this paper presents an efficient global optimization approach to the problem of constrained contour energy minimization for the object boundary extraction. In the method, with a given contour energy function, differen...
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this paper presents an efficient global optimization approach to the problem of constrained contour energy minimization for the object boundary extraction. In the method, with a given contour energy function, different target boundaries can be modeled as constrained global optimal solutions under different constraints expressed as a set of parameters characterizing the target contour interior structure. To search for the constrained global optimal solution, a fast and efficient global approach based on mean field annealing (MFA) is employed to avoid local minima. An illustrative example of three target boundaries in a synthetic image modeled as constrained global energy minimum contours with different constraint parameters is successfully located using the derived algorithm. A conventional variational based deformable contour method (Wang et al., 2002) withthe same energy function and constraint fails to achieve the same task. Experimental evaluations and comparisons with other methods on ultrasound pig heart, MRI knee, and CT kidney images where gaps, blur contour segments having complex shape and inhomogeneous interiors have been conducted with most favorable results
Order-k Markov model can be used in many fields such as natural language understanding, coding, mobile path prediction and so on to make prediction and then control. But the model has to face the problem of state spac...
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Order-k Markov model can be used in many fields such as natural language understanding, coding, mobile path prediction and so on to make prediction and then control. But the model has to face the problem of state space expansion. Taking the mobile path prediction as the research background, the paper firstly proposes a step-k Markov model and validates its feasibility. Secondly, a hybrid Markov predictor model is put forward based on the step-k Markov model. the complexity of the hybrid Markov model is O(N) while the order-k Markov model is O(N 2 ). And the memory demand of the hybrid Markov model is O(N 2 ) while order-k Markov model is O(N 3 ). Finally, it is proved that the hybrid Markov predictor can get close performance with order-k Markov predictor at much lower expense by conditional entropy analysis and user mobility data analysis. Also it can alleviate the zero probability problem in order-k Markov model to some extent. the hybrid Markov predictor is more practical than order-k Markov predictor under WLAN
this paper discusses the role played by signal detection algorithms in the mobile robot map building problem. Typical mapping techniques make the assumption that the internal signal detection, which is required to pro...
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this paper discusses the role played by signal detection algorithms in the mobile robot map building problem. Typical mapping techniques make the assumption that the internal signal detection, which is required to produce an (r, rho) point estimate, is ideal. that is, the probability of detecting the signal is unity, and the probabilities of a false alarm or missed detection are zero. In the case of grid mapping, this allows for the occupancy probability to be distributed under the constraint of a unity summation amongst affected cells. In the case of SLAM, this allows for a feature's (x,y) coordinates to be modeled with (Gaussian) probability density functions. this paper shows that typical signal detection algorithms contain all the necessary measurement models to exactly calculate the map occupancy estimates. Furthermore, once restrictive signal assumptions are relaxed, its shown that evidence theory and not Bayesian theory should be used in the combination and updating of the map estimates. the ideas presented in this paper are demonstrated in the field robotics domain using a millimeter wave radar sensor. Target presence and absence beliefs are derived directly from signal likelihood ratios as opposed to a priori assigned constants as is typical for mapping algorithms. Results obtained from outdoor sensing experiments, show the improvement of this new model, given targets of fluctuating radar cross section (RCS)
the paper refers to intelligent industrial automation. the objective is to present key elements and methods for best practice, as well as some results obtained. the first part presents an ontology for automated cognit...
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the paper refers to intelligent industrial automation. the objective is to present key elements and methods for best practice, as well as some results obtained. the first part presents an ontology for automated cognition (cognitics), where, based on information and time, the main cognitive concepts, including those of complexity, knowledge, expertise, learning, intelligence abstraction, and concretization are rigorously defined, along with corresponding metrics and specific units. Among important conclusions at this point are the fact that reality is much too complex to be approached better than through much simplified models, in very restricted contexts. Another conclusion is the necessity to be focused on goal. Extensions are made here for group behavior. the second part briefly presents a basic law governing the choice of overall control architecture: achievable performance level of control system in terms of agility, relative to process dynamics, dictates the type of approaches which is suitable, in a spectrum which ranges from simple threshold-based switching, to classical closed-loop calculus (PID, state space multivariable systems, etc.), up to "impossible" cases where additional controllers must be considered, leading to cascaded, hierarchical control structures. For complex cases such as latter ones, new tools and methodologies must be designed, as is typical in O 3 NEIDA initiative, at least for software components. Finally, a large part of the paper presents a case study, a mobile robot, i.e. an embedded autonomous system with distributed, networked control, featuring industry-grade components, designed withthe main goal of robust functionality. the case illustrates several of the concepts introduced earlier in the paper.
A macro-micro dual-drive high acceleration precision XY-stage is presented in this paper. Combining macro with micro actuator, a system of large workspace and high acceleration with high resolution of motion is develo...
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A macro-micro dual-drive high acceleration precision XY-stage is presented in this paper. Combining macro with micro actuator, a system of large workspace and high acceleration with high resolution of motion is developed. Two linear voice coil motors (VCM) are used into the macro motion, and two PZT-driven micro stages of high frequency are mounted on each motor to compensate the position error. A novel elastic decoupling mechanism is used in the stage to avoid the moving gap. the high resolution linear encoder is integrated into the closed-loop feedback, which is used to measure the position of the output end of macro stage and micro stage. By using the mechanical dynamic simulation and FEA method, the decoupling mechanism and the micro mechanism are optimized, and the dynamic characteristics of the high acceleration stage are investigated, which is based on the rigid-flexible dynamic analysis of mechanical system. the simulation results show that this new configuration allows a workspace of 25/spl times/25mm/sup 2/ and an acceleration exceeding 100m/s/sup 2/ with a resolution of motion better than 10nm. the significantly improved performance of the XY-stage can meet the requirement of the rapid development of IC bonding technology.
A method is proposed for estimating states of 2-D GM models by using noisy observations in the case when the input to the dynamic system and the observation errors are unknown except for bounds on their magnitude or e...
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ISBN:
(纸本)0780386531
A method is proposed for estimating states of 2-D GM models by using noisy observations in the case when the input to the dynamic system and the observation errors are unknown except for bounds on their magnitude or energy. the designed state estimator is composed of a set in state space rather than a single vector. It is shown that the optimal estimator is the smallest set, which contains the unknown system state. A recursive algorithm is developed which calculates a time-varying ellipsoid in the state space.
this paper uses weekly closing price of Shenzhen Integrated Index to research on the volatility of Shenzhen Stock Market based on a logistic forecasting model. the results prove that most of the results are reasonably...
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ISBN:
(纸本)0780386531
this paper uses weekly closing price of Shenzhen Integrated Index to research on the volatility of Shenzhen Stock Market based on a logistic forecasting model. the results prove that most of the results are reasonably exact, whereas only several forecasting outcome produce a little deviation. In addition, the paper also adopts the statistical methods of the ME, MAE, RMSE, MAPE to test the out-of-sample. the results show us that the error statistical test results of the ME, MAE, MAPE are all the same, however only the results of RMSE have a little error.
We propose a method of person authentication based on lip movement by using the kernel mutual subspace method. Algorithms for person authentication using lip movements have already been developed, but in case that the...
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We propose a method of person authentication based on lip movement by using the kernel mutual subspace method. Algorithms for person authentication using lip movements have already been developed, but in case that the distribution of lip images has a nonlinear structure, the identification accuracy is degraded because the conventional recognition methods do not assume such nonlinearity. We have investigated the distributions of lip images in feature space and experimentally obtained their nonlinear properties. To solve the problem of nonlinearity, we propose a labiate person authentication method based on the kernel mutual subspace method. We evaluated accuracy and stability of the proposed method with demonstration experiments results.
Usually, effects of a system under PID control with considerable time delay are not satisfied. However, model-based predictive control is an advanced control strategy that uses a move optimization method for achieving...
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ISBN:
(纸本)0780386531
Usually, effects of a system under PID control with considerable time delay are not satisfied. However, model-based predictive control is an advanced control strategy that uses a move optimization method for achieving satisfactory closed-loop dynamic responses of complex systems. this paper describes a real time scheme that utilizes DMC algorithm to deal with a large pure time delay in process and adopts nuclear radiation to measure thickness of battery separator. the system has been designed and implemented in one of the battery separator plants of China. And the method of measure and control has wide applicability for similar continuous thin material process.
We have developed and evaluated a set of speaker normalization procedures derived by bilinear transform (BLT) to compensate for variations in vocal tract lengths of different classes of speakers. the warping factors a...
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ISBN:
(纸本)0780386531
We have developed and evaluated a set of speaker normalization procedures derived by bilinear transform (BLT) to compensate for variations in vocal tract lengths of different classes of speakers. the warping factors are estimated using the average third formants and their bandwidth, leaving out the exhaustive search. the MFCC of the testing data are transformed by the warped Mel filterbanks to match the models of the training data. the effectiveness of this set of speaker normalization procedures is examined in an experimental study performed using an isolated digit database of man, woman and children comparing to other standard speaker normalization method. the results of experiments demonstrate their capacity to achieve recognition accuracy increase of 19.5% and 16.5% at the best.
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