In recent years, the collection of various data coming from anatomical and functional imagery is becoming very common for the study of a given pathology, and their aggregation generally allows for a better medical decision in clinical studies. However, it is difficult to simulate the human ability of image fusion when algorithms of image processing are piled up merely. On the basis of the review of researches on psychophysics and physiology of human vision, this paper presents an effective multi-resolution image data fusion methodology, which is based on discrete wavelet transform theory and self-organizing features mapping neural network (SOFMNN), to simulate the processes of images recognition and understanding implemented in the human vision system. Through the two-dimensional wavelet transform, original images canbe decomposed into different typeti of details and levels. The integration de can be built using self-organizing neural networks, just like the automatic work in human brain. As an example, the fusion process is applied in the clinical case: the study of some particular disease by MRlSPECT fusion. Results are presented and evaluated, and a preliminary clinical validation is achieved. The assessment of the method is encouraging, allowing its application on several clinical diagnostic problems.
A hybrid genetic/simulated annealing approach is established for solving the thermal generator scheduling problem. It develops a method for encoding generator schedules in the hybrid approach. The method leads to a large chance of producing feasible schedules in the solution process. For the infeasible schedules produced, methods are developed to restore their feasibility. The methods developed are incorporated into the hybrid algorithm GAA2 for the determination of the most economical schedules. The original GAA2 developed previously is employed to access the cost of the generator schedules. The hybrid approach has the ability to deal with the nonconvexity of the scheduling problem. Its usefulness is demonstrated by its application to a real-life power system consisting of 13 generators.
Unbalance in steady-state operation is of general concern in power transmission, but it can be especially emphasised when transmission is over long distances, owing to the increasing influence of line parameter asymmetries on operating conditions as transmission-line lengths increase. Specifically in the context of long-distance transmission, the paper first develops procedures for evaluating the dependence of operating unbalance on the precise sets of connections when conductor transpositions are made at discrete points along the length of a transmission line. These are included in detailed phase-variable models for transmission-line sections, and on them is based an overall form of Newton-Raphson solution. The methods are applied to a representative long-distance transmission interconnection operating at 220kV and having two separated points of conductor transposition. The inherent operating unbalance in the interconnection is evaluated for a range of power-transfer conditions, and the extent to which the unbalance can be lowered by discrete transposition is quantified. The interaction of load unbalance and transmission-line parameter asymmetry is investigated, and load distributions are identified which lead to the greatest increase and the greatest reduction in unbalance in transmission. For an optimal choice of conductor transposition from a total of 35 possible sets of connections involving the two transposition points, the paper then investigates the further lowering of operating unbalance which saturated-reactor forms of shunt compensation at selected locations can additionally offer. In total, the paper provides a comprehensive investigation of operating unbalance in long-distance transmission from which several conclusions are drawn.
The worldwide trend for the deregulation of the electricity generation and transmission industries has led to dramatic changes in system operation and planning procedures. The optimum approach to transmission-expansion planning in a deregulated environment is an open problem especially when the responsibilities of the organisations carrying out the planning work need to be addressed. To date there is a consensus that the system operator and network manager perform the expansion planning work in a centralised way. However, with an increasing input from the electricity market, the objectives, constraints and approaches toward transmission planning should be carefully designed to ensure system reliability as well as meeting the market requirements. A market-oriented approach for transmission planning in a deregulated environment is proposed. Case studies using the IEEE 14-bus system and the Australian national electricity market grid are performed. In addition, the proposed method is compared with a traditional planning method to further verify its effectiveness.
2068 Objectives Development of tumor necrosis is often accompanied by metabolic heterogeneity. Partial volume correction (PVC) of FDG images of heterogeneous tumor that involves unknown irregular shape and non-uniform concentrations is difficult. We propose a new approach that integrated PVC with factor analysis that is applicable to dynamic FDG PET images of tumors. Methods A 60-min dynamic FDG PET was performed on 11 SCID mice with implanted U87 glioblastoma. Eight out of 11 that had tumor necrosis were investigated. Factor analysis was applied to the dynamic tumor images with up to 4 factors. PVC was performed on the factor image of the tumor component as follows. A segment boundary was determined using a threshold t1(%) of maximum, and the segment image was smoothed using the scanner’s resolution. The smoothed image was then subtracted from the original factor image. The procedure was repeated 3 more times with different thresholds (t2, t3, and t4(%)), which were selected for each animal to minimize the sum of square of the residual image. Results Regardless of the number of factors used in the factor analysis, the FDG kinetics of all tumor tissues were represented by only a single factor (i.e., no other factor images showed tumor structure). The kinetic characteristics of the corresponding factor were close to the kinetics obtained from the tumor ROI. The smoothed PVC images were visually similar to the original tumor factor images. Quantitatively, relative difference between the smoothed PVC image and the original factor image was averaged 9.2±1.1% (range: 8.2 to 11.3%) for all 8 tumors studied. The total activity of the PVC images was within 5% of that of the original factor image. Conclusions This new method using factor analysis followed by stepwise correction procedure successfully converted dynamic tumor FDG images to PVC images of tumor FDG kinetics. This approach is expected to improve quantitation of FDG uptake in tumors with necrosis for longitudinal tumor progression studies.
Proper power system load models are playing more important roles in power system stability analysis in today's stressed power systems. Different load models may cause a large difference in stability analytical results. Measurement based load modeling gives a closer look at the real power system loads and their dynamic characteristics. In this paper, genetic algorithms and evolutionary programming based system identification is used to locate the best available parameters for proper load modeling based on real field test data taken in North China.
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