• Journal of Periodontal and Implant Science
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• v.53 no.4
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• pp.248-258
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• 2023

Purpose: This study aimed to characterize the early stages of periodontal disease and determine the optimal period for its evaluation in a mouse model. The association between the duration of ligation and its effect on the dentogingival area in mice was evaluated using micro-computed tomography (CT) and histological analysis. Methods: Ninety mice were allocated to an untreated control group or a ligation group in which periodontitis was induced by a 6-0 silk ligation around the left second maxillary molar. Mice were sacrificed at 1, 2, 3, 4, 5, 8, 11, and 14 days after ligature placement. Alveolar bone destruction was evaluated using micro-CT. Histological analysis was performed to assess the immune-inflammatory processes in the periodontal tissue. Results: No significant difference in alveolar bone loss was found compared to the control group until day 3 after ligature placement, and a gradual increase in alveolar bone loss was observed from 4 to 8 days following ligature placement. No significant between-group differences were observed after 8 days. The histological analysis demonstrated that the inflammatory response was evident from day 4. Conclusions: Our findings in a mouse model provide experimental evidence that ligature-induced periodontitis models offer a consistent progression of disease with marginal attachment down-growth, inflammatory infiltration, and alveolar bone loss.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.115-117
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• 2022

Whitebox encryption technique is a method of preventing exposure of encryption keys by mixing encryption key information with a software-based encryption algorithm. Whitebox encryption technique is attracting attention as a technology that replaces conventional hardware-based security encryption techniques by making it difficult to infer confidential data and keys by accessing memory with unauthorized reverse engineering analysis. However, in the encryption and decryption process, a large lookup table is used to hide computational results and encryption keys, resulting in a problem of slow encryption and increased memory size. In particular, it is difficult to apply whitebox cryptography to low-cost, low-power, and light-weight Internet of Things products due to limited memory space and battery capacity. In addition, in a network environment that requires real-time service support, the response delay time increases due to the encryption/decryption speed of the whitebox encryption, resulting in deterioration of communication efficiency. Therefore, in this paper, we analyze whether the AES-based whitebox(WBC-AES) proposed by S.Chow can satisfy the speed and memory requirements based on the experimental results.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.239-260
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• 2023

The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.261-272
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• 2023

This research concentrates on the potential explosion hazards that could arise from unforeseen accidents in the rapidly proliferating hydrogen refueling stations and Energy Storage System(ESS) facilities. It underscores the pivotal role of structural protection technology in alleviating such risks. The research contributes primary data for the formulation of structure protection design by assessing the impact resistance across various reinforcement techniques used in cement composites. The experimental results elucidate that reinforced concrete, serving as the quintessential structural material, exhibits a 20% advancement in impact resistance in comparison to its non-reinforced counterpart. In situations typified by rapid loads, such as those seen with high-velocity impacts, the reinforcement of the matrix with fibers is demonstrably more beneficial than local reinforcement. These insights accentuate the importance of judiciously choosing the reinforcement method to augment impact resistance in structural design.

• Journal of the Korean Geotechnical Society
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• v.22 no.9
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• pp.5-14
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• 2006

Generally, the sediments contain significant water, clay, colloidal fraction and contaminants, and can result in soft strata with high initial void, and its potential hazards in subsurface environments exist. Electrokinetic technique has been used in sedimentation for volume reduction of slurry tailing wastes and in remediation for extraction of contaminants from contaminated soils. In this research, the coupled effects of sedimentation and remediation of contaminated sediments are focused using electrokinetic sedimentation and remediation techniques from experimental aspects. A series of laboratory experiments including variable conditions such as initial solid content of the specimen, concentration level of the contaminant, and magnitude of applied voltage are performed with the contaminated sediment specimens mixed with ethylene glycol. Commercially available high specification Kaolin was used to simulate slurried sediment. From the test results, the settlement of specimen increases with increasing of applied voltage and decreasing of solid content and contamination level. The density of specimen increases due to settlement of specimen in the process of electrokinetic sedimentation and decreases due to extraction of organic contaminant in the process of electrokinetic remediation.

• Journal of the Korean Geotechnical Society
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• v.26 no.4
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• pp.37-45
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• 2010

Salt cementation, a typical naturally-cemented phenomenon, may occur due to water evaporation under the change of climate. Capillary force may influence the distribution of cement in granular soils. This study addresses the effect of capillary force on salt cementation using five different techniques: cone penetration test, electrical conductivity measurement, photographic imaging technique, nondestructive imaging technique, and process monitoring by elastic wave. Glass beads modeling a particulate media was mixed with salt water and then dried in an oven to create the cementation condition. Experimental results show that salt cementation highly concentrates at the top of the small particle size specimens and at the middle or the bottom of the large particle specimens. The predicted capillary heights are similar to the locations of high salt concentration in the cemented specimens. Five suggested methods show that the behavior of salt-cemented granular media heavily depends on the capillary force.

• Composites Research
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• v.20 no.5
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• pp.49-55
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• 2007

Nanocomposite blade for dicing semiconductor wafer is investigated for micro/nano-device and micro/nano-fabrication. While metal blade has been used for dicing of silicon wafer, polymer composite blades are used for machining of quartz wafer in semiconductor and cellular phone industry in these days. Organic-inorganic material selection is important to provide the blade with machinability, electrical conductivity, strength, ductility and wear resistance. Maintaining constant thickness with micro-dimension during shaping is one of the important technologies fer machining micro/nano fabrication. In this study the fabrication of blade by wet processing of mixing conducting nano ceramic powder, abrasive powder phenol resin and polyimide has been investigated using an experimental approach in which the thickness differential as the primary design criterion. The effect of drying conduction and post pressure are investigated. As a result wet processing techniques reveal that reliable results are achievable with improved dimension tolerance.