Soft tissue osteosarcoma with telangiectatic features: MR imaging findings in two cases

Extraskeletal osteosarcomas are rare tumors, and the telangiectatic variety is the least common histological variety in this group. This report describes the clinical and MR imaging findings in two cases arising in the pretibial soft tissues. Both tumors demonstrated marked inhomogeneity with T2-weighted spin echo and STIR sequences. One of the tumors revealed numerous fluid levels within the lesion. A review of the MRI features of these tumors is provided. Osteosarcoma with telangiectatic features should be considered in the differential diagnosis of a soft tissue mass with fluid-fluid levels in patients 40 years of age or older.     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

Second Generation of Active Structural Control in Civil Engineering

Abstract: Research and development in active control of civil engineering structures in the United States has approximately a 20-year history. Under the leadership of the U.S. National Science Foundation in forming the U.S. Panel on Structural Control Research and in launching a program initiative in the area of structural control, there has been a surge of interest in this research field. Recent progress in active control research in the United States is first summarized in this paper. In the second part, an attempt is made to identify areas of research needs and to recommend future directions for the next generation of active control activities in civil engineering.     

Training Neural Networks by Adaptive Random Search Techniques

A relatively simple stochastic optimization procedure based on the adaptive random search algorithm is presented to train artificial neural networks of the type encountered in applied mechanics applications. After discussing some essential features of the algorithm that influence its search efficiency, a procedure is outlined for replacing the back-propagation training approach by the new method in order to train networks involving high-dimensional parameter vectors. The method is successfully used in conjunction with a multilayer network involving a parameter vector of very high dimension. It is shown that the adaptive random search approach shifts the training effort from the user to the computer by exchanging additional computer search effort for easier training tasks on the part of the user. Extensive simulation studies are presented to provide statistically significant results related to the characteristics of the stochastic training approach. Guidelines are provided for applying the method to generic neural network training episodes.     

Data based model free hysteresis identification for a nonlinear structure

In the last two decades, the damage detection for civil engineering structures has been widely treated as a modal analysis problem and most of the currently available vibration-based system identification approaches are based on modal parameters, namely the natural frequencies, mode shapes and damping ratios, and/or their derivations, which are suitable for linear systems. Nonlinearity is generic in engineering structures. For example, the initiation and development of cracks in civil engineering structures as typical structural damages are nonlinear process. One of the major challenges in damage detection, early warning and damage prognosis is to obtain reasonably accurate identification of nonlinear performance such as hysteresis which is the direct indicator of damage initiation and development under dynamic excitations. In this study, a general data-based identification approach for hysteretic performance in form of nonlinear restoring force using structural dynamic responses and complete and incomplete excitation measurement time series was proposed and validated with a 4-story frame structure equipped with smart devices of magneto-rheological (MR) damper to simulate nonlinear performance. Firstly, as an optimization method, the least-squares technique was employed to identify the system matrices of an equivalent linear system of the nonlinear structure model basing on the excitation force and the corresponding vibration measurements with impact test when complete and incomplete excitations; and secondly, the nonlinear restoring force of the structure was identified and compared with the test measurements finally. Results show that the proposed data-based approach is capable of identifying the nonlinear behavior of engineering structures and can be employed to evaluate the damage initiation and development of different structure under dynamic loads.     

A probabilistic approach for the construction of regional earthquake response spectra

Five scenario earthquakes plausible for the Los Angeles metropolitan region, and one numerical approximation of the 1994 Northridge M_w 6.7 event, provide the database of the proposed methodology that is applied for the construction of regional earthquake response spectra. The methodology involves two main stages of data compaction. In the first stage, the Karhunen–Loève (K–L) decomposition of the excitation temporal covariance matrix is performed. In the second stage, the dominant eigenvectors are analytically approximated with Chebyshev polynomials, thus being converted from eigenvectors to eigenfunctions. This compact analytical representation of the nonstationary excitation data provides an exact closed-form solution for the nonstationary response of linear multi-degree-of-freedom systems. Furthermore, statistical inference analysis for the response variables is conducted, which leads to the construction of regional probabilistic response spectra based on the Log-Normal probability model for the response variables.