• Journal of Institute of Control, Robotics and Systems
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• v.14 no.4
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• pp.385-391
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• 2008

To effectively cope with a complexity of kinematic metrology due to workspace enlargement of the planar stage, the linear induction motor is suggested as its new driving source. Especially, the linear induction motor under uniform plate type of secondary doesn't inherently have a periodical force ripple which is generally shown in the brushless DC motor. But, it presents a poor transient characteristic at zero or low speed zone owing to time delay of flux settling, resulting in slow response. To improve the servo property of linear induction motor and apply successfully it to the precision stage, this paper discusses a modified vector control methodology. The controller has a novel input form, fixed d-axis current, q-axis current and forward-fed DC current, to control thrust force and normal force of the linear induction motor independently. Influence of the newly introduced input and the feasibility of controller are validated experimentally.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.241-249
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• 2020

The role of money in the modern economy highly determines the intensity and the development of the macroeconomy. The money supply is assumed to be as much as money demand, which reflects the economic character of a country and indicates the growth and development of macroeconomy. In Indonesia, the money supply (M1) is related to the economic dynamics in either the monetary market or the goods market. This research aims at analyzing factors that influence the money supply and to what extent the economic factors affect the money supply in Indonesia. The analysis method used in this research was Vector Autoregressive (VAR) with some variables, such as money supply (M1), interest rate, and Gross Domestic Product (GDP) from the 1st quarter of 2001 until the 1st quarter of 2013. The data collection method was in the form of data compilation from credible sources, such as Bank of Indonesia (BI), Central Bureau of Statistics (CBS), and International Financial Statistics (IFS). To obtain adequate analysis results, several tests were taken, such as unit-root test, Granger causality test, and optimal lag. VAR analysis formulates the correlation among independent variables, so it also sees the study of impulse response and matrix decomposition.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.491-498
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• 2015

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

• Current Optics and Photonics
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• v.4 no.3
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4239-4243
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• 2014

Background: This study investigated the influence of curcumin on HOX transcript antisense RNA (HOTAIR)-mediated migration of cultured renal cell carcinoma (RCC) cells. Materials and Methods: Five RCC cell lines (769-P, 769-P-vector, 769-P-HOTAIR, 786-0, and Kert-3 ) were maintained in vitro. The expression of HOTAIR mRNA was determined by quantitative real-time PCR and cell migration was measured by transwell migration assay. The effects of different concentrations of curcumin (0 to $80{\mu}mol/L$) on cell proliferation was determined by the CCK-8 assay and influence of non-toxic levels (0 to $10{\mu}M$) on the migration of RCC cells was also determined. Results: Comparison of the 5 cell lines indicated a correlation between HOTAIR mRNA expression and cell migration. In particular, the migration of 769-P-HOTAIR cells was significantly higher than that of 769-P-vector cells. Curcumin at $2.5-10{\mu}M$ had no evident toxicity against RCC cells, but inhibited cell migration in a concentration-dependent manner. Conclusions: HOTAIR expression is correlated with the migration of RCC cells, and HOTAIR may be involved in the curcumin-induced inhibition of RCC metastasis.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1155-1157
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• 2002

This paper presents the use of a simple genetic algorithm for the tuning of a proportional-integral speed controller for an induction motor drive. The influence of population size, generation number and rate of mutation on the convergence of the genetic algorithm is investigated. On Matlab/Simulink environment, this paper proposes an optimal GA-PI controller of indirect vector control for induction motor drive system. The simulation results verify that the system has a more robust to the parameter variation than classical PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.488-492
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• 2004

In this paper, the design of a speed sensorless vector control system for induction motor is performed by using a new sliding mode technique based on current model flux observer. A current and flux observer based on the current estimation error is constructed. The proposed current observer includes a sliding mode function, which is derivative of the flux. That is, sliding mode observer which allows the estimation of both the rotor speed and flux based on the measurement of motor terminal quantities, would be proposed. And, a synergetic speed controller using the estimated speed signal is designed to stabilize the speed loop. Simulation results are presented to confirm the theoretical analysis, and to show the system performance with different observer gains and the influence of the motor parameter.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.55-59
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• 1997

In general, Vector control of synchronous reluctance motor(SynRM) is performed under the assumptions that all the parameters are constant and magnetizing flux saturation and iron loss effect can be negligible. Under these assumptions, however, torque nonlinear characteristic can be a possible performance deterioration when precision torque control is needed and operating speed is high. This paper proposes the method, in the Synchronous Reluctance Motor (SynRM), which select appropriate stator d,q-axis currents that the influence of iron core loss on the developed torque can be minimized, and shows that the proposed method is comparable to the algorithm which compensates the iron core loss effect.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.40-47
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• 2003

In this paper, we propose a new method for reducing the computational overhead of fine-to-coarse multi-resolution motion estimation (MRME) at the finest resolution level by searching for the region to consider motion vectors of the coarsest resolution subband. At this time, if half-pel accuracy motion estimation (HPAME) is used in the baseband where influence a lot of effect to the reconstructed image, we can have the motion vector exactly But, this method causes to higher computational overhead. So we suggest the method to the computational overhead by using selective interpolation. Experimental results show that the proposed algorithm gives better results than the traditional algorithms from image quality.