Piecewise adaptive controller design based on ECP model 730 magnetic levitation system

Many new techniques have been developed in recent years. One of the most notable techniques is the magnetic levitation train, or Maglev. A Maglev uses electro-magnetic control systems to levitate a vehicle in a short distance away from a guide way vertically. The idea was firstly patented in Germany by 'Transrapid'. After a few decades of development, the Maglev train has been already used for public service in China. The project studied a similar dynamic control system to control the lift of a maglev train. An ECP Mode 730 Magnetic levitation plant was used in the development of the control system. The system modelling was identified first and then a PID controller were designed, simulated and implemented. It was found that the characteristics of a PID controller are not good enough to such a maglev plant which requires a quicker response and almost no overshoot. A deadbeat controller later was designed to handle the maglev system which could give a much quicker response and no overshoot. The simulation results for a deadbeat controller suggested the overshoot of the system when the step input is 2cm is 0.013 (less than 0.0065%) which can be almost neglected. The settling time for the system response is 0.231 seconds and it has only 0.031 seconds‟ difference from the desired time. While applying the designed deadbeat controller to the plant, some real-world problems such as oscillations and control errors occurred. The problem was solved at the end and system performance became much better but the small oscillation still exists. It was believed that the small oscillation was coming from the hardware of maglev plant itself. In comparison with a classic PID controller, it was found the settling time has been improved at least 55% at the linearization point and the overshoot was reduced. However, when it comes up to a large movement from the linearization point there was no improved at all. There is a need to apply adaptive control techniques in the further work

A New Feature Extraction Algorithm Based on Orthogonal Regularized Kernel CCA and Its Application

In this paper, an orthogonal regularized kernel canonical correlation analysis algorithm (ORKCCA) is proposed. ORCCA algorithm can deal with the linear relationships between two groups of random variables. But if the linear relationships between two groups of random variables do not exist, the performance of ORCCA algorithm will not work well. Linear orthogonal regularized CCA algorithm is extended to nonlinear space by introducing the kernel method into CCA. Simulation experimental results on both artificial and handwritten numerals databases show that the proposed method outperforms ORCCA for the nonlinear problems

Analyzing Macro-Level Ecological Change and Micro-Level Farmer Behavior in Manas River Basin, China

Environmental degradation is closely related to unreasonable land use behaviors by farmers. In this study, participatory rural assessment (PRA) is used to conduct a detailed survey of farmers and plots and to collect relevant natural and social statistics. The accuracy of remote sensing data is verified by comparative analysis, and the change in status of various land use types in each research period is reflected by the change in the dynamic degree and change in range. We examine how farmers’ attitudes and behaviors affect environmental degradation, using a sample of 403 farmers in China’s Manas River Basin. Due to age, education, income and other differences, farmers’ land use behaviors, as well as their attitude toward and feelings about environmental degradation, vary greatly. We found that most farmers considered the environment to be very important to their lives and crop production, but nearly 21% did not know the causes of environmental degradation and nearly 8% did not consider the environmental impacts of their crop production activities. A new model for oasis expansion—land integration—is presented here. This model can increase the area of cultivated land, reduce cultivated land fragmentation, save irrigation water, improve the field microclimate and form a good ecological cycle. Through land transfer, ecological compensation and ecological protection incentives, the government should guide farmers’ land use behaviors toward cooperation with the river basin’s ecological protection and land use planning

Analysis of Ecological and Economic Benefits of Rural Land Integration in the Manas River Basin Oasis

Land consolidation is an effective means of promoting the regularization of fragmented arable land, optimizing the allocation of land resources and improving the environment in farming areas, as well as an important means of increasing the economic returns of farming households, and it is important to scientifically assess the ecological and economic benefits of agricultural land consolidation. In this study, participatory rural assessment (PRA) was used to investigate, in detail, the meaning, satisfaction and changes in farmland rehabilitation before and after implementation. The accuracy of the remote sensing data was verified through an experiment on the net cultivation coefficient. We used a sample of 447 farmers from nine villages in Manas County to study the differences in plot area, crop unit value, income and irrigation before and after the farmers’ integration. We found that, after the integration of farmland, the cultivated area increased significantly, the crop unit yield increased by at least 42.66%, the average income of farmers increased by a value of RMB 4324/ha and the water savings were all higher than 7.18 m3/ha. At the same time, after the integration of farmland, the number of plots was significantly reduced, the arable land became more regular and the microclimate of the farmland improved significantly. The government and individuals should follow the concept and construction requirements of the “community of life in mountain, water, forest, lake, grass and sand”, consider the economic and ecological benefits of land consolidation, ensure the quality of farmland ecosystems, actively explore new models of land consolidation and stimulate the economic vitality of rural areas

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human–computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net