• Journal of Industrial Technology
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• v.25 no.B
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• pp.141-147
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• 2005

A target of this paper is to get some elementary experimental data on the energy harvesting using a piezoelectric material. A THUNDER series piezo material (TH7-R), which has been developed by NASA engineer is selected for this study. In order to provide a mechanical energy to the piezoelectric material, a mechanical motion vibrator and its driving electronics are designed. Using a simple PWM control, the excitation frequency of vibrating mechanical motion is varied. The generated electric power as a function of the excitation frequency is monitored and analyzed. This initial experiment shows a possible energy source using a piezoelectric material for the application to low-power consumed small electronic devices such as RFID, MEMS, and etc.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.6
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• pp.92-98
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• 2008

The target of this paper is to study on the usefulness of the SSHI technique as a wireless electrical power supply when it is driven by mechanical vibrations of low frequency. A THUNDER series a piezoelectric material (TH7-R), which has been developed by a NASA engineer is selected for this study. A mechanical motion vibrator supplies piezoelectric material with mechanical energy. An optical fiber sensor and a pulse generating circuit are used to accomplish the parallel-SSHI technique. As a result of this study, energy harvesting using SSHI technique results in a significant increase of the electrical power flow.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.265-274
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• 2022

This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.33.2-33.2
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• 2011

The 'NREL Phase VI' model with a 10.06m diameter was tested in the NASA Ames tunnel to make a reference data of the computational models. The test was conducted at the one rotational speed, blade tip speed 38m/s and the Reynolds number of the sectional airfoils in that test was around 1E6. The 1/5 scale down model of the 'NREL Phase VI' model was used in this paper to study the power characteristics in low Reynolds number region, 0.1E6 ~ 0.4E6 which is achievable range for the conventional wind tunnel facilities. The torque generated by the blade was directly measured by using the torque sensor installed in the rotating axis for a given wind speed and rotational speed. The power characteristics below the stall condition, lambda > 4, was presented in this paper. The power coefficient is very low in the condition below the Re. 0.2E6 and rapidly increases as the Re. increases. And it still increases but the variation is not so big in the condition above the Re. 0.3E6. This results shows that to study the performance of the wind turbine blade by using the scaled down model, the Re. should be larger than the 0.3E6.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• Smart Structures and Systems
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• v.7 no.1
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• pp.1-13
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• 2011

It has been shown in the literature that active adjustment of the intake area of a jet engine has potential to improve its fuel efficiency. This paper presents the design and control of a novel proof-of-concept active jet engine intake using Nickel-Titanium (Ni-Ti or Nitinol) shape memory alloy (SMA) wire actuators. The Nitinol SMA material is used in this research due to its advantages of high power-to-weight ratio and electrical resistive actuation. The Nitinol SMA material can be fabricated into a variety of shapes, such as strips, foils, rods and wires. In this paper, SMA wires are used due to its ability to generate a large strain: up to 6% for repeated operations. The proposed proof-of-concept engine intake employs overlapping leaves in a concentric configuration. Each leaf is mounted on a supporting bar than can rotate. The supporting bars are actuated by an SMA wire actuator in a ring configuration. Electrical resistive heating is used to actuate the SMA wire actuator and rotate the supporting bars. To enable feedback control, a laser range sensor is used to detect the movement of a leaf and therefore the radius of the intake area. Due to the hysteresis, an inherent nonlinear phenomenon associated with SMAs, a nonlinear robust controller is used to control the SMA actuators. The control design uses the sliding-mode approach and can compensate the nonlinearities associated with the SMA actuator. A proof-of-concept model is fabricated and its feedback control experiments show that the intake area can be precisely controlled using the SMA wire actuator and has the ability to reduce the area up to 25%. The experiments demonstrate the feasibility of engine intake area control using an SMA wire actuator under the proposed design.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.89-101
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• 2007

This paper investigates the effects of the fusion of multifrequency (C and L bands) polarimetric SAR data in land-cover classification. NASA JPL AIRSAR C and L bands data were used to supervised classification in an agricultural area to simulate the integration of ALOS PALSAR and Radarsat-2 SAR data to be available. Several scattering features derived from target decomposition based on eigen value/vector analysis were used as input for a support vector machines classifier and then the posteriori probabilities for each frequency SAR data were integrated by applying a probabilistic ratio model as a decision level fusion methodology. From the case study results, L band data had the proper amount of penetration power and showed better classification accuracy improvement (about 22%) over C band data which did not have enough penetration. When all frequency data were fused for the classification, a significant improvement of about 10% in overall classification accuracy was achieved thanks to an increase of discrimination capability for each class, compared with the case of L band Shh data.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.4
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• pp.28-35
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• 2012

Recently, as the social interest increase has been focused on new renewable energy system to build-up sustainable urban planning system, the related studies have been actively conducting. As well as in other areas, the construction area, which accounts for over 40% of the total energy consumption, clearly showed this tendency. Whereas, various landscape facilities applying renewable energy equipments have been manufactured and installed, systematic study available for planning and designing is rarely found in Korea. This study is expected to contribute to the landscape planning and designing by quantifying the energy-efficient and economic advantages of the renewable energy system for landscape facilities. For this purpose, the analysis on the energy-efficiency and economic values under the scenario that the current fossil power supply for the streetlights in urban parks in Seoul, Daegu, and Incheon were replaced by photovoltaic power generation were performed through RETScreen$^{(R)}$ a clean energy simulation program based on the NASA Satellite Meteorological Data. As a result, the generated power and the economic values vary depending on the climatic features of the appointed cities. This study will be used to build up the effective decision-making in applying the clean renewable system to the plan and design of landscaping.