• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.53-66
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• 2023

Recently, there has been a significant interest in the development of autonomous driving simulation environment based on digital twin. In the development of such digital twin-based simulation environment, many researches has been conducted not only performance and functionality validation of autonomous driving, but also generation of virtual training data for deep learning. However, such digital twin-based autonomous driving simulation system has the problem of requiring a significant amount of time and cost for the system development and the data construction. Therefore, in this research, we aim to propose a method for rapidly designing and implementing a digital twin-based autonomous driving simulation system, using only the existing 3D models and high-definition map. Specifically, we propose a method for integrating 3D model of FBX and NGII HD Map for the Busan EDC area into CARLA, and a method for adding and modifying CARLA functions. The results of this research show that it is possible to rapidly design and implement the simulation system at a low cost by using the existing 3D models and NGII HD map. Also, the results show that our system can support various functions such as simulation scenario configuration, user-defined driving, and real-time simulation of traffic light states. We expect that usability of the system will be significantly improved when it is applied to broader geographical area in the future.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.171-178
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• 2024

Exercises using a Balance Board (BB) are effective in developing balance, strengthening core muscles, and improving physical fitness and concentration. In particular, the Smart Balance Board (SBB), which integrates with various digital content, provides appropriate feedback compared to traditional balance boards, maximizing the effectiveness of the exercise. However, most systems only offer visual and auditory feedback, failing to evaluate the impact on user engagement, interest, and the accuracy of exercise postures. This study proposes an Immersive Smart Balance Board (I-SBB) that utilizes multiple sensors to enable training with various feedback mechanisms and precise postures. The proposed system, based on Arduino, consists of a gyro sensor for measuring the board's posture, a communication module for wired/wireless communication, an infrared sensor to guide the user's foot placement, and a vibration motor for tactile feedback. The board's posture measurements are smoothly corrected using a Kalman Filter, and the multi-sensor data is processed in real-time using FreeRTOS. The proposed I-SBB is shown to be effective in enhancing user concentration and engagement, as well as generating interest, by integrating with diverse content.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.769-779
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• 2024

Purpose To determine the pros and cons of an artificial intelligence (AI) model developed to diagnose acute rib fractures in chest CT images of patients with chest trauma. Materials and Methods A total of 1209 chest CT images (acute rib fracture [n = 1159], normal [n = 50]) were selected among patients with chest trauma. Among 1159 acute rib fracture CT images, 9 were randomly selected for AI model training. 150 acute rib fracture CT images and 50 normal ones were tested, and the remaining 1000 acute rib fracture CT images was internally verified. We investigated the diagnostic accuracy and errors of AI model for the presence and location of acute rib fractures. Results Sensitivity, specificity, positive and negative predictive values, and accuracy for diagnosing acute rib fractures in chest CT images were 93.3%, 94%, 97.9%, 82.5%, and 95.6% respectively. However, the accuracy of the location of acute rib fractures was low at 76% (760/1000). The cause of error in the diagnosis of acute rib fracture seemed to be a result of considering the scapula or clavicle that were in the same position (66%) or some ribs that were not recognized (34%). Conclusion The AI model for diagnosing acute rib fractures showed high accuracy in detecting the presence of acute rib fractures, but diagnosis of the exact location of rib fractures was limited.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.518-528
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• 2014

In this paper, we propose a method to recognize the action direction of human by developing 4D space-time (4D-ST, [x,y,z,t]) features. For this, we propose 4D space-time interest points (4D-STIPs, [x,y,z,t]) which are extracted using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the proposed features are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding image planes in training step. We can recognize the directions of actors in the test video since our training sets, which are projections of 3D-S volumes and 4D-STIPs to various image planes, contain the direction information. The process for recognizing action direction is divided into two steps, firstly we recognize the class of actions and then recognize the action direction using direction information. For the action and direction of action recognition, with the projected 3D-S volumes and 4D-STIPs we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. For the action recognition, features are trained by support vector data description (SVDD) according to the action class and recognized by support vector domain density description (SVDDD). For the action direction recognition after recognizing actions, each actions are trained using SVDD according to the direction class and then recognized by SVDDD. In experiments, we train the models using 3D-S volumes from INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and recognize action direction by constructing a new SNU dataset made for evaluating the action direction recognition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.63-69
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• 2013

This paper related with the performance of SE-MMA (Signed-Error MMA) that is the reduction of computational operation number in algorithm than MMA blind eualization algorithm which are possible to elimination of intersymbol interferance in the band limited and time dispersive nonlinear communication channel. In MMA algorithm which are possible to reduction of amplitude and phase rotation by intersymbol interference that is occurred in channel without using the training sequence, it uses the error signal that is the difference of the equalizer output and constant modulus, the statisticlly characteristic of transmitted signal. But in SE-MMA, it uses the polarity of the error signal, then it is possible to reduce the updating the tap coefficient and to simplify the H/W implementation. The computer simulation were performed in order to compare the performance of SE-MMA and conventional MMA algorithm. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve, SER were used. As a result of simulation, the SE-MMA has more fast convergence speed than the MMA. But in the other index after reaching the seady state, it gives more worst performance values in the used index.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Proceedings of the KSPS conference
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• 1997.07a
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• pp.228-229
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• 1997

1 Intonation is important. It cannot be ignored. To convince students of the importance of intonation, we can use sentences with two very different interpretations according to intonation. Example: "I thought it would rain" with a fallon "rain" means it did not rain, but with a fall on "thought" and a rise on "rain" it means that it did rain. 2 Although complex, intonation is structured. For both teacher and student, the big job of tackling intonation is made simpler by remembering that intonation can be analysed into systems and units. There are three main systems in English intonation: Tonality (division into phrases) Tonicity (selection of accented syllables) Tone (the choice of pitch movements) Examples: Tonality: My brother who lives in London is a doctor. Tonicity: Hello. How ARE you. Hello. How are YOU. Tone: Ways to say "Thank you" 3 In deciding what to teach, we must distinguish what is universal from what is specifically English. This is where contrastive studies of intonation are very valuable. Usually, for instance, division into phrases (tonality) works in broadly similar ways across languages. Some uses of pitch are also similar across languages - for example, very high pitch may signal excitement or urgency. 4 Although most people think that intonation is mainly about pitch (the tone system), actually accent placement (tonicity) is probably the single most important aspect of English intonation. This is because it is connected with information focus, and the effects on interpretation are very clear-cut. Example: They asked for coffee, so I made them coffee. (The second occurrence of "coffee" must not be accented). 5 Ear-training is the beginning of intonation training in the VeL approach. First, students learn to identify fall vs rise vs fall-rise. To begin with, single words are used, then phrases and sentences. When learning tones, the fIrst words used should have unstressed syllables after the stressed syllable (Saturday) to make the pitch movement clearer. 6 In production drills, the fIrst thing is to establish simple neutral patterns. There should be no drama or really special meanings. Simple drills can be used to teach important patterns: Example: A: Peter likes football B: Yes JOHN likes football TOO A: Mary rides a bike B: Yes JENny rides a bike TOO 7 The teacher must be systematic and let learners KNOW what they are learning. It is no good using new patterns and hoping that students will "pick them up" without noticing. 8 Visual feedback of fundamental frequency with a computer display can help students learn correct patterns. The teacher can use the display to demonstrate patterns, or students can practise by themselves, imitating recorded models.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.239-243
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• 2002

Since 1990s, many companies or enterprises have been interested in Career Development or Career Management of Personnel in Korea. In the result of those interesting, some companies developed or built a program or computer system on Career Development or Career Management for their personnel. But their systems or programs are just the level to mange what kind of on-the-job training taken, ones experience where he or she worked, one who has a certificate of qualification ring a position and etc. This is not fit the field of construction industry and is not sufficient to mange workers at a construction company and their career. Because a construction project success depends on ones ability including know-how, skill and experience and these dependence is higher than any other industry's projects. The level of management on the career of construction company's personnel should be lower and expended than now and the career management of a construction company should include individual experience and personal history(period attained a project or a work, what kind of project or work, and etc)attained in performing each construction project and show ones career development process to each person. This system gives participants a chance to educate oneself and evaluate the level of ones training by showing individual records to each member of staff. And the company gains the advantage of the staffs ability to easily solve problems as well as the improvement of the organizations capabilities.