Forced vibration frequency response for a permanent magnetic planetary gear

The time and frequency forced responses for the permanent magnetic planetary gear drive were computed and analyzed. The influence of air gap between sun gear and planetary gear to frequency forced responses is discussed. Results show that the changes of air gap have obvious effects on the low frequency vibration amplitude of elements. When air gap is too large or too small, it will seriously affects the dynamic performance of the system. So, selecting rational system structure parameters is very important, which can avoid system elements generating larger vibration

Signaling at the neuromuscular synapse

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 1995.Includes bibliographical references.by Xuejun Zhu.Ph.D

Learning to Price Supply Chain Contracts against a Learning Retailer

The rise of big data analytics has automated the decision-making of companies and increased supply chain agility. In this paper, we study the supply chain contract design problem faced by a data-driven supplier who needs to respond to the inventory decisions of the downstream retailer. Both the supplier and the retailer are uncertain about the market demand and need to learn about it sequentially. The goal for the supplier is to develop data-driven pricing policies with sublinear regret bounds under a wide range of possible retailer inventory policies for a fixed time horizon. To capture the dynamics induced by the retailer's learning policy, we first make a connection to non-stationary online learning by following the notion of variation budget. The variation budget quantifies the impact of the retailer's learning strategy on the supplier's decision-making. We then propose dynamic pricing policies for the supplier for both discrete and continuous demand. We also note that our proposed pricing policy only requires access to the support of the demand distribution, but critically, does not require the supplier to have any prior knowledge about the retailer's learning policy or the demand realizations. We examine several well-known data-driven policies for the retailer, including sample average approximation, distributionally robust optimization, and parametric approaches, and show that our pricing policies lead to sublinear regret bounds in all these cases. At the managerial level, we answer affirmatively that there is a pricing policy with a sublinear regret bound under a wide range of retailer's learning policies, even though she faces a learning retailer and an unknown demand distribution. Our work also provides a novel perspective in data-driven operations management where the principal has to learn to react to the learning policies employed by other agents in the system

Linear Network Coding Based Fast Data Synchronization for Wireless Ad Hoc Networks with Controlled Topology

Fast data synchronization in wireless ad hoc networks is a challenging and critical problem. It is fundamental for efficient information fusion, control and decision in distributed systems. Previously, distributed data synchronization was mainly studied in the latency-tolerant distributed databases, or assuming the general model of wireless ad hoc networks. In this paper, we propose a pair of linear network coding (NC) and all-to-all broadcast based fast data synchronization algorithms for wireless ad hoc networks whose topology is under operator's control. We consider both data block selection and transmitting node selection for exploiting the benefits of NC. Instead of using the store-and-forward protocol as in the conventional uncoded approach, a compute-and-forward protocol is used in our scheme, which improves the transmission efficiency. The performance of the proposed algorithms is studied under different values of network size, network connection degree, and per-hop packet error rate. Simulation results demonstrate that our algorithms significantly reduce the times slots used for data synchronization compared with the baseline that does not use NC.Comment: 9 pages, 9 figures, published on China Communications, vol. 19, no. 5, May 202

Free vibration of the electromechanical integrated magnetic gear system

The electromechanical integrated magnetic gear (EIMG), in which the field modulated magnetic gear, drive and control are integrated, is proposed in this paper. The dynamic model of the EIMG system with four subsystems is founded and the model assumptions are given. Then, the electromagnetic coupling stiffnesses are calculated by the finite element method and the dynamic differential equations are deduced. On the basis of the modal analyses of the EIMG system, the changes of the natural frequencies with the system parameters are discussed. The results show that the electromagnetic coupling sitffnesses change periodically with the relative rotation angles. The EIMG system has five torsional modes and five transverse modes, which have entirely different modal characteristics. The natural frequencies of the EIMG system are affected greatly by the system parameters

Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review

In the financial services industry, forecasting the risk factor distribution conditional on the history and the current market environment is the key to market risk modeling in general and value at risk (VaR) model in particular. As one of the most widely adopted VaR models in commercial banks, Historical simulation (HS) uses the empirical distribution of daily returns in a historical window as the forecast distribution of risk factor returns in the next day. The objectives for financial time series generation are to generate synthetic data paths with good variety, and similar distribution and dynamics to the original historical data. In this paper, we apply multiple existing deep generative methods (e.g., CGAN, CWGAN, Diffusion, and Signature WGAN) for conditional time series generation, and propose and test two new methods for conditional multi-step time series generation, namely Encoder-Decoder CGAN and Conditional TimeVAE. Furthermore, we introduce a comprehensive framework with a set of KPIs to measure the quality of the generated time series for financial modeling. The KPIs cover distribution distance, autocorrelation and backtesting. All models (HS, parametric and neural networks) are tested on both historical USD yield curve data and additional data simulated from GARCH and CIR processes. The study shows that top performing models are HS, GARCH and CWGAN models. Future research directions in this area are also discussed

Epidemiology and associations with climatic conditions of Mycoplasma pneumoniae and Chlamydophila pneumoniae infections among Chinese children hospitalized with acute respiratory infections

BACKGROUND: The incidence of severe acute respiratory tract infections in children caused by Mycoplasma pneumoniae (syn. Schizoplasma pneumoniae) and Chlamydophila pneumoniae (formerly Chlamydia pneumoniae) varies greatly from year to year and place to place around the world. This study investigated the epidemiology of M. pneumoniae and C. pneumoniae infections among children hospitalized with acute respiratory infections in Suzhou, China in the year 2006, and associations between incidence rates and climatic conditions. METHODS: Nasopharyngeal aspirates obtained from 1598 patients (aged 26.4 ± 28.3 months; range, 1 month to 13 years) were analyzed with real-time PCR and ELISA. Meteorological data were obtained from the weather bureau. RESULTS: About 18.5% of patients were infected with M. pneumoniae and, C. pneumoniae, or both. Isolated M. pneumoniae infection was positively correlated with increasing age (χ(2) = 34.76, P < 0.0001). Incidence of M. pneumoniae infection was seasonal with a peak in summer (P < 0.0001) and minimum in winter (P = 0.0001), whereas C. pneumoniae infection was low only in autumn (P = 0.02). Monthly mean temperature was strongly correlated with the incidence of M. pneumoniae infection (r = 0.825, P = 0.001). CONCLUSIONS: M. pneumoniae and C. pneumoniae are important infectious agents in hospitalized children with acute respiratory tract infections. M. pneumoniae infection showed a strong direct correlation with environmental temperature