• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11c
• /
• pp.569-577
• /
• 2000

The position of tobacco leaves is affluence to the quality. To evaluate its quality, sample leaves was collected according to the position of attachment. In Korea, the position was divided into four classes such as high, middle, low and inside positioned leaves. Until now, the grade of standard sample was determined by human expert from korea ginseng and tobacco company. Many research were done by the chemical and spectrum analysis using NIR and computer vision. The grade of tobacco leaves mainly classified into 5 grades according to the attached position and its chemical composition. In high and low positioned leaves shows a low level grade under grade 3. Generally, inside and medium positioned leaf has a high level grade. This is the basic research to develop a real time tobacco leaves grading system combined with portable NIR spectrum analysis system. However, this research just deals with position recognition and grading using the color machine vision. The RGB color information was converted to HSI image format and the sample was all investigated using the bundle of tobacco leaves. Quality grade and position recognition was performed through well known general error back propagation neural network. Finally, the relationship about attached leaf position and its grade was analyzed.

• Journal of Engineering Education Research
• /
• v.27 no.2
• /
• pp.3-12
• /
• 2024

The purpose of this study was to evaluate how well the 2020 vision presented by the Accreditation Board for Engineering Education of Korea(ABEEK) had been achieved, and to objectively examine the social status. It was very necessary for the development of engineering education in Korea to provide room for improvement by diagnosing how well the ABEEK, one of the major engineering education communities, was achieving its own vision. In order to achieve the objectives of the study, research methods such as literature review, survey research, and expert advisory committee were used. To evaluate the level of achievement of the Vision 2020 of the ABEEK, the analysis was based on the response results of 61 people who had experience as a member of the steering committe. In addition, the vision and mission of the 23 countries that are currently signatory members of the Washington Accord were surveyed, and the social responsibility and financial independence of the 20 countries that joined the signatory member countries before 2020 were compared with each other. As a result of the analysis, the item of securing international equivalence in engineering education received the most positive evaluation, and the social compensation efforts for accreditied graduates received the least evaluation. The ABEEK was evaluated as having a medium level of social responsibility and a low level of financial independence. Based on the results of this research, we proposed ways the ABEEK to contribute to the improvement of Korean engineering education.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.277-280
• /
• 2004

In recent die industry, web-based production control system is applied widely because of the improvement of IT technology. In result, many researches are published about remote monitoring at a long distance. The target of this study is to develop Die Discrimination System using web-based vision, and CAD API when client discriminates die in process at a long distance. Special feature of this system is to use 2D vision image and to match with DB. We can get discrimination result enough to want with short time and a little low precision in web-monitoring by development of this system.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.2 s.95
• /
• pp.68-73
• /
• 1999

Generally, most of machining processes are done according to the dimention of the draft made by CAD. However, there are many cases that a sample is given without the draft because of the simplicity of the shape in the machining of 2D objects. To cut the same shape as the given sample, this paper proposes the method to extract the geometric information about a large sample using the robot vision and to draw the demensional draft for the machining. Because the resolution of one frame in the vision system is too low, it is necessary to set up a camera according to the desired resolution and to capture the image moving along the contour. And the overall outline can be compounded of the sequentially captured images. In the experiment, we compared the product after the cutting with the original sample and found that the size of two objects was coincided within the allowed error bound.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.678-682
• /
• 2000

The purpose of this study is to develop a powerful 6-axes general welding robot utilizing a low cost vision system. The developed vision system is composed of a CCD camera, a PC with windows 98 OS, and a PC-Robot communication program using Visual C++. A test was carried out to investigate whether the welding torch can precisely follow up the welding path. It shows that the result of this study can readily be applied to practical welding operations.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.2
• /
• pp.101-108
• /
• 2015

In this paper, a robust traffic light detection method is proposed by using vision sensor and DGPS(Difference Global Positioning System). The conventional vision-based detection methods are very sensitive to illumination change, for instance, low visibility at night time or highly reflection by bright light. To solve these limitations in visual sensor, DGPS is incorporated to determine the location and shape of traffic lights which are available from traffic light database. Furthermore the geometric relationship between traffic light and vision sensor is used to locate the traffic light in the image by using DGPS information. The empirical results show that the proposed method improves by 51% in detection rate for night time with marginal improvement in daytime environment.

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2341-2343
• /
• 2002

This paper describes a vision-based line recognition and control of driving direction for an AGV(autonomous guided vehicle). As navigation guide, black stripe attached on the corridor is used. Binary image of guide stripe captured by a CCD camera is used. For detect the guideline quickly and extractly, we use for variable thresholding algorithm. this low-cost line-tracking system is efficiently using pc-based real time vision processing. steering control is studied through controller with guide-line angle error. This method is tested via a typical agv with a single camera in laboratory environment.

• Electronics and Telecommunications Trends
• /
• v.34 no.4
• /
• pp.15-22
• /
• 2019

Recent technological advances in three-dimensional (3D) sensing devices and machine learning such as deep leaning has enabled data-driven 3D applications. Research on artificial intelligence has developed for the past few years and 3D deep learning has been introduced. This is the result of the availability of high-quality big data, increases in computing power, and development of new algorithms; before the introduction of 3D deep leaning, the main targets for deep learning were one-dimensional (1D) audio files and two-dimensional (2D) images. The research field of deep leaning has extended from discriminative models such as classification/segmentation/reconstruction models to generative models such as those including style transfer and generation of non-existing data. Unlike 2D learning, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become increasingly popular owing to advances in 3D vision technology, the generation/acquisition of 3D data is still very difficult. Even if 3D data can be acquired, post-processing remains a significant problem. Moreover, it is not easy to directly apply existing network models such as convolution networks owing to the various ways in which 3D data is represented. In this paper, we summarize technological trends in AI-based 3D content generation.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.1
• /
• pp.225-231
• /
• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Journal of Sensor Science and Technology
• /
• v.33 no.2
• /
• pp.112-116
• /
• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.