• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.83-90
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• 2012

In this paper, an intelligent visual surveillance system based on a NIOS II embedded platform is implemented. By this time, embedded based visual surveillance systems were restricted for a special purpose because of high dependence upon hardware. In order to improve the restriction, we implement a flexible embedded platform, which is available for various purpose of applications. For high speed processing of software based programming, we improved performance of the system which is integrated the SOPC type of NIOS II embedded processor and image processing algorithms by using software programming and C2H(The Altera NIOS II C-To-Hardware(C2H) Acceleration Compiler) compiler in the core of the hardware platform. Then, we constructed a server system which globally manage some devices by the NIOS II embedded processor platform, and included the control function on networks to increase efficiency for user. We tested and evaluated our system at the designated region for visual surveillance.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.379-390
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• 2019

In this paper, we propose a novel deep neural network model for Vision-and-Language Navigation (VLN) named LVLN (Landmark-based VLN). In addition to both visual features extracted from input images and linguistic features extracted from the natural language instructions, this model makes use of information about places and landmark objects detected from images. The model also applies a context-based attention mechanism in order to associate each entity mentioned in the instruction, the corresponding region of interest (ROI) in the image, and the corresponding place and landmark object detected from the image with each other. Moreover, in order to improve the success rate of arriving the target goal, the model adopts a progress monitor module for checking substantial approach to the target goal. Conducting experiments with the Matterport3D simulator and the Room-to-Room (R2R) benchmark dataset, we demonstrate high performance of the proposed model.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.192-198
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• 2018

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.447-454
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• 2018

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.23-29
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• 2019

Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.259-271
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• 2019

It is important to obtain reasonable predictions of the extent of the damage during maritime accidents such as ship collisions and groundings. Many fracture models based on different mechanical backgrounds have been proposed and can be used to estimate the extent of damage involving ductile fracture. The goal of this study was to compare the damage extents provided by some selected fracture models. Instead of performing a new series of material constant calibration tests, the fracture test results for the ship building steel EH36 obtained by Park et al. (2019) were used which included specimens with different geometries such as central hole, pure shear, and notched tensile specimens. The test results were compared with seven ductile fracture surfaces: Johnson-Cook, Cockcroft-Latham-Oh, Bai-Wierzbicki, Modified Mohr-Coulomb, Lou-Huh, Maximum shear stress, and Hosford-Coulomb. The linear damage accumulation law was applied to consider the effect of the loading path on each fracture surface. The Swift-Voce combined constitutive model was used to accurately define the flow stress in a large strain region. The reliability of these simulations was verified by the good agreement between the axial tension force elongation relations captured from the tests and simulations without fracture assignment. The material constants corresponding to each fracture surface were calibrated using an optimization technique with the minimized object function of the residual sum of errors between the simulated and predicted stress triaxiality and load angle parameter values to fracture initiation. The reliabilities of the calibrated material constants of B-W, MMC, L-H, and HC were the best, whereas there was a high residual sum of errors in the case of the MMS, C-L-O, and J-C models. The most accurate fracture predictions for the fracture specimens were made by the B-W, MMC, L-H, and HC models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.464-470
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• 2021

Automobiles have been used as simple means of transportation, but recently, as automobiles are rapidly becoming intelligent and smart, and automobile preferences are increasing, research on IT technology convergence is underway, requiring basic high-performance functions such as driver's convenience and safety. As a result, autonomous driving and semi-autonomous vehicles are developed, and these technologies sometimes deviate from lanes due to environmental problems, situations that cannot be judged by autonomous vehicles, and lane detectors may not recognize lanes. In order to improve the performance of lane departure from the lane detection system of autonomous vehicles, which is such a problem, this paper uses fast recognition, which is a characteristic of YOLO(You only look once), and is affected by the surrounding environment using CSI-Camera. We propose a lane detection system that recognizes the situation and collects driving data to extract the region of interest.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.35-42
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• 2021

In the article, the authors investigate the economic security system in the conditions of the powers transformation. It is substantiated that economic security acts as a certain system that includes components and at the same time acts as a subsystem of the highest order. It is determined that the economic security system of regions acting as a system has its subsystems, which include: production, financial, environmental, innovation, investment and social subsystems. The parameters of the economic security system include relative economic independence, economic stability and self-development of economic systems, and it is proved that an important feature of economic security in addition to its systemic nature is multi-vector. It is substantiated that the monitoring of ensuring the economic security system of the development of economic systems of different levels in the conditions of the powers transformation should contain the analysis of social, economic and ecological development of regions; spheres of possible dangers of the development of regional economic systems; the nature of the threats; the degree of the possibility of threats; time perspective of economic development threats; possible consequences of losses for economic entities; the impact of threats to the object of the economic entities' activity; possible asymmetry of economic development of regional economic entities. Possible threats as a consequence of the powers transformation have been identified. A PEST analysis of the impact of factors of different nature on economic security and the development of regional economic systems in the powers transformation is carried out. A recurrent ratio is proposed for the economic security system in the conditions of the powers transformation.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Composites Research
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• v.22 no.1
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• pp.22-31
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• 2009

Recently the wind energy has been alternatively used as a renewable energy resource instead of the mostly used fossil fuel due to its lack and environmental issues. This work is to propose a structural design and analysis procedure for development of the 500W class small wind turbine system which will be applicable to relatively low speed region like Korea and for the domestic use. The wind turbine blade was performed structural analysis including stress, deformation, buckling, vibration and fatigue. In addition, the blade should be safe from the impact damage due to FOD(Foreign Object Damage) including the bird strike. MSC.Dytran was used in order to analyze the bird strike penomena on the blade, and the applied method Arbitrary Lagrangian-Eulerian was evaluated by comparison with the previous study results. Finally, the structural test was carried out and its test results were compared with the estimated results for evaluation of the designed structure.