• 한국항행학회논문지
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• 제14권6호
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• pp.874-881
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• 2010

무인기의 자동 비행 제어 시스템은 기체의 형태, 크기, 무게 등의 정적 및 동적 변화에 따라 스스로 비행계수를 조정하여 목표 비행궤적을 정확히 따라가도록 제어할 필요가 있다. 본 논문에서는 PID 제어 기법을 이용하는 비행제어시스템에 기계학습모듈(MLM)을 추가하여 기체의 특성 변화에 따라 제어계수를 비행중 실시간 자동으로 조정하는 시스템을 제안한다. MLM은 선형회귀분석과 보정학습을 이용하여 설계되었으며 MLM을 통해 학습된 제어계수의 적합성을 평가하는 평가모듈(EvM)을 함께 모델링 하였다. 이 시스템은 FDC 비버 시뮬레이터를 기반으로 실험하였으며 그 결과를 분석 제시하였다.

• 아시안잔디학회지
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• 제7권1호
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• pp.31-34
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• 1993

There is a growing concern in the United Stares over the environmental and human health implications associated with heavy use of water, pesticides, and inorganic ferilizers in maintaining picture perfect golf courses. There is also a growing awareness that a beautiful course is not necessarily a healthy course. The following discussion reviews the interrelationship of turfgrass and the soil that supports it and provides basic information on currently available alternatives to turf management practices that feature intensive application of inorganic fertilizers. water and pesticides. Soil is a dynamic natural environment in which microorganisms play an important role. Soil contains a large mass of microorganisms which produce thousands of enzymes that can catalyze the transformation and degradation of many organic molecules. (In top soil under optimum conditions may contain 10 billion cells per gram of soil.). Turfgrass and the soil which supports it are interdependent. The natural organic cycle as applied to turf and soil begins with healthy vigorous grass plants storing up the sun's energy in green plant tissues as chemical energy. Animals obtain energy by eating plants and when plants and animals die, their wastes are returned to the soil and provide "food" for soil microorganisms. In the next step of the organic cycle soil microorganisms break down complex plant tissues into more basic forms and make the nutrients available to grass roots. Finally, growing plants extract the available nutrients from the soil. By free operation of this organic cycle, natural grasslands have some of the most fertile soils on earths.

• Earthquakes and Structures
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• 제4권6호
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• pp.607-627
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• 2013

The application of shape memory alloys (SMAs) to the seismic response reduction of civil engineering structures has attracted growing interest due to their self-centering feature and excellent fatigue performance. The loading rate dependence of SMAs raises a concern in the seismic analysis of SMA-based devices. However, the implementation of micromechanics-based strain-rate-dependent constitutive models in structural analysis software is rather complicated and computationally demanding. This paper investigates the feasibility of replacing complex rate-dependent models with rate-independent constitutive models for superelastic SMA elements in seismic time-history analysis. Three uniaxial constitutive models for superelastic SMAs, including one rate-dependent thermomechanical model and two rate-independent phenomenological models, are considered in this comparative study. The pros and cons of the three nonlinear constitutive models are also discussed. A parametric study of single-degree-of-freedom systems with different initial periods and strength reduction factors is conducted to examine the effect of the three constitutive models on seismic simulations. Additionally, nonlinear time-history analyses of a three-story prototype steel frame building with special SMA-based damping braces are performed. Two suites of seismic records that correspond to frequent and design basis earthquakes are used as base excitations in the seismic analyses of steel-braced frames. The results of this study show that the rate-independent constitutive models, with their parameters properly tuned to dynamic test data, are able to predict the seismic responses of structures with SMA-based seismic response modification devices.

• Journal of Magnetics
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• 제18권3호
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• pp.370-374
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• 2013

This paper introduces an innovative hybrid array consisting of both permanent and electro magnets. It will enable us to develop an active control mechanism for underdamped electro-dynamic suspension (EDS) Maglev systems. The proposed scheme is based on the Halbach array configuration which takes the major technical advantage from the original Halbach characteristics: a strongly concentrated magnetic field on one side of the array and a cancelled field on the opposite side. In addition, the unique feature of the proposed concept only differs from the Halbach array with permanent magnets. The total magnetic field of the array can be actively controlled through the current of the electro-magnet's coils. As a result, the magnetic force produced by the proposed hybrid array can also be controlled actively. This study focuses on the magnetic characteristics and capability of the proposed array as compared to the basic Halbach concept. The results show that the proposed array is capable of producing not only an equivalent suspension force of the basic Halbach permanent magnet array but also a controlled mode. Consequently, the effectiveness of the proposed array confirms that this study can be used as a technical framework to develop an active control mechanism for an EDS Maglev system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• Earthquakes and Structures
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• 제15권6호
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• pp.701-713
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• 2018

Many bridges in Algeria were constructed without taking into account the seismic effect in the design. The implantation of a new regulation code RPOA-2008 requires a higher reinforcement ratio than with the seismic coefficient method, which is a common feature of the existing bridges. For better perception of the performance bridge piers and evaluation of the risk assessment of existing bridges, fragility analysis is an interesting tool to assess the vulnerability study of these structures. This paper presents a comparative performance of bridge piers designed with the seismic coefficient method and the new RPOA-2008. The performances of the designed bridge piers are assessed using thirty ground motion records and incremental dynamic analysis. Fragility curves for the bridge piers are plotted using probabilistic seismic demand model to perform the seismic vulnerability analysis. The impact of changing the reinforcement strength on the seismic behavior of the designed bridge piers is checked by fragility analysis. The fragility results reveal that the probability of damage with the RPOA-2008 is less and perform well comparing to the conventional design pier.

• Advances in Computational Design
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• 제4권1호
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• pp.1-13
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• 2019

Recent generations of smartphones offer accelerometer sensors as a standard feature. While this has led to the development of a number of related applications (apps), there has been no study on their comparative or individual performance against a benchmark. This paper investigates the comparative performance of a number of smartphone accelerometer apps amongst themselves and to a calibrated benchmark accelerometer. A total of 12 apps were selected for testing out of 90 following an initial review. The selected apps were subjected to sinusoidal vibration testing of varying frequency and the response of each compared against the calibrated baseline accelerometer. The performance of apps was quantified using analysis of variance (ANOVA) and test of significance was carried out. The apps were then compared for a realistic dynamic scenario of measuring the acceleration response of a bridge due to the passage of a French Train $\grave{a}$ Grande Vitesse (TGV) in a laboratory environment.

• 한국가시화정보학회지
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• 제20권2호
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• pp.70-77
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• 2022

Skeletal muscle tissues feature cellular heterogeneity, including differentiated myofibers, myoblasts, and satellite cells. Thanks to the presence of undifferentiated myoblasts and satellite cells, skeletal muscle tissues can self-regenerate after injury. In skeletal muscle regeneration, the collective motions among these cell types must play a significant role, but little is known about the dynamic collective behavior during the regeneration. In this study, we constructed in vitro platform to visualize the migration behavior of skeletal muscle cells in specific conditions that mimic the biochemical environment of injured skeletal muscles. We then visualized the spatiotemporal distribution of stresses arising from the differential collectiveness in the cellular clusters under different conditions. From these analyses, we identified that the heterogeneous population of muscle cells exhibited distinct collective migration patterns in the injury-mimicking condition, suggesting selective activation of a specific cell type by the biochemical cues from the injured skeletal muscles.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3359-3366
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• 2023

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.