• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.45-48
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• 2019

Detection of crosswalk is an important issue for the blind to walk without the help of others. There is a braille block on the sidewalk, which helps the blind to walk. On the other hand, crosswalk is more dangerous due to the moving vehicles. However, there is no appropriate means to induce the blind. In this paper, we propose a method to detect crosswalk in front of a blind and estimate its direction using an image sensor. We adopt multi-ROIs and make their binary versions. In order to determine whether it is a crosswalk, two features are extracted; one is the number of crossing in the binary image and the other is the ratio of white area. We can also estimate the direction of the crosswalk through the slope of the projection data. We evaluated the performance using experimental dataset and the proposed algorithm showed 80% accuracy of detection.

• The Journal of the Korean dental association
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• v.44 no.12 s.451
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• pp.816-829
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• 2006

The purpose of this study was to determine the level of perception and preference between dentists and lay people to altered facial profile. The assessors consisted of 40 dentists and 54 lay people, the survey was performed using questionnaire asking the order of perception and preference. The profiles presented in the questionnaire were based on the profile of one man and one woman, each morphed according to anterior or posterior direction of maxilla and mandible. The results were as follows. 1. In antero-posterior change of man and woman s profile, both dentists and lay people were sensitive to relatively skeletal profile (convex profile) changes than skeletal profile (concave profile) changes. 2. At least dentists needed to be perceived a 2 mm change in convex profile and a 3 mm change in concave profile and lay people needed to be perceived a 2 mm change in convex profile and a 3 mm change in concave profile for profile view. 3. Dentists are more sensitive in perception of man s profile change than lay people, but there is no significant differences between dentists and lay people in sensitivity of detection for woman s profile changes . 4. It seems that there is a general concordance between dentists and lay people in there perception of man s and woman s facial profile. This information might be clinician in comprehensive perception and preference of dentists and lay people to altered facial profile.

• Journal of the Ergonomics Society of Korea
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• v.31 no.3
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• pp.421-426
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• 2012

As headlamp technology advances, newly developed various headlamps were introduced in the market. The objective of this study is to quantitatively analyze the detection distance of the recently developed LED headlamps and existing headlamps, complying with specific technical standard. Background: The detection distance of headlamps is very important to prevent automobile accident at night time. The studies of detection distance of LED, Halogen and HID headlamp have been conducted, but no study has shown the detection distance of pedestrian target with various colors (Black, White, Blue). Method: The experiment of detection distance was conducted with 30 people, which divide into 2 groups as 15 men and 15 women. Automatic transferable target on the rail was manufactured in order to reduce the error of study's result, and ANOVA also conducted to analyze the main effect with sign color, sex and headlamp classified by detection distance. In addition, the luminance by average detection distance was measured as well. Results: The detection distance of headlamps was HID > LED > Halogen. The luminance measure of LED headlamp was lower than HID and Halogen headlamps. Conclusion: The headlamp performs a very significant role for safety at night time but it needs to be improved through assessment of visual characteristics. Also, it needs to be suggested the need of test method for dynamic detection distance concerning technical development is suggested.

• ETRI Journal
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• v.36 no.5
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• pp.865-875
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• 2014

Voice phishing (vishing) uses social engineering, based on people's trust in telephone services, to trick people into divulging financial data or transferring money to a scammer. In a vishing attack, a scammer often modifies the telephone number that appears on the victim's phone to mislead the victim into believing that the phone call is coming from a trusted source, since people typically judge a caller's legitimacy by the displayed phone number. We propose a system named iVisher for detecting a concealed incoming number (that is, caller ID) in Session Initiation Protocol-based Voice-over-Internet Protocol initiated phone calls. Our results demonstrate that iVisher is capable of detecting a concealed caller ID without significantly impacting upon the overall call setup time.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.9-16
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• 2018

In this paper, a kidnapping detection scheme in which human pose estimation is used to classify accurately between kidnapping cases and normal ones is proposed. To estimate human poses from input video, human's 10 joint information is extracted by OpenPose library. In addition to the features which are used in the previous study to represent the size change rates and the regularities of human activities, the human pose estimation features which are computed from the location of detected human's joints are used as the features to distinguish kidnapping situations from the normal accompanying ones. A frame-based kidnapping detection scheme is generated according to the selection of J48 decision tree model from the comparison of several representative classification models. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, the detection accuracy of our newly proposed scheme is compared with that of the previous scheme. According to the experiment results, the proposed scheme could detect kidnapping situations more 4.73% correctly than the previous scheme.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3423-3426
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• 2012

Background: The objective of this study was to measure the antibody content of NuTu-19 ovarian cancer cells in serum samples using a quartz crystal microbalance (QCM) immunosensor. Materials and Methods: NuTu-19 cells were first cultured onto the electrode surfaces of crystals in Dulbecco's modified Eagle medium, and then specified amounts of immunized serum samples of immunized rabbit were also added. The change in mass caused by specific adsorbtion of antibodies of NuTu-19 to the surfaces of the crystals was detected. Results: The change in resonance frequency of crystals caused by immobilization of NuTu-19 cells was from 83 to 429Hz. The antibody content of NuTu-19 detected was 341ng/ul. The frequency shifts were linearly dependent on the amount of antibody mass in the range of 69 to 340ng. The positive detection rate and the negative detection rate were 80% and 100%, respectively. Conclusion: This immunoassay provides a viable alternative to other early ovarian cancer detection methods and is particularly suited for health screening of the general population.

• Current Optics and Photonics
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• v.4 no.3
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.110-116
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• 2021

This paper proposed a real-time earlobe detection system using deep learning on the web. Existing deep learning-based detection methods often find independent objects such as cars, mugs, cats, and people. We proposed a way to receive an image through the camera of the user device in a web environment and detect the earlobe on the server. First, we took a picture of the user's face with the user's device camera on the web so that the user's ears were visible. After that, we sent the photographed user's face to the server to find the earlobe. Based on the detected results, we printed an earring model on the user's earlobe on the web. We trained an existing YOLO v5 model using a dataset of about 200 that created a bounding box on the earlobe. We estimated the position of the earlobe through a trained deep learning model. Through this process, we proposed a real-time earlobe detection system on the web. The proposed method showed the performance of detecting earlobes in real-time and loading 3D models from the web in real-time.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.