Adaptive Magnetorheological Sliding Seat System for Ground Vehicles

Magnetorheological (MR) fluids (MRFs) are smart fluids that have reversible field dependent rheological properties that can change rapidly (typically 5 - 10 ms time constant). Such an MRF can be changed from a free flowing fluid into a semi-solid when exposed to a magnetic field. The rapid, reversible, and continuous field dependent variation in rheological properties can be exploited in an MRF-based damper or energy absorber to provide adaptive vibration and shock mitigation capabilities to varying payloads, vibration spectra, and shock pulses, as well as other environmental factors. Electronically controlled electromagnetic coils are typically used to activate the MR effect and tune the damping force so that feedback control implementation is practical and realizable. MR devices have been demonstrated as successful solutions in semi-active systems combining advantages of both passive and active systems for applications where piston velocities are relatively low (typically < 1 m/s), such as seismic mitigation, or vibration isolation. Recently strong interests have focused on employing magnetorheological energy absorbers (MREAs) for high speed impact loads, such as in helicopter cockpit seats for occupant protection in a vertical crash landing. This work presents another novel application of MREAs in this new trend - an adaptive magnetorheological sliding seat (AMSS) system utilizing controllable MREAs to mitigate impact load imparted to the occupant for a ground vehicle in the event of a low speed frontal impact (up to 15 mph). To accomplish this, a non-linear analytical MREA model based on the Bingham-plastic model and including minor loss effects (denoted as the BPM model) is developed. A design strategy is proposed for MREAs under impact conditions. Using the BPM model, an MREA is designed, fabricated and drop tested up to piston velocities of 5 m/s. The measured data is used to validate the BPM model and the design strategy. The MREA design is then modified for use in the AMSS system and a prototype is built. The prototype MREA is drop tested and its performance, as well as the dynamic behavior in the time domain, is described by the BPM model. Next, theoretical analysis of the AMSS system with two proposed control algorithms is carried out using two modeling approaches: (1) a single-degree-of-freedom (SDOF) rigid occupant (RO) model treating the seat and the occupant as a single rigid mass, and (2) a multi-degree-of-freedom (MDOF) compliant occupant (CO) model interpreting the occupant as three lumped parts - head, torso and pelvis. A general MREA is assumed and characterized by the Bingham-plastic model in the system model. The two control algorithms, named the constant Bingham number or Bic control and the constant stroking force or Fc control, are constructed in such a way that the control objective - to bring the payload to rest while fully utilizing the available stroke - is achieved. Numerical simulations for both rigid and compliant occupant models with assumed system parameter values and a 20 g rectangular crash pulse for initial impact speeds of up to 7 m/s (15.7 mph) show that overall decelerations of the payload are significantly reduced using the AMSS compared to the case of a traditional fixed seat. To experimentally verify the theoretical analysis, a prototype AMSS system is built. The prototype seat system is sled tested in the passive mode (i.e. without control) for initial impact speeds of up to 5.6 m/s and for the 5th percentile female and the 95th percentile male. Using the test data, the CO model is shown to be able to adequately describe the dynamic behavior of the prototype seat system. Utilizing the CO model, the control algorithms for the prototype seat system are developed and a prototype controller is formulated using the DSPACE and SIMULINK real time control environments. The prototype seat system with controller integrated is sled tested for initial impact speeds of up to 5.6 m/s for the 5th female and 95th male (only the 95th male is tested for the Bic control). The results show that the controllers of both control algorithms successfully bring the seat to rest while fully utilizing the available stroke and the decelerations measured at the seat are substantially mitigated. The CO model is shown to be effective and a useful tool to predict the control inputs of the control algorithms. Thus, the feasibility and effectiveness of the proposed adaptive sliding seat system is theoretically and experimentally verified

A Case Study of Marketing Strategy and Logistics System of Changhong, a Chinese Home Appliances Enterprise

The growing process of Changhong, a representative Chinese home appliance enterprise, is described in this paper. Changhong succeeded in transforming from a former state-run military-radar factory into China’s leading consumer-electronics manufacturer. However it has been experiencing hardship since 1998 and reported significant losses in 2004.The success and failure of Changhong marketing strategy are expounded.This paper reports Changhong’s recent efforts in strategy shift. It shows Changhong is attempting to integrate upstream and downstream resources, and trying to use logistics and supply chain management as competitive business weapons, faced with new challenges in the high-end products market

Learning Diverse Image Colorization

Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization. Our goal is to model the diversity intrinsic to the problem of colorization and produce multiple colorizations that display long-scale spatial co-ordination. We learn a low dimensional embedding of color fields using a variational autoencoder (VAE). We construct loss terms for the VAE decoder that avoid blurry outputs and take into account the uneven distribution of pixel colors. Finally, we build a conditional model for the multi-modal distribution between grey-level image and the color field embeddings. Samples from this conditional model result in diverse colorization. We demonstrate that our method obtains better diverse colorizations than a standard conditional variational autoencoder (CVAE) model, as well as a recently proposed conditional generative adversarial network (cGAN).Comment: This revision to appear in CVPR1