Placeholder image for staff profiles

Juyan Zhang

Postgraduate Research Student

Academic and research departments

Department of Chemical and Process Engineering.

My publications


Zhang Juyan, Yao Xuhui, Misra Ravi K, Cai Qiong, Zhao Yunlong (2020)Progress in electrolytes for beyond-lithium-ion batteries, In: Journal of materials science & technology44pp. 237-257 Elsevier Ltd
The constant increase in global energy demand and stricter environmental standards are calling for advanced energy storage technologies that can store electricity from intermittent renewable sources such as wind, solar, and tidal power, to allow the broader implementation of the renewables. The grid-oriented sodium-ion batteries, potassium ion batteries and multivalent ion batteries are cheaper and more sustainable alternatives to Li-ion, although they are still in the early stages of development. Additional optimisation of these battery systems is required, to improve the energy and power density, and to solve the safety issues caused by dendrites growth in anodes. Electrolyte, one of the most critical components in these batteries, could significantly influence the electrochemical performances and operations of batteries. In this review, the definitions and influences of three critical components (salts, solvents, and additives) in electrolytes are discussed. The significant advantages, challenges, recent progress and future optimisation directions of various electrolytes for monovalent and multivalent ions batteries (i.e. organic, ionic liquid and aqueous liquid electrolytes, polymer and inorganic solid electrolytes) are summarised to guide the practical application for grid-oriented batteries.
Chen T, Zhang J (2009)On-line statistical monitoring of batch processes using Gaussian mixture model, In: IFAC Proceedings: Advanced Control of Chemical Processes7(PART 1)pp. 667-672
The statistical monitoring of batch manufacturing processes is considered. It is known that conventional monitoring approaches, e.g. principal component analysis (PCA), are not applicable when the normal operating conditions of the process cannot be sufficiently represented by a Gaussian distribution. To address this issue, Gaussian mixture model (GMM) has been proposed to estimate the probability density function of the process nominal data, with improved monitoring results having been reported for continuous processes. This paper extends the application of GMM to on-line monitoring of batch processes, and the proposed method is demonstrated through its application to a batch semiconductor etch process.
Yu T, Zhao M, Zhong J, Zhang J, Xiao P (2017)Low-complexity graph-based turbo equalization for single-carrier and multi-carrier FTN signaling, In: IET Signal Processing11(7)pp. 838-845 Institution of Engineering and Technology
We propose a novel turbo detection scheme based on the factor graph serial-schedule belief propagation equalization algorithm with low complexity for single-carrier faster-than-Nyquist (FTN) and multicarrier FTN signaling. In this work, the additive white Gaussian noise channel and multi-path fading channels are both considered. The iterative factor graph-based equalization algorithm can deal with severe intersymbol interference and intercarrier interference introduced by the generation of single-carrier and multi-carrier FTN signals, as well as the effect of multi-path fading. With the application of Gaussian approximation, the complexity of the proposed equalization algorithm is significantly reduced. In the turbo detection, Low density parity check code is employed. The simulation results demonstrate that the factor graph-based turbo detection method can achieve satisfactory performance with low complexity.
Kristan M, Pflugfelder R, Leonardis A, Matas J, Porikli F, Cehovin L, Nebehay G, Fernandez G, Vojir T, Gatt A, Khajenezhad A, Salahledin A, Soltani-Farani A, Zarezade A, Petrosino A, Chan CS, Milton A, Bozorgtabar B, Li B, Heng C, Ward D, Kearney D, Monekosso D, Karaimer HC, Xiao J, Rabiee HR, Zhu J, Gao J, Zhang J, Xing J, Huang K, Lebeda K, ELHelw M, Cao L, Maresca ME, Lim MK, Felsberg M, Remagnino P, Bowden R, Goecke R, Stolkin R, Lim SY, Satoh S, Maher S, Poullot S, Wong S, Chen W, Hu W, Zhang X, Li Y, Niu Z (2013)The Visual Object Tracking VOT2013 challenge results, In: 2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW)pp. 98-111 IEEE
Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge. net)
Zhang J, Basson L, Leach M (2009)Review of Life Cycle Assessment Studies of Coal-fired Power Plants with Carbon Capture and Storage, In: 2009 INTERNATIONAL CONFERENCE ON SUSTAINABLE POWER GENERATION AND SUPPLY, VOLS 1-4pp. 2108-2114
Chen T, Zhang J (2010)On-line multivariate statistical monitoring of batch processes using Gaussian mixture model, In: COMPUTERS & CHEMICAL ENGINEERING34(4)pp. 500-507 PERGAMON-ELSEVIER SCIENCE LTD
Zhang J, Seet B-C, Lie T-T, Foh CH (2013)Opportunities for Software-Defined Networking in Smart Grid, In: 2013 9TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATIONS AND SIGNAL PROCESSING (ICICS)
Marinos A, Razavi A, Moschoyiannis Sotiris, Krause Paul, Damiani E, Zhang J, Chang R (2009)RETRO: A Consistent and Recoverable RESTful Transaction Model, In: 2009 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, VOLS 1 AND 2pp. 181-188 IEEE Computer Society
Lynch SA, Paul DJ, Townsend P, Matmon G, Kelsall RW, Ikonic Z, Harrison P, Zhang J, Norris DJ, Cullis AG, Pidgeon CR, Murzyn P, Murdin B, Bain M, Gamble HS (2005)Silicon quantum cascade lasers for THz sources, In: 2005 IEEE LEOS Annual Meeting Conference Proceedings (LEOS)pp. 727-728
He B, Zhang J, Chen T, Yang X (2013)Penalized Reconstruction-Based Multivariate Contribution Analysis for Fault Isolation, In: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH52(23)pp. 7784-7794 AMER CHEMICAL SOC