Data mining is a methodology for the extraction of knowledge from data, especially, knowledge relating to a problem that we want to solve. Data mining from simulation outputs is performed in this paper, it focuses on ...
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Data mining is a methodology for the extraction of knowledge from data, especially, knowledge relating to a problem that we want to solve. Data mining from simulation outputs is performed in this paper, it focuses on techniques for extracting knowledge from simulation outputs for beer production and optimizing devices and labors with certain target. We first set up one simulation model for beer production process and construct optimization objective. Then we set up one data mining model based on witness miner. The mining results show that the model is able to fund important information affecting target, make manager diagnose the bottlenecks of the beer production process, and help manager to make decisions rapidly under uncertainty.
The Gyroscope-free inertial navigation system can measure the direction, velocity and position of a vehicle after a suitable configuration and mechanization. Because of system error effect, the navigation error of a G...
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The Gyroscope-free inertial navigation system can measure the direction, velocity and position of a vehicle after a suitable configuration and mechanization. Because of system error effect, the navigation error of a Gyroscope-free INS is deeply affected by the time influence. This paper presents three important research issues. The first is the navigation equations of a Gyro free (six-accelerometer) INS that is based on the tangent-plane coordinate. The second is error equations of the proposed strap-down six-accelerometer INS. The third is a novel method for the integration of the six-accelerometer INS and the GPS. A superior result of simulation is found that the integrated INS/GPS can possess a more accurate navigation error, it almost have two orders less than conventional INS. The novel method of integrating six-accelerometer INS and GPS would be considered in auto-pilot and UAV design for the future application.
In this paper, it discuss the analytical method of ATBM interception via TVM (Tracking Via Missile), it use the modified CLOS(Command to Line of Sight) in the terminal phase. The major difference between conventional ...
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In this paper, it discuss the analytical method of ATBM interception via TVM (Tracking Via Missile), it use the modified CLOS(Command to Line of Sight) in the terminal phase. The major difference between conventional CLOS is observation (Reference) point, which is changed from the ground to seeker "lock-on" position in collision course. The second difference occur in the constant P, which is the ratio of speed of missile and target, the value is decreased from 1.5 to 0.7-0.5 and form as a head-on collision. It also give a rigorous study for some methods of CLOS, such as Locke method, Jalali-Naini method and Wen's method, to make some refining results. Under some assumption, the paper give a prediction for the best lock-on point, and the angle of lock-on, it can obtain a collision with the most accurate and the best energy consumption in the interception course.
Two improvements are introduced into vicinal-risk-minimization based support vector (SV) algorithm. Since the misclassified samples must be support vectors, a scheme for pruning hard-to-learn samples from the training...
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Two improvements are introduced into vicinal-risk-minimization based support vector (SV) algorithm. Since the misclassified samples must be support vectors, a scheme for pruning hard-to-learn samples from the training set based on support vectors is presented. The parameter's determination of Gaussian vicinal function is proposed to be modified, based on the maximum likelihood criterion. Preliminary experimental results show that the pruning scheme and improvement of the parameter's determination of vicinal function much improved vicinal SV algorithm's generality, and can outperform support vector machine (SVM) by about 0.5% in test accuracy.
Summary form only given. This special session has been motivated by the growing importance of data-driven modeling in Earth sciences, climate modeling, meteorological and oceanographic applications, geophysical data p...
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Summary form only given. This special session has been motivated by the growing importance of data-driven modeling in Earth sciences, climate modeling, meteorological and oceanographic applications, geophysical data processing, and hydrology. Of particular interest are the methodological aspects of learning methods, with the clarification of the advantages and limitations of learning techniques in the context of specific applications. This panel includes informal presentations by the session co-chairs followed by questions and answers from the audience. Topics of discussion include the following: 1) to identify major types of problems encountered in this field; 2) how to estimate the quality of data-driven models; 3) what are specific characteristics of data sets in climate modeling/Earth sciences that make them different from other applications; and 4) try to come to an agreement on possible benchmark data sets in this field.
Three typical path planning methods, i.e. artificial potential field, probabilistic path planning and biologically inspired neural network, were introduced. The analysis and comparisons were made in relating to genera...
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Three typical path planning methods, i.e. artificial potential field, probabilistic path planning and biologically inspired neural network, were introduced. The analysis and comparisons were made in relating to general aspects of the complexity, robustness and adaptability of the methods.
This paper firstly examines the possibility of using data mining in such a case-based system and then puts forward a case-based system framework based on data mining techniques. Some data mining approaches in this sys...
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ISBN:
(纸本)0780384032
This paper firstly examines the possibility of using data mining in such a case-based system and then puts forward a case-based system framework based on data mining techniques. Some data mining approaches in this system are proposed.
The Simple Object Access Protocol (SOAP) is recognized as a more promising middleware for electronic commerce applications among other leading candidates such as CORBA. Many recent polls reveal however that security a...
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BandPass Sampling (BPS) is an undersampling technique by intentional aliasing. BPS enables one to have an interface between the IF stage and the ADC in a radio receiver. Conventional uniform BPS at Nyquist rate normal...
BandPass Sampling (BPS) is an undersampling technique by intentional aliasing. BPS enables one to have an interface between the IF stage and the ADC in a radio receiver. Conventional uniform BPS at Nyquist rate normally results in a low Signal-to-Noise Ratio (SNR) due to noise spectrum aliasing. The noise (e.g. kT/C noise introduced in a voltage-mode sampler) is combined in each of the Nyquist bands within the bandwidth of the sampling device. Also timing jitter causes a performance degradation in BPS. In this paper, signal spectrum aliasing, noise aliasing and jitter effects in BPS is analyzed. It is verified by simulation that NonUniform Sampling (NUS) has the potential to suppress signal spectrum aliasing and relax the requirement on the anti-aliasing (AA) filter. Jitter effects in BPS are compared to LowPass Sampling (LPS) case. However, a signal cannot be reconstructed from its nonuniform samples by using only ideal lowpass filtering (classic Shannon’s reconstruction). Finally, signal reconstruction in the presence of noise and jitter are investigated for three Reconstruction Algorithms (RAs) aimed at NUS.
Bandpass sampling (BPS) is an undersampling technique by intentional aliasing. Conventional uniform discrete sampling within an f/sub s/ band normally results in a bad signal-to-noise ratio (SNR) due to signal spectru...
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Bandpass sampling (BPS) is an undersampling technique by intentional aliasing. Conventional uniform discrete sampling within an f/sub s/ band normally results in a bad signal-to-noise ratio (SNR) due to signal spectrum aliasing. The noise combined in each of the f/sub s/ bands below the highest frequency of the signal (the so called noise spectrum aliasing) and timing jitter are two causes of performance degradation in BPS system. Nonuniform BPS has the potential to suppress signal spectrum aliasing due to the aperiodic property of nonuniform sampling (NUS). In this paper, the frequency spectra of uniform sampling (US) and NUS are analyzed, signal spectrum aliasing, noise spectrum aliasing and jitter effects in BPS are studied. Finally, the performance of reconstruction algorithms (RAs) for nonuniform BPS in the presence of sources of performance degradation are discussed based on simulations.
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