This study aimed to evaluate the reliability factors, and identify causes of error in direct anthropometry method. After training three beginner anthropometrists and following the instructions of anthropometric standards, 48 body dimensions of 42 male students were measured three times. In other words, the physical dimensions of each subject were measured for 9 times. All participants were wearing uniforms during anthropometry, with bare feet. Differences in values of Repeated Measurement Test were explored using SPSS software version 11. The same software was employed to evaluate, through calculating ICC index, the correlation between anthropometrists. Inter-Observer repeated measurement test showed significant difference in the measurements taken in 3, 7 and 1 dimension (s) by the three anthropometrists. The average measurement was significantly different at 16 dimensions; this, however, showed no difference at 32 dimensions. Measurements taken by anthropometrist 1 had ICC values of 0.26 (Min) and 0.99 (Max); these values were 0.48 (Min) and 1.00 (Max) for anthropometrist 2 and 0.23 (Min) and 0.98 (Max) for antropometrist 3. The maximum and minimum values of ICC index in all three anthropometrists were respectively close to and above 0.98, and lower than 0.5. High value of ICC in the measured dimensions indicated high reliability of repeated measurements. The decreasing value of some indexes can be attributed to such factors as random error, poor design of measurements tool (which in turn leads to random error), the long time devoted to measurement process, high number of dimension measured, changes in posture of subjects and deviation from the standard position.

Steering assist system controls the force transfer behavior of the steering system and improves the steering probability of the vehicle. Moreover, it is an interface between the diver and vehicle. Fault detection in electrical assisted steering systems is a challenging problem due to frequently use of these systems. This paper addresses the fault detection and reconstruction in automotive electrical steering assist systems. Two types of faults including sensor fault and actuator fault are investigated. In this paper, four different model-based fault detection methods including Luenberger Observer method, Parity space method, decoupling filter of fault from disturbance method and the unknown input Observer are studied. In each method, a sensor and actuator fault is investigated based on the model of the system. Moreover, we examine a method for the fault reconstruction based on the sliding mode Observer. Finally, these methods are applied to an automotive electrical steering assist system. The results are presented and thoroughly discussed.

Background: Histopathologic assessment of liver tissue is an essential step in management and follow-up of non-alcoholic fatty liver disease (NAFLD) while inter- and intra-Observer variations limit the accuracy of these assessments.Objectives: The aim of this study was to assess the inter- and intra-Observer reproducibility of histopathologic assessment of liver biopsies based on NAFLD activity score (NAS) scoring system.Materials and Methods: The anonymous liver biopsy samples of 100 consecutive NAFLD suspected adults were randomly assigned to four pathologists. Then, the samples were randomly reassigned to the pathologists for the second time in a way that each sample would be evaluated by two different pathologists. Biopsies were revisited by their first evaluator after two months. The results were reported based on NAS scoring system.Results: Inter-Observer agreement of the pathology scores based on NAS scoring system was acceptable for steatosis, lobular inflammation, and fibrosis, but not for hepatocyte ballooning. The intra-Observer agreement was acceptable in all scales, with lowest intra-class correlation observed for lobular inflammation.Conclusions: NAS scoring system has good overall inter- and intra-Observer agreement, but more attention should be given to defining the hepatocyte ballooning and lobular inflammation, and training the pathologists to improve the accuracy of pathology reports.

In this paper, an innovative adaptive output feedback control scheme is proposed for general multi-input multi-output (MIMO) plants with unknown parameters in a regulation task; such that the outputs of the plant converge to zero as well as the control gains remain uniformly bounded. First an adaptive Observer is designed to estimate the state variables and system parameters by using the inputs and outputs of the plant. Then a linear combination of the estimated states by adaptive control gains is used to design a robust adaptive controller. Some theorems are given to show the convergence of the modeling errors and the control gains. The proposed controller is used to control a two degree of freedom robot manipulator such that the robot moves from any initial configuration to zero position. Simulation results exhibit the effectiveness of the proposed scheme to control the robot manipulator with different initial conditions and parameter perturbations.

This paper concerns the design of a neural state Observer for nonlinear dynamic systems with noisy measurement channels and in the presence of small model errors. The proposed Observer consists of three feedforward neural parts, two of which are MLP universal approximators, which are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN), which is being updated on-line. The off-line trained parts are able to generate state estimations instantly and almost accurately, if there are not catastrophic errors in the mathematical model used. The contribution of the on-line adapting part is to compensate the remainder estimation error due to uncertain parameters and/or unmodeled dynamics. A time delay term is also added to compensate the arising differential effects in the Observer. The proposed Observer can learn the noise cancellation property by using noise corrupted data sets in the MLPs off-line training. Simulation results in two case studies show the high effectiveness of the proposed state observing method.

Structural vibration control is one of the most important features in structural engineering. Real-time information about seismic resultant forces is required for deciding module of intelligent control systems. Evaluation of lateral forces during an earthquake is a complicated problem considering uncertainties of gravity loads amount and distribution and earthquake characteristics. An artificial neural network (ANN) has been trained in this article to estimate these forces. This ANN was trained on the results of time history analysis of a three-story building under 702 different loadings. Results of numerical examples verify that the trained ANN can predict the expected forces with negligible deviations.

In this paper, a neural network model reference adaptive system speed Observer is designed, which can be used inspeed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In otherwords, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Thenequations of the reference model and adaptive model have been extracted. Existence of the speed-dependent functionsin the reference model causes error in speed estimation. In order to reduce error, the reference model equations areupdated unlike the standard MRAS method. The adaptive model equations have also been discrete and they have beenrewritten so as to be represented by a linear neural network (ADALINE). On this basis, the so-called back propagationhas been used to compute online, in recursive form, the machine linear speed. Finally, the estimated speed is used formotor speed control. The simulations show the efficiency of the method.