• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.217-220
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• 2024

Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during the 2015 Nepal earthquake.

• Journal of Platform Technology
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• v.11 no.5
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• pp.110-125
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• 2023

In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.110-114
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• 2012

Purpose : PET/CT is widely used for early checking up of cancer and following up of pre and post operation. Image reconstruction method is advanced with mechanical function. We want to evaluate image quality of each reconstruction program based on time of flight (TOF). Materials and Methods : After acquiring phantom images during 2 minutes with Gemini TF (Philips, USA), Biograph mCT (Siemens, USA) and Discovery 690 (GE, USA), we reconstructed image applied to Astonish TF (Philips, USA), ultraHD PET (Siemens, USA), Sharp IR (GE, USA) and not applied. inside of Flangeless Esser PET phantom (Data Spectrum corp., USA) was filled with $^{18}F$-FDG 1.11 kBq/ml (30 Ci/ml) and 4 hot inserts (8. 12. 16. 25 mm) were filled with 8.88 kBq/ml (240 ${\mu}Ci/ml$) the ratio of background activity and hot inserts activity was 1 : 8. Inside of triple line phantom (Data Spectrum corp., USA) was filled with $^{18}F$-FDG 37 MBq/ml (1 mCi). Three of lines were filled with 0.37 MBq (100 ${\mu}Ci$). Contrast ratio and background variability were acquired from reconstruction image used Flangeless Esser PET phantom and resolution was acquired from reconstruction image used triple line phantom. Results : The contrast ratio of image which was not applied to Astonish TF was 8.69, 12.28, 19.31, 25.80% in phantom lid of which size was 8, 12, 16, 25 mm and it which was applied to Astonish TF was 6.24, 13.24, 19.55, 27.60%. It which was not applied to ultraHD PET was 4.94, 12.68, 22.09, 30.14%, it which was applied to ultraHD PET was 4.76, 13.23, 23.72, 31.65%. It which was not applied to SharpIR was 13.18, 17.44, 28.76, 34.67%, it which was applied to SharpIR was 13.15, 18.32, 30.33, 35.73%. The background variability of image which was not applied to Astonish TF was 5.51, 5.42, 7.13, 6.28%. it which was applied to Astonish TF was 7.81, 7.94, 6.40 6.28%. It which was not applied to ultraHD PET was 6.46, 6.63, 5.33, 5.21%, it which was applied to ultraHD PET was 6.08, 6.08, 4.45, 4.58%. It which was not applied to SharpIR was 5.93, 4.82, 4.45, 5.09%, it which was applied to SharpIR was 4.80, 3.92, 3.63, 4.50%. The resolution of phantom line of which location was upper, center, right, which was not applied to Astonish TF was 10.77, 11.54, 9.34 mm it which was applied to Astonish TF was 9.54, 8.90, 8.88 mm. It which was not applied to ultraHD PET was 7.84, 6.95, 8.32 mm, it which was applied to ultraHD PET was 7.51, 6.66, 8.27 mm. It which was not applied to SharpIR was 9.35, 8.69, 8.99, it which was applied to SharpIR was 9.88, 9.18, 9.00 mm. Conclusion : Image quality was advanced generally while reconstruction program which is based on time of flight was used. Futhermore difference of result compared each manufacture reconstruction program showed up, however this is caused by specification of instrument of each manufacture and difference of reconstruction algorithm. Therefore we need further examination to find out appropriate reconstruction condition while using reconstruction program used for advance of image quality.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.201-211
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• 2012

This paper addresses a Full Disk Encryption hardware processor for SATA HDD in a single FPGA design, and shows its experimental result using an FPGA board. The proposed processor mainly consists of two blocks: the first block processes XTS-AES block cipher which is the IEEE P1619 standard of storage media encryption and the second block executes the interface between SATA Host (PC) and Device (HDD). To minimize the performance degradation, we designed the XTS-AES block with the 4-stage pipelined structure which can process a 128-bit block per 4 clock cycles and has 4.8Gbps (max) performance. Also, we implemented the proposed design with Xilinx ML507 FPGA board and our experiment showed 140MB/sec read/write speed in Windows XP 32-bit and a SATA II HDD. This performance is almost equivalent with the speed of the direct SATA connection without FDE devices, hence our proposed processor is very suitable for SATA HDD Full Disk Encryption environments.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.59-69
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• 2018

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) - non-orthogonal multipleaccess (NOMA) downlink system for 5G wireless communications. This work consider the imperfectchannel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as on-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beam forming design which establishes on majorization minimization (MM) technique to find the optimal transmit beam forming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.39-49
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• 2000

In this paper, we propose an optimizing feature extraction method for multiclass problems assuming normal distributions. Initially, We start with an arbitrary feature vector Assuming that the feature vector is used for classification, we compute the classification error Then we move the feature vector slightly in the direction so that classification error decreases most rapidly This can be done by taking gradient We propose two search methods, sequential search and global search In the sequential search, an additional feature vector is selected so that it provides the best accuracy along with the already chosen feature vectors In the global search, we are not constrained to use the chosen feature vectors Experimental results show that the proposed algorithm provides a favorable performance.