To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

PURPOSES : This study aims to suggest how to utilize "standby data" of shared mobility that does not contain personal information and examine whether "standby data" can derive existing shared mobility operation analysis items similarly. METHODS : An existing Personal Mobility (PM) traffic pattern analysis was performed by identifying the user (User ID) and the user's route in a time frame. In this study, the PM traffic pattern analysis focuses on a vehicle (ID of the standby vehicle) and its standby location. We examined whether the items derived from the User ID-based traffic pattern analysis could also be derived from the standby Vehicle ID-based analysis. RESULTS : The analysis showed that all five items (traffic volume by time slot, peak time, average travel time, average travel distance, and average travel speed) of the existing User ID-based PM travel analysis result could be derived similarly using the standby Vehicle ID-based PM traffic analysis. However, the disadvantage is that the average driving distance is calculated as a straight-line distance. It seems possible to overcome this limitation by correcting the average driving distance through linkage analysis with road network data. However, it is not possible to derive the instantaneous maximum speed or acceleration/deceleration. CONCLUSIONS : In an era in which various means of transportation are being introduced, data sharing is not preferred because of legal issues.Consequently, it is difficult to understand the use of new means of transportation and formulate new policies. To address this, data sharing can be active based on standby data that is not related to personal information.

To conduct numerical simulation of a disposal repository of the spent nuclear fuel, it is necessary to numerically simulate the entire domain, which is composed on numerous finite elements, for at least several tens of thousands of years. This approach presents a significant computational challenge, as obtaining solutions through the numerical simulation for entire domain is not a straightforward task. To overcome this challenge, this study presents the process of producing the training data set required for developing the machine learning based hybrid solver. The hybrid solver is designed to correct results of the numerical simulation composed of coarse elements to the finer elements which derive more accurate and precise results. When the machine learning based hybrid solver is used, it is expected to have a computational efficiency more than 10 times higher than the numerical simulation composed of fine elements with similar accuracy. This study aims to investigate the usefulness of generating the training data set required for the development of the hybrid solver for disposal repository. The development of the hybrid solver will provide a more efficient and effective approach for analyzing disposal repository, which will be of great importance for ensuring the safe and effective disposal of the spent nuclear fuel.

Currently, the development of evaluation technology for vibration and shock loads transmitted to spent nuclear fuel and structural integrity of spent nuclear fuel under normal conditions of transport is progressing in Korea by the present authors. Road transportation tests using surrogate spent nuclear fuel were performed in September, 2020 using a test model of KORAD-21 transportation cask and sea transportation tests were conducted from September 30 to October 4, 2021. Finally, the shake table tests and rolling test were conducted from October 31 to November 2, 2022. The shake table test was performed with the input load produced conservatively from the data obtained from the road and sea transportation tests. The test input was produced based on the power spectral densities and shock response spectrums from the transportation tests. In addition to the test inputs from the road and sea tests, sine sweep input and half sine input were used to verify the vibration characteristics of assemblies under boundary conditions during normal conditions of transport. Because the input load of the shake table test was produced conservatively, a slightly larger strain than the strain value measured in road and sea transportation tests was measured from the shake table tests. In the case of the sea test, it is considered that the process of enveloping the data in the 20 to 80 Hz range generated by the engine propeller system was performed excessively conservatively. As a result of analyzing the test results for the difference in boundary conditions, it was confirmed that the test conditions of loading the basket generated a relatively large strain compared to the conditions of loading the disk assembly for the same input load. Therefore, it is concluded that a transportation cask having a structure in which a basket and a disk are separated, such as KORAD-21, is more advantageous in terms of vibration shock load characteristics under normal conditions of transport than a transportation cask having an integral internal structure in which a basket and a disk are a single unit. However, this effect will be insignificant because the load itself transmitted to the disk assembly is very small.

The purpose of this study is to detect future signals and changes in nuclear-related research to apply safeguards by design to new nuclear facilities or to determine nuclear fuel cycle-related research and development (R&D) activities. First, a total of 2,029 scientific articles published between 2015 and 2022 in the journal of “Nuclear Engineering and Technology” by the Korean Nuclear Society were collected. The authors of the scientific article used their expertise and knowledge to select keywords that can properly represent the article. Therefore, in this study, the keywords of each scientific article were analyzed using the technique of text mining. We then calculated the “word frequency” and “term frequency-inverse document (TF-IDF)” values of the keywords. Consequently, significant words such as “reactor,” “nuclear,” and “fuel” were extracted, which were represented as word clouds. Furthermore, keywords extracted through text mining were quantitatively classified into weak or strong signals using a keyword emergence map (KEM). The KEM is a tool that can explore future signals because essential keywords have a high frequency of appearance, and newer keywords are more important than older keywords. The KEM results showed no keywords in the strong-signal area in the field of nuclear academia. However, keywords such as “deep learning,” “earthquake,” “zircaloy,” and “CFD” were confirmed to be distributed in the weak signal area. A weak signal indicates the most probable topic that could become a strong signal in the near future. The weak signal methodology can be applied to predict future nuclear scientific trends in the rapidly changing world. Based on the results of the study, changes in the subject of nuclear-related scientific articles over the past eight years and future signals were interpreted. The results confirmed that this method can be applied to safeguards measures of new nuclear facilities in the design stage and can be used to detect R&D activities related to the nuclear fuel cycle in advance.

Background: Osteoarthritis is a common condition with an increasing prevalence and is a common cause of disability. Osteoarthritic pain decreases the quality of life, and simple gait training is used to alleviate it. Knee osteoarthritis limits joint motion in the sagittal and lateral directions. Although many recent studies have activated orthotic research to increase knee joint stabilization, no study has used patellar tendon straps to treat knee osteoarthritis. Objects: This study aimed to determine the effects of patellar tendon straps on kinematic, mechanical, and electromyographic activation in patients with knee osteoarthritis. Methods: Patients with knee osteoarthritis were selected. After creating the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), leg length difference, Q-angle, and thumb side flexion angle of the foot were measured. Kinematic, kinetic, and muscle activation data during walking before and after wearing the orthosis were viewed. Results: After wearing the patellar tendon straps, hip adduction from the terminal stance phase, knee flexion from the terminal swing phase, and ankle plantar flexion angle increased during the pre-swing and initial swing phases. The cadence of spatiotemporal parameters and velocity increased, and step time, stride time, and foot force duration decreased. Conclusion: Based on the results of this study, the increase in plantar flexion after strap wearing is inferred by an increase due to neurological mechanisms, and adduction at the hip joint is inferred by an increase in adduction due to increased velocity. The increase in cadence and velocity and the decrease in gait speed and foot pressure duration may be due to joint stabilization. It can be inferred that joint stabilization is increased by wearing knee straps. Thus, wearing a patellar tendon strap during gait in patients with knee osteoarthritis influences kinematic changes in the sagittal plane of the joint.

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model’s performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

최근 3차원 공간정보와 행정정보를 융합하여 다양한 도시문제의 과학적 해결을 지원하는 디지털트윈이 공공분야의 스마트시티와 공간정보 행정업무에 도입되고 있다. 이 연구의 목적은 공공분야에 디지털트윈을 확산하기 위해 필요한 공간정보의 정책방향을 제시하는데 있다. 먼저, 이론적 고찰을 통해 디지털트윈에 대한 개념을 검토하였고, 언론보도 등에 나타난 디지털트윈에 대한 주요 키워드를 도출하였다. 두 번째, 디지털트윈 국가정책과 디지털트윈국토 지자체 시범사업, 공공기관의 디지털트윈 사업 특성을 고찰하였다. 세 번째, 정책 및 제도적 관점, 사업적 관점, 활용성의 관점에서 공공분야 디지털트윈이 어떠한 이슈와 문제점을 가지고 있는지 도출하였고, 이를 해결하기 위한 정책방향을 제안하였다. 이 연구의 결과는 향후 공공분야 디지털트윈 확산을 위한 정책적 시사점을 제공할 것이다.

Since delivery food has become a new dietary culture, this study examines consumer awareness through big data analysis. We present the direction of delivery food for healthy eating culture and identify the current state of consumer awareness. Resources for big data analysis were mainly articles written by consumers on various websites; the collection period was divided into before and after COVID-19. Results of the big data analysis revealed that before COVID-19, delivery food was recognized as a limited product as a meal concept, but after COVID-19, it was recognized as a new shopping list and a new product for home parties. This study concludes by suggesting a new direction for healthy eating culture.