The University of Science and Technology Beijing , formerly known as Beijing Steel and Iron Institute before 1988, is a national key university in Beijing, China. USTB's metallurgy and materials science programmes are highly regarded in China. Wikipedia.
University of Science and Technology Beijing | Date: 2016-03-15
This present invention provides a method for preparing a stainless reinforcing steel bar resistant to corrosion of chloride ions, and belongs to the technical field of corrosion-resistant materials. This method particularly comprises the steps of: selecting a reinforcing steel bar blank, and performing oil removing, rust removing, water washing, and drying treatments on the surface of the reinforcing steel bar blank to be treated, or directly performing sand blasting or shot blasting on a reinforcing steel bar blank whose surface is only slightly rusted; placing the reinforcing steel bar blank in a chromium-containing environment, and keeping at a certain temperature for a certain time such that chromium in the environment is capable of diffusing into the surface of the reinforcing steel bar blank to form a chromium-containing diffusion layer, wherein an area in the diffusion layer where the weight content of Cr exceeds 12% meets the basic component requirements for a stainless steel, and this area is the effective diffusion layer described in this invention; and performing cooling treatment on the heat diffusion treated reinforcing steel bar. In this invention, a reinforcing steel bar blank is pre-formed, a heat diffusion technique is optimized, and the corrosion resistance to chloride ions of the stainless reinforcing steel bar of this invention is superior to that of the 316L stainless reinforcing steel bar.
University of Science and Technology Beijing | Date: 2017-01-09
The present invention provides a sector-shaped section of a continuous casting machine for heavy reduction of a solidified terminal of a continuously cast slab and a heavy reduction method. The sector-shaped section of a continuous casting machine comprises an upper frame, a lower frame, an upper driving roller, a lower driving roller, a left driven roller set, a right driven roller set, a screw-down gear and clamping cylinders which are positioned on the sector-shaped section; the clamping cylinders are used for clamping the upper frame and the lower frame and keeping the upper frame and the lower frame at a set interval; the upper driving roller is connected with the screw-down gear; the upper driving roller is connected to the upper frame through a bearing base; the lower driving roller is connected to the lower frame through a bearing base; the left driven roller set and the right driven roller set, are respectively positioned on two sides of the driving rollers; the left driven roller set is used for clamping the cast slab before reduction; the right driven roller set is used for clamping the cast slab after reduction; and the diameter ratio of the driving rollers to the driven rollers is 1.1:1-2:1. The deformation permeability of the present invention is increased, equivalent to the deformation of one rolling pass of a two-roller mill with a relatively large roller diameter, which facilitates improvement on the looseness and segregation of the center area of the continuously cast slab.
News Article | May 16, 2017
Smart homes need smart batteries. Current systems overindulge on power, which can shorten the life of batteries and the devices they power. Future batteries may get an intelligence boost, though. A collaborative research team based in Beijing, China, has proposed a novel programming solution to optimize power consumption in batteries. The scientists, from the Institute of Automation, the Chinese Academy of Sciences, and the School of Automation and Electrical Engineering at the University of Science and Technology Beijing, published their results in IEEE/CAA Journal of Automatica Sinica (JAS), a joint publication of the IEEE and the Chinese Association of Automation. "In smart home energy management systems, the intelligent optimal control of [the] battery is a key technology for saving power consumption," Prof. Qinglai Wei, with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, wrote in the paper. To develop a system in which batteries can learn and optimize their power consumption, Wei and his team turned to adaptive dynamic programming. This method breaks down one big problem - how to best use batteries in smart home systems - into smaller problems. The answer to each small problem builds into the answer to the big problem, and, as the circumstances of the question change, the system can examine all the small answers to see if and how the big answer adapts. Wei and his team are the first to use this method while also considering the physical charging and discharging constraints of the battery. The algorithm learns which inputs, such as the demand for power from a device, lead to which outputs, such as providing power. By continually questioning the link between input and output, the algorithm learns more about the best times to charge and to discharge to limit power consumed from the grid. To extend the battery life, every iteration of learning is constrained by the understanding that the battery can only charge and discharge to certain limits. Anything more, and the battery could experience excessive wear. "The battery [makes] decisions to meet the demand of the home load according to the real-time electricity rate," Wei wrote, noting that the objective of optimal control is to find the ideal balance for each battery state (charging, discharging, and idle) within the battery's constraints, while still minimizing the power needed from the grid. To further extend the lifetime of batteries in smart home systems, Wei and his team will next examine how the damage caused by frequently switching between charging and discharging modes may be avoided. Fulltext of the paper is available: http://ieeexplore. http://html. IEEE/CAA Journal of Automatica Sinica (JAS) is a joint publication of the Institute of Electrical and Electronics Engineers, Inc (IEEE) and the Chinese Association of Automation. JAS publishes papers on original theoretical and experimental research and development in all areas of automation. The coverage of JAS includes but is not limited to: Automatic control/Artificial intelligence and intelligent control/Systems theory and engineering/Pattern recognition and intelligent systems/Automation engineering and applications/Information processing and information systems/Network based automation/Robotics/Computer-aided technologies for automation systems/Sensing and measurement/Navigation, guidance, and control. To learn more about JAS, please visit: http://ieeexplore.
News Article | May 17, 2017
A collaborative research team based in Beijing, China, has proposed a novel programming solution to optimize power consumption in batteries. The scientists, from the Institute of Automation, the Chinese Academy of Sciences, and the School of Automation and Electrical Engineering at the University of Science and Technology Beijing, published their results in IEEE/CAA Journal of Automatica Sinica (JAS), a joint publication of the IEEE and the Chinese Association of Automation. "In smart home energy management systems, the intelligent optimal control of [the] battery is a key technology for saving power consumption," Prof. Qinglai Wei of the Chinese Academy of Sciences wrote in the paper. To develop a system in which batteries can learn and optimize their power consumption, Wei and his team turned to adaptive dynamic programming. This method breaks down one big problem—how best to use batteries in smart home systems—into smaller problems. The answer to each small problem contributes to the answer to the big problem, and, as the circumstances change, the system can examine all the small answers to see if and how the big answer adapts. Wei and his team are the first to use this method while also considering the physical charging and discharging constraints of the battery. The algorithm learns which inputs, such as the demand for power from a device, lead to which outputs, such as providing power. By continually questioning the link between input and output, the algorithm learns more about the best times to charge and to discharge to limit power consumed from the grid. To extend battery life, every iteration of learning is constrained by the understanding that the battery can only charge and discharge to certain limits. Anything more, and the battery could experience excessive wear. "The battery [makes] decisions to meet the demand of the home load according to the real-time electricity rate," Wei wrote, noting that the objective of optimal control is to find the ideal balance for each battery state (charging, discharging, and idle) within the battery's constraints, while still minimizing the power needed from the grid. To further extend the lifetime of batteries in smart home systems, Wei and his team will next examine how the damage caused by frequently switching between charging and discharging modes may be avoided. Explore further: 'Virtual batteries' could lead to cheaper, cleaner power More information: Qinglai Wei et al, Optimal constrained self-learning battery sequential management in microgrid via adaptive dynamic programming, IEEE/CAA Journal of Automatica Sinica (2017). DOI: 10.1109/JAS.2016.7510262
News Article | April 17, 2017
XI'AN, China--(BUSINESS WIRE)--Forbes announced its list of “Forbes 30 under 30 Asia” of 2017, the annual ranking of Asia’s brightest young entrepreneurs, innovators and game changers. Yeahmobi CEO Xiaowu Zou is named in the list, based on his outstanding performance on entrepreneurship. Forbes sifted through thousands of nominations and then convened a panel of judges – from Kaifu Lee and Jean Liu to Jimmy Choo and Sonny Bill Williams to bring out the list. The Asian list is a continuation of the global expansion of the Forbes 30 under 30 franchise – a franchise that includes alumni such as Palmer Luckey from Oculus, Evan Spiegel and Bobby Murphy from Snapchat, basketball superstar LeBron James and K-pop star G-Dragon. Forbes’s announcement remarked Zou’s outstanding performance managing Chinese marketing company Yeahmobi. The company reported a profit of $13 million on revenues of $93 million last year. Throughout the process of startup, Zou holds on to one opinion: to find the thing he truly loves. As the now 29-year-old entrepreneur said in an interview, startup is a long and arduous paths, with numerous setbacks and obstacles awaiting. The weapon used to conquer the route is to be clear that what you do is what you truly love to do. Zou claims he made through the difficult path bearing in mind that marketing and connecting good products with potential users is where his true passion lies on. Like many young Chinese students, after obtaining Bachelor’s degree from the University of Science and Technology Beijing, Zou chose to seek further education abroad. He went to the University of Arizona and obtained Master’s degree there. During his studies abroad, Zou started the business specialized in marketing, monetizing traffic of his blog. He soon accumulated his first bucket of gold, and went back China to found his overseas marketing company Yeahmobi with several friends. Now Yeahmobi grows to a world’s leading mobile advertising platform designated to help mobile technology companies, app developers and e-commerce platforms to acquire active users, monetize inventory and reach rapid growth in new markets. The company also won “best mobile ad service” and “mobile champion of China channel partner” titles from Google, and on mobile marketing analytics platform AppsFlyer’s performance index, ranked 12th among global counterparts.
Agency: GTR | Branch: NERC | Program: | Phase: Research Grant | Award Amount: 203.66K | Year: 2016
Our overall aim is to build earthquake resilience in China by improving (a) the assessment of seismic hazard and risk from earthquakes and consequent events and (b) the communication and use of probabilistic information in the development of more proportionate and risk-based strategies for disaster risk reduction. We will build on and extend a recently-developed historical catalogue for earthquakes, extend it for the first time to include consequent events (landslides, debris/mud-flows, outburst floods), unify this new database with modern instrumental data, use state-of the art statistical techniques to quantify the associated uncertainties, and incorporate social science-based understanding of risk communication and governance to improve policy development and implementation. The work programme will be carried out in Si-chuan (including the 2008 Wenchuan earthquake) and Yun-nan provinces. While they are both tectonically active, and mountainous, and thus vulnerable not only to earthquakes but also to consequent hazards of earthquake-triggered landslides and flooding, Si-chuan is one of the wealthiest provinces in China, while Yun-nan is one of poorest. These differences in wealth, combined with the recency of the devastating 2008 Wenchuan in Si-chuan compared to the more attenuated memory of the 1996 Lijiang earthquake in Yun-nan, make for a natural experiment in which to test the efficacy of improved probabilistic assessment of risk and associated uncertainty to people and property by earthquakes, and consequent event hazards, in supporting more risk-based approaches to disaster reduction. This project will promote long-term sustainable growth in earthquake prone regions of China by improving both the assessment of earthquake hazard and consequent event risk and the communication, understanding, and use of the resulting probabilistic forecasts for disaster risk reduction by policymakers and local publics. It addresses several specific capacity gaps identified in successive Chinese national disaster risk reduction strategies. As well as engaging with policymakers at both the national and local levels to improve the effectiveness of emergency planning and building code regulation, we will also engage directly with local publics to enhance public understanding of risk and capacity to deal with it. In so doing, the project will also fulfil the UKs Official Development Assistance (ODA) commitment to promoting the economic development and welfare of developing countries by drawing on UKs science base to address a key vulnerability differentially affecting the very poorest in China.
Princeton University, Tsinghua University, University of Science and Technology Beijing | Date: 2015-08-18
Emulsion breaking and phase separation is achieved by droplet adhesion. An emulsion breaking device includes a channel having distinct adjacent zones with distinctly different surface wettability characteristics, namely, solvophilic and solvophobic surfaces. The device is positioned such that the upstream portion of the device is configured to be wetted by the continuous phase of the emulsion, and the downstream portion of the device is configured to be wetted by the dispersed phase of the emulsion. As the emulsion flows from the upstream zone to the downstream zone, the change in surface wettability characteristics promotes adhesion of the dispersed phase as the dispersed phase wets the surface of the downstream portion of the channel, which results in breaking of the emulsion. Subsequent collection of the broken emulsion in a collection vessel results in separation of the disparate phases to facilitate their recapture and recycling.
Wang W.,University of Science and Technology Beijing
Reliability Engineering and System Safety | Year: 2012
Industrial plant maintenance is an area which has enormous potential to be improved. It is also an area attracted significant attention from mathematical modellers because of the random phenomenon of plant failures. This paper reviews the recent advances in delay-time-based maintenance modelling, which is one of the mathematical techniques for optimising inspection planning and related problems. The delay-time is a concept that divides a plant failure process into two stages: from new until the point of an identifiable defect, and then from this point to failure. The first stage is called the normal working stage and the second stage is called the failure delay-time stage. If the distributions of the two stages can be quantified, the relationship between the number of failures and the inspection interval can be readily established. This can then be used for optimizing the inspection interval and other related decision variables. In this review, we pay particular attention to new methodological developments and industrial applications of the delay-time-based models over the last few decades. The use of the delay-time concept and modeling techniques in other areas rather than in maintenance is also reviewed. Future research directions are also highlighted. © 2012 Elsevier Ltd.
Wu X.Z.,University of Science and Technology Beijing
Structural Safety | Year: 2015
An important issue regarding the use of probabilistic predictions for complex engineering systems is characterising the dependence structure among its correlated performance functions, which are driven by dependent or independent basic random variables. The interrelationship of these performance functions can be attributed to the same random variables and the cross correlation among the input parameters. An assessment of joint failure probability for an engineering system is proposed, which is associated with the correlated performance functions using a copula-based method by conveying the dependence structure of the performance functions. The method is demonstrated with four simple engineering problems, i.e., (a) bivariate distribution in which two predetermined performance functions are associated with each other; (b) pile bearing capacity in which the performance functions are related with the soil internal friction and the compressive strength of a concrete pile; (c) pipe flow in which the performance function of three pipes in a sewer system is assessed with six independent random variables; and (d) retaining wall in which the failure criteria for defining the performance functions include overturning failure about the toe point, sliding failure along the base, and bearing capacity instability considering uncertain soil properties. The computational efficiency is evaluated using the results based on the conventional bounding methods. The joint failure probability expressed by copulas provides a means to obtain the joint probabilities of multiple failure modes, which pave the way for an objective description of the overall failure probability of a practical engineering problem. © 2014 Elsevier Ltd.
University of Science and Technology Beijing | Date: 2016-01-15
This disclosure relates to a preparation method of a low-pH controlled-release intelligent corrosion inhibitor. The low-pH controlled-release intelligent corrosion inhibitor comprises a hydrogel with low pH responsiveness and a corrosion inhibiting substance having the capacity of corrosion inhibition. That is, a corrosion inhibiting substance is wrapped in a low-pH sensitive hydrogel. The swelling degree of the pH sensitive hydrogel may be changed according to the amounts of monomers and crosslinking agents so as to control the releasing speed of the corrosion inhibiting substance. By the soaking experiment and the measurements of electrochemical polarization curves and alternating impedance spectra, the sensitive and long-lasting features of the low-pH controlled-release intelligent corrosion inhibitor are indicated. Therefore, the advantageous effects of this disclosure lies in that: 1) the system enables the releasing speed of the corrosion inhibiting substance to be controlled by pH; 2) the system enables long-lasting effect and high corrosion inhibition efficiency of the corrosion inhibiting substance; and 3) the system has broad applicability.