• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.639-645
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• 2012

In this paper, we propose a method that gives motion command to a mobile robot to recognize human being's hand gesture. Former way of the robot-controlling system with the movement of hand used several kinds of pre-arranged gesture, therefore the ordering motion was unnatural. Also it forced people to study the pre-arranged gesture, making it more inconvenient. To solve this problem, there are many researches going on trying to figure out another way to make the machine to recognize the movement of the hand. In this paper, we used third-dimensional camera to obtain the color and depth data, which can be used to search the human hand and recognize its movement based on it. We used HMM method to make the proposed system to perceive the movement, then the observed data transfers to the robot making it to move at the direction where we want it to be.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.1-8
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• 2020

If a dangerous situation arises in a place where out of reach from the human, UAVs can be used to determine the size and location of the situation to reduce the further damage. With this in mind, this paper sets the minimum value of the roll, pitch, and yaw using beta flight to detect the UAV's smooth hovering, integration, and derivative (PID) values to ensure that the UAV stays horizontal, minimizing errors for safe hovering, and the camera uses Open CV to install the Raspberry Pi program and then HSV (color, saturation, Brightness) using the color palette, the filter is black and white except for the red color, which is the closest to the fire we want, so that the UAV detects the image in the air in real time. Finally, it was confirmed that hovering was possible at a height of 0.5 to 5m, and red color recognition was possible at a distance of 5cm and at a distance of 5m.

• Journal of Adhesion and Interface
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• v.25 no.2
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• pp.56-62
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• 2024

In this study, LiDAR-detectable black hollow-structured materials are synthesized using different reducing agents to evaluate their applicability to LiDAR sensor. Initially, white SiO₂/TiO₂ core/shell (WST) materials are fabricated via a sol-gel method, followed by a reduction using ascorbic acid (AA) and sodium borohydride (SB). After the reduction, subsequent etching of the SiO₂ core leads to the formation of two different black hollow-structured materials (AA-BHT and SB-BHT). The lightness (L^*) and near-infrared (NIR) reflectance (R%) of AA-BHT are measured as ca. 19.1 and 34.5 R%, and SB-BHT shows values of ca. 11.5 and 31.8 R%, respectively. While AA-BHT exhibits higher NIR reflectance compared to SB-BHT, it displays slightly lower blackness. Compared with core/shell structured materials, improved NIR reflectance of both AA-BHT and SB-BHT is attributed to the morphology of hollow- structured materials, which increase light reflection at the interface between air and black TiO₂ according to the Fresnel's reflection principle. Consequently, both AA-BHT and SB-BHT are effectively detected by the commercially available LiDAR sensors, validating their suitability as black materials for autonomous vehicle and environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.123-125
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• 2021

Pregnant women's psychological health affects the health of the fetus. Therefore, health care for pregnant women is essential for a healthy fetus. One of the symptoms of pregnancy among many pregnant women is the depression of emotional ups and downs. One way to relieve this depression is to use light therapy and color therapy by using lighting. Adjust the color temperature of the light so that it affects the emotions through color. For example, ceiling lights in car dealerships are set up like a sun-light, or low color temperature are used to create a comfortable mood in facilities like spas. In this paper, we use image sensors to identify the psychological state and change the color temperature of the lighting in real time. The study was conducted to relieve postpartum depression by using the psychological effects of pregnant women with easily purchased lighting devices.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.