Machine acceleration time series prediction for dimensional accuracy of 3D printed parts

This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.

LTE系统中的Mean-OTDOA定位算法

由于LTE蜂窝网中远近效应的影响,终端测量到的邻近基站信号的定位参数会存在较大的偏差,导致OTDOA定位方法(到达时间差定位法)估计的终端位置存在较大误差。基于此,提出一种改进的Mean-OTDOA定位算法。首先估计终端与各基站的时延,然后对终端与多基站的距离测量值进行平均,作为OTDOA定位方法中的参考距离,最后利用泰勒级数展开法对终端位置进行估计。仿真结果表明,该算法可提高终端的定位精度,在基站数目为5、测量误差标准差为50 m时,本算法的均方根误差比OTDOA算法降低了5.2039 m,且随着基站数目的增加,定位精度的改善程度优于OTDOA算法。

角度测量下双机协同standoff目标跟踪

基于最大化目标位置估计精度,针对两架无人机(UAV)仅有角度测量的情况,提出一种新的协同随机运动目标standoff跟踪控制方法.以目标位置估计均方根误差(RSME)作为性能指标,建立其与UAV观测几何构型之间的关系模型,进而确定了最优跟踪时UAV最优观测几何构型.采用扩展信息滤波实现目标状态的融合估计;考虑平台性能、碰撞规避、安全距离等约束条件,采用非线性模型预测控制(NMPC)实现UAV协同分布式在线优化控制.仿真结果表明该算法在确保最优观测构型和跟踪精度的同时有效地提高了算法实时性.

基于最大自相关及最小能量比的MB-OFDM-UWB系统定时算法

该文针对IEEE 802.15.3a提案及ECMA-368标准采用的多频带OFDM超宽带系统,通过算法改进和仿真给出了适用于该系统的定时同步的完整方案。用基于前导序列第1频带信息的最大自相关法进行帧检测及粗定时,用基于全部3个频带信息的最小能量比值法进行细定时。对定时位置两次进行修正,保证了算法的性能。仿真表明该定时同步方案可以把残余定时偏差控制在较小的范围内,从而可以被频域信道估计及均衡吸收,同时该方案具有较低的复杂度。

基于混合Taguchi-GA算法的MIMO-OFDM信号检测

为了避免传统MIMO-OFDM信号检测方法具有的计算量过大而导致的算法复杂度高的问题,设计了一种基于混合Taguchi方法和GA算法的MIMO-OFDM信号检测方法,首先建立了MIMO-OFDM信号检测的模型,然后依据信号检测模型建立目标函数,将目标函数作为混合Taguchi-GA算法的适应度函数,通过个体在信号检测问题的解空间中进行不断地选择、交叉和变异等操作来求解全局最优解,为了进一步增加算法的全局寻优能力,通过Taguchi方法进一步在交叉和变异之间产生新个体;最后,定义和描述了基于混合Taguchi和GA算法的MIMO-OFDM信号检测算法,仿真实验表明,文中方法能有效进行信号检测,与其他方法相比,在BPSK调制和16QAM调制情况下,均具有较小的BER均方误差。

Interleaved-OFDMA中基于子空间的上行链路载波频偏估计方法

在OFDMA系统中,通过为每个用户分配不同的子载波可以实现并行数据传输.采用Interleaved子载波分配方法可以提高频率分集和系统容量,但是发射机和接收机之间的载波频率偏移会破坏子载波间的正交性,从而导致本用户的载波间干扰(ICI)以及用户间干扰(MUI).本文提出了一种基于子空间的两阶段频偏搜索方法,该方法只采用一个OFDMA符号块就可以实现Interleaved-OFDMA上行链路多个用户频偏的联合估计,并且不需要知道接入的用户个数及用户所占用的子信道,因此适用于随机分配子信道的情况.仿真结果验证了算法的精确度和有效性.

Estimating van Genuchten Model Parameters of Undisturbed Soils Using an Integral Method

The van Genuchten model is the most widely used soil water retention curve （SWRC） model. Two undisturbed soils （clay and loam） were used to evaluate the accuracy of the integral method to estimate van Genuchten model parameters and to determine SWRCs of undisturbed soils. SWRCs calculated by the integral method were compared with those measured by a high speed centrifuge technique. The accuracy of the calculated results was evaluated graphically, as well as by root mean square error （RMSE）, normalized root mean square error （NRMSE） and Willmott＇s index of agreement （1）. The results obtained from the integral method were quite similar to those by the centrifuge technique. The RMSEs （4.61 ×10^-5 for Eum-Orthic Anthrosol and 2.74 × 10^-4 for Los-Orthic Entisol） and NRMSEs （1.56 × 10^-4 for Eum- Orthic Anthrosol and 1.45 ×10^-3 for Los-Orthic Entisol） were relatively small. The 1 values were 0.973 and 0.943 for Eum-Orthic Anthrosol and Los-Orthic Entisol, respectively, indicating a good agreement between the integral method values and the centrifuge values. Therefore, the integral method could be used to estimate SWRCs of undisturbed clay and loam soils.

Cokriging of Soil Cation Exchange Capacity Using the First Principal Component Derived from Soil Physico-Chemical Properties

As soil cation exchange capacity （CEC） is a vital indicator of soil quality and pollutant sequestration capacity,a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties.In Qingdao,China,107 soil samples were collected.Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test.The first two principal components （PC1 and PC2） together explained 60.2% of the total variance of soil physico-chemical properties.The PC1 was highly correlated with CEC （r=0.76,P0.01）,whereas there was no significant correlation between CEC and PC2 （r=0.03）.The PC1 was then used as an auxiliary variable for the prediction of soil CEC.Mean error （ME） and root mean square error （RMSE） of kriging for the test dataset were-1.76 and 3.67 cmolc kg-1,and ME and RMSE of cokriging for the test dataset were-1.47 and 2.95 cmolc kg-1,respectively.The cross-validation R2 for the prediction dataset was 0.24 for kriging and 0.39 for cokriging.The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation.In addition,principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.

Application of SALTMED and HYDRUS-1D models for simulations of soil water content and soil salinity in controlled groundwater depth

Salinization is a gradual process that should be monitored.Modelling is a suitable alternative technique that saves time and cost for the field monitoring.But the performance of the models should be evaluated using the measured data.Therefore,the aim of this study was to evaluate and compare the SALTMED and HYDRUS-1D models using the measured soil water content,soil salinity and wheat yield data under different levels of saline irrigation water and groundwater depth.The field experiment was conducted in 2013 and in this research three controlled groundwater depths,i.e.,60(CD60),80(CD80)and 100(CD100)cm and two salinity levels of irrigation water,i.e.,4(EC4)and 8(EC8)dS/m were used in a complete randomized design with three replications.Soil water content and soil salinity were measured in soil profile and compared with the predicted values by the SALTMED and HYDRUS-1D models.Calibrations of the SALTMED and HYDRUS-1D models were carried out using the measured data under EC4-CD100 treatment and the data of the other treatments were used for validation.The statistical parameters including normalized root mean square error(NRMSE)and degree of agreement(d)showed that the values for predicting soil water content and soil salinity were more accurate in the HYDRUS-1D model than in the SALTMED model.The NRMSE and d values of the HYDRUS-1D model were 9.6%and 0.64 for the predicted soil water content and 6.2%and 0.98 for the predicted soil salinity,respectively.These indices of the SALTMED model were 10.6%and 0.81 for the predicted soil water content and 11.0%and 0.97 for the predicted soil salinity,respectively.According to the NRMSE and d values for the predicted wheat yield(9.8%and 0.91,respectively)and dry matter(2.9%and 0.99,respectively),we concluded that the SALTMED model predicted the wheat yield and dry matter accurately.

A New Method of Embedded Fourth Order with Four Stages to Study Raster CNN Simulation

A new Runge-Kutta （PK） fourth order with four stages embedded method with error control is presentea m this paper for raster simulation in cellular neural network （CNN） environment. Through versatile algorithm, single layer/raster CNN array is implemented by incorporating the proposed technique. Simulation results have been obtained, and comparison has also been carried out to show the efficiency of the proposed numerical integration algorithm. The analytic expressions for local truncation error and global truncation error are derived. It is seen that the RK-embedded root mean square outperforms the RK-embedded Heronian mean and RK-embedded harmonic mean.

基于WR分解的多用户多天线OFDM系统半盲信道估计

在多用户多天线OFDM上行信道的模型下,提出一种改进的基于WR分解的半盲信道估计算法。改进算法利用接收信号的统计特性和子空间分解法直接估计白化矩阵W,避免了对导频信号的依赖和模糊矩阵的求解问题。在已知W矩阵后,根据正交导频的设计方案,由最小均方误差准则得到Q矩阵。计算机仿真结果表明,系统的信道冲击响应可以被很好的跟踪,且当信噪比达到20 dB时,改进算法的RMSE比LS算法降低约1～1.5 dB。与此同时,正交导频的长度对于改进算法的性能具有较大的影响,当导频信号的长度达到整个OFDM符号块长度的1/5时,改进算法的RMSE比LS算法降低约1～2 dB。

Comparison of seed morphology of two ginkgo cultivars

Ginkgo biloba L. is a precious relic tree species with important economic value. Seeds, as a vital reproductive organ of plants, can be used to distinguish cultivars of the species. We chose 400 seeds from two cultivars of ginkgo(“Fozhi” and “Maling”;200 seeds for each cultivar) as the study material and used the Gielis equation to fit the projected shape of these seeds. The coefficients of variation(CV) in root mean squared errors(RMSE)obtained from the fitted data were used to compare the level of inter-cultivar variations in seed shape. We also used the covariance analysis to compare the allometric relationships between seed weights and projected areas of these two cultivars. The Gielis equation fitted well the seedshapes of two ginkgo cultivars. The lower CV in RMSE of cultivar “Fozhi” than “Maling” indicated a less symmetrical seed shape in the latter than the former. The bootstrap percentile method showed that the seed shape differences between the two cultivars were significant. However, there was no significant difference in the exponents between the seed weights and the projected areas of these two cultivars.Overall, the significant differences in shapes between the seeds of two ginkgo cultivars were well explained by the Gielis equation;this model can be further extended to compare morphological differences in other ginkgo cultivars, and even for plant seeds or animal eggs that have similar oval shapes.

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa

Digital Elevation Models (DEMs) depict the configuration of the earth surface and are being applied in many areas in earth and environmental sciences. In this study, the accuracy of the Advanced Land Observing Satellite World 3D Digital Surface Model version 2.1 (ALOS W3D30), the Shuttle Radar Topography Mission Digital Elevation Model version 3.0 (SRTM30) and the Advanced Space borne Thermal Emission and Reflection Radiometer Global DEM version 2.0 (ASTER GDEM2) was statistically assessed using high accuracy GPS survey data. Root-Mean-Square errors of ~5.40 m, ~7.47 m and ~20.03 m were obtained for ALOS W3D30, SRTM30 and ASTER GDEM2 respectively. In further analyses, we discovered that ALOS W3D30 and SRTM30 were much more accurate in regions where the height intervals were within 201 m - 400 m and >801 m. ALOS W3D30 proved to be the most accurate DEM that best represents the topography of the earth’s surface and could be used for some earth and environmental applications in Nigeria. We recommend that this study should serve as a guide in the use of any of these DEMs for earth and environmental applications in Nigeria.

CARRIER FREQUENCY OFFSET ESTIMATION FOR INTERLEAVED OFDMA UPLINK BASED ON SUBSPACE PROCESSING

This paper investigates Carrier Frequency Offset (CFO) estimation in the uplink of the Orthogonal Frequency-Division Multiple Access (OFDMA) systems with the interleaved subcarrier assignment. CFOs between the transmitters and the uplink receiver will destroy orthogonality among different subcarriers, hence resulting in inter-carrier interference and multiuser interference. A two-stage frequency offset estimation algorithm based on subspace processing is proposed. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. Compared with the previously known methods, it not only has a relatively low implementation complexity but is also suitable for random subchannel assignment.

Time resource management of OAR based on fuzzy logic priority for multiple target tracking

For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming（CCP） employing fuzzy logic priority is proposed for opportunistic array radar（OAR）. In this scheme,the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section（RCS）, the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Crame ′r-Rao lower bound（BCRLB） provides us with a low bound of localization estimation root-mean-square error（RMSE）, and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm（GA） to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.

A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.

Statistical Analysis of Groundwater Table Depths in Upper Swarnamukhi River Basin

Development and effective utilization of groundwater resources is essential in semi-arid regions for activities such as water supply and irrigation. The present study aims to analyze statistically the groundwater data at the Chinnagottigallu, Yerravaripalem, Chandragiri, Sriramapuram, Tirupati, Renigunta, Karvetinagaram and Yerpedu piezometric stations of upper Swarnamukhi river basin in the drought prone Rayalaseema region of Andhra Pradesh, India and to develop models through multiple linear correlation and regression analysis. The monthly rainfall and groundwater data at the raingauge and piezometric stations of the basin for the period 2001-2006 were collected from the Groundwater and Irrigation Departments of the region. It is observed from the analysis that the groundwater table depth in any period is influenced by the rainfall in the period and, the rainfall and groundwater table depth in the previous period. The study also reveals that the effect of antecedent groundwater table depth is more pronounced than that of rainfall and antecedent rainfall. The models proposed may be adopted for the estimation of groundwater table depths to effectively plan and efficiently manage groundwater resources of the basin.

Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.

Trends of Shoreline Position: An Approach to Future Prediction for Balasore Shoreline, Odisha, India

The present study aims to analyze the shift in shoreline due to coastal processes and formulate available for best estimate of future shoreline positions based on precedent shorelines. Information on rates and trends of shoreline change can be used to improve the understanding of the underlying causes and potential effects of coastal erosion which can support informed coastal management decisions. In this paper, researchers go over the changes in the recent positions of the shoreline of the Balasore coast for the 38 years from 1975 through 2013. The study area includes the Balasore coastal region from Rasalpur to Udaypur together with Chandipur, Choumukh, Chandrabali as well as Bichitrapur. Transects wise shoreline data base were developed for approximately 67 kilometers of shoreline and erosional/accretional scenario has also been analysed by delineating the shoreline from Landsat imageries of 1975, 1980, 1990, 1995, 2000, 2005, 2010 and 2013. A simple Linear Regression Model and End Point Rate (EPR) have been adopted to take out the rate of change of shoreline and its future positions, based on empirical observations at 67 transects along the Balasore coast. It is found that the north eastern part of Balasore coast in the vicinity of Subarnarekha estuary and Chandrabali beach undergo high rates of shore line shift. The shoreline data were integrated for long- (about 17 years) and short-term (about 7 years) shift rates analysis to comprehend the shoreline change and prediction. For the prediction of future shoreline, the model has been validated with the present shoreline position (2013). The rate of shoreline movement calculated from the fixed base line to shoreline position of 1975, 1980, 1990, 1995, 2000, 2005 and 2010 and based on this, the estimated shoreline of 2013 was calculated. The estimated shoreline was compared with the actual shoreline delineated from satellite imagery of 2013. The model error or positional shift at each sample point is observed. The positional error varies from??4.82 m to 212.41 m. It has been found that model prediction error is higher in the left hand side of river Subarnarekha. The overall error for the entire predicted shoreline was found to be 41.88 m by Root Mean Square Error (RMSE). In addition, it was tested by means difference between actual and predicted shoreline positions using “t” test and it has been found that predicted shore line is not significantly different from actual shoreline position at (t132 = 0.278) p < 0.01.