• Nuclear Engineering and Technology
• /
• v.52 no.12
• /
• pp.2717-2724
• /
• 2020

Silicon, especially silicon in the form of SiO₂, is a major component of rocks. Final spent fuel storages, which are being designed, are located in suitable rock formations in the Earth's crust. Reduction of the uncertainty of silicon neutron scattering and capture is needed; improved silicon evaluations have been recently produced by the ORNL/IAEA collaboration within the INDEN project. This paper deals with the nuclear data validation of that evaluation performed at the LR-0 reactor by means of critical experiments and measurement of reaction rates. Large amounts of silicon were used both as pure crystalline silicon and SiO₂ sand. The critical moderator level was measured for various core configurations. Reaction rates were determined in the largest core configuration. Simulations of the experimental setup were performed using the MCNP6.2 code. The obtained results show the improvement in silicon cross-sections in the INDEN evaluations compared to existing evaluations in major libraries. The new Thermal Scattering Law for SiO₂ published in ENDF/B-VIII.0 additionally reduces the discrepancy between calculation and experiments. However, an unphysical peak is visible in the neutron spectrum in SiO₂ obtained by calculation with the new Thermal Scattering Law.

• Journal of Information Processing Systems
• /
• v.19 no.1
• /
• pp.109-117
• /
• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.740-753
• /
• 2023

Text recognition in natural scene images is a challenging problem in computer vision. The accurate identification of ship number characters can effectively improve the level of ship traffic management. However, due to the blurring caused by motion and text occlusion, the accuracy of ship number recognition is difficult to meet the actual requirements. To solve these problems, this paper proposes a dual-branch network based on the CRNN identification network. The network couples image restoration and character recognition. The CycleGAN module is used for blur restoration branch, and the Pix2pix module is used for character occlusion branch. The two are coupled to reduce the impact of image blur and occlusion. Input the recovered image into the text recognition branch to improve the recognition accuracy. After a lot of experiments, the model is robust and easy to train. Experiments on CTW datasets and real ship maps illustrate that our method can get more accurate results.

• Smart Structures and Systems
• /
• v.31 no.1
• /
• pp.45-56
• /
• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• Structural Engineering and Mechanics
• /
• v.88 no.1
• /
• pp.67-81
• /
• 2023

The reinforced concrete (RC) structures might need strengthening or upgradation due to adverse environmental conditions, design defects, modification requirements, and to prolong the expected lifespan. The RC beams have been efficiently strengthened using the near surface mounted (NSM) approach over the externally bonded reinforcing (EBR) system. In this study, the performance of RC beam elements strengthened with NSM-steel rebars was investigated using an experimental program and nonlinear finite element modeling (FEM). Nine medium-sized, rectangular cross-section RC beams total in number made up for the experimental evaluation. The beams strengthened with varying percentages of NSM reinforcement, and the number of grooves was assessed in four-point bending experiments up to failure. Based on the experimental evaluation, the load-displacement response, crack features, and failure modes of the strengthened beams were recorded and considered. According to the experimental findings, NSM steel greatly improved the flexural strength (up to about 84%) and stiffness of RC beams. The flexural response of the tested beams was simulated using a 3D non-linear finite element (FE) model. The findings of the experiments and the numerical analysis showed good agreement. The effect of the NSM groove and reinforcement on the structural response was then assessed parametrically.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.2
• /
• pp.73-81
• /
• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.3
• /
• pp.1645-1651
• /
• 2015

This paper describes the changes of shape accuracy in workpiece materials depending on the turning clearance angle. The experiments started from choosing three workpiece materials, SM45C(machine structural carbon steel), STS303(stainless steel) and SCM415 (chrome-molybdenum steel). The experiments showed specifically how features of selected materials changed when they were processed with diverse machining depths, 0.1 mm, 0.2 mm and 0.3 mm, with various negative angles, $0.0^{\circ}(-6.0^{\circ})$, $0.3^{\circ}(-6.3^{\circ})$ and $0.9^{\circ}(-6.9^{\circ})$, and called cutting edge inclination starting from a fixed rotational speed, 2,500 rpm, focusing on the feed rate, 0.07 mm/rev and 0.10 mm/rev. The results of the accuracy of processing, cylindricity, deviation from coaxiality, etc. were compared using the graph and table. The accuracy of cylindricity in the order of degree $0.0^{\circ}{\rightarrow}0.3^{\circ}{\rightarrow}0.9^{\circ}$ depending on the workpiece materials showed the best cylindricity when it was $0.9^{\circ}$. In conclusion, the accuracy improved in specific degrees irrespective of the quality of the materials when the bite negative angles increased. This means that workability improved in these experiments. In addition, the processing shape changed depending on depth of the cut and feed rate.

• The Bulletin of The Korean Astronomical Society
• /
• v.43 no.1
• /
• pp.41.1-41.1
• /
• 2018

We introduce a current status and future plans of Korea Microlensing Telescope Network (KMTNet) microlensing experiments, which include an observational strategy, pipeline, event-finder, and collaborations with Spitzer. The KMTNet experiments were initiated in 2015. From 2016, KMTNet observes 27 fields including 6 main fields and 21 subfields. In 2017, we have finished the DIA photometry for all 2016 and 2017 data. Thus, it is possible to do a real-time DIA photometry from 2018. The DIA photometric data is used for finding events from the KMTNet event-finder. The KMTNet event-finder has been improved relative to the previous version, which already found 857 events in 4 main fields of 2015. We have applied the improved version to all 2016 data. As a result, we find that 2597 events are found, and out of them, 265 are found in KMTNet-K2C9 overlapping fields. For increasing the detection efficiency of event-finder, we are working on filtering false events out by machine-learning method. In 2018, we plan to measure event detection efficiency of KMTNet by injecting fake events into the pipeline near the image level. Thanks to high-cadence observations, KMTNet found fruitful interesting events including exoplanets and brown dwarfs, which were not found by other groups. Masses of such exoplanets and brown dwarfs are measured from collaborations with Spitzer and other groups. Especially, KMTNet has been closely cooperating with Spitzer from 2015. Thus, KMTNet observes Spitzer fields. As a result, we could measure the microlens parallaxes for many events. Also, the automated KMTNet PySIS pipeline was developed before the 2017 Spitzer season and it played a very important role in selecting the Spitzer target. For the 2018 Spitzer season, we will improve the PySIS pipeline to obtain better photometric results.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.9 no.5
• /
• pp.269-276
• /
• 2014

In recent, autonomous navigation techniques to avoid obstacles have been studied by using unmanned aircraft vehicles(UAVs) since the increment of UAV's interest and utilization. Particularly, autonomous navigation based UAVs are utilized in several areas such as military, police, media, and so on. However, there are still some problems to avoid obstacle when UVAs perform autonomous navigation. For instance, the UAV can not forward in the corner of corridors even though it utilizes the improved vanish point algorithm that makes an autonomous navigation system. Therefore, in this paper, we propose an obstacle avoidance technique based on immune algorithm for autonomous navigation of Quadrotor. The proposed algorithm is consisted of two steps such as 1) single color discrimination and 2) multiple color discrimination. According to the result of experiments, we can solve the previous problem of the improved vanish point algorithm and improve the performance of autonomous navigation of Quadrotor.