Automatic flaw detection on signals in tube support plate region

An automatic flaw detection system for signals in tube support plate (TSP) regions in steam generators in nuclear power plants, was created using the affine transformation for preprocessing, wavelet transformation, deconvolution, and orthogonal conjugate spectrum filtering for feature extraction, and the fuzzy inference system for evaluation. Tube support plate data were extracted from an entire tube data set. The affine transformation was used to suppress TSP signals since they were not of interest. This, however, did not suppress the TSP signal perfectly but left residuals. The residual signal was then transformed using wavelets for feature extraction. The wavelet transformation with the residual data provides time and frequency information that are the important factors in determining patterns of flaw and noise signals. The convolution method was used to extract the flaw candidates, as well. By removing the impulse response of TSP signals, we could distinguish the flaw candidates from noise. Orthogonal conjugate spectrum filtering was also used to extract the flaw candidates by suppressing TSP signals in the frequency domain. With these three methods, the flaw candidates were extracted and given evaluation values. A fuzzy inference system (FIS) was used to evaluate the flaw candidates extracted in the feature extraction stage. The FIS simply summed up the evaluation-values of a flaw candidate and provided the total. In the decision-making procedure, any flaw candidate with the total of 0.5 or higher evaluation value, was categorized as a real flaw signal

Initial operational capability of future weapon systems and field test method using digital twin

The application of artificial-intelligence technology to weapon systems, is difficult to use in military applications for such reasons as killing or wounding problems, ethical problems, and field environment considerations; therefore, more-sophisticated techniques and development are required for testing and evaluation. There is an increasing demand for advanced weapon systems that incorporate new technologies, such as artificial intelligence, and for electrification, and a new paradigm that can confirm and verify the performance required for field operations is required. Therefore, through case studies on digital twins, a plan for field testing and initial operational capability in future weapon systems was developed. Five development directions were identified, including policies and systems, organization, establishment of infrastructure, and utilization plans, to establish a field test and initial operational capability. In addition, studies on economic ripple effects and cost-effectiveness were conducted based on application cases of digital twins in the private sector. An optimal method for users to safely verify performance by utilizing the digital twin method to overcome the difficulties of testing and evaluating weapon systems with new technologies from the time when the weapon system requirements are determined to mass production is proposed

Technological trends in wheeled armoured vehicle from patent analysis

This study analyzed the domestic and foreign patent applications and technology trends of wheeled armored vehicles. Patents for wheeled armored vehicles have been steadily filed since 2009. As the technology development of armoured vehicles has achieved maturity, this technology has entered stabilization period. The scope of patent technology search is to select patent documents from the five major patent application countries that have applied for a large number of patents globally in both quantity and quality, and the search focused on patents published and registered between 1990 and August 2019 using the WIPS ON program. In particular, the main technologies of wheeled armored vehicles, such as protection, mobility, firepower, and body technology, were analyzed. Technological trend data from patents can be used to create requirements for weapons systems. If technology trends are not sufficiently reflected when raising requirements for a weapon system, problems such as obsolete technology development and lack of operational effectiveness may occur, if an excessive required operational capability (ROC) is set without considering the technology, there is a risk that the target performance may not be reached, costs may be incurred and additional time may be needed. So, for the development of weapon systems, it is necessary to analyze the trends of major technologies and find promising technologies in the long term and of wheeled armored vehicles and the latest development trends of major countries are investigated, as well as the technical information necessary for the establishment of the maneuvering system, which is the cornerstone of Army TIGER 4.0

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Proactive and Sustainable Transport Investment Strategies to Balance the Variance of Land Use and House Prices: A Korean Case

The transport infrastructure sustaining the ascension of land values while synergizing with the industries is a condition optimized for economic sustainability. In general, although transport investment aims to create a more reliable, less congested, better-connected transport network, the secondary aim is to facilitate balanced and sustainable development by enhancing accessibility to infrastructures. Although the current investment principle in Korea more or less reflects the primary purpose, the second aim is not fully reflected and might be too strict about measuring the balanced and sustainable influence on the regional economy. Considering that the house price is an output of regional production, this research tried to establish more proactive quantitative models explaining how ‘transport accessibility to infrastructure’ raises the apartment price in South Korea while interacting with the industries. This study achieved four main results according to the model. First, most urban infrastructures raise apartment prices per square meter about ten times higher than most industries, given a percentage change. Second, the synergy between industrial sales and infrastructural accessibility was negative in most cases, showing a limit of infrastructural investment alone to facilitate sustainable development. Third, an impoverished area tends to conclude positive synergies between industries and infrastructures, justifying more infrastructural investment in those poor areas. Finally, a public service behaves as infrastructure, which re-examines public services’ functionality of the prime water. Conclusively, this research proved that accessibility to core infrastructures is essential in conjunction with land use status resulting from industrial geography to rebalance Korean apartment prices for sustainable investment in transportation sectors