• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.21 no.6
• /
• pp.15-21
• /
• 2022

This study considered the internal flow considering the internal shape of the CNG filling nozzle, which is widely distributed in Korea. The CNG filling nozzle is the last part to pass through in the CNG filling process and has a significant influence on the filling efficiency. The mechanism was identified by disassembling the CNG filling nozzle and performing a flow analysis according to the mechanism. Consequently, the energy loss owing to eddy currents in the flow was determined, and modeling was proposed to reduce the energy loss by simplifying the shape and parts.

• Advances in nano research
• /
• v.13 no.3
• /
• pp.297-312
• /
• 2022

Coughing and breath shortness are common symptoms of nano (small) cell lung cancer. Smoking is main factor in causing such cancers. The cancer cells form on the soft tissues of lung. Deformation behavior and wave vibration of lung affected when cancer cells exist. Therefore, in the current work, phase velocity behavior of the small cell lung cancer as a main part of the body via an exact size-dependent theory is presented. Regarding this problem, displacement fields of small cell lung cancer are obtained using first-order shear deformation theory with five parameters. Besides, the size-dependent small cell lung cancer is modeled via nonlocal stress/strain gradient theory (NSGT). An analytical method is applied for solving the governing equations of the small cell lung cancer structure. The novelty of the current study is the consideration of the five-parameter of displacement for curved panel, and porosity as well as NSGT are employed and solved using the analytical method. For more verification, the outcomes of this reports are compared with the predictions of deep neural network (DNN) with adaptive optimization method. A thorough parametric investigation is conducted on the effect of NSGT parameters, porosity and geometry on the phase velocity behavior of the small cell lung cancer structure.

• Journal of the Korean Society for Heat Treatment
• /
• v.35 no.6
• /
• pp.319-326
• /
• 2022

Direct extrusions of an aluminum 7075 alloy were carried out using 1500 ton machine with and without die cooling system. Cooling of extrusion die has been performed by the flow of liquid nitrogen and controlled by laser thermometer. Billet was 180 mm in diameter and 500 mm in length. The preheating temperatures of billet, container and die were 390℃, 400℃ and 450℃, respectively. Ram speed was kept with 1.25 mm/sec first. The change of ram speed was carried out during extrusion according to the observation of surface defects such as crack or tearing. Extrudates of 8.3 m in length, 100 mm in width and 15 mm in thickness were obtained to observe and analyze surface defects by optical microscopy and EBSD (Electron BackScattered Diffraction). In case of extrusion without die cooling cracks on the surface and tearing in the corner of extrudate occurred in the middle stage and developed in size and frequency during the late stage of extrusion. At the extrusion with die cooling the occurrence of defects could be suppressed on the most part of extrudate. EBSD micrographs showed that cracks and tearings have been resulted from the same origin. Surface defects were generated at the boundaries of grains formed by secondary recrystallization due to surface overheating during extrusion.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.73-78
• /
• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• Nuclear Engineering and Technology
• /
• v.54 no.3
• /
• pp.948-958
• /
• 2022

Since reliability and security of man-machine system increasingly depend on reliability of human, human reliability analysis (HRA) has attracted a lot of attention in many fields especially in nuclear engineering. Dependence assessment among human tasks is a important part in HRA which contributes to an appropriate evaluation result. Most of methods in HRA are based on experts' opinions which are subjective and uncertain. Also, the dependence influencing factors are usually considered to be constant, which is unrealistic. In this paper, a new model based on Dempster-Shafer evidence theory (DSET) and fuzzy number is proposed to handle the dependence between two tasks in HRA under uncertain and dynamic situations. First, the dependence influencing factors are identified and the judgments on the factors are represented as basic belief assignments (BBAs). Second, the BBAs of the factors that varying with time are reconstructed based on the correction BBA derived from time value. Then, BBAs of all factors are combined to gain the fused BBA. Finally, conditional human error probability (CHEP) is derived based on the fused BBA. The proposed method can deal with uncertainties in the judgments and dynamics of the dependence influencing factors. A case study is illustrated to show the effectiveness and the flexibility of the proposed method.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.26 no.1
• /
• pp.61-68
• /
• 2023

In North Korea, the production of courage tombs continues, and it is known that the activity rate is higher than that of old tombs. However, pot seedling are planted using hoes and shovels used for planting old tombs with exposed roots and low activity rates. This is believed to result in excessive force when stepping on the container grave with a hoe in the planting process, resulting in the collapse of the container grave or the waste of labor due to the creation of unnecessary planting holes. Therefore, when planting courage graves at North Korean afforestation sites, it is necessary to improve the work of making planting holes using general hoes in a way that improves labor productivity in a more efficient manner. As part of inter-Korean technical cooperation to improve the North Korean afforestation method, this study was conducted with the aim of developing efficient container seedlings and using them for North Korean forest restoration projects. It is believed that developing planting equipment exclusively for container graves for forest restoration in North Korea in South Korea and providing equipment and production technology to North Korea can contribute to the development of forest restoration technology in North Korea. If the Yonggeomyo Development Planting Equipment is provided to North Korea, it will be a realistic inter-Korean forest cooperation project to avoid international sanctions by recognizing the excellence of the development products by directly using its own materials through technical cooperation.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.230-236
• /
• 2009

Double-front construction machinery, which was designed for complicated tasks, requires intelligent systems that can provide the quantitative work analysis needed to determine effective work procedures and that can provide operational and cognitive support for operators. Construction work environments are extremely complicated, however, and this makes state identification difficult. We therefore defined primitive static states (PSS) that are determined using on-off data for the lever inputs and manipulator loads for each part of the grapple and front and that are completely independent of the various environmental conditions and operator skill levels. To confirm the usefulness of PSS, we performed experiments with a demolition task by using our virtual reality simulator. We confirmed that PSS could robustly and accurately identify the work states and that untrained skills could be easily inferred from the PSS-based work analysis. We also confirmed in skill-training experiments that advice information using PSS-based skill analysis greatly improved work performance. We thus confirmed that PSS can adequately identify work states and are useful for work analysis and skill improvement.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.91-94
• /
• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• Nuclear Engineering and Technology
• /
• v.54 no.12
• /
• pp.4684-4692
• /
• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• 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.