• Journal of the Korean Home Economics Association
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• v.44 no.7 s.221
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• pp.21-39
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• 2006

In modem society, elderly people's continual working is not only effective for realizing elderly welfare but is also an efficient scheme for improving national competitive power with the aging of societies. Therefore, the purpose of this study was to obtain confirmed data on the methods by which people can prepare for elderly people's working and increase the workforce participation rate. The study subjects were 300 male and female elderly people over the age of 60, living and working in Seoul. The method of sampling was purposive sampling in which the subject are extracted and assigned according to their occupations and gender. The results were as follows. Firstly, elderly people have a strong desire for employment stability. Secondly, they lack knowledge on obtaining information about jobs and approaching their desired job field. Thirdly, the subjects suggested that it is necessary to raise the level of social recognition about elderly people's working and to develop an environment that is conducive for increasing the working rate of the elderly.

• 한국항공운항학회:학술대회논문집
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• 2015.11a
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• pp.231-236
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• 2015

Ever since the permission of overseas travel by the Chinese government in 1983, the Chinese outbound travel market has been growing at an expanding rate. The Republic of Korea has been able to reap great benefits from this growth, and this has been noticeable especially in the aviation market. Chinese passengers count for 23.9% of the total passengers at Incheon International Airport (IIA), and it is no doubt Chinese passengers will remain important customers for IIA. Given the fact that China has such a huge territory and possesses a population of more than 1.3 billion, there rises the need to divide China into smaller regions. By doing so, the different thoughts Chinese passengers have, and the actions they show in terms of travel and airport service consumption according to differing regions can be figured out. With knowledge of the differences, IIA can provide different services to fit Chinese passengers from differing regions, hence increasing the total consumer value and satisfaction rate of IIA. This study has divided China into 5 different regions according to official regional recognition of China, and difference of economical, social factors, etc... With the results obtained from this study, different strategies can be implemented for the 5 regions.

• Korean Journal of Health Education and Promotion
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• v.19 no.1
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• pp.1-15
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• 2002

In this study, it is aimed to clarify the status of safety accident and safety health education and risk factors on safety accidents among the selected high school students. The study is accomplished by the survey which includes a total of 558 students sampled from the six high school. The survey took for 10 days from the date of 3th July 2000 to 13th July 2000 and the results were as follows; 1. The rate of experience of safety health education during last one year was 26.4% and the rate of accident during last one month was 20.6%. 2. The risk factors of safety accident during last one year were understanding degree of safety health education(R.R.=2.81), experience of smoking(R.R.=2.55), educational level of mother (R.R=l.63) From the results of this study, we are to propose as follow; Because the purpose of safety health education is the practice, it is important to induce changing the recognition and behavior. Therefore the government have to build up experience place, to bring up professional teacher of safety health education. It need to develope a reference of each subject and to utilize various media. Especially it need to take of education comprehensive health and environmental inducing safety health subject.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.525-531
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• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.61-67
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• 2015

The existing methods for control between smart devices in near used Bluetooth, WiFi-Direct, or socket communication using Wi-Fi. However, those have a problem that can not use when operating system of each smart devices is different or when socket server is not working. In this paper, we proposed a new near control technology using High frequencies in audible frequency between smart devices to supplement the problem of existing methods. High frequencies use micro-phone and speaker of smart device and are a control signals that is combined high frequencies within 18kHz ~ 22kHz among audible frequency range. The proposed technology using High frequencies do not need any extra communication modules or socket servers and can use the most smart devices without operating system of devices. To evaluate the performance of the proposed technology, we developed a music play and music control application applied the proposed technology and tested a control experiment using the developed applications. The control success rate was 97% and recognition rate of surrounding people about using high frequencies was under 5%. Therefore, the proposed technology will be the useful technology to control between smart devices in near.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.145-150
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• 2020

최근 음성인식 분야에서 신경망 기반의 종단간 모델이 제안되고 있다. 해당 모델들은 음성을 직접 입력받아 전사된 문장을 생성한다. 음성을 직접 입력받는 모델의 특성상 데이터의 품질이 모델의 성능에 많은 영향을 준다. 본 논문에서는 이러한 종단간 모델의 문제점을 해결하고자 음성인식 결과를 후처리하기 위한 멀티모달 기반 모델을 제안한다. 제안 모델은 음성과 전사된 문장을 입력 받는다. 입력된 각각의 데이터는 Encoder를 통해 자질을 추출하고 주의집중 메커니즘을 통해 Decoder로 추출된 정보를 전달한다. Decoder에서는 전달받은 주의집중 메커니즘의 결과를 바탕으로 후처리된 토큰을 생성한다. 본 논문에서는 후처리 모델의 성능을 평가하기 위해 word error rate를 사용했으며, 실험결과 Google cloud speech to text모델에 비해 word error rate가 8% 감소한 것을 확인했다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.345-346
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• 2023

As the shape and size of detectable objects diversifying recognition and segmentation algorithms have been developed to acquire accurate shape information. Although a high density of data captured by the repetition of scanning improves the accuracy of algorithms the high dense data decreases the efficiency due to its large size. This paper proposes standardization algorithms using the feature of structural members on indoor point cloud data to improve the process. First of all we determine the reduction rate of the density based on the features of the target objects then the data reduction algorithm compresses the data based on the reduction rate. Second the data arrangement algorithm rotates the data until the normal vector of data is aligned along the coordinate axis to allow the following algorithms to operate properly. Final the data arrangement algorithm separates the rotated data into their leaning axis. This allows reverse engineering of indoor point clouds to obtain the efficiency and accuracy of refinement processes.