No abstract

The overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model of the overhead crane was established and the sliding mode control (SMC) was proposed that ensured the existence of sliding motion in the presence of payload and hoisting height variations, and viscous frictions. To maximize the benefits derived from the proposed control method, novel sliding slope-update based on intelligent neural-network and fuzzy algorithms were developed to tune the controller, guaranteeing precise tracking of the actuated variables as well as regulation of the unactuated variables. The proposed methods adjust predetermined value of the sliding manifold’s slope in response to variations in hoisting heights. Control applications were conducted, and results based on graphical, integral absolute error (IAE), and integral time absolute error (ITAE) proved the effectiveness of the proposed algorithms. It was observed that the response of the controller with back-propagation-trained neural-network was more effective relative to that of the fuzzy algorithm.

Concentrate grade and tailings grade are two vital parameter indexes in a flotation process. To detect the grade succinctly and continuously, a soft sensor based on case-based reasoning (CBR) is proposed. Historic production data is first switched into the form of a case. The case problem includes feed grade, raw ore grade, raw ore ferrous oxide content, raw ore magnetic iron content, target concentrate grade, target tailings grade, dosage of four kinds of reagents; the case solution includes concentrate grade and tailings grade. Simulation result shows that the CBR soft sensor has a higher accuracy and speed in forecasting both concentrate grade and tailings grade when compared with soft sensors supported by other algorithms. The application result in a Chinese iron core dressing mill indicates that the soft sensor presented by this paper causes no damage to people and it can forecast product quality in real-time.

No abstract

Since 2016, China has officially regarded blockchain technology as a subversive innovation that will fundamentally transform major industries. Current blockchain projects in China are dominated by private or consortium blockchains that have their accessibility firmly controlled; for public blockchains such as Bitcoin and Ethereum, access is free for all. Not surprisingly, by weakening state control of digital data, public blockchains may neutralise China’s decades of efforts in building internet filtering systems. The existing development trajectory of private and consortium blockchains is likely to advance steadfastly, and citizens and companies in China may be required to use state-controlled blockchains.