EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use.

Dual-Band Tunable Recursive Active Filter

This letter presents a novel recursive active filter topology that provides dual-band performance, with independent tuning capability in both bands. The dual-band operation is achieved by using two independent feedback lines. Additionally, linear phase shifters based on left-handed cells are included in these two branches in order to tune the center frequency of both pass bands.

From irradiance to output power fluctuations: the pv plant as a low pass filter

The power generated by large grid-connected photovoltaic (PV) plants depends greatly on the solar irradiance. This paper studies the effects of the solar irradiance variability analyzing experimental 1-s data collected throughout a year at six PV plants, totaling 18 MWp. Each PV plant was modeled as a first order filter function based on an analysis in the frequency domain of the irradiance data and the output power signals. An empiric expression which relates the filter parameters and the PV plant size has been proposed. This simple model has been successfully validated precisely determining the daily maximum output power fluctuation from incident irradiance measurements.

Methodology for adapting the parameters of a fuzzy system using the extended Kalman filter

When we try to analyze and to control a system whose model was obtained only based on input/output data, accuracy is essential in the model. On the other hand, to make the procedure practical, the modeling stage must be computationally efficient. In this regard, this paper presents the application of extended Kalman filter for the parametric adaptation of a fuzzy model

Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process