• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.1-9
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• 2014

With the development of quantum computer, the development of the quantum-inspired search method applying the features of quantum mechanics and its application to engineering problems have emerged as one of the most interesting research topics. This algorithm stores information by using quantum-bit superposed basically by zero and one and approaches optional values through the quantum-gate operation. In this process, it can easily keep the balance between the two features of exploration and exploitation, and continually accumulates evolutionary information. This makes it differentiated from the existing search methods and estimated as a new algorithm as well. Thus, this study is to suggest a new minimum weight design technique by applying quantum-inspired search method into structural optimization of planar truss. In its mathematical model for optimum design, cost function is minimum weight and constraint function consists of the displacement and stress. To trace the accumulative process and gathering process of evolutionary information, the examples of 10-bar planar truss and 17-bar planar truss are chosen as the numerical examples, and their results are analyzed. The result of the structural optimized design in the numerical examples shows it has better result in minimum weight design, compared to those of the other existing search methods. It is also observed that more accurate optional values can be acquired as the result by accumulating evolutionary information. Besides, terminal condition is easily caught by representing Quantum-bit in probability.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.43-50
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• 2018

Biometric information computing is greatly influencing both a computing system and Big-data system based on the bio-information system that combines bio-signal sensors and bio-information processing. Unlike conventional data formats such as text, images, and videos, biometric information is represented by text-based values that give meaning to a bio-signal, important event moments are stored in an image format, a complex data format such as a video format is constructed for data prediction and analysis through time series analysis. Such a complex data structure may be separately requested by text, image, video format depending on characteristics of data required by individual biometric information application services, or may request complex data formats simultaneously depending on the situation. Since previous bio-information processing computing systems depend on conventional computing component, computing structure, and data processing method, they have many inefficiencies in terms of data processing performance, transmission capability, storage efficiency, and system safety. In this study, we propose an improved biosensing converged big data computing architecture to build a platform that supports biometric information processing computing effectively. The proposed architecture effectively supports data storage and transmission efficiency, computing performance, and system stability. And, it can lay the foundation for system implementation and biometric information service optimization optimized for future biometric information computing.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.85-98
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• 2009

The tau-p inversion algorithm is widely employed to generate starting models with most computer programs, which implement refraction tomography. This algorithm emphasises the vertical resolution of many layers, and as a result, it frequently fails to detect even large lateral variations in seismic velocities, such as the decreases which are indicative of shear zones. This study demonstrates the failure of the tau-p inversion algorithm to detect or define a major shear zone which is 50m or 10 stations wide. Furthermore, the majority of refraction tomography programs parameterise the seismic velocities within each layer with vertical velocity gradients. By contrast, the Generalized Reciprocal Method (GRM) inversion algorithms emphasise the lateral resolution of individual layers. This study demonstrates the successful detection and definition of the 50m wide shear zone with the GRM inversion algorithms. The existence of the shear zone is confirmed by a 2D analysis of the head wave amplitudes and by numerous closely spaced orthogonal seismic profiles carried out as part of a later 3D refraction investigation. Furthermore, an analysis of the shot record amplitudes indicates that a reversal in the seismic velocities, rather than vertical velocity gradients, occurs in the weathered layers. The major conclusion reached in this study is that while all seismic refraction operations should aim to provide as accurate depth estimates as is practical, those which emphasise the lateral resolution of individual layers generate more useful results for geotechnical and environmental applications. The advantages of the improved lateral resolution are obtained with 2D traverses in which the structural features can be recognised from the magnitudes of the variations in the seismic velocities. Furthermore, the spatial patterns obtained with 3D investigations facilitate the recognition of structural features such as faults which do not display any intrinsic variation or 'signature' in seismic velocities.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.226-233
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• 2013

In this study, In Laver's automatic drying device, laver thickness measurement and control devices that are associated with. Disconnect the water and steam, after put a certain amount of the mixture(water and laver) in the mold. In process, Laver of the size and thickness (weight) to determine, constant light source to detect and image LED Lamp occur Vision Sensor (Camera) prepare, then the values of these state of the image is transmitted in real time embedded computers. Built-in measurement and control with the purpose of the application of each of the channels separately provided measurements are displayed on a monitor, And servo signals sent to each of the channels and it become so set function should be. In this paper, the laver drying device, prior to the laver thickness measurement and control devices that rely on the experience of existing workers directly laver manually adjust the thickness of the lever, but the lever by each channel relative to the actuator by installing was to improve the quality. In addition, The effect of productivity gains and labor savings are.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.607-612
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• 2019

The softness of the crashpad part is one of the important factors which affect the interior perceived quality of the vehicle interior. And while improving the softness of the crashpad part, every effort to lower the production cost has been going on. The PU foaming process for the crashpad part depends on the understanding of a lot of processes, tools and material properties. Therefore, to achieve the requirement of the customer for the interior part's visual quality, the integrated design techniques are investigated to correlate the processes, tool design, material design and the computer aided analysis. In this paper, IMG (In Mold Grain) designed concept is firstly developed to integrate the skin preforming, plastic injection molding of the substrate and the foaming process in a tool within reduced processes. Through the application of this technology, softness of crashpad is improved by 40% compared to the conventional vacuum molding method, and the existing process is reduced by 50% by integrating the injection process and the manufacturing process. And by integrating the injection mold and the skin mold and removing the foaming mold, the number of molds are reduced from 3 to 1, resulting in 20% reduction in the cost of applying a medium-sized passenger car.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.495-509
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• 2020

This study is a theoretical research to explore ways to effectively learn AI in the age of intelligent information driven by artificial intelligence (hereinafter referred to as AI). The emphasis is on presenting a teaching method to make AI education accessible not only to students majoring in mathematics, statistics, or computer science, but also to other majors such as humanities and social sciences and the general public. Given the need for 'Explainable AI(XAI: eXplainable AI)' and 'the importance of storytelling for a sensible and intelligent machine(AI)' by Patrick Winston at the MIT AI Institute [33], we can find the significance of research on AI storytelling learning model. To this end, we discuss the possibility through a pilot study targeting general students of an university in Daegu. First, we introduce the AI storytelling(AI+ST) learning method[30], and review the educational goals, the system of contents, the learning methodology and the use of new AI tools in the method. Then, the results of the learners are compared and analyzed, focusing on research questions: 1) Can the AI+ST learning method complement algorithm-driven or developer-centered learning methods? 2) Whether the AI+ST learning method is effective for students and thus help them to develop their AI comprehension, interest and application skills.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.599-608
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• 2023

The increasing interest in soil moisture data using satellite data for applications of hydrology, meteorology, and agriculture has led to the development of methods for generating soil moisture maps of variable resolution. This study demonstrated the capability of generating soil moisture maps using Sentinel-1 and Sentinel-2 data provided by Google Earth Engine (GEE). The soil moisture map was derived using synthetic aperture radar (SAR) image and optical image. SAR data provided by the Sentinel-1 analysis ready data in GEE was applied with normalized difference vegetation index (NDVI) based on Sentinel-2 and Environmental Systems Research Institute (ESRI)-based Land Cover map. This study produced a soil moisture map in the research area of Victoria, Australia and compared it with field measurements obtained from a previous study. As for the validation of the applied method's result accuracy, the comparative experimental results showed a meaningful range of consistency as 4-10%p between the values obtained using the algorithm applied in this study and the field-based ones, and they also showed very high consistency with satellite-based soil moisture data as 0.5-2%p. Therefore, public open data provided by GEE and the algorithm applied in this study can be used for high-resolution soil moisture mapping to represent regional land surface characteristics.