• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.37 no.2
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• pp.78-84
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• 2001

Escape behaviour of the anchovy (Engralius japonica, total length 4-7cm) at the inside wing net and bag net in the anchovy boat seine was observed by underwater video camera in order to clarify the relationship between visual stimulus of the gear or relative water flow inside gear and reacting behaviour. The vertical attenuation coefficient of underwater illuminance in the offshore of Keoje island and Tongyoung was ranged from 0.24 to 1.03 and it could be affect visual range and visual contrast of the fishing gear. The relative water flow at the joint part between inside wing and bagnet while towing was 1.5 times higher than at the middle part of inside wing or fore part of bag net, but it was estimated under than maximum swimming speed of 4-7 cm anchovy. The mean escaping number of anchovy from end part of inside wing of 30 cm mesh to out side for a minute within visual range of video camera was 455 and anchovy swimming forward from bag net through flapper was 308. These results revealed anchovy could escape as voluntary response in spite of higher visual stimulus or higher water flow.

• Journal of Biosystems Engineering
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• v.19 no.2
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• pp.124-137
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• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.325-335
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• 1996

Visual features are crucial for monitoring the growth state, indexing the drying performance, and grading the quality of oak mushrooms. A computer vision system with neural net information processing technique was utilized to quantize quality factors of a dried oak mushrooms distributed over the cap and gill sides. In this paper, visual feature extraction algorithm were integrated with the neural net processing to deal with various fuzzy patterns of mushroom shapes and to compensate the fault sensitiveness of the crisp criteria and heuristic rules derived from the image processing results. The proposed algorithm improved the segmentation of the skin features of each side, the identification of cap and gill surfaces, the identification of stipe states and removal of the stipe, etc. And the visual characteristics of dried oak mushrooms were analyzed and primary visual features essential to tile quality evaluation were extracted and quantized. In this study, black and white gray images were captured and used for the algorithm development.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.91-97
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• 2014

In this paper, we design and implement a mobile game based on windows phone platform. Visual Studio and the Windows Mobile software development kit(SDK) and developer tool kits (DTK) make it possible to create software for the Windows Mobile platform in Visual C#. Also The XNA Framework is the set of .NET libraries that developers will code against to build games. XNA Framework support PC and XBOX and a ZUNE HD multiplex platform environment and 2D and 3D games provides the necessary functions to a development. XNA Framework is to make it easier to develop games because the codes which are succeeded creates with automatic, implements a game development relation resource and the game logic. The key idea of our game is that we use shooting and ranking system to raise fun.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.238-243
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• 1998

This paper presents a new approach to visual servoing with the stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using a binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only at around a desired location but also at the other locations. The suggested technique can guide a robot manipulator to the desired location without giving such priori knowledge as the relative distance to the desired location or the model of an object even if the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by the simulation and experimental results and compared with the case of conventional method fur a SCARA robot.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.105-114
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• 2006

In this paper, 3D input and output systems for a web-based real-time 3D video communication system using IEEE 1394 digital cameras, Intel Xeon Server system and Microsoft Directshow library is proposed. And some conditions for optimizing the operations of the stereo camera, 3D display and signal processing system are analyzed. Input & output systems are carefully selected, which can satisfy the required optimization conditions and the final 3D video communication system is implemented by using three optimized devices. The overall control system is developed with Microsoft Visual C++.Net and Microsoft DirectX 9.1 SDK. Some experimental results show that the observer can feel the natural presence from multi-view(4-view) 3D video of server system in real-time and also can feel the natural presence from 3D video of client system and finally suggest an application possibility of the proposed web-based real-time 3D video communication in real fields.

• Journal of the Korean Wood Science and Technology
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• v.23 no.2
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• pp.55-69
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• 1995

The primary objective of this research was to investigate optimum manufacturing condition of thin composite panels composed of sawdust, polyethylene film and polypropylene net. At the study the experiment was designed to make thin board in which sawdust offers effectiveness as core composing material, polyethylene as adhesive with added urea resin, and polypropylene as stiffness and flexibility in the composition panel. 100 types of thin composite panels were manufactured according to press-lam and mat-forming process of various hot pressing conditions(pressure, temperature and time). They were tested and compared with control boards on bending properties(MOR, MOE, SPL, WML), internal bond strength, thickness swelling, linear expansion and water absorption. At the same time the visual inspections of each types of panels were accomplished. The physical and mechanical properties of composite types passed by visual inspection were analyzed by Tukey's studentized range test. From the statistical analysis, the optimum manufacturing condition of thin composite panels were selected. Compared with two manufacturing processes, mat-forming process performed better than press-lam process in all tested properties. The optimum manufacturing conditions resulted from the experiment and statistical analysis were able to determine as following: the press temperature was shown the most good result at 130$^{\circ}C$ in mat forming process and 140$^{\circ}C$ press lam process, the press time 4 min in both processes, but the press pressure was 25-10kg/$cm^2$ in mat forming and 15k/$cm^2$ press lam process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.626-634
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• 2012

It is required that system can seamlessly identify and manage change history and comprehensive assessment of several types of data as well as individual information of feeding and water environment for scientific and systematic management of fish farming environment and fish farmer. In this study, we implemented the system which can present and simulate current status of water quality and feeding based on th historical data of them, and check changes of state step by step using visual C++. In addition, we proposed the entropy model which can be comprehensive analysis about water quality and feed status information based on knowledge of fisheries. It can be the foundation to create high-level environment model reflecting the more diverse fisheries knowledge such as disease.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.360-363
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• 2022

According to the 2018 Kaggle ML & DS Survey, among the proportions of frameworks for machine learning and data science, TensorFlow and Keras each account for 41.82%. It was found to be 34.09%, and in the case of development programming, it is confirmed that about 82% use Python. A significant number of machine learning and deep learning structures utilize the Keras framework and Python, but in the case of Python, distribution and execution are limited to the Python script environment due to the script language, so it is judged that it is difficult to operate in various environments. This paper implemented a machine learning and deep learning system using C# and Keras running in Visual Studio 2019. Using the Mnist dataset, 100 tests were performed in Python 3.8,2 and C# .NET 5.0 environments, and the minimum time for Python was 1.86 seconds, the maximum time was 2.38 seconds, and the average time was 1.98 seconds. Time 1.78 seconds, maximum time 2.11 seconds, average time 1.85 seconds, total time 37.02 seconds. As a result of the experiment, the performance of C# improved by about 6% compared to Python, and it is expected that the utilization will be high because executable files can be extracted.