• Restorative Dentistry and Endodontics
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• v.39 no.4
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• pp.276-281
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• 2014

Objectives: The purpose of this study was to compare the cutting efficiency of a newly developed microprojection tip and a diamond-coated tip under two different engine powers. Materials and Methods: The apical 3 mm of each root was resected, and root-end preparation was performed with upward and downward pressure using one of the ultrasonic tips, KIS-1D (Obtura Spartan) or JT-5B (B&L Biotech Ltd.). The ultrasonic engine was set to power-1 or -4. Forty teeth were randomly divided into four groups: K1 (KIS-1D / Power-1), J1 (JT-5B / Power-1), K4 (KIS-1D / Power-4), and J4 (JT-5B / Power-4). The total time required for root-end preparation was recorded. All teeth were resected and the apical parts were evaluated for the number and length of cracks using a confocal scanning micrscope. The size of the root-end cavity and the width of the remaining dentin were recorded. The data were statistically analyzed using two-way analysis of variance and a Mann-Whitney test. Results: There was no significant difference in the time required between the instrument groups, but the power-4 groups showed reduced preparation time for both instrument groups (p < 0.05). The K4 and J4 groups with a power-4 showed a significantly higher crack formation and a longer crack irrespective of the instruments. There was no significant difference in the remaining dentin thickness or any of the parameters after preparation. Conclusions: Ultrasonic tips with microprojections would be an option to substitute for the conventional ultrasonic tips with a diamond coating with the same clinical efficiency.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.300-306
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• 2021

Due to the development of IT technology, hardware technologies applied to IoT equipment have recently been developed, so smart systems using low-cost, high-performance RF and computing devices are being developed. However, in the infrastructure environment where a large amount of IoT devices are installed, big data collection causes a load on the collection server due to a bottleneck between the transmitted data. As a result, data transmitted to the data collection server causes packet loss and reduced data throughput. Therefore, there is a need for an efficient big data collection technique in an infrastructure environment where a large amount of IoT devices are installed. Therefore, in this paper, we propose an efficient big data collection technique in an infrastructure environment where a vast amount of IoT devices are installed. As a result of the performance evaluation, the packet loss and data throughput of the proposed technique are completed without loss of the transmitted file. In the future, the system needs to be implemented based on this design.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.307-313
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• 2021

Most of the stores of small business owners are those that use a large number of electrical devices, and in particular, there are many stores that use a cold storage system. In severe cases, there is a lot of power load on the store, which can cause a loss to the assets in the store as the power supply is cut off. Accordingly, in this paper, an LSTM-based power load prediction system was designed to measure the energy demand rate of stores and to save energy. Since it can be used as a data-based power saving system for small and medium-sized stores, it is expected to be used as a data-based power demand prediction system for small businesses in the future, and to be used in the field of preventing damage due to power load.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.213-220
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• 2024

This paper proposes an optimization strategy for performing Federated Learning between devices and edge clouds using WebAssembly (WASM). The proposed strategy aims to maximize efficiency by conducting partial training on devices and the remaining training on edge clouds. Specifically, it mathematically describes and evaluates methods to optimize data transfer between GPU memory segments and the overlapping of computational tasks to reduce overall training time and improve GPU utilization. Through various experimental scenarios, we confirmed that asynchronous data transfer and task overlap significantly reduce training time, enhance GPU utilization, and improve model accuracy. In scenarios where all optimization techniques were applied, training time was reduced by 47%, GPU utilization improved to 91.2%, and model accuracy increased to 89.5%. These results demonstrate that asynchronous data transfer and task overlap effectively reduce GPU idle time and alleviate bottlenecks. This study is expected to contribute to the performance optimization of Federated Learning systems in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.221-228
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• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.

• Restorative Dentistry and Endodontics
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• v.36 no.5
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• pp.377-384
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• 2011

Objectives: The purpose of this study was to observe the change in the viscoelastic properties of thermoplasticized injectable root canal filling materials as a function of temperature and to compare the handling characteristics of these materials. Materials and Methods: Three commercial gutta perchas and Resilon (Pentron Clinical Technologies) in a pellet form were heated in the Obtura-II system (Obtura Spartan) at $140^{\circ}C$ and $200^{\circ}C$, and the extrusion temperature of the thermoplasticized materials was measured. The viscoelastic properties of the materials as a function of temperature were evaluated using a rheometer. The elastic modulus G', viscous modulus G", loss tangent tan${\delta}$, and complex viscosity ${\eta}^*$ were determined. The phase transition temperature was determined by both the rheometer and a differential scanning calorimeter (DSC). The consistency of the materials was compared under compacting pressure at $60^{\circ}C$ and $40^{\circ}C$ by a squeeze test. Results: The three gutta perchas had dissimilar profiles in viscoelastic properties with varying temperature. The phase transition of softened materials into solidification occurred at $40^{\circ}C$ to $50^{\circ}C$, and the onset temperatures obtained by a rheometer and a DSC were similar to each other. The onset temperature of phase transition and the consistency upon compaction pressure were different among the materials (p < 0.05). Resilon had a rheologically similar pattern to the gutta perchas, and was featured between high and low-flow gutta perchas. Conclusions: The rheological characteristics of the thermoplasticized root canal filling materials changed under a cooling process. The dissimilar viscoelastic properties among the materials require different handling characteristics during an injecting and compacting procedure.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.