility information on previous frame and hierarchical spatial tree. Our proposed method improved maximum 44%, minimum 14% compared with S&W method based on hardware occlusion query. And the performance is increased 25% and 17% respectively, compared to maximum and minimum performance of CHC method which is based on occlusion culling method.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.698-707
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• 2017

This paper deals with predictive control methods of mobile robots for reference trajectory tracking control. Predictive control methods using predictive model are known as effective schemes that minimize the future errors between the reference trajectories and system states; however, the amount of real-time computation for the predictive control are huge so that their applications were limited to slow dynamic systems such as chemical processing plants. Lately with high computing power due to advanced computer technologies, the predictive control methods have been applied to fast systems such as mobile robots. These predictive controllers have some control parameters related to control performance. But these parameters have not been optimized. In this paper we employed the genetic algorithm to optimize the control parameters of the predictive controller for mobile robots. The improved performances of the proposed control method are demonstrated by the computer simulation studies.