126 research outputs found
Switching delay in self-powered nonlinear piezoelectric vibration energy harvesting circuit: mechanism, effects and solution
No full textSelf-powered synchronized switching harvesting on an inductor (SSHI) circuits have been proved to greatly increase the performance of a PVEH device and peak detector-based self-powered switches are widely used. In practice, however, the switch is impossible to turn on simultaneously at peak displacements due to nonlinear components, so that switching delay(SD) always exists. Furthermore, the SD will degrade the performance of PVEH devices, so it must be reduced. Therefore, for this kind of SSHI circuits, the purpose of this paper is to explore basic causes of generating SD and investigate the corresponding solution. Firstly, theoretical model of SD in self-powered parallel (SP-PSSHI) is derived and the SD is first proved to be positive. Then effects of key component parameters on the SD are studied. Based on above results, an improved SP-PSSHI (ISP-PSSHI) circuit is proposed by adding a voltage divider and its SD is proved to be less than that of the SP-PSSHI circuit. Circuit simulations validate theoretical results and also expose that there are optimal resistor and capacitor of the envelope detector for achieving the maximum harvested power. In the end, experimental results show that the ISP-PSSHI circuit can improve the averaged harvested power about 11 percent more than that of the SP-PSSHI circuit under choosing optimal components.</p
Elastic-electro-mechanical modeling and analysis of piezoelectric metamaterial plate with a self-powered synchronized charge extraction circuit for vibration energy harvesting
No full textStructural vibrations usually exist in the form of low frequency and broadband elastic waves, so cantilever-like harvesters are not appropriate due to space limitations and high quality factor. Piezoelectric metamaterial plate with local resonators (PMP-LR) has been explored to overcome it. However, how to model and analyze the whole energy harvesting system is still a challenge. In this paper, a self-powered synchronized charge extraction circuit is presented and connected to the PMP-LP as the interface circuit. An elastic-electro-mechanical model is built based on the Kirchhoff plate theory and equivalent impedance method, where equivalent impedance of the self-powered synchronized charge extraction circuit is first derived. Then the elastic-electro-mechanical model is numerically solved by using the Bloch theorem and wave finite element method. By numerical simulations, it is found that the synchronized charge extraction circuit has few effects on vibration bandgaps of the PMP-LR. While by inserting an inductor parallel with the clamped capacitor of the piezoelectric patch, we can see that a new dispersion curve is induced by the electrical resonance and the inductor is beneficial for low-frequency and broadband vibration energy harvesting. In particular, the inductor can greatly improve the harvesting performance when the resonant frequency is equal to the excitation frequency. In the end, experiments are done and the results are consistent with the numerical ones. Excitingly, the output voltage amplitude of the piezoelectric patch is enlarged about 200% after using the resonant inductor
A new approach to analyse the underwater vibration of double layer ribbed cylinder
No full textAnalyzing double layer ribbed cylinder’s underwater vibration is of great importance to the design and evaluation of vessels’ dynamic performance of operation and human comfort level. The two main traditional methods that are applied to solve this work are Flügge’s equations of motion and numerical method, such as FEM. Even though both methods have their own special advantages, complicated model developing process and comparative low accuracy (compared with data from real measurement) are their common drawbacks. In this paper, Neural Network is used to analyze the dynamic character of this double layer ribbed cylinder. Real measurement data are used to construct Neural Network, and after this Neural Network model is well built, comparison studies are processed between the vibration data of some positions of interest on the cylinder acquired from Neural Network and real test, respectively. The results show that Neural Network can be used to analyse the dynamic character of double layer ribbed cylinder and is of high accuracy and timesaving. The next step of this research will be concentrated on using more representative vibration data to develop Neural Network model
tibetanword segmentation as syllable tagging using conditional random field
No full textIn this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He.In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He
Construction of fast retrieval model of e-commerce supply chain information system based on Bayesian network
No full textBayesian network is a kind of uncertainty knowledge expression and reasoning tool, and it is an effective means to solve problems in related fields such as information retrieval. Considering the characteristics of e-commerce supply chain supply information and Bayesian network, a cognitive big data analysis method for intelligent information system is designed. The model uses a set of information sample documents to describe the query requirements and the documents to be detected. By calculating the similarity between them, the return results of the general search engine are sorted, thereby retrieving the supply chain supply information required by the user. Through numerical results, the precision of the source information retrieval model based on Bayesian network is also significantly higher than that of the trust network model and the inference network model, and the experimental data shows that the Bayesian network model has better retrieval performance than the trust network model and the inference network model. Therefore, when conducting large-scale e-commerce supply chain supply information collection, Bayesian network-based source information retrieval model is effective.Chu, YP (corresponding author), Hubei Univ Econ, Sch Business Adm, Wuhan, Peoples R China.
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Uncertainty extraction based multi-fault diagnosis of rotating machinery
No full textFeature extraction has always been a significant research topic for in-situ fault diagnosis applications. In this research, measurement uncertainty of vibration signal is defined and extracted as a pre-processing step for statistical feature calculation. An Empirical Mode Decomposition (EMD) detrending method combined with hurst exponent criterion is applied to extract uncertainty. Decision tree and Least Square Support Vector Machine (LS-SVM) algorithms are introduced as statistical feature selector and classifier respectively. Misalignment, rub-impact, pedestal looseness as well as eccentricity faults are set on experimental rig in sequence for data collecting and to test the proposed method. As the diagnosis accuracy shows, the extracted uncertain components are more sensitive to rotor faults compared to original vibration signal. HE-EMD (Hurst Exponent-Empirical Mode Decomposition) is proved a rational tool to pre-process vibration signal for an enhanced diagnosis ability. This paper shows effectiveness of a multi-fault diagnosis method with uncertainty components as state indicator and thus provides new approaches for condition monitoring of rotating machinery
GEORGE FRANCIS MCLEAN: A PHILOSOPHER IN THE SERVICE OF HUMANITY
No full textGeorge Francis McLean is a philosopher in the service of humanity. At present he holds the titles of Professor Emeritus at the School of Philosophy of The Catholic University of America (CUA) in Washington, D.C., and Director of the Centre for Culture and Values.Yet anyone who knows him knows that this says only a small part of who he is. Over the years, McLean has been a scholar and a teacher, but most importantly he has worked to democratize philosophy – promoting the research of philosophers coming from many different cultural traditions, and publishing the academic work of teams of scholars from countries and regions around the globe
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