• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.156-161
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• 2003

It is an interest problem to predict substance distributions in three-dimensional space. Recently, a research field as Geostatistics is advanced. It is a kind of inter- or extrapolation mathematically. Some useful means for the inter- and extrapolation are known, in which slide window method with neural networks is hopeful one. We propose multi-dimensional extrapolation using multi-layer neural networks and the slide-window method. The multi-dimensional extrapolation is not similar to one-dimension. It has plural algorithms. We researched line predictors and local-plain predictors I two-dimensional space. The both predictors are equivalent; however, in multi-dimensional extrapolation, it is very important to find the direction of predictions. Especially, since the slide window method requires information to predict the future in sampling data, if they are not ordered appropriately in the direction, the predictor cannot operate. We tested the extrapolation for typical two-dimensional functions, and found an excellent character of slide-window method based on local-plain. By using the method, we can extrapolate the function until twice-outer regions of the definitions.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.129-135
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• 2016

In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to ${\alpha}-{\beta}$ tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.

• Journal of IKEEE
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• v.28 no.2
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• pp.180-186
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• 2024

This paper proposes an efficient fault diagnosis method for ITSC(Inter-Turn Short Circuit) in three-phase induction motors using CNN. By utilizing only the D-axis component of the D-Q synchronous coordinate system, it compares SWM(Slide Window Method) and GAF(Gramian Angular Field) methods for image encoding. Results show GAF achieving ~74% accuracy, while SWM achieves ~65%, indicating GAF's superiority by 9%. Learning time (~14.74s) remains consistent, particularly with epochs ≤ 100, showcasing faster learning.

• Korean Institute of Interior Design Journal
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• v.18 no.5
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• pp.101-110
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• 2009

In case of general residential house, photovoltaic can be installed at roof, wall, and any other places. But, in case of apartment building, there has not enough roof space to install photovoltaic panels to supply enough electricity. Actually, apartment building roof and facade wall (exclude the balcony window space) is not enough space to produce and supply the electricity to residents by installing PV panel. Generally, the space of facade balcony with windows in facade wall at apartment building occupied about $70{\sim}80%$, in all facade space. So, if we could use the balcony and windows space in facade as PV to generating electricity, there could contribute the energy saying. But, PV cell is opacify. So if it installed at front window area in apartment building, residents may have displeasure for that opacity character. But the other hand, residents are not always in house especially in day time that is exactly good time for generating electricity by PV. If we can use PV at the facade balcony with window without collusion of resident's displeasure, there have good attraction to using sustainable energy. Hence, this study suggests the design of facade balcony window style PV by considering resident's living pattern in apartment building. The methods of this study are as follows. At first, this study surveyed to the residents about residential time in their home and asked user demand by Delphi survey. At second, this study designed balcony open style PV system which oriented to the user demand. At third, this study tests designed result performance by computer simulation that compared design result with old design. As a result, For the purpose of satisfying the resident demand, there designed sliding window style which slide the several door systems to the one side. That would be make balcony absolute open scenery to the residents. Hence, the designed system performance results were as follows. When we compare the small apartment and large apartment, smaller one has good performance than larger one. Because resident's residential time characteristic. And that has more good electronic performance than vertical style that is similar to roof style.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.1-18
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• 2014

Over the past years RFID/SN has been an elementary technology in a diversity of applications for the ubiquitous environments, especially for Internet of Things. However, one of obstacles for widespread deployment of RFID technology is the inherent unreliability of the RFID data streams by tag readers. In particular, the problem of false readings such as lost readings and mistaken readings needs to be treated by RFID middleware systems because false readings ultimately degrade the quality of application services due to the dirty data delivered by middleware systems. As a result, for the higher quality of services, an RFID middleware system is responsible for intelligently dealing with false readings for the delivery of clean data to the applications in accordance with the tag reading environment. One of popular techniques used to compensate false readings is a sliding window filter. In a sliding window scheme, it is evident that determining optimal window size intelligently is a nontrivial important task in RFID middleware systems in order to reduce false readings, especially in mobile environments. In this paper, for the purpose of reducing false readings by intelligent window adaption, we propose a new adaptive RFID data cleaning scheme based on window sliding for a single tag. Unlike previous works based on a binomial sampling model, we introduce the weight averaging. Our insight starts from the need to differentiate the past readings and the current readings, since the more recent readings may indicate the more accurate tag transitions. Owing to weight averaging, our scheme is expected to dynamically adapt the window size in an efficient manner even for non-homogeneous reading patterns in mobile environments. In addition, we analyze reading patterns in the window and effects of decreased window so that a more accurate and efficient decision on window adaption can be made. With our scheme, we can expect to obtain the ultimate goal that RFID middleware systems can provide applications with more clean data so that they can ensure high quality of intended services.