• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.127-132
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• 2022

As the IoT technology is more developed, it is more important for the accuracy of IoT data. Since the IoT data supports a different formats and protocols, it is often happened that the IoT system is failed or the incorrect data is generated with the unreliable IoT devices(sensor, actuator). Because the abnormality of IoT device or the user situation is not detected correctly, this problem makes the user to be unsatisfied with the IoT system. This study proposes the decision methodology of IoT data consistency whether the IoT data is generated in normal range or not by using the mathematical functions('gradient descent function' and 'linear regression function'). It may be concluded that the gradient function method is suitable for the IoT data which the 'increasing velocity' is related with the next generated pattern(eg. sensor devices), the linear regression function method is suitable for the IoT data which the 'the difference from linear regression function' is related with the next generated pattern in case the data has a linear pattern(eg. water meter, electric meter).

• Korean Architects
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• s.551
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• pp.62-63
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• 2015

• 한국도로학회:학술대회논문집
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• 2002.10a
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• pp.195-198
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• 2002

• Tunnel and Underground Space
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• v.23 no.5
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• pp.353-361
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• 2013

Previous studies for analyzing optimal haulage routes of dump trucks in open pit mines mostly used raster data. However, the raster data has several problems in performing optimal route analyses: (1) the jagged appearance of haulage roads according the cell resolution often causes overestimation of the travel cost; (2) it difficult to trace the topological relationships among haulage roads. These problems can be eliminated by using vector network data, however a new method is required to reflect the performance characteristics of a dump truck according to terrain gradient changes. This study presents a new method to create vector network data with the consideration of terrain gradient for analyzing optimal haulage routes of dump trucks in open pit mines. It consists of four procedures: (a) creating digital elevation models, (b) digitizing haulage road networks, (c) calculating the terrain gradient of haulage roads, and (d) calculating the average speed and travel time of a dump truck along haulage roads. A simple case study at the Roto South pit in the Pasir open pit coal mine, Indonesia is also presented to provide proof that the proposed method is easily compatible to ArcGIS Network Analyst software and is effective in finding optimal haulage routes of dump trucks with considering terrain gradient in open pit mines.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.343-346
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• 1998

In this paper a radiation problem for a finite microstrip antenna structure is analyzed. For the analysis of finite structures we utilize the equivalent volume current. Intergral equation for the unknown equivalent volume current induced on a finite microstrip structure is derived and solved by the use of conjugate gradient-fast fourier. transform (CG-FFT) method. Some numerical examples are radiation patterns derived by the equivalent volume current solved by the conjugate gradient-fast fourier transform.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.149-156
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• 1995

This paper suggests a new experimental system and protocol of mechanical analysis for arterial cross-section research. So far, most methods of arterial studies have been focused on the deformation measurement in longitudinal and circumferential direction. The deformation in radial direction has been theoretically assumed by Poisson's ratio and/or the incompressibility of arterial wall. Also, the radial gradient of strains are neglected. In fact, the radial deformation and radial gradient of strains against blood pressure are important to be observed in the pathological point of view of artery. Proposed experimental system and protocol are to measure the deformation of cross-sectional artery. Also, this method enables to measure the deformation of anterior, posterior, and side site of cross-sectional area. It is meaningful to correlate the mechanically experimented data with pathological data of athroscIerotic artery.

• KSBB Journal
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• v.14 no.1
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• pp.109-113
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• 1999

Supercritical $CO_2$ chromatography was applied for purification of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from fish oil. Various supercritical $CO_2$ pressures were tested to find out the pressure effects on solubility and selectivity of low fatty acids in the silver nitrate column. The solubility of low fatty acids was increased as the supercntical $CO_2$ Pressure increased. However, the selectiviy between low fatty acids and EPA waw decreased. Stepwise density gradient method was applied to increase the purification efficiency of EPA. Low fatty acids were easily separated at the early elution steps with low $CO_2$ densities. Successive fractions containing 92.1~97.8% of EPA were collected. The average concentration of three purified fractions was 95.6% with the recovery rate of 30%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.128-134
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• 2009

Iris recognition is a biometric technology which can identify a person using the iris pattern. Recently, using iris information is used in many fields such as access control and information security. But Perform complex operations to extract features of the iris. because High-end hardware for real-time iris recognition is required. This paper is appropriate for the embedded environment using local gradient histogram embedded system using iris feature extraction methods have implement. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform noticeably improves recognition performance and it is noted that the processing time of the local gradient histogram transform is much faster than that of the existing method and rotation was also a strong attribute.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.153-162
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• 2019

Recently, it is necessary to develop a technology for quantitatively evaluating railway vibration to prevent civil complaints about orbital structures caused by railway noise and normal operation of ultra-precise equipment of orbital industrial complexes. The existing analytical method requires a very complicated dynamic response model, and it is difficult to secure the reliability of the result due to the inaccuracy of the demand model. Therefore, in this paper, we propose a railway vibration evaluation algorithm and system that deduce the vibration value generated from railway operation by using Linear Regression and Gradient Descent technique based on actual measurement railway vibration database that classifies factors affecting railway vibration. The prediction results obtained by the proposed algorithm show higher efficiency and accuracy than the existing analytical methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2418-2425
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• 1994

Although the error backpropagation(EBP) algorithm based on the gradient descent method is a widely-used learning algorithm of neural networks, learning sometimes takes a long time to acquire accuracy. This paper develops a novel learning method to alleviate the problems of EBP algorithm such as local minima, slow speed, and size of structure and thus to improve performance by adopting other new networks. Gaussian Potential Function networks(GPFN), in parallel with multilayer neural networks. Empirical simulations show the efficacy of the proposed algorithm in function approximation, which enables us to train networks faster with the better generalization capabilities.