• The Korean Journal of Vision Science
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• v.20 no.4
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• pp.403-411
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• 2018

Purpose : The purpose of this study is to analyze how the certain efforts of the optical shops affect on opticians' job satisfaction and organizational commitment, and to analyze how opticians' job satisfaction and organizational commitment affect on the customer orientation and to suggest the method of maintaining competitiveness. Methods : Two hundred opticians took participations on the surveys via the Internet survey method and social network system (SNS), and SPSS 18.0 statistics program was used for data analysis; frequency analysis, T-test, factor analysis, reliability analysis, and multiple linear regression analysis were conducted. Results : It is analyzed the differences on the job satisfaction and organizational commitment in accordance with type of optical shops, conducting 5 working days/week and flexible time. As a result, higher job satisfaction is presented with 5 working days/week. Relationships with co-workers statistically affect on job satisfaction positively and emotional labor and work overload statistically affect on job satisfaction negatively. In addition, relationships with co-workers and reward statistically affect on organizational commitment positively and emotional labor and work overload statistically affect on organizational commitment negatively. And opticians' job satisfaction and organizational commitment statistically affect on customer satisfaction positively. Conclusion : It is necessary for the optical shops to make an effort for their opticians to improve the job satisfaction and organizational commitment. And to do so, it is needed to form trusting and respecting relationships with co-workers or superiors. In addition, it is necessary to have continuous communication and education for opticians' self-management. Also, it is needed to establish an effective reward system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.377-391
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• 2019

Recently, system studies using tokens and block chains for authentication, access control, etc in IoT environment have been going on at home and abroad. However, existing token-based systems are not suitable for IoT environments in terms of security, reliability, and scalability because they have centralized characteristics. In addition, the system using the block chain has to overload the IoT device because it has to repeatedly perform the calculation of the hash et to hold the block chain and store all the blocks. In this paper, we intend to manage the access rights through tokens for proper access control in the IoT. In addition, we apply the Tangle to configure the P2P distributed ledger network environment to solve the problem of the centralized structure and to manage the token. The authentication process and the access right grant process are performed to issue a token and share a transaction for issuing the token so that all the nodes can verify the validity of the token. And we intent to reduce the access control process by reducing the repeated authentication process and the access authorization process by reusing the already issued token.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.86-94
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• 2019

This paper describes the development of unmanned vehicle remote control system which is configured with steering and accelerating/braking hardware to improve the sense of reality and safety of control. Generally, in these case of the remote control system, a joystick-type device is used for steering and accelerating/braking control of unmanned vehicle in most cases. Other systems have been developing using simple steering wheel, but there is no function of that feedback the feeling of driving situation to users and it mostly doesn't include the accelerating/braking control hardware. The technology of feedback means that a reproducing the feeling of current driving situation through steering and accelerating/braking hardware when driving a vehicle in person. In addition to studying feedback technologies that reduce unfamiliarity in remote control of unmanned vehicles, it is necessary to develop the remote control system with hardware that can improve sense of reality. Therefore, in this study, the reliable remote control system is developed and required system specification is defined for applying force-feedback haptic control technology developed through previous research. The system consists of a steering-wheel module similar to a normal vehicle and an accelerating/braking pedal module with actuators to operate by feedback commands. In addition, the software environment configured by CAN communication to send feedback commands to each modules. To verify the reliability of the remote control system, the force-feedback haptic control algorithms developed through previous research were applied, to assess the behavior of the algorithms in each situation.

• Korean Journal of Heritage: History & Science
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• v.48 no.4
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• pp.74-93
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• 2015

Geophysical survey of Korea was introduced in Nara National Research Institute of Cultural Heritage in 1995. At that time, it has been activated geophysical survey of architecture and civil engineering in Korea. But there was no exploration experts to be combined the archaeology. For this reason, National Research Institute of Cultural Heritage has introduced the physical exploration. Through the expert exchanges South Korea and Japan carried out joint exploration. And it has increased the reliability of the exploration method and exploration results. It is GPR the most method commonly in geophysical exploration. There are many usability before excavation because of good resolution. However, the shallow GPR penetration depth has limitations in large mounds. We were able to take advantage of the resistivity analysis program to study the underground structure to deep through the experts exchange. We was able to get a good result that overcomes the limitations of GPR exploration in a number of burial mounds including Naju bokamri by the resistivity analysis program. In particular, we confirmed the location of the burial main body by compares the results of exploration and excavation results. In the future we will perform a convergence research of exploration and archaeology through a variety of joint research. In addition we will have to build a new network of archaeological science.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Journal of IKEEE
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• v.26 no.2
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• pp.160-168
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• 2022

In this paper, we propose a 3D mesh reconstruction method from a single image using deep learning and a sphere shape transformation method. The proposed method has the following originality that is different from the existing method. First, the position of the vertex of the sphere is modified to be very similar to the 3D point cloud of an object through a deep learning network, unlike the existing method of building edges or faces by connecting nearby points. Because 3D point cloud is used, less memory is required and faster operation is possible because only addition operation is performed between offset value at the vertices of the sphere. Second, the 3D mesh is reconstructed by covering the surface information of the sphere on the modified vertices. Even when the distance between the points of the 3D point cloud created by correcting the position of the vertices of the sphere is not constant, it already has the face information of the sphere called face information of the sphere, which indicates whether the points are connected or not, thereby preventing simplification or loss of expression. can do. In order to evaluate the objective reliability of the proposed method, the experiment was conducted in the same way as in the comparative papers using the ShapeNet dataset, which is an open standard dataset. As a result, the IoU value of the method proposed in this paper was 0.581, and the chamfer distance value was It was calculated as 0.212. The higher the IoU value and the lower the chamfer distance value, the better the results. Therefore, the efficiency of the 3D mesh reconstruction was demonstrated compared to the methods published in other papers.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.385-391
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• 2022

Quality evaluation of beef carcasses is an important issue in the livestock industry. Recently, through the AI monitor system based on artificial intelligence, the quality manager can receive help in making accurate decisions based on the analysis of beef carcass images or result information. This artificial intelligence dataset is an important factor in judging performance. Existing datasets may have different surface orientation or resolution. In this paper, we proposed a two-stage classification model that can efficiently manage the grades of beef carcass image using deep learning. And to overcome the problem of the various conditions of the image, a new dataset of 1,300 images was constructed. The recognition rate of deep network for 5-grade classification using the new dataset was 72.5%. Two-stage evaluation is a method to increase reliability by taking advantage of the large difference between grades 1++, 1+, and grades 1 and 2 and 3. With two experiments using the proposed two stage model, the recognition rates of 73.7% and 77.2% were obtained. As this, The proposed method will be an efficient method if we have a dataset with 100% recognition rate in the first stage.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.423-435
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• 2021

The purpose of this paper is to verify the influence of the characteristics of Chinese online video on users' subscription satisfaction and continuous subscription intention. For this purpose, this paper conducted online and offline questionnaire for customers who had subscription experiences within one month from April 16 to May 16, 2021. A total of 338 questionnaires were collected, except for 55 dishonest responses and non-subscription responses. A total of 283 copies were used for empirical analysis. The analysis results are as follows. First, before implementing the hypothesis verification of this paper, the results of analyzing the rationality and reliability of the measurement concept show six factors of online video characteristics, such as accuracy, social impact, service quality, content diversity, information, and entertainment. Second, among the six factors of the characteristics of online video, accuracy, social influence, service quality, and information have a "positive (+)" impact on subscription satisfaction. Third, in addition to the content diversity and entertainment in the characteristics of online video, the other four factors have a positive impact on consumers' continuous subscription intention. Fourth, subscription satisfaction has a positive impact on continuous subscription intention. Fifth, there is a media effect of subscription satisfaction between the characteristics of online video and the intention of continuous subscription.

• Journal of Family Resource Management and Policy Review
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• v.26 no.2
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• pp.53-70
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• 2022

This study aimed to develop a scale to measure the family strength of single-parent families. We analyzed the everyday life and demands of single-parent families using the theory of family strength to draw 78 items that encompass family basis, relationships, roles, social networks and family culture. Using a sample of 286 single-parent families through an online survey platform, we examined the factor structure of the items and selected 48 items based on the results of the factor analysis. Reliability, criterion and construct validity were also examined. The final scale comprised of five domains ; basis, parents' role, work-life balance, social network, lifestyle and household management. This scale can be used as an assessment measure of the family strength of single-parent families for consulting, case management and suggesting various programs in the field. This merit will help enhance the quality of programing for single-parent families at the Healthy Family Support Center and the development of family strength scales for various types of families.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.