EEG Dipole Source Localization using Deep Neural Network

Electroencephalography (EEG) inverse problem is a typical inverse problem, in which the electrical activity within the brain is reconstructed based on EEG data collected from the scalp electrodes. In this paper, the four-layer concentric head model is used for simulation firstly, four deep neural network models including a multilayer perceptron (MLP) model and three convolutional neural networks (CNNs) are adopted to solve EEG inverse problem based on equal current dipole (ECD) model. In the simulations, 100,000 samples are generated randomly, of which 60% are used for network training and 20% are used for cross-validation. Eventually, the generalization performance of the model using the optimal function is measured by the errors in the rest 20% testing set. The experimental results show that the absolute error, relative error, mean positioning error and standard deviation of the four models are extremely low. The CNN with 6 convolutional layers and 3 pooling layers (CNN-3) is the best model. Its absolute error is about 0.015, its relative error is about 0.005, and its dipole position error is 0.040±0.029 cm. Furthermore, we use CNN-3 for source localization of the real EEG data in Working Memory. The results are in accord with physiological experience. The deep neural network method in our study needs fewer calculation parameters, takes less time, and has better positioning results.

Palindromic Vectors, Symmetropy and Symmentropy as Symmetry Descriptors of Binary Data

Today, the palindromic analysis of biological sequences, based exclusively on the study of “mirror” symmetry properties, is almost unavoidable. However, other types of symmetry, such as those present in friezes, could allow us to analyze binary sequences from another point of view. New tools, such as symmetropy and symmentropy, based on new types of palindromes allow us to discriminate binarized 1/f noise sequences better than Lempel–Ziv complexity. These new palindromes with new types of symmetry also allow for better discrimination of binarized DNA sequences. A relative error of 6% of symmetropy is obtained from the HUMHBB and YEAST1 DNA sequences. A factor of 4 between the slopes obtained from the linear fits of the local symmentropies for the two DNA sequences shows the discriminative capacity of the local symmentropy. Moreover, it is highlighted that a certain number of these new palindromes of sizes greater than 30 bits are more discriminating than those of smaller sizes assimilated to those from an independent and identically distributed random variable.

Study on the tool setting method of micro-milling tool based on in-line holography

Aiming at the various shortcomings of existing tool setting methods, this paper proposes a coaxial holographic tool setting method for tiny tools. Based on the research and analysis of the principle of holographic imaging and the key issues of holographic images, a set of holographic tool setting detection device for micro milling tool was built, and the micro milling tool measurement was carried out on the five-axis machining center using standard tools. experiment. Experimental results show that the tool setting device can efficiently perform tool setting detection of micro-milling tool. Compared with the measurement results of the high-precision external presetting instrument, the relative error of the contact tool setting instrument is 0.033%, and the relative error of the holographic tool setting prototype is 0.007%, which is more effective in realizing the tool setting of tiny tools. Detection. This result verifies the feasibility of the coaxial holographic tool setting method for micro tool, that is, holographic measurement can be used for high-precision tool setting of micro milling tool.

CFD MODELLING AND VALIDATION OF AMBIENT FACTORS IN EVAPORATIVE COLD STORE FOR PEACH STORAGE

Spatial variation of temperature and relative humidity were estimated with Computational Fluid Dynamics (CFD) at top, middle and base levels for peach storage at +1oC and 90% relative humidity and verified with measured data in a cold store with evaporative cooling system. Storage temperature was +1oC and relative humidity 90% for peach storage. Ansys Fluent Software was used for CFD modelling. CFD models were validated with sensors measurements. Results were evaluated by using descriptive statistics, relative error and variance analyses. Mean difference between the model and measurements was calculated as 0.51oC for ambient temperature and as 3.47 % for relative humidity. Relative error of the CFD model was calculated as 9.77 for the ambient temperature and 1.29 for relative humidity for peach storage. The developed CFD models estimated the ambient factors with an acceptable error in the evaporative cold store for peach storage.

Information Quality in Bibliographic Databases

The aim of the article is to analyze the quality of information contained in the bibliographic databases Web of Science and Scopus. The results of the presented study prove that due to errors existing in the analyzed databases, there are often cases of significant underestimation of bibliometric parameters. In extreme cases, the relative error of a given parameter exceeds several dozen per cent, which concerns both individual authors and entire scientific journals. The publication presents a discussion on sources of errors in bibliographic databases and methods of their effective search and correction.

Application of the space-based optical interferometer towards measuring cosmological distances of quasars.

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. It is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. As any of the basic observational parameters become larger, the relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

Numerical Approach of Symmetric Traveling Salesman Problem Using Simulated Annealing

The aim of this paper is to elaborate the performance of Simulated Annealing (SA) algorithm for solving traveling salesmen problems. In this paper, SA algorithm is modified by using the interaction between outer and inner loop of algorithm. This algorithm produces low standard deviation and fast computational time compared with benchmark algorithms from several research papers. Here SA uses a certain probability as indicator for finding the best and worse solution. Moreover, the strategy of SA as cooling to temperature ratio is still given. Thirteen benchmark cases and thirteen square grid symmetric TSP are used to see the performance of the SA algorithm. It is shown that the SA algorithm has promising results in finding the best solution of the benchmark cases and the squared grid TSP with relative error 0 - 7.06% and 0 – 3.31%, respectively. Further, the SA algorithm also has good performance compared with the well-known metaheuristic algorithms in references.

DEM PARAMETERS CALIBRATION OF MIXED BIOMASS SAWDUST MODEL WITH MULTI-RESPONSE INDICATORS

Taking hybrid biomass sawdust as the material, carry out the simulation calibration experiment with JKR contact model based on DEM principle. The Plackett-Burman factorial experiment is carried out by Design-Expert for 10 related factors. Combined with the steepest climbing test scheme, according to the Box-Behnken experiment, the parameter calibration of the multi-response Indicators is completed. The results are as follows: the Poisson's ratio of hybrid sawdust is 0.30, the density is 399.22kg·m-3, the recovery coefficient between sawdust particles is 0.47, the rolling friction coefficient between sawdust particles is 0.39, and the parameter of surface energy density between sawdust particles (JKR) is 0.29J·m-2. Through the comparative verification experiment, it can be seen that the relative error of the repose angle is 3.41%, and the relative error of the stress-time response curve is less than 6.36%, which verifies the reliability of the calibration method, and provides a theoretical reference for the study of the constitutive characteristics of biomass materials and the densification mechanism.

An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model

Remote sensing excels in estimating regional evapotranspiration (ET). However, most remote sensing energy balance models require researchers to subjectively extract the characteristic parameters of the dry and wet limits of the underlying surfaces. The regional ET accuracy is affected by wrong determined ideal pixels. This study used Landsat images and the METRIC model to evaluate the effects of different dry and wet pixel combinations on the ET in the typical steppe areas. The ET spatiotemporal changes of the different land cover types were discussed. The results show that the surface temperature and leaf area index could determine the dry and wet limits recognition schemes in grassland areas. The water vapor flux data of an eddy covariance system verified that the relative error between the ETd,METRIC and ETd,GES of eight DOYs (day of the year) was 18.8% on average. The ETMETRIC values of the crop growth season and the ETIMS of eight silage maize irrigation monitoring stations were found to have a relative error of 11.1% on average. The spatial distribution of the ET of the different land cover types in the study area was as follows: ETwater &gt; ETarable land &gt; ETforest land &gt; ETunutilized land &gt; ETgrassland &gt; ETurban land.