• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.95-103
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• 2024

The diversity of smart EV(electric vehicle)-related industries is increasing due to the growth of battery-based eco-friendly electric vehicle component material technology, and labor-intensive industries such as logistics, manufacturing, food, agriculture, and service have invested in and studied automation for a long time. Accordingly, various types of robots such as autonomous mobile robots and collaborative robots are being utilized for each process to improve industrial engineering such as optimization, productivity management, and work management. The technology that should accompany this unmanned automobile industry is unmanned automatic charging technology, and if autonomous mobile robots are manually charged, the utility of autonomous mobile robots will not be maximized. In this paper, we conducted a study on the technology of unmanned charging of autonomous mobile robots using charging terminal docking and undocking technology using an unmanned charging system composed of hardware such as a monocular camera, multi-joint robot, gripper, and server. In an experiment to evaluate the performance of the system, the average charging terminal recognition rate was 98%, and the average charging terminal recognition speed was 0.0099 seconds. In addition, an experiment was conducted to evaluate the docking and undocking success rate of the charging terminal, and the experimental results showed an average success rate of 99%.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.94-102
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• 1997

Purpose: There have been some efforts to diagnose intracranial aneurysm through a non-invasive method using MRA, although the process may be difficult when the lesion is less than 3mm. The present study prospectively compares the results of high resolution, fast speed slice interpolation MRA and DSA thereby examing the potentiality of primary non-invasive screening test. Materials and Methods: A total of 26 cerebral aneurysm lesions from 14 patients with subarachnoid hemorrhage from ruptured aneurysm (RA) and 5 patients with unruptured aneurysm(UA). In all subjects, MRA was taken to confirm the vessel of origin, definition of aneurysm neck and the relationship of the aneurysm to nearby small vessels, and the results were compared with the results of DSA. The images were obtained with 1.5T superconductive machine (Vision, Siemens, Erlangen, Germany) on 4 slabs of MRA using slice interpolation. The settings include TR/TE/FA=30/6.4/25, matrix $160{\times}512$, FOV $150{\times}200$, 7minutes 42 seconds of scan time, effective thickness of 0.7 mm and an entire thickness of 102. 2mm. The images included structures from foramen magnum to A3 portion of anterior cerebral artery. MIP was used for the image analysis, and multiplanar reconstruction (MPR) technique was used in cases of intracranial aneurysm. Results: A total of 26 intracranial aneurysm lesions from 19 patients with 2 patients having 3 lesion, 3 patients having 2 lesions and the rest of 14 patients having 1 lesion each were examined. Among those, 14 were RA and 12 were UA. Eight lesions were less than 2mm in size, 9 lesions were 3-5mm, 7 were 6-9mm and 2 were larger than IOmm. On initial exams, 25 out of 26 aneurysm lesions were detected in either MRA or DSA showing 96% sensitivity. Specificity cannot be estimated since there was no true negative of false positive findings. When MRA and MPR were used concurrently for the confirmation of size and shape, the results were equivalent to those of DSA, while in the confirmation of aneurysm neck and parent vessels, the concurrent use of MRA and MPR was far superior to the sole use of either MRA or DSA. Conclusion: High resolution MRA using slice interpolation technique showed equal results as those of DSA for the detection of intracranial aneurysm, and may be used as a primary non-invasive screening test in the future.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.5 no.4
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• pp.309-322
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• 2007

This study deals with the irradiation effects on some selected components which are being used in an Advanced Spent Fuel Conditioning Process (ACP). Irradiation test components have a higher priority from the aspect of their reliability because their degradation or failure is able to critically affect the performance of an ACP equipment. Components that we chose for the irradiation tests were the AC servo motor, potentiometer, thermocouples, accelerometer and CCD camera. ACP facility has a number of AC servo motors to move the joints of a manipulator and to operate process equipment. Potentiometers are used for a measurement of several joint angles in a manipulator. Thermocouples are used for a temperature measurement in an electrolytic reduction reactor, a vol-oxidation reactor and a molten salt transfer line. An accelerometer is installed in a slitting machine to forecast an incipient failure during a slitting process. A small CCD camera is used for an in-situ vision monitoring between ACP campaigns. We made use of a gamma-irradiation facility with cobalt-60 source for an irradiation test on the above components because gamma rays from among various radioactive rays are the most significant for electric, electronic and robotic components. Irradiation tests were carried out for enough long time for total doses to be over expected threshold values. Other components except the CCD camera showed a very high radiation hardening characteristic. Characteristic changes at different total doses were investigated and threshold values to warrant at least their performance without a deterioration were evaluated as a result of the irradiation tests.

• Journal of Bio-Environment Control
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• v.6 no.3
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• pp.183-189
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• 1997

This study was performed to Investigate size of droplet sprinkled from mini-sprinkler. Twelve different kinds of the sprinkler having various structures and sizes of nozzle orifices were selected and tested. Diameters of the droplet reached at several distances from a sprinkler were measured by a machine vision system and the volume median diameters (VMM) were determined statistically. The size of droplet was not affected much by the size of nozzle orifice of a sprinkler but was rather more affected by structure of the sprinkler, especially by the shape of spreader of the sprinkler. Experiment of varying pressure of sprinkling water validated that the size of droplet was inversely proportional to water pressure powered by 1/3. Hence the size of droplet at any water pressure could be easily estimated from experimental data. The size of droplet increased as travel distance of the droplet increases in a relationship of and order function. The size of droplet of the tested sprinkler were in the ranges of 100-300fm within 1m of droplet travel distance, 230~470${\mu}{\textrm}{m}$ within 1~2m of droplet travel distance and 300~770${\mu}{\textrm}{m}$ within 2~3m of droplet travel distance.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.245-254
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• 2002

IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.481-489
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• 2014

In this paper, the automatic standby power blocking socket outlet to reduce standby power by blocking power threshold is implemented. Where, the standby power means a flowing power when a disused power electronic is plugged into the socket outlet. The proposed socket outlet can cut off the standby power by establishing a proper block power threshold electronic device according to each electronic device because it can monitor the amount of power through the smart machines such as the real-time PC or mobile phone and directly control the blocking power threshold. The software is implemented by using Visual Studio software, code vision and SN8 C studio, and the hardware is embodied in ATmega128, SN8F27E93S, USB to UART, and relay etc. Through the simulation, we find that the standby power of the proposed method is similar to that of the conventional method in case of the cellular phone but the standby power of the proposed method is much less than that of the conventional method in case of the computer, air conditioning, and set-top box. Therefore, it is proved that the proposed socket outlet has a superior performance in terms of the standby power.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.