Energy-security-based contactless battery charging system for roadway-powered electric vehicles

This paper proposes an encrypted contactless charging system for roadway-powered electric vehicles (EVs), where the energy can be specifically transferred from the electric supply to authorized EVs. The key of the proposed energy encryption scheme is to utilize the random-like Gaussian map as the security key to chaotically regulate the charging circuit of the electric supply. In such way, the energy can be wirelessly transferred to authorized EVs, while unauthorized EVs cannot illegally acquire the electric energy without knowledge of the security key. Hence, the proposed energy encryption scheme can significantly improve the secure performance of the roadway EV charging system. In this paper, the simulated and experimental results are both provided to illustrate the effectiveness of the proposed the encrypted contactless charging system for multiple roadway-powered EVs. © 2015 IEEE.published_or_final_versio

An efficient wireless power transfer system with security considerations for electric vehicle applications

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Quantitative comparison of permanent magnet linear machines for ropeless elevator

Paper no. YD-026263This paper presents a quantitative comparison of three topologies of double-sided long-stator type permanent magnet linear machines (PMLMs) as possible candidates for the ropeless elevator propulsion system. First, the parameters of each PMLM topology are designed using the same criteria. Then the finite element method (FEM) is employed to evaluate the performance of each topology. Specifically, the translator mass, propulsion forces, detent forces, and no-load EMFs are analyzed and compared. The quantitative comparison results show that the Halbach array PMLM configuration is preferable for the ropeless elevator application because of its small detent force as well as low total mass. © 2015 IEEE.postprin

Modular inductive power transmission system for high misalignment electric vehicle application

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A positioning-tolerant wireless charging system for roadway-powered electric vehicles

published_or_final_versio

Far-infrared optical properties of the pyrochlore spin ice compound Dy2Ti2O4

Near normal incident far-infrared reflectivity spectra of [111] dysprosium titanate (Dy2Ti2O4) single crystal have been measured at different temperatures. Seven phonon modes (eight at low temperature) are identified at frequency below 1000 cm-1. Optical conductivity spectra are obtained by fitting all the reflectivity spectra with the factorized form of the dielectric function. Both the Born effective charges and the static optical primitivity are found to increase with decreasing temperature. Moreover, phonon linewidth narrowering and phonon modes shift with decreasing temperature are also observed, which may result from enhanced charge localization. The redshift of several low frequency modes is attributed to the spin-phonon coupling. All observed optical properties can be explained within the framework of nearest neighbor ferromagnetic(FM) spin ice model

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape