• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.621-628
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• 2015

This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.17-24
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• 2009

This paper suggests the background image and the algorism of detecting an accident at crossroads by using the sequence of traffic light at crossroads, which is installed within the crossroads, in order to detect an accident within crossroads. A method of using the existing image contains a problem that the accident-detection ratio gets lower in a situation that noise occurs loudly given using new accident model, the confused situation, or sound source. This study used the accident detection by developing a filter of using the property of histogram in the sequence of traffic light at crossroads and the background image, in order to reduce misjudgment of an accident caused by external shadow, vehicle stoppage, vehicle headlight, and externally environmental influence. As a result of experimenting by acquiring 15 actual accident images in order to examine the performance of the suggested algorism, the accident was detected in all the 15 videos. Even as for a new accident model, the accident within crossroads could be detected.

• 한국사회정책
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• v.23 no.1
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• pp.179-202
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• 2016

This study analyses the effects of the major educational policies, focusing on the private tutoring prohibition policy(PTPP), on the intergenerational class mobility(ICM) by using Korea Labor and Income Panel Study(KLIPS) 1st-12th surveys. Because private tutoring(shadow education) can be effective for academic achievement of children of above middle classes that spend most private tutoring expenditure and have more information on education, the private tutoring prohibition policy can increase the intergenerational mobility. This study confirms this possibility. Even when the overlapping effect of the middle school equalization polity is controlled for, there is still high effect of the PTPP. We think that we still need to examine the level of intergenerational mobility with PTPP cohort with that of later cohorts in the future. We also emphasize the compositive effect of the several consistent policies such as middle school and high school equalization polices and the PTPP and length and continuity of the policies for the higher mobility.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.63-68
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• 2022

In schools and corporate buildings, public WiFi Access Points are installed on the ceilings of hallways. In the case of an architectural structure in which a WiFi signal enters through a steel door made of a material with high signal attenuation, Internet connection is frequently cut off or fails when the door is closed. To solve this problem, our research implements an economical and portable WiFi extender using a Raspberry Pi and an auxiliary battery. Commercially available WiFi extenders have limitations in the location where the power plug is located, and WiFi extension using the WiFi hotspot function of an Android smartphone is possible only in some high-end models. However, because the proposed device can be installed at the position where the Wi-Fi reception signal is the best inside the door, the WiFi range can be extended while minimizing the possibility of damage to the original signal. Experimental results show that it is possible to eliminate the shadows of radio waves and to provide Internet services in the office when the door is closed, to the extent that web browsing and real-time video streaming for 720p are possible.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.243-250
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• 2023

Chest X-ray is radiological examination for xeamining the lungs and haert, and is particularly widely used for diagnosing lung disease. Since the quality of these chest X-rays can affect the doctor's diagnosis, the process of evaluating the quality must necessarily go through. This process can involve the subjectivity of radiologists and is manual, so it takes a lot of time and csot. Therefore, in this paper, based on the chest X-ray quality assessment guidelines used in clinical settings, we propose a tool that automates the five quality assessments of artificial shadow, coverage, patient posture, inspiratory level, and permeability. The proposed tool reduces the time and cost required for quality judgment, and can be further utilized in the pre-processing process of selecting high-quality learning data for the development of a learning model for diagnosing chest lesions.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.402-410
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• 2020

Purpose: The purpose of this research is to eliminate communication shadowing loss of the 'smart safety management system'. The 'smart safety management system' can monitor and relay real time data of workers working in high risk workplace (i.e: industrial scene, disaster scene). The data will provide the rescue team the 'golden hour' in their rescue operations. Method: In this research, safety tag was designed and implemented so that it acts as a repeater for the user. Result: In other words, when communication in-between the safety tag and headquarters' communication terminal is jeopardized, the safety tag will act as a repeater-terminal for other safety tags in the area. Conclusion: The research tested if a specific building with communication shadowing loss problem was resolved when safety tags were implemented. Communication shadowing was first identified in-between the safety tag and headquarters' communication terminal. When extra safety tags were deployed in the same situation, the results showed that the communication shadowing loss was resolved. The repeater safety tags could resolve communication shadowing loss of up to three basement levels in this test building.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.117-125
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• 2008

The Object tracking by real-time image analysis is one of the major concerns in computer vision and its application fields. The Object detection process of real-time images must be preceded before the object tracking process. To achieve the stable object detection performance in the exterior environment, adaptive background model generation methods are needed. The adaptive background model can accept the nature's phenomena changes and adapt the system to the changes such as light or shadow movements that are caused by changes of meridian altitudes of the sun. In this paper, we propose a robust background model generation method effective in an illegal parking auto-detection application area. We also provide a evaluation method that judges whether a moving vehicle stops or not. As the first step, an initial background model is generated. Then the differences between the initial model and the input image frame is used to trace the movement of object. The moving vehicle can be easily recognized from the object tracking process. After that, the model is updated by the background information except the moving object. These steps are repeated. The experiment results show that our background model is effective and adaptable in the variable exterior environment. The results also show our model can detect objects moving slowly. This paper includes the performance evaluation results of the proposed method on the real roads.

• Horticultural Science & Technology
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• v.28 no.4
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• pp.567-573
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• 2010

Research was initiated to investigate early establishment characteristics and germination pattern of fine-textured fescues (FF). Six varieties from Chewings fescue ($Festuca$ $rubra$ L. ssp. $commutata$ Gaud., CF), creeping red fescue ($F.$ $rubra$ L. ssp. $rubra$ Gaud., CRF), hard fescue ($F.$ $ovina$ ssp. $longifolia$ Thuill., HF) and sheep fescue ($F.$ $ovina$ L., SF) were evaluated in the study. An alternative environmental condition requiring a FF germination test by International Seed Testing Association (ISTA) was applied in the experiment, consisting of 8-hr light at $25^{\circ}C$ and 16-hr dark at $15^{\circ}C$ (ISTA conditions). Daily and cumulative germination patterns were measured and analyzed on a daily basis. Significant differences were observed in germination pattern, days to the first germination, days to 50% germination, days to 60% germination, and germination rate. The final germination percentage was variable with species and varieties, being 40.25 to 82.00% at the end of study. There were considerable variations in early germination characteristics and germination pattern among FF species. The first germination in all entries except HF was initiated between 5 and 6 DAS (days after seeding) under ISTA conditions, while HF between 6 and 7 DAS, being 1 day later. It was 8 to 10 DAS in days to the 50% germination, which was 2 to 4 days after the first germination date. Days to the 60% germination were 9.10 to 14.80 DAS under ISTA conditions, being 5.70 days in differences among the entries. CF 'Jamestown II' and 'Shadow II' and HF 'Aurora Gold' were the fast varieties. The slowest one was HF 'Rescue 911'. Among FF species, turf establishment speed was becoming faster in CRF, SF, HF and CF in this order. Information on differences in germination characteristics and pattern from this study would be usefully applied for golf course design and construction, when established with FF.