Xiaofeng Zhang

Shanghai Jiao Tong University

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Guangbin Zhang

Institute of Soil Science

Jingyao Shi

Chinese Academy of Sciences

Yupei Qiao

Guizhou Normal University

Shuyuan Li

Shaanxi Normal University

Guangbin Zhang

Shaanxi Normal University

Mingliang Han

Shaanxi Normal University

Yunyun Deng

Shaanxi Normal University

Shulin Sun

Shanxi Normal University

Huan Kang

Shaanxi Normal University

Shijie Hao

China University of Petroleum, Beijing

Cooperative Institutions

Chinese Academy of Sciences

Shaanxi Normal University

Shanghai Jiao Tong University

Zhejiang University

University of Chinese Academy of Sciences

Xi'an Jiaotong University

University of Science and Technology Beijing

Central South University

China University of Petroleum, Beijing

Tianjin University

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A robot finger joint driven by hybrid multi-DOF piezoelectric ultrasonic motor

Sensors and Actuators A Physical (2011)

Xiaofeng Zhang Guangbin Zhang Kentaro Nakamura Sadayuki Ueha

Ultrasonic motor

Vibrator (electronic)

Piezoelectric motor

10.1016/j.sna.2011.05.023

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Citations (55)

Mechanically alloyed composite anode materials based on SiO–SnxFeyCz for Li-ion batteries

Journal of Materials Chemistry A (2013)

Bo Liu Ali Abouimrane Dennis E. Brown Xiaofeng Zhang Yang Ren

A new family of composite materials as anodes for lithium-ion batteries, SiO–SnxFeyCz (x : y : z molar ratio), was synthesized by mechanical alloying. SiO is preferable because of its high capacity and the Sn–Fe–C alloys could be used as a buffer material to extend the cycle life of cells. Rather than expensive and toxic cobalt, we selected cheap and environmentally benign iron instead. The aim of this work is to find the optimal point by tuning the composition of Sn–Fe–C in the hope of obtaining better electrochemical performance. Different combinations were studied by high-energy X-ray diffraction and electrochemical methods. The results indicated that carbon can improve the cycle life, the amount of iron affects phase formation greatly, and the FeSn2 phase should be avoided because of its detrimental effect on cycle life. The 50 wt% SiO–50 wt% Sn30Fe30C40 composition was studied using the pair distribution function and Mössbauer spectroscopy. This material exhibits high specific capacity (900 mA h g−1 at C/6 rate) with good cycle life and rate capability. These results indicate that SiO–SnxFeyCz are promising candidate anode materials for commercial rechargeable lithium batteries.

Molar ratio

Carbon fibers

10.1039/c3ta00101f

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Citations (25)

Acoustic Radiation Force of a Gaussian Beam Incident on Spherical Particles in Water

Ultrasound in Medicine & Biology (2012)

Xiaofeng Zhang Guangbin Zhang

Acoustic Radiation Force

Gaussian beam

10.1016/j.ultrasmedbio.2012.06.014

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Citations (42)

Protecting Data Privacy from Being Inferred from High Dimensional Correlated Data

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (2014)

Huafeng Ba Xiaoming Gao Xiaofeng Zhang Zhenyu He

In the era of big data, privacy becomes a challenging issue which already attracts a good number of research efforts. In the literature, most of existing privacy preserving algorithms focus on protecting users' privacy from being disclosed by making the set of designated semi-id features indiscriminate. However, how to automatically determine the appropriate semi-id features from high-dimensional correlated data is seldom studied. Therefore, in this paper we first theoretically study the problem and propose the IPFS algorithm to find all possible features forming the candidate semi-id feature set which can infer users' privacy. Then, the KIPFS algorithm is proposed to find the key features from the candidate semi-id feature set. By anonymizing the key feature set, called as key inferring privacy features (KIPFS), users' privacy is protected. To evaluate the effectiveness and the efficacy of the proposed approach, two state-of-the-art algorithms, i.e., K-anonymity and t-closeness, applied on the designated semi-id feature set are chose as the baseline algorithms and their revised versions are applied on the KIPFS for the performance comparison. The promising results showed that by anonymizing the identified KIPFS, both aforementioned algorithms can achieve better performance than the original ones in terms of efficiency and data quality.

10.1109/wi-iat.2014.139

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Citations (8)

[EEMD-ICA Applied in Signal Extraction in Functional Near-Infrared Spectroscopy].

PubMed (2015)

Yutong Zha Guangda Liu Rundong Zhou Xiaofeng Zhang Jun-qi Niu

Currently, functional near-infrared spectroscopy (fNIRS) is widely used in the field of Neuroimaging. To solve the signal-noise frequency spectrum aliasing in non-linear and non-stationary fNIRS characteristic signal extraction, a new joint multi-resolution algorithm, EEMD-ICA, is proposed based on combining Independent Component Analysis with Ensemble Empirical Mode Decomposing. After functional brain imaging instrument detected the multi-channel and multi-wavelength NIR optical density signals, EEMD was performed to decompose measurement signals into multiple intrinsic mode function according to the signal frequency component. Then ICA was applied to extract the interest data from IMFs into ICs. Finally, reconstructed signals were obtained by accumulating the ICs set. EEMD-ICA was applied in de-noising Valsalva test signals which were considered as original signals and compared with Empirical Mode Decomposing and Ensemble Empirical Mode Decomposing to illustrate validity of this algorithm. It is proved that useful information loss during de-noising and invalidity of noise elimination are completely solved by EEMD-ICA. This algorithm is more optimized than other two de-noising methods in error parameters and signal-noise-ratio analysis.

Aliasing

SIGNAL (programming language)

Functional near-infrared spectroscopy

Source

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Citations (1)

Fabrication of the double-layer millimeter wave frequency selective surface by femtosecond laser

Optik (2023)

Cong Wang Xuesong Chen Xianshi Jia Kai Li Huiyong Liang

Radome

Extremely high frequency

10.1016/j.ijleo.2023.170726

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Citations (0)

Yan Xu Xiaofeng Zhang Li Liu Wenming ZHANG

10.1142/9789814689007_0030

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Citations (0)

Analytical study on discharge process of multiphase air-core pulsed alternators

Engineering Research Express (2021)

Ying Zhang Xuhui Wen Wenchao Li Jialin Wei Xiaofeng Zhang

Abstract This paper has investigated the discharge process of a four-phase air-core pulsed alternator. A mathematical model of the short-circuit current, relating to firing angles of discharge thyristors and taking the current coupling and field current attenuation into account, is established. Compared with the conventional trial-and-error method and existing phase peak current model, the proposed model has considered the attenuation trend of the field current in the discharge process and derived the intuitive expression of the resultant short-circuit current. Firstly, the state equation model of a four-phase air-core pulsed alternator is established. Meanwhile, the simulation comparison indicates that the results of the state equation model are close to the finite-element model. Then, the segmented formula of resultant short-circuit current is derived based on the voltage equations of the armature winding circuits and the approximate attenuation coefficient of the field current. Finally, the segmented formula is verified with the finite-element method, and some preliminary experiments for field windings are carried out. The results show that this method can well describe the decay trend of field current and discharge current. It is helpful for selecting firing angles to generate the desired current amplitude and waveform in the future.

Alternator

10.1088/2631-8695/ac41b3

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DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation Benchmark

arXiv (Cornell University) (2024)

Haodong Li Haicheng Qu Xiaofeng Zhang

With the rapid development of large vision language models (LVLMs), these models have shown excellent results in various multimodal tasks. Since LVLMs are prone to hallucinations and there are currently few datasets and evaluation methods specifically designed for remote sensing, their performance is typically poor when applied to remote sensing tasks. To address these issues, this paper introduces a high quality remote sensing LVLMs dataset, DDFAV, created using data augmentation and data mixing strategies. Next, a training instruction set is produced based on some high-quality remote sensing images selected from the proposed dataset. Finally, we develop a remote sensing LVLMs hallucination evaluation method RSPOPE based on the proposed dataset and evaluate the zero-shot capabilities of different LVLMs. Our proposed dataset, instruction set, and evaluation method files are available at https://github.com/HaodongLi2024/rspope.

Benchmark (surveying)

10.48550/arxiv.2411.02733

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Characteristics and supercapacitive performance of nanoporous bamboo leaf-like CuO

Chemical Physics Letters (2017)

Feng Yang Xiaofeng Zhang Ying Yang Shijie Hao Lishan Cui

Nanoporous

10.1016/j.cplett.2017.11.047

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Citations (29)