Within the cascade approach to the design of state and perturbation observers, an original method for estimating the derivatives of external perturbations that affect a controlled object is proposed. This method is based on virtual dynamic models compiled by the sequential differentiation of some derivatives of the object’s state variables. Observers are constructed as replicas of virtual models. A cascade design procedure for the observers of the derivatives of external perturbations with piecewise linear corrections is developed. In this procedure, the motions separation method in the virtual space of observation errors is implemented. The observer’s variables and also their corrections serve as estimates of the derivatives of external perturbations. They can be used to design combined feedback control and/or to perform current diagnosis of the control process.

The problems that are being talked about here are related to the scientific field of supply chain management. This article focuses on the preferred control algorithm selection for large-scale railway network logistics for the special case. In this particular case, the dispatch and destination stations belong to the various regional rails. The statistical approach is applied here to the choice of the preferred control algorithm for rail cargo logistics. The peculiarity of our approach is the application of standard score z-test modifications for timing characteristics comparison. The results obtained can be used for logistics process optimization and improvement. These findings may be applicable to large-scale railway networks and transportation networks that combine the different modes of transport.

Ultrasound guided biopsy is a popular biopsy technology. An important issue for this technology is biopsy needle detection. However, the contrast of ultrasound images is low and the involved background is complicated. Existing needle detection methods cannot realize the fast and accurate needle detection. To address this issue, we have proposed a new needle detection algorithm by combining fuzzy enhancement with Hough transform. In the proposed method, the improved fuzzy enhancement method is firstly utilized to improve the image contrast, and image thinning algorithm is used to extract the linear skeleton. Then, Hough transform is adopted to detect the straight line. Finally, the endpoint detected using Hough transform is corrected based on the intensity correlation of pixels. The proposed method has been evaluated on the ultrasound prostate image sequences including 32 frames where each frame is of size 800×600. Experimental results demonstrate that the proposed method generally has higher detection accuracy than other compared methods in that it can provide 6.7% misdetection rate and its mean length and angle errors are 8.8 pixels and 0.98 degree, respectively. Meanwhile, the implementation time of the proposed method is 25 ms. Indeed, the proposed algorithm can detect the linear surgical instruments accurately and meet the demand of clinical practices.

When random disturbances are regularly introduced into a dynamical system over time, its small-signal stability is determined by the energy of perturbations accumulated in the system. To analyze this perturbation energy, this paper proposes a novel physically motivated Lyapunov modal analysis (LMA) framework, which combines selective modal analysis with the spectral decompositions of specially chosen Lyapunov functions. This approach allows the modal interactions in dynamical systems to be characterized and estimated in connection with specific state variables. Conventional participation factors characterize the relative contribution of the system modes and state variables to the evolution of states and modes, respectively. In contrast, the proposed Lyapunov participation factors characterize similar contributions to corresponding Lyapunov functions, which determine the integral energy associated with the states and modes on an infinite or finite time interval. This allows the estimation of modal interactions in terms of total energy produced by their mutual actions over time. Using a two-area four-machines power system, we demonstrate that LMA reliably identifies resonant modal interactions, merging of modes, and loss of stability, even for a linear model, and associates them with certain state variables. The Lyapunov participation factors corresponding to the selected part of the system spectrum can be calculated independently and serve as a basis for rapid real-time calculations of critical mode behaviors in large-scale dynamical systems.