Search

A method of using nonlinear constrained optimization to design gradient waveforms for magnetic resonance imaging is described. Artificial constraints on waveform shape imposed by multilobe designs are eliminated by defining the waveform as a set of discrete amplitudes. These amplitudes are the parameters determined in the optimization procedure, subject to the constraints defined by imaging conditions and the specific gradient hardware system of interest. Formulation and solution of the waveform optimization problem are described and example waveforms are presented for a variety of design objectives and constraint sets. Most design objectives can be expressed as linear or quadratic functions of the discrete parameter set, and most constraint functions are linear. Thus, linear and quadratic programming techniques can be utilized to solve the optimization problem. Among the objectives considered are: 1) minimize RMS current; 2) minimize waveform slewing; 3) minimize waveform moments to reduce motion induced dephasing; 4) minimize echo time (TE) for given imaging and motion refocusing conditions; 5) maximize the gradient amplitude during RF application and sampling and the area of the phase encoding waveform to maximize resolution; and 6) minimize or maximize the gradient b factor or diffusion sensitivity. This optimal design procedure produces physically realizable waveforms which optimally achieve specific imaging and motion artifact reduction goals, and it is likely to reduce waveform design time by making it more scientifically (rather than heuristically) based.

This paper focuses on the design of nonlinear coordinated generator excitation and superconducting magnetic energy storage (SMES) control to enhance the transient stability of an electrical power system. An immersion and invariance (I&I) nonlinear control design technique is used to achieve power angle stability and frequency and voltage regulation, and to ensure that the closed-loop system is asymptotically stable. The main contributions of this paper are: (1) use of a generator excitation and SMES combination to improve transient stability, (2) demonstrating the applicability of nonlinear control theory (particularly the I&I methodology) for the design of a stabilizing (state) feedback controller in a single machine infinite bus system to enhance transient system performance and (3) developing a methodology that can employ the additional degree of freedom in power systems that include the combined excitation and SMES and advanced nonlinear control in a way that can further improve the dynamic transient performance of the system. In order to show the effectiveness of the proposed controller design, numerical simulation results are provided to illustrate the performance in maintaining stability and simultaneously achieving improvements in voltage regulation during small and large disturbances, and to show that our proposed nonlinear controller has more advantages by comparing it with the control performance of two existing nonlinear controllers and a conventional linear controller, in particular, feedback linearizing and coordinated passivation controllers, and a power system stabilizer (PSS), respectively. [ABSTRACT FROM AUTHOR]

Stereotactic body radiation therapy (SBRT), which delivers a much higher fractional dose than conventional treatment in only a few fractions, is an effective treatment for liver metastases. For patients who are treated under free-breathing conditions, however, respiration-induced tumor motion in the liver is a concern. Limited clinical information is available related to the impact of tumor motion and treatment technique on the dosimetric consequences. This study evaluated the dosimetric deviations between planned and delivered SBRT dose in the presence of tumor motion for three delivery techniques: three-dimensional conformal static beams (3DCRT), dynamic conformal arc (DARC), and intensity-modulated radiation therapy (IMRT). Five cases treated with SBRT for liver metastases were included in the study, with tumor motions ranging from 0.5 to 1.75 cm. For each case, three different treatment plans were developed using 3DCRT, DARC, and IMRT. The gantry/multileaf collimator (MLC) motion in the DARC plans and the MLC motion in the IMRT plans were synchronized to the patient’s respiratory motion. Retrospectively sorted four-dimensional computed tomography image sets were used to determine patient-organ motion and to calculate the dose delivered during each respiratory phase. Deformable registration, using thin-plate-spline models, was performed to encode the tumor motion and deformation and to register the dose-per-phase to the reference phase images. The different dose distributions resulting from the different delivery techniques and motion ranges were compared to assess the effect of organ motion on dose delivery. Voxel dose variations occurred mostly in the high gradient regions, typically between the target volume and normal tissues, with a maximum variation up to 20%. The greatest CTV variation of all the plans was seen in the IMRT technique with the largest motion range (D99: -8.9%, D95: -8.3%, and D90: -6.3%). The greatest variation for all 3DCRT plans was less than 2% for D95. Dose variations for DARC fell between the 3DCRT and IMRT techniques. The dose volume histogram variations for normal organs were negligible. Therefore, the IMRT technique may be a preferable treatment choice in cases where the target volume and critical organs are in close proximity, or when normal organ protection is a high priority, provided that motion effect for the target volume can be managed. [ABSTRACT FROM AUTHOR]

During the last decade, databases have been growing rapidly in size and number as a result of rapid advances in database capacity and management techniques. This expansive growth in data and databases has caused a pressing need for the development of more powerful techniques to convert the vast pool of data into valuable information. For the purpose of strategic and decision-making, many companies and researchers have recognized mining useful information and knowledge from large databases as a key research topic and as an opportunity for major revenues and improving competitiveness. In this paper, we will explore a new rule generation algorithm (based on rough sets theory) that can generate a minimal set of rule reducts, and a rule generation and rule induction program (RGRIP) which can efficiently induce decision rules from conflicting information systems. All the methods will also be illustrated with numerical examples. [ABSTRACT FROM AUTHOR]