Reliability-based design optimization of problems with correlated input variables using a Gaussian Copula

The reliability-based design optimization (RBDO) using performance measure approach for problems with correlated input variables requires a transformation from the correlated input random variables into independent standard normal variables. For the transformation with correlated input variables, the two most representative transformations, the Rosenblatt and Nataf transformations, are investigated. The Rosenblatt transformation requires a joint cumulative distribution function (CDF). Thus, the Rosenblatt transformation can be used only if the joint CDF is given or input variables are independent. In the Nataf transformation, the joint CDF is approximated using the Gaussian copula, marginal CDFs, and covariance of the input correlated variables. Using the generated CDF, the correlated input variables are transformed into correlated normal variables and then the correlated normal variables are transformed into independent standard normal variables through a linear transformation. Thus, the Nataf transformation can accurately estimates joint normal and some lognormal CDFs of the input variable that cover broad engineering applications. This paper develops a PMA-based RBDO method for problems with correlated random input variables using the Gaussian copula. Several numerical examples show that the correlated random input variables significantly affect RBDO results.     

Reliability-based design optimization with confidence level under input model uncertainty due to limited test data

For obtaining a correct reliability-based optimum design, the input statistical model, which includes marginal and joint distributions of input random variables, needs to be accurately estimated. However, in most engineering applications, only limited data on input variables are available due to expensive testing costs. The input statistical model estimated from the insufficient data will be inaccurate, which leads to an unreliable optimum design. In this paper, reliability-based design optimization (RBDO) with the confidence level for input normal random variables is proposed to offset the inaccurate estimation of the input statistical model by using adjusted standard deviation and correlation coefficient that include the effect of inaccurate estimation of mean, standard deviation, and correlation coefficient.     

Position control of a mobile inverted pendulum system using radial basis function network

This article presents the implementation of position control of a mobile inverted pendulum (MIP) system by using the radial basis function (RBF) network. The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is a nonlinear system whose dynamics is nonholonomic. The goal of this study was to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique scheme is used as a neural network control method for the MIP. The back-propagation learning algorithm of the RBF network is derived for online learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the desired position of the MIP.     

Iteratively constrained selection of word alignment links using knowledge and statistics

Word alignment is a crucial component in applications that use bilingual resources. Statistical methods are widely used because they are portable and allow simple system building. However, pure statistical methods often incorrectly align functional words in the English–Korean language pair due to differences in the typology of the languages and a lack of knowledge. Knowledge is inevitably required to correct errors and to improve word alignment quality. In this paper, we introduce an effective method that uses an iterative process to incorporate knowledge into the word alignment system. The method achieved significant improvements in word alignment and its application: statistical machine translation.     

A case-based reasoning approach to cognitive map-driven tacitknowledge management

Knowledge is at the heart of knowledge management. In literature, a lot of studies have been suggested covering the role of knowledge in improving the performance of management. However, there are few studies about investigating knowledge itself in the arena of knowledge management. Knowledge circulating in an organization may be explicit or tacit. Until now, literature in knowledge management shows that it has mainly focused on explicit knowledge. On the other hand, tacit knowledge plays an important role in the success of knowledge management. It is relatively hard to formalize and reuse tacit knowledge. Therefore, research proposing the explication and reuse of tacit knowledge would contribute significantly to knowledge management research. In this sense, we propose using cognitive map (CM) as a main vehicle of formalizing tacit knowledge, and case-based reasoning as a tool for storing CM-driven tacit knowledge in the form of frame-typed cases, and retrieving appropriate tacit knowledge from the case base according to a new problem. Our proposed methodology was applied to a credit analysis problem in which decision-makers need tacit knowledge to assess whether a firm under consideration is healthy or not. Experiment results showed that our methodology for tacit knowledge management can provide decision makers with robust knowledge-based support.     

Suppression of superconductivity in the Er1− x Pr x Ba2Cu3O7−δ

Measurements of electrical resistivity. X-ray diffraction patterns, magnetic susceptibility and thermoelectric power of the Er_1-x Pr _x Ba₂Cu₃O_7- system have been made. The superconducting transition temperature was found to decrease monotonically with praseodymium concentration, x. From the susceptibility data, it was determined that the valence of praseodymium lies between +3 and +4. The thermoelectric power was found to increase with x, and the slopes of dS/dT were negative except for the case x= 0. The. tendency of the thermopower to change with increasing praseodymium concentration has been qualitatively explained using the theory for strongly correlated systems.     

Video Panorama for 2D to 3D Conversion

Accurate depth estimation is a challenging, yet essential step in the conversion of a 2D image sequence to a 3D stereo sequence. We present a novel approach to construct a temporally coherent depth map for each image in a sequence. The quality of the estimated depth is high enough for the purpose of2D to 3D stereo conversion. Our approach first combines the video sequence into a panoramic image. A user can scribble on this single panoramic image to specify depth information. The depth is then propagated to the remainder of the panoramic image. This depth map is then remapped to the original sequence and used as the initial guess for each individual depth map in the sequence. Our approach greatly simplifies the required user interaction during the assignment of the depth and allows for relatively free camera movement during the generation of a panoramic image. We demonstrate the effectiveness of our method by showing stereo converted sequences with various camera motions.     

Forecasting trends of high-frequency KOSPI200 index data using learning classifiers

Recently many statistical learning techniques have been applied to the prediction of financial variables. The aim of this paper is to conduct a comprehensive study of the applications of statistical learning techniques to predict the trend of the return of high-frequency Korea composite stock price index (KOSPI) 200 index data using the information from the one-minute time series of spot index, futures index, and foreign exchange rate. Through experiments, it is observed that the spot index change is better predictable with high-frequency time series data and the futures index information significantly improves the prediction accuracy of the return trends of the spot index for high-frequency index data, while the information of exchange rate does not. Also, dimension reduction process before training helps to increase the accuracy and dramatically for some classifiers. In addition, the trained classifiers with which a virtual trading strategy is applied to, noticeable better profits can be achieved than just a buy-and-hold-like strategy.     

Experimental investigation on the detachable thermosiphon for conduction-cooled superconducting magnets

A detachable thermosiphon, as a transient thermal switch for conduction-cooled superconducting magnet, is designed, fabricated and tested. A thermosiphon between the first and second stages of a cryocooler can reduce the cool-down time of a conduction-cooled superconducting magnet by using the large cooling capacity of the first stage. The thermosiphon is a very efficient heat transfer device until all the working fluid in it freezes (off-state). After the working fluid freezes and the second stage temperature becomes lower than that of the first stage, however, the thermosiphon then becomes a conduction heat leak path between two stages of the cryocooler. Considering a very small cooling capacity of the second stage of the cryocooler around 4.2 K, the conduction heat loss is not negligible. Therefore, a detachable thermosiphon, made of a metal bellows, is considered to be able to eliminate such a conduction heat leak. The mock-up magnet is cooled down with the thermosiphon and the thermodynamic states of the thermosiphon and the mock-up magnet are precisely examined during the whole cool-down process. At off-state, the thermosiphon is detached mechanically from the magnet. In this way, the conduction heat leak path through the thermosiphon wall is completely eliminated. This paper describes the detailed transient operation of the detachable thermosiphon using nitrogen as the working fluid.     

Low-Complexity Symbol Detector for MIMO-OFDM-Based Wireless LANs

In this brief, a low-complexity hardware architecture for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) symbol detectors with two transmit and two receive antennas is proposed. The detectors support two MIMO-OFDM schemes of space-frequency block coded OFDM and space-division multiplexing OFDM in order to achieve higher performance and throughput. However, symbol detection processes for these two schemes have high computational complexity, which is a burden to hardware implementation of MIMO-OFDM symbol detectors. In order to reduce complexity, the proposed symbol detector is designed with shared architecture, where similar functional blocks are merged and share the hardware resources, and results in the reduction of logic gates by 34% over a conventional architecture employing two individual detectors