• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.605-611
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• 2023

Accidents at construction sites have a very high rate of fatalities due to the nature of being prone to severe injury patients. In order to reduce the mortality rate of severely injury patients, quick response is required, and some systems that detect accidents using AI technology and cameras have been devised to respond quickly to accidents. However, since existing accident detection systems use only a single camera, there are blind spots, Thus, they cannot detect all accidents at a construction site. Therefore, in this paper, we present the system that minimizes the detection blind spot by using multiple cameras. Our implemented system extracts feature points from the images of multiple cameras with the YOLO-pose library, and inputs the extracted feature points to a Long Short Term Memory-based recurrent neural network in order to detect accidents. In our experimental result, we confirme that the proposed system shows high accuracy while minimizing detection blind spots by using multiple cameras.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.259-264
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• 2019

This paper proposes an image prediction system architecture for deep running in enterprise environment. Easily transform into an artificial intelligence platform for an enterprise environment, and allow sufficient deep-running services to be developed and modified even in Java-centric architectures to improve the shortcomings of Java-centric enterprise development because artificial intelligence platforms are concentrated in the pipeline. In addition, based on the proposed environment, we propose a more accurate prediction system in the deep running architecture environment that has been previously learned through image forecasting experiments. Experiments show 95.23% accuracy in the image example provided for deep running to be performed, and the proposed model shows 96.54% accuracy compared to other similar models.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.749-755
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• 2022

Electricity is essential energy in modern society, such as being used in various industries. However, the rate of fires occurring on electric wiring to deal with it is very high. In this work, we implemented a system to predict the temperature change of an electric circuit through analysis using various regression models. To do so, we collected the temperature data of 27 types of magnetic switches which control electric circuits as well as trained the regression models by using the collected temperature data. In our experiments, we confirmed that the regression models can be trained at a sufficiently usable level since the difference between the actual temperature and predicted temperature is about 4℃. The results of our work will be useful to predict the temperature of electric circuits and preventing fires on them.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.561-570
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• 2018

Artificial Intelligence is one of the technologies that are being discussed in the Fourth Industrial Revolution, attracting the attention from companies around the world and this technology is being applied to various industries such as education, finance, automobile, etc. AI integrated ChatBot is a system designed to respond to user questions according to defined response rules. This system is gradually expanded from simple inquiry responses for intelligent virtual assistant service, weather, traffic, schedule, etc. to service provisions through user pattern analysis, to solidify its position as a life-style service. As a result, research on AI integrated ChatBot platform has become necessary. Therefore, this study suggests the design and implementation of an intelligent chatbot service platform for small businesses.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.1-12
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• 2024

As the service robot market grows among the food technology industry, the quality of robot service that affects consumer behavioral intentions in the restaurant industry has become important. Serving robots, which are common in restaurants, reduce employee work through order and delivery, but because they do not respond to service failures, they increase customer dissatisfaction as well as increase employee work. In order to improve the quality of service beyond the simple function of receiving and serving orders, functions of recovery effort, fairness, empathy, responsiveness, and certainty of the process after service failure, such as serving employees, are also required. Accordingly, we assumed the type of failure of restaurant serving service as two internal and external factors, and developed a serving robot with a vocational ethics module to respond with a professional ethical attitude when the restaurant serving service fails. At this time, the expression and action of the serving robot were developed by adding a failure mode reflecting failure recovery efforts and empathy to the normal service mode. And by recruiting college students, we tested whether the service robot's response to two types of service failures had a significant effect on evaluating the robot. Participants responded that they were more uncomfortable with service failures caused by other customers' mistakes than robot mistakes, and that the serving robot's professional ethical empathy and response were appropriate. In addition, unlike the robot's favorability, the evaluation of the safety of the robot had a significant difference depending on whether or not a professional ethical empathy module was installed. A professional ethical empathy response module for natural service failure recovery using generative artificial intelligence should be developed and mounted, and the domestic serving robot industry and market are expected to grow more rapidly if the Korean serving robot certification system is introduced.

• The Journal of Korean Medicine
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• v.33 no.4
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• pp.60-68
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• 2012

Objectives: By grasping trends in research, technology, and general characteristics of learning support tools, this study was conducted to present a model for research on Korean Medicine (KM) to make use of information technology to support teaching and learning. The purpose is to improve the future clinical competence of medical personnel, which is directly linked to national health. Methods: With papers and patents published up to 2011 as the objects, 438 papers were extracted from "Web of Science" and 313 patents were extracted from the WIPS database (DB). Descriptive analysis and network analysis were conducted on the annual developments, academic journals, and research fields of the papers, patents searched were subjected to quantitative analysis per application year, nation, and technology, and an activity index (AI) was calculated. Results: First, research on medical learning support tools has continued to increase and is active in the fields of computer engineering, education research, and surgery. Second, the largest number of patent applications on medical learning support tools were made in the United States, South Korea, and Japan in this order, and the securement of remediation technology-centered patents, rather than basic/essential patents, seemed possible. Third, when the results of the analysis of research trends were comprehensively analyzed, international research on e-PBL- and medical simulation-centered medical learning support tools was seen to expand continuously to improve the clinical competence of medical personnel, which is directly linked to national health. Conclusions: The KM learning support tool model proposed in the present study is expected to be applicable to computer-based tests at KM schools and to be able to replace certain functions of national KM doctor license examinations once its problem DB, e-PBL, and TKM simulator have been constructed. This learning support tool will undergo a standardization process in the future.

• Archives of design research
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• v.19 no.5 s.67
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• pp.213-222
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• 2006

This paper presents MIDAS (Media Interaction Design Authoring System), an authoring toolkit for designers and artists to develop working prototypes in new interaction design projects. Field research were conducted to identify the requirements and a case study of designing new interactive products was carried out to examine the feasibility of the new tool. MIDAS provides easier ways of integrating hardware and software, to manage a wide range of electric input and output elements and to employ 3D Augmented Reality technology within conventional multimedia authoring tools, such as Director and Flash, which are popularly used by designers. MIDAS was used in case study projects of design education as well as by voluntary designers for evaluation. From the result of case studies, it was found that many design projects were successfully accomplished using MIDAS. Designers who participated in the projects reported that MIDAS not only helped them to concentrate more on ideation but also was very easy to use as they implemented the physical interface concepts without advanced engineering skills. It is expected that MIDAS can also support prototyping in interactive media an, tangible user interface development and related human computer interaction fields.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2715-2720
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• 2014

Median filtering is very effective to remove impulse type noises, so it has been widely used in many signal processing applications. However, due to the time complexity of its non-linearity, median filtering is often used using a small filter window size. A lot of work has been done on devising fast median filtering algorithms, but most of them can be efficiently applied to input data with finite integer values like images. Little work has been carried out on fast 2-d median filtering algorithms that can deal with real-valued 2-d data. In this paper, a fast and simple median 2-d filter is presented, and its performance is compared with the Matlab's 2-d median filter and a heap-based 2-d median filter. The proposed algorithm is shown to be much faster than the Matlab's 2-d median filter and consistently faster than the heap-based algorithm that is much more complicated than the proposed one. Also, a more efficient median filtering scheme for 2-d real valued data with a finite range of values is presented that uses higher-bit integer 2-d median filtering with negligible quantization errors.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.260-276
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• 2024

The purpose of this study is to explore whether pre-service teachers majoring in earth science improve their perception of computational thinking through STEAM classes focused on engineering-based wave power plants. The STEAM class involved designing the most efficient wave power plant model. The survey on computational thinking practices, developed from previous research, was administered to 15 Earth science pre-service teachers to gauge their understanding of computational thinking. Each group developed an efficient wave power plant model based on the scientific principal of turbine operation using waves. The activities included problem recognition (problem solving), coding (coding and programming), creating a wave power plant model using a 3D printer (design and create model), and evaluating the output to correct errors (debugging). The pre-service teachers showed a high level of recognition of computational thinking practices, particularly in "logical thinking," with the top five practices out of 14 averaging five points each. However, participants lacked a clear understanding of certain computational thinking practices such as abstraction, problem decomposition, and using bid data, with their comprehension of these decreasing after the STEAM lesson. Although there was a significant reduction in the misconception that computational thinking is "playing online games" (from 4.06 to 0.86), some participants still equated it with "thinking like a computer" and "using a computer to do calculations". The study found slight improvements in "problem solving" (3.73 to 4.33), "pattern recognition" (3.53 to 3.66), and "best tool selection" (4.26 to 4.66). To enhance computational thinking skills, a practice-oriented curriculum should be offered. Additional STEAM classes on diverse topics could lead to a significant improvement in computational thinking practices. Therefore, establishing an educational curriculum for multisituational learning is essential.