• Imaging Science in Dentistry
• /
• v.52 no.2
• /
• pp.187-195
• /
• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.104-108
• /
• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• International Journal of Korean Welding Society
• /
• v.3 no.1
• /
• pp.34-38
• /
• 2003

It has been used many kinds of horizontal butt-welding methods at block-to-block erection stage in shipbuilding companies. For examples, some companies use conventional FCAW process with one side or both sides groove joint welding, others use carriage with torch holder type mechanized welding method. Although lots of efforts were done until now, some problems in quality and productivity still remain in ship's hull welding. In this study, we have attempted to raise productivity and quality on horizontal position of welding with following 3 items. 1) Prepare groove condition with no root gap for making easy fit-up work. 2) Develop improved MAG (100% $CO_2$ gas shielding) welding process with solid wire for making sound root bead from one side. 3) Develop and apply quite new automatic welding carriage. The stability of new welding process was confirmed by conducting mechanical tests of weldments to verify the soundness of weldments.

• International Journal of High-Rise Buildings
• /
• v.11 no.2
• /
• pp.115-124
• /
• 2022

Welding is a significant part of the construction industry. Since most high-rise building construction structures rely on a robust metal frame welded together, welding defect can damage welded structures and is critical to safety and quality. Despite its importance and heavy usage in construction, the labor shortage of welders has been a continuous challenge to the construction industry. To deal with the labor shortage, the ultimate goal of this study is to design and develop an automated robotic welding system composed of a welding machine, unmanned ground vehicle (UGV), robotic arm, and visual sensors. This paper proposes and focuses on automated weaving using the robotic arm. For automated welding operation, a microcontroller is used to control the switch and is added to a welding torch by physically modifying the hardware. Varying weave patterns are mathematically programmed. The automated weaving is tested using a brush pen and a ballpoint pen to clearly see the patterns and detect any changes in vertical forces by the arm during weaving. The results show that the weave patterns have sufficiently high consistency and precision to be used in the actual welding. Lastly, actual welding was performed, and the results are presented.

• Composites Research
• /
• v.20 no.4
• /
• pp.18-24
• /
• 2007

The mechanical and thermal properties of PAN-based/rayon-based carbon fabrics interply hybrid composite materials have been studied. Mechanical properties including tensile and interlaminar shear strengths were improved with increasing amount of continuous PAN-based carbon fabrics. The erosion rate and insulation index were determined through the torch test. Continuous rayon-based carbon fabrics composite indicated relatively low ablation resistant property. The thermal conductivity of hybrid composite of spun PAN-based/continuous rayon-based carbon fabrics is lower than that of the continuous PAN-based carbon fabrics composite.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.5
• /
• pp.42-52
• /
• 1997

Computer aided process planning and Offline programming are decisive factors in successful implementation of automated robotic production. However, conventional offline programming procedure has proven ineffective due to time-consuming teaching process for robot programming and due to inefficient system modeling. The paper presents an efficient procedure to semi-automatically generate robot job programs for ship block welding applications. In the research, the teaching positions are automatically determined by predefined rules which are functions of the type and the dimensions of the given welding section of ship block. And a sequence of robot movements and welding conditions such as welding type, welding current, welding speed, and welding torch orientation, are determined by use of Standard Program which is experimentally proved to work well for the welding wection group. Finally, a robot program for the welding section is generated automatically. Based on the algorithm, a offline automatic teaching software is developed. The paper presents also the algorithm and structure of the software.

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.2
• /
• pp.74.2-75
• /
• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• Ocean Systems Engineering
• /
• v.13 no.4
• /
• pp.367-384
• /
• 2023

Herein, we present the design and development of an efficient finite element analysis model for thermal plate forming in shipbuilding. Double curvature shells in the ship building industries are primarily formed through the thermal forming technique. Thermal forming involves heating of steel plates using heat sources like oxy-acetylene gas torch, laser, and induction heating, etc. The differential expansion and contraction across the plate thickness cause plastic deformation and bending of plates. Thermal forming is a complex forming technique as the plastic deformation and bending depends on many factors such as peak temperature, heating and cooling rate, depth of heated zone and many other secondary factors. In this work, we develop an efficient finite element analysis model for the thermo-mechanical analysis of thermal forming. Different simulations are reported to study the effect of various parameters affecting the process. Temperature dependent properties are used in the analysis and the finite element analysis model is used to identify the critical flame velocity to avoid recrystallization of plate material. A spring connected plate is modeled for structural analysis using spring elements and that helps in identifying the resultant shapes of various thermal forming patterns. Finally, detailed simulation results are reported to establish the efficacy, applicability and efficiency of the designed and developed finite element analysis model.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.22 no.1
• /
• pp.91-104
• /
• 2024

Safe radiation management is essential not only for operational nuclear power plants but also for nuclear plants to be decommissioned. When spent nuclear fuel is present on-site, meticulous radiation emergency plans are necessary to ensure safety. In Korea, numerous radiation emergency plans have been established for operational nuclear reactors. These plans delineate distinct response mitigation measures for white, blue, and red emergencies. However, clear regulations are yet to be devised for radiation emergency plans for reactors to be decommission. Therefore, this study investigated the decommissioning plan and status of Kori unit 1 to comprehensively analyze the current status of decommissioning safety in Korea. In this study, radiation emergency plans of decommissioning nuclear power plants abroad were reviewed to confirm radiation emergency action levels. Furthermore, radioactive waste treatment facilities, to be used for decommissioning reactors in Korea were evaluated. Moreover, the study assessed emergency plans (especially, emergency initiating conditions) for operational nuclear power plants in Korea for potential use in the decommissioning phase. This study proposed an emergency initiating condition that can be used for decommissioning reactors in Korea. Considering the anticipated introduction of plasma torch melting facility in Korea, this study examined the conditions of radiation emergency plans can be altered. This study identified effective measures and guidelines for managing radiological emergency initiating conditions, and effective decommissioning of nuclear power plants in Korea.

• Convergence Security Journal
• /
• v.24 no.3
• /
• pp.203-208
• /
• 2024

The identification and classification of victims in the county border area is one of the important issues. The personnel that can appear in the military border area are comprised of North Korean soldiers, U.S. soldiers, South Korean soldiers, and the general public, and are currently being confirmed through CCTV. They were classified into true categories and learned through transfer learning. The PyTorch machine learning library was used, and the dataset was utilized by crawling images corresponding to each item shared on Google. The experimental results show that each item is classified with an accuracy of 98.7500%. Future research will explore ways to distinguish more systematically and specifically by going beyond images and adding video or voice recognition.