a. ghanbarzadeh

In a walking cycle design, maximizing the upright balance should be considered in addition to the kinematic constraints, energy consumption rate must be considered. The purpose of this study is to find the optimal step length obtained for each person according to the physical features. In this research, in order to minimize energy consumption rate by considering maximum balance two cost function were defined. the fall cost function was designed based on the concept of MOS and balance index. To investigate the upright balance and to reconstruct the movement pattern, Data from normal walking gait of healthy subjects was taken and seven links model was defined. In this study, the optimal step length was obtained for the person with height, weight and gait cycle characteristics. it is shown that for a person of 92kg mass and 1.87 meters height, the best step length in walking would be 0.54 meters. In this study, the kinematic and kinetic characteristics of human motion were identified by the analysis of gait patterns on a treadmill. Through the calculation of the balance index, individuals are helped to find the optimal step length for which the maximum balance is achieved. The results of the study can provide the optimal step length to correct the gait pattern.
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The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), which are extracted from the vibration signals of the test data. Effectiveness and novelty of this proposed method are illustrated by using the experimentally obtained the bearing vibration data based on laboratory application. In this research, based on the fast kurtogram method in the time-frequency domain, a technique for the first time is presented using other types of statistical features instead of the kurtosis. For this study, the problem of four classes for bearing fault detection is studied using various statistical features. This study is conducted in four stages. At first, the stability of each feature for each fault mode is investigated, then resistance to load change as well as failure growth is studied. At the end, the resolution and fault detection for each feature using the comparison with a determined pattern and the coherence rate is calculated. From the above results, the best feature that is both resistant and repeatable to different variations, as well as the suitable accuracy of detection and resolution, is selected and with comparing to the kurtosis feature, it is found that this feature is not in a good condition in compared with other statistical features such as harmmean and median. The results show that the accuracy of the proposed approach is 100% by using the proposed neural network, even though it uses only two features.

The protective mechanisms of zinc-rich coatings include cathodic protection driven by the more active Zn particles and barrier-type protections due to the formation of zinc oxide and sealling the pores between the zinc particles are discussed. Methods of evaluating the performance of these coatings are also discussed and electrochemical evaluation as one of the most important method that can provide quantitative results has been more studied. At the end, two ideas of research performed to improve the electrical conductivity of the coatings to increase the longevity of cathodic protection and improved barrier property to prevent penetration of corrosive agents and slow down intake of zinc metal powder is provided.