• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.715-721
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• 2018

This study examined energy saving and elevating the worker's neuro energy (comfort, concentration, physical, and psychological stability) by compensating for the unpleasant tactile sensation to stimulate auditory and olfactory senses and reduce energy consumption. The experiment was conducted in an environment test room under the test conditions of temperature $25[^{\circ}C]$, relative humidity 50[RH%], illumination 1,000[lux] and air current speed 0.02[m/sec] by stimulating the auditory senses with a 1/f change in rhythm and the olfactory senses with an aroma scent. The experiment utilized the method of EEG, which evaluates human body's psychological status via tactile means, and the method of the vibra image, which evaluates the learning abilities, HRV and human body's psychological status via non-tactile means. The subjects were selected as eight university students (four males and four females) in their 20s, the type that have high relative ${\alpha}$(8~13[Hz]) activation in occipital lobe, which brings the highest level of mind stability and concentration, who had no difficulty in physical activities. The subjects' posture and physical activity was fixed to 1met - when the subjects are seated and relaxing in a comfortable environment - and their clothes condition was standardized as 0.7clo. As a result, the sentimental and psychological stability and concentration were the highest in the multisensory stimulation of jasmine scent and change rhythm of an a=1.106 sound source. In addition, under this condition, the relative $M{\alpha}$ and relative $M{\beta}$ increased by 70.49[%] and 89.72[%], respectively; the HRT decreased by 39.09[%]; and the fatigue and tension/anxiety decreased by 36.85[%] and 15.54[%], respectively.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.725-739
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• 2022

This study aims to analyze what elementary school pre-service teachers focused on and what challenges they faced in designing and producing a guided science inquiry program based on augmented reality (AR) and to provide some implications for teachers' professionalism and teacher education. To this end, focusing on the cases of pre-service teachers who designed and created AR-based guided inquiry programs, the researchers extracted and categorized the pre-service teachers' focus and challenges from the program design and production stages. As a result, in the program design stage, the pre-service teachers tried to construct scenarios that could promote students' active inquiry process. At the same time, drawing on the unique affordances of AR, the pre-service teachers focused on creating vivid visual data in a 3D environment and making meaningful connections between virtual and real-world activities. The pre-service teachers faced challenges in making use of the advantages of AR technology and designing an inquiry program due to a lack of background knowledge about CoSpaces, a content creation program. In the program production stage, the pre-service teachers tried to make their program easy to handle to improve students' concentration on inquiry activities. In addition, challenges of programming using CoSpaces were reported. Based on these results, educational implications were discussed in terms of the pedagogical uses of AR and teachers' professionalism in adopting AR in science inquiry.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.33-48
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• 2023

In this paper we consider automatic speech recognition (ASR) for Korean speech data in which elderly persons randomly speak a sequence of words such as animals and vegetables for one minute. Most of the speakers are over 60 years old and some of them are dementia patients. The goal is to compare deep-learning based ASR models for such data and to find models with good performance. ASR is a technology that can recognize spoken words and convert them into written text by computers. Recently, many deep-learning models with good performance have been developed for ASR. Training data for such models are mostly composed of the form of sentences. Furthermore, the speakers in the data should be able to pronounce accurately in most cases. However, in our data, most of the speakers are over the age of 60 and often have incorrect pronunciation. Also, it is Korean speech data in which speakers randomly say series of words, not sentences, for one minute. Therefore, pre-trained models based on typical training data may not be suitable for our data, and hence we train deep-learning based ASR models from scratch using our data. We also apply some data augmentation methods due to small data size.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.357-363
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• 2024

As AR display virtual technology enters public learning life extensively, the way in which reality and virtual connection are connected is also changing. The purpose of this paper is to study the expression between the 3D connection sensory information visualization experience and virtual reality enhancement through the visual direction sensory information visualization experience of the plane. It is analyzed by examining the basic setting method compared to the current application of AR display and flat visualization cases. The scope of this paper is to enable users to have a better experience through the relationship with sensory visualization, centering on eye tracking technology in the four categories of AR display connection design: gesture connection, eye tracking, voice connection, and sensor. Focusing on eye tracking technology through AR display interaction and current application and comparative analysis of flat visualization cases, the geometric consistency of visual figures, light and color consistency, combination of multi-sensory interaction methods, rational content display, and smart push presented sensory visualization in virtual reality more realistically and conveniently, providing a simple and convenient sensory visualization experience to the audience.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1196-1206
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• 2021

Purpose To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30-50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learning-based AI algorithm for CDSS.

• Journal of Welding and Joining
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• v.32 no.4
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• pp.39-45
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• 2014

Today, the development of digitized information media and info-communications are bringing many changes. Due to the development of IT thechnology, we can learn wherever, whenever, regardless of time and place. Machine drawing subject is a very important in mechanical engineering course, but it's studyed only basic theory in a short period, average 1~2weeks. So that, students think that the mechanical drawing is of minor importance. Such ideas make them difficult to impove sense of space in isometric drawing and drawing skill. Therefore, in this paper, augmented reality-based contents through the system, Mechanical Drawing of education to meet the effectiveness and satisfaction, student learning can be spontaneously it was construct self-system. And, Theoretical part of the Mechanical Drawing is proposed ensure more efficient and easier training. In this paper, we were test operation for user effectualness of proposed service at Korea Polytechnics Colleges a industrial facilities management in Daegu. Target user are 66 students, and The students were divided into experimental group and comparison group. Experimental results, experimental group was able to do systematically experience many Projection Drawing and Pictorial Drawing in short schooltime. And, The test operation results showed that have the possibility to meet education effectiveness and user satisfaction in this augmented reality-based contents system.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.243-250
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• 2016

Recently, public interest in virtual reality (VR) and augmented reality (AR) has increased. Therefore, computer graphics-related research has been actively conducted. This has included research on virtual space related to human posture implementation. However, such research has focused on general posture in humans. This paper presents a system with reference to the basic posture in sports climbing and the inverse kinematics method for generating the positions and behavior of virtual characteristics in a three-dimensional virtual space. The simulation based on the inverse kinematics method, produced with an inverse kinematics solver and initial pose animation from motion capture, provides realistic and natural movement. We designed a simulation system to generate correct posture and motions similar to those in sports climbing by applying the basic procedure of sports climbing. The simulation system provides help for producing content about sports climbing, such as learning programs for novice climbers and sports climbing games.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.558-561
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• 2009

In this paper, we propose Simple Frame Marker(SFMarker) to support recognition of characters and images included in a marker in augmented reality. If characters are inserted inside of marker and are recognised using Optical Character Recognition(OCR), it doesn't need marker learning process before an execution. It also reduces visual disturbance compared to 2D barcode marker due to familarity of characters. Therefore, proposed SFMarker distinguishes Square SFMarker that embeds images from Rectangle SFMarker with characters according to ratio of marker and applies different recognition algorithms. Also, in order to reduce preprocessing of character recognition, SFMarker inserts direction information in border of marker and extracts it to execute character recognition fast and correctly. Finally, since the character recognition for every frame slows down tracking speed, we increase the speed of recognition process using the result of character recognition in previous frame when frame difference is low.