Turn-turn short circuit fault management in permanent magnet machines

This paper presents a systematic study on turn-turn short circuit fault and ways to manage them to provide a basis for comparison of the various options available. The possible methods to reduce the likelihood of the winding SC fault and the fault mitigation techniques related to such faults are discussed. A Finite Element (FE) analysis of a surface-mount Permanent Magnet (PM) machine under application of different mitigation techniques during a turn-turn fault is presented. Both machine and drive structural adaptations for different fault mitigation techniques are addressed. Amongst the investigated fault mitigation techniques, the most promising solution is identified and validated experimentally. It is shown that the shorting terminal method adopting vertical winding arrangement is an effective method in terms of the implementation, reliability and weight

On Long Range Dependence and Token Buckets

The Long Range Dependence (LRD) property of actual tra#c in today's network applications has been shown to have significant impact on network performance. In this paper we consider the problem of optimally dimensioning token bucket parameters for LRD tra#c. We first empirically illustrate the di#erent behavior of token buckets when acting on LRD vs. SRD tra#c with identical average and peak rates. The comparison shows that LRD tra#c requires higher token rates and larger bucket sizes. In this paper we investigate the use of a statistical model to analytically determine optimal bucket parameters under various optimization criteria. The model is based on Fractional Brownian Motion and takes into account the degree of LRD. We apply the model to several aggregation scenarios of MPEG video sources. The analytic results are validated against empirical results. Minimum token bucket parameter curves obtained by analysis and via experiments match well. This is particularly true in the region relevant to the adopted optimization criteria. Thus, the analytic approach presented here is e#ective in optimally sizing token buckets for LRD tra#c, and has potential in wider contexts under di#erent tra#c conditions, as well as for various optimization criteria

Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device

The bioelectrical impedance analysis (BIA) method is widely used to predict percent body fat (PBF). However, it requires four to eight electrodes, and it takes a few minutes to accurately obtain the measurement results. In this study, we propose a faster and more accurate method that utilizes a small dry electrode-based wearable device, which predicts whole-body impedance using only upper-body impedance values. Such a small electrode-based device typically needs a long measurement time due to increased parasitic resistance, and its accuracy varies by measurement posture. To minimize these variations, we designed a sensing system that only utilizes contact with the wrist and index fingers. The measurement time was also reduced to five seconds by an effective parameter calibration network. Finally, we implemented a deep neural network-based algorithm to predict the PBF value by the measurement of the upper-body impedance and lower-body anthropometric data as auxiliary input features. The experiments were performed with 163 amateur athletes who exercised regularly. The performance of the proposed system was compared with those of two commercial systems that were designed to measure body composition using either a whole-body or upper-body impedance value. The results showed that the correlation coefficient ( r 2 ) value was improved by about 9%, and the standard error of estimate (SEE) was reduced by 28%