• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.144-152
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• 2007

We propose a 3D modeling method for surface inspection of non-linear moving object. The laser lines reflect the surface curvature. We can acquire 3D surface information by analyzing projected laser lines on object. ill this paper, we use multi-line laser to make use of robust of single stripe method and high speed of single frame. Binarization and channel edge extraction method were used for robust laser line extraction. A new labeling method was used for laser line labeling. We acquired sink information between each 3D reconstructed frame by feature point matching, and registered each frame to one whole image. We verified the superiority of proposed method by applying it to container damage inspection system.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.445-456
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• 2017

In this paper, we propose a method to detect spam tweets containing unhealthy information by using an n-gram dictionary under limited labeling. Spam tweets that contain unhealthy information have a tendency to use similar words and sentences. Based on this characteristic, we show that spam tweets can be effectively detected by applying a Naive Bayesian classifier using n-gram dictionaries which are constructed from spam tweets and normal tweets. On the other hand, constructing an initial training set requires very high cost because a large amount of data flows in real time in a twitter. Therefore, there is a need for a spam detection method that can be applied in an environment where the initial training set is very small or non exist. To solve the problem, we propose a method to generate pseudo-labels by utilizing twitter's retweet function and use them for the configuration of the initial training set and the n-gram dictionary update. The results from various experiments using 1.3 million korean tweets collected from December 1, 2016 to December 7, 2016 prove that the proposed method has superior performance than the compared spam detection methods.

• International Journal of Oral Biology
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• v.33 no.3
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• pp.83-89
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• 2008

Glutamate-induced cobalt uptake reveals non-NMDA glutamate receptors (GluRs) in rat taste bud cells. Previous studies suggest that glutamate-induced cobalt uptake in taste cells occurs mainly via kainate type GluRs. Cobaltstained cells were immunoreactive against GluR6 and KA1 subunits of GluRs. However, the functions of those type of receptors are not known yet. It is important question which types of taste cells are cobalt-stained when stimulated by glutamate and whether they express these kinds of GluRs. Circumvallate and foliate papilla of Sprague-Dawley rats (45-60 days old) were used. A cobalt-staining technique combined with immunohistochemistry against specific markers for taste bud cell types, such as blood group H antigen (BGH), $\alpha$-gustducin (Gus), or neural cell adhesion molecule (NCAM) was employed. We also performed double labeling of GluR6 or KA1 subunits of GluR with each specific marker for taste bud cell types. Lots of cobaltstained taste bud cells expressed Gus-like immunoreactivity, and subsets of the cobalt stained cells appeared NCAM- or BGH-like immunoreactivity. Stimulation with 1 mM glutamate significantly increased the number of cobaltstained cells in Gus-like immunoreactive cells, but not in NCAM- or BGH-like immunoreactive cells. In the double labeling experiments, GluR6 and KA1 subunits of GluRs were mainly expressed with Gus. These results suggest that kainate glutamate receptors preferentially expressed in type II taste bud cells in rat.

• Korean Journal of Head & Neck Oncology
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• v.15 no.1
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• pp.3-8
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• 1999

Objective: The expression of Ki67, a proliferation marker, and p27, a cyclin dependent kinases(CDKs) inhibitor, has been studied in various human neoplasms. This study was carried out to determine whether these markers are useful in distinguishing benign from malignant lesions of the thyroid or predicting biologic behavior of malignant lesions. Material and Methods: Using immunohistochemical techniques with monoclonal antibodies to Ki67 and p27, we analyzed the expression of Ki67 and p27 in various thyroid disorders(25 follicular adenomas, 47 follicular carcinomas, 16 papillary carcinomas, 20 adenomatous goiters and 40 normal thyroid tissues). The labeling indices(LIs) were determined by counting cells expressing these markers in 1000 cells per immunostained slide. Results: Neoplastic thyroid diseases showed higher expression of Ki67 and lower expression of p27 than non-neoplastic diseases(p<0.05). The expression of p27 was significantly different between follicular adenomas($LI=55.4{\pm}5.7$) and follicular carcinomas($LI=23.2{\pm}10.2$). There was, however, no significant correlation between the degree of Ki67 and p27labeling indices and types of carcinoma or clinical aggressiveness of diseases. Conclusion: The degree of Ki67 and p27 expression was useful in distinguishing between benign from malignant thyroid lesions, particulary between follicular adenoma and follicular carcinoma, but was not directly proportional to the tumor aggressiveness.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.15-21
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• 2007

We propose a 3D modeling method for surface inspection of non-constant velocity moving object. 1'lie laser lines reflect tile surface curvature. We can acquire 3D surface information by analyzing projected laser lines on object. In this paper, we use multi-line laser to improve the single stripe method and high speed of single frame. Binarization and edge extraction of frame image were proposed for robust laser each line extraction. A new labeling method was used for laser line labeling. We acquired some feature points for image matching from the frame data and juxtaposed the frames data to obtain a 3D shape image. We verified the superiority of proposed method by applying it to inspect container's damages.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.718-723
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• 2011

In this paper, we propose a visual multi-touch air input device using vision cameras. The implemented device provides a barehanded interface which copes with the multi-touch operation. The proposed device is easy to apply to the real-time systems because of its low computational load and is cheaper than the existing methods using glove data or 3-dimensional data because any additional equipment is not required. To do this, first, we propose an image processing algorithm based on the HSV color model and the labeling from obtained images. Also, to improve the accuracy of the recognition of hand gestures, we propose a motion recognition algorithm based on the geometric feature points, the skeleton model, and the Kalman filter. Finally, the experiments show that the proposed device is applicable to remote controllers for video games, smart TVs and any computer applications.

• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• Journal of Platform Technology
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• v.11 no.3
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• pp.56-67
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• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.2
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• pp.144-152
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• 2011

Most of the models for the vehicle routing problems studied in the literature assumed constant travel times. However, those approaches may give infeasible solutions when traffic congestion causes delays in travel time. To overcome such difficulty, there have been some researches considering the change of the travel time which is called the time dependent vehicle routing problem (TDVRP). TDVRP assumes that the travel time between two locations is not only affected by the distance traveled, but by many other factors including the time of the day. In this paper, we propose a branch-and-price algorithm to solve the TDVRP. The time dependent property of the travel time is dealt with an enumeration scheme with bounding procedures in the column generation procedure identifying a profitable route. The proposed algorithm guarantees the "Non-passing" property to be held in the solutions. The algorithm was tested on problems composed of the Solomon's benchmark instances for 25 and 50 nodes. Computational results are reported.