The Non-equidistant Multivariable New Information Optimization NMGRM (1,n) Based on New Information Background Value Constructing

Applying the principle in which new information should be used fully and modeling method of Grey system for the problem of lower precision as well as lower adaptability in non-equidistant multivariable MGM(1,n)model, taking the mean relative error as objective function, and taking the modified values of response function initial value as design variables, based on accumulated generating operation of reciprocal number, a non-equidistant multivariable new information optimization MGRM(1,n) model was put forward which was taken the mth component as the initialization. Based on index characteristic of grey model, the characteristic of integral and new information principle, the new information background value in non-equidistant multivariable new information optimization MGRM(1,n) was researched and the discrete function with non-homogeneous exponential law was used to fit the accumulated sequence and the formula of new information background value was given. The new information optimization MGRM(1,n) model can be used in non-equal interval & equal interval time series and has the characteristic of high precision as well as high adaptability. Example validates the practicability and reliability of the proposed model. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.218

Effective connectivity of dorsal and ventral visual pathways in chunk decomposition

Chunk decomposition is defined as a cognitive process which breaks up familiar items into several parts to reorganize them in an alternative approach. The present study investigated the effective connectivity of visual streams in chunk decomposition through dynamic causal modeling (DCM). The results revealed that chunk familiarity and perceptual tightness made a combined contribution to highlight not only the "what" and the "where" streams, but also the effective connectivity from the left inferior temporal gyrus to the left superior parietal lobule

Inverse Displacement Analysis of a General 6R Manipulator Based on the Hyper-Chaotic Least Square Method

The hyper-chaotic least square method for finding all real solutions of nonlinear equations was proposed and the inverse displacement analysis of a general 6R manipulator was completed. Applying the D-H method, a 4 × 4 matrix transform was obtained and the first type twelve constrained equations were established. Analysing the characteristics of the matrix, the second type twelve constrained equations were established by adding variables and restriction. Combining the least square method with hyper-chaotic sequences, the hyper-chaotic least square method based on utilizing a hyper-chaotic discrete system to obtain and locate initial points to find all the real solutions of the nonlinear questions was proposed. The numerical example was given for two type constrained equations. The results show that all the real solutions have been obtained, and it proves the correctness and validity of the proposed method

Efficient longitudinal relaxation time measurement of 129Xe via bias-approach

We proposed a new bias-approach method for measuring longitudinal relaxation time of 129Xe. Compared with the delayed pulse method, the measuring time of the proposed single-scan bias-approach is significantly decreased by more than 20 times, and the differences are 2% and 0.7% of two sample cell A and B respectively. By giving a small static magnetic field in the transverse domain, a π-pulse-induced 87Rb magnetometer signal bias with respect to the longitudinal magnetization of xenon was obtained. We then use a π/2 pulse to find the half-life time of longitudinal relaxation. Since the bias-approach has a low requirement of the systematic robust, it naturally becomes a more efficient method to measure the longitudinal relaxation time with respect to the noble gas in the vapor cell

Cross-Axis Coupling Effects in Single-Axis Nuclear Magnetic Resonance Gyroscopes

Nuclear magnetic resonance gyroscopes (NMRGs) may be operated in an environment with violent vibration that usually contains both linear components and angular components. To analyze the influence of angular vibration on an NMRG, cross-axis coupling effects are studied. The cross-axis rotation rates induce an equivalent magnetic field. Its influence can be described by the Bloch equations. The approximate frequency shift and amplitude of the spin oscillator with an equivalent magnetic field in the cross-axis were obtained, which was validated by numerical simulation. The findings show that the angular vibration component leads to a remarkable error for the NMRG. When the angular vibration frequency is near the Larmor frequency, the oscillation frequency of the spins may be locked to the angular vibration frequency, destroying the NMRG’s ability to measure rotation rates. The cross-axis coupling problem should be considered in the design of an NMRG and corresponding inertial navigation systems

The characteristic analysis of the built-in vector atomic magnetometer in a nuclear magnetic resonance oscillator

We analyze the amplitude-frequency, phase-frequency, signal amplification, linear range, and vector characteristics of the built-in vector atomic magnetometer operating at extreme off-resonance condition in a nuclear magnetic resonance oscillator, which makes possible its performance improvement by a balanced strategy in optimizing the parameters based on the proposed model. The experiment validates our prediction of the amplitude-frequency characteristic, and the numerical simulation indicates that the applied carrier field with following demodulation procedure holds the potential to give one order of magnitude, which is experimentally-validated to have at least twice, signal enhancement and enable the vector characteristic, where a large longitudinal static field and an appropriate transverse relaxation time are preferred to have optimized characteristics depending on different applications

Data-driven decision support for rail traffic control: A predictive approach

Advanced decision support for rail traffic control is significant for enhancing the safety and quality of railway transport service. Data-driven methods have shown powerful learning ability and wide extensibility for prediction, classification, and decision-making problems. In this paper, we propose a hybrid prediction model based on deep forest (DF) ensemble learning to analyze the two common rail traffic control actions, i.e., changing the dwelling times and running times. This basic concept is to mimic the decision-making of rail traffic controllers, providing them with advanced decisions/control actions using data-driven learning algorithms. According to the decision-making approach of rail traffic controllers, the learning process of the model is split into two stages, i.e., learning the type of action (ToA) and the number of changes (NoC) in the dwelling times and running times (i.e., how many dwelling times or running times have been changed compared with the planned ones). The first stage is a classification problem; thus, DF classifier with the synthetic minority oversampling technique (SMOTE) is employed to deal with imbalanced data. In the second stage, the DF regressor treats the NoC in the dwelling and running times as numerical variables and utilizes the information from stage one, i.e., the prediction results of the classification model, to make predictions. The proposed model is calibrated on train operation data from two high-speed railway lines in China. The experimental results and comparative analyses show that the proposed method provides advanced and timely decision support for controllers. These characteristics of the proposed model are imperative for supporting the dynamic decision-making of controllers to manage railway traffic.ISSN:0957-417