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The single input single output (SISO) system with known strong interference is widely used in various occasions. Due to its strong interference, the control accuracy is hard to guarantee. To solve this problem, an improved generalized predictive control (IGPC) algorithm is developed. The IGPC firstly builds the difference equation CARIMA (Controlled Auto-Regressive Integrated Moving-Average) model of the SISO system and then treats the system as a two input single output (TISO) system and calculates its predictive vector, then transforms it into a SISO system and uses the TISO system predictive vector to calculate the SISO system control increment. A new parameter called phase coefficient is added to inhibit the control lag. Simulations are performed to make the comparison among the traditional GPC, PID control, velocity synchronization control (VSC), fuzzy adaptive PID control (FAPID), model-based robust PID control (BPID) and the IGPC. Results show that IGPC has best performance compared to the others. Finally, experiments are developed which proved that the IGPC algorithm has a higher accuracy in the SISO system with known strong interference than that of VSC. [ABSTRACT FROM AUTHOR]

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure andH∞performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

While the Internet and World Wide Web have put a huge volume of low-quality information at the easy access of an information gathering system, filtering out irrelevant information has become a big challenge. In this paper, a Web data mining and cleaning strategy for information gathering is proposed. A data-mining model is presented for the data that come from multiple agents. Using the model, a data-cleaning algorithm is then presented to eliminate irrelevant data. To evaluate the data-cleaning strategy, an interpretation is given for the mining model according to evidence theory. An experiment is also conducted to evaluate the strategy using Web data. The experimental results have shown that the proposed strategy is efficient and promising. [ABSTRACT FROM AUTHOR]

AbstractWith the high-speed and high-frequency operation of CPU chips, as well as miniaturization and dense assembly of integrated circuits, the heat generated by the Raspberry Pi CPU is constantly increasing. To address the urgent need for efficient Raspberry Pi CPU heat dissipation and ensure normal operation within a safe temperature range, this study first established a model to simulate the CPU's thermal physics parameters during operation using CFD simulation software. An experimental platform was then set up to validate the CFD simulation results. Based on the validated CFD model, initial heat dissipation design was carried out. On this basis, the heat dissipation system was optimized by altering the heat sink’s length, width, fin height, thickness, spacing, and the thermal grease’s thermal conductivity between the heat sink and CPU chip. A surrogate model was established using the response surface methodology, and the NSGA-II genetic algorithm was employed for multi-objective optimization of the heat dissipation system’s cooling performance. Compared to the initial solution, the chip temperature was reduced by 6.19 °C, but the heat sink mass was increased by 0.65 g. By conducting multi-objective optimization design and making reasonable choices for the parameters, a suitable and an optimum design, in terms of both economics and efficiency, is obtained. These results demonstrate that this research can provide theoretical guidance and technical support for practical production. Furthermore, the methods and findings from this study can be applied to the design of heat dissipation systems for similar applications. [ABSTRACT FROM AUTHOR]