• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.133-138
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• 2010

We present a new multimodal biometric authentication method, which performs both feature-level fusion and decision-level fusion. After generating support vector machines for new features made by integrating face and voice features, the final decision for authentication is made by integrating decisions of face SVM classifier, voice SVM classifier and integrated features SVM clssifier. We justify our proposal by comparing our method with traditional one by experiments with XM2VTS multimodal database. The experiments show that our multilevel fusion algorithm gives higher recognition rate than the existing schemes.

• The Journal of Korean Physical Therapy
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• v.33 no.3
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• pp.155-161
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• 2021

Purpose: The purpose of this study was to find out the personal characteristics of physical therapists, dementia awareness and dementia attitude, and to find out what relationship is there between personal characteristics and dementia awareness and dementia attitude. Methods: Participants in this study surveyed physical therapists who are members of the Association of Korean Physical Therapists on their awareness of dementia, and conducted online surveys from January 28 to February 27, 2021. The survey questions used in the survey consisted of 29 questions in total, including 9 general characteristics of the participant, 10 questions on perception of dementia, and 10 attitudes toward dementia. All 104 participants were surveyed, and 100 surveys were analyzed, excluding 4 surveys with insufficient responses. Results: In this study, the correct answer rate for all items in the dementia awareness sub-item was 65%, and the dementia attitude-related sub-items were generally positive. However, there was no significant correlation between personal characteristics such as gender, age, educational background, treatment target, treatment experience and dementia awareness, and no correlation with dementia attitude was significant. Conclusion: Regardless of personal characteristics such as gender, age, treatment target, and treatment experience, a positive attitude and correct recognition of dementia can improve the quality of treatment with dementia patients and increase the reliability of patients and caregivers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1266-1272
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• 2022

With the recent development of computer vision technology, image processing-based mechanical devices are being developed to realize autonomous driving. The camera-taken images of image processing-based machines are invisible due to scattering and absorption of light in foggy conditions. This lowers the object recognition rate and causes malfunction. The safety of the technology is very important because the malfunction of autonomous driving leads to human casualties. In order to increase the stability of the technology, it is necessary to apply an efficient haze removal algorithm to the camera. In the conventional haze removal method, since the haze removal operation is performed regardless of the haze concentration of the input image, excessive haze is removed and the quality of the resulting image is deteriorated. In this paper, we propose an automatic haze removal method that removes haze according to the haze density of the input image by applying Ngo's Haziness Degree Evaluator (HDE) to Kim's haze removal algorithm using Hazy Particle Map. The proposed haze removal method removes the haze according to the haze concentration of the input image, thereby preventing the quality degradation of the input image that does not require haze removal and solving the problem of excessive haze removal. The superiority of the proposed haze removal method is verified through qualitative and quantitative evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.21-28
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• 2022

Focusing on the differences in perceptions of color education fields between game majors and game designers. As a result, game major college students were surveyed to have a low recognition rate for the color field, the other group were not. for resolve the gap in perception, it's suggested need to re-educate systematic color education of game major college students and color experience of game designers in terms of securing expertise. it's expected to be presenting the direction of fostering game designers with color sense that contributes for the competitiveness of the game industry.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.59-66
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• 2022

This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.28-31
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• 2003

In the present work, a main purpose is to propose a fuzzy integral-based aggregation framework to complementarily combine partial information due to lack of completeness. Based on Choquet integral (CI) viewed as monotone expectation, we take into account complementary, non-interactive, and substitutive aggregations of different sources of defective information. A CI-based system representing upper, conventional, and lower expectations is designed far handling three aggregation attitudes towards uncertain information. In particular, based on Choquet integrals for belief measure, probability measure, and plausibility measure, CI$\_$bi/-, CI$\_$pr/ and CI$\_$pl/-aggregator are constructed, respectively. To illustrate a validity of proposed aggregation framework, multiple matching systems are developed by combining three simple individual template-matching systems and tested under various image variations. Finally, compared to individual matchers as well as other traditional multiple matchers in terms of an accuracy rate, it is shown that a proposed CI-aggregator system, {CI$\_$bl/-aggregator, CI$\_$pl/-aggregator, Cl$\_$pl/-aggregator}, is likely to offer a potential framework for either enhancing completeness or for resolving conflict or for reducing uncertainty of partial information.

• Annual Conference of KIPS
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• 2010.04a
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• pp.615-618
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• 2010

본 논문은 900MHz 대역의 RFID를 이용하여 고속도로 자동 요금징수 시스템을 개발하고, 차량에 부착한 태그를 RFID 리더가 인식하는 효율을 높이기 위하여 최적의 태그부착위치와 안테나 설치위치에 대하여 연구하고 속도에 따른 인식률을 연구 하였다. 연구결과 RFID 리더의 높이는 170cm일 때 지표면과의 각도 $80^{\circ}$와 차량 진행 방향과 RFID 리더의 안테나 면과의 각도 $90^{\circ}$일 때 최적의 RFID 리더 설치 위치를 나타냈고, 태그의 위치는 차량 전면 유리 운전석 쪽 아래 모서리에서부터 가로 10cm와 세로 10cm에서 가장 좋게 나타났다. 차량과 RFID 리더간의 거리에 따른 차량의 속도별에 의한 인식률은 차량과 RFID 리더간의 수평 거리가 25cm 이하일 때 30km/h에서 80km/h까지 모든 경우에 100%의 인식률을 보임으로써 빠른 속도로 톨게이트를 통과하여도 충분히 인식할 수 있을 것으로 나타났다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.154-157
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• 2022

In the existing Tensorflow CNN environment, the object detection method is a method of performing object labeling and detection by Tensorflow itself. However, with the advent of YOLO, the efficiency of image object detection has increased. As a result, more deep layers can be built than existing neural networks, and the image object recognition rate can be increased. Therefore, in this paper, the detection ability and speed were compared and analyzed by designing an object detection system based on Darknet and YOLO and performing multi-layer construction and learning based on the existing convolutional neural network. For this reason, in this paper, a neural network methodology that efficiently uses Darknet's learning is presented.