基于Midas GTS的某近海深基坑开挖三维有限元数值模拟分析

为研究近海深基坑在桩锚支护体系下土体开挖变形规律,考虑水流渗透作用,运用Midas GTS对某深基坑开挖过程进行模拟,并结合已有资料进行分析。结果表明:该深基坑开挖后的第一步为最危险施工步,桩顶土体最大位移为13.97 mm,越往下开挖支护体系越完善,位移变化越小,实际监测最大位移值为15.36 mm;整个等效地连墙在钢腰梁与锚索位置处向基坑内部变形收缩程度最大,为6.6 mm,符合实际变形规律。Midas GTS能有针对性地指导现实施工阶段,可为基坑监测布置设置点提供参考价值。

基于Midas GTS/NX的新疆沙漠输水明渠风积土岸坡稳定性分析

为了准确评估各类工况对新疆沙漠输水明渠风积土岸坡稳定性的影响。通过开展室内土工试验及对该渠道岸坡地质工况进行了调查,采取Midas GTS/NX软件中的强度折减法对输水明渠在未加固和加固两种工况下的竣工期、降雨期、输水期和水位骤降4个时期的二维岸坡进行稳定性计算,通过对比岸坡在未加固与加固两个工况下4个时期的整体位移量、等效塑性应变区和安全系数,对岸坡整体稳定及加固效果给出了分析及评价。结果表明:渠道未加固与加固两个工况下4个时期下风积土岸坡均处于稳定状态,安全系数均大于1.30;渠道位于输水期时,坡体前缘水压增加,有效提高渠道岸坡的安全系数,但水位骤降对风积土岸坡稳定性影响最大,降雨次之;未加固时,4个时期的岸坡等效塑性应变区呈圆弧状且已贯通;经加固后,岸坡等效塑性应变区大体沿衬砌呈直线分布,滑移带位移变形得到较好控制,极大程度降低了滑坡灾害发生的可能性。研究结果在一定程度上可以为新疆沙漠风积土输水渠道工程建设提供参考。

基于Midas GTS NX的废弃矿山边坡稳定性分析

开展矿山边坡稳定性分析对识别矿山地质灾害隐患及评估边坡治理措施有效性具有重要意义。以新疆乌鲁木齐市米东区绍兴砂场废弃采砂矿山边坡为例,针对矿区内边坡A及边坡B进行研究。采用Midas GTS NX岩土分析软件有限元强度折减法对边坡进行模拟计算,分析恢复治理前后矿山边坡的稳定性。结果表明,应用Midas GTS NX软件对边坡进行数值模拟分析,所得模拟结果科学直观,在边坡稳定性分析中表现出较好的效果;边坡A经工程措施治理后,稳定性系数明显增大,x轴方向位移量减小了93.9%,最大剪应变分散在各分级台阶的坡脚处,减小了95.5%,潜在滑移面消失,边坡达到稳定状态;通过削高填低、坡脚压实、削坡分级及排水工程等工程措施治理边坡,可有效增加边坡稳定性,消除边坡地质灾害隐患。

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

基于Midas GTS/NX的马莲村滑坡稳定性分析

马莲村滑坡在暴雨之后出现了明显的变形,通过现场勘察、钻探、探井等勘察手段得出初步结论。为进一步查明马莲村滑坡成因机制,通过现有资料,系统分析马莲村滑坡的基本特征、结构特征、变形特征及影响因素,对稳定性及其形成机理分析研究,利用Midas GTS/NX有限元数值模拟软件,采用强度折减法对马莲村滑坡的三个典型剖面在天然工况、天然+地震工况下的稳定性进行分析。结果表明,天然工况和地震工况下滑坡处于稳定状态;暴雨工况下处于欠稳定状态,需要采取工程措施对其进行防治。

三亚GTS1-2与GTS13型探空仪探空数据对比

为了解探空仪换型引起的观测数据的差异,文章运用方差分析、多重比较等手段对2020年三亚探空仪平行观测期间GTS1-2(旧型)与GTS13(新型)两种探空仪在925~500 hPa等压面上的温度、相对湿度和层间厚度三个观测要素进行定量对比。结果表明:两种探空仪在不同规定等压面上的温度差具有方差齐性,显示两种探空仪有较好的测温稳定性;两种探空仪在不同规定等压面上的相对湿度差具有很好的方差齐性,显示两种探空仪有良好的测湿稳定性;两种探空仪在不同规定等压面上的层间厚度差不具有方差齐性,显示两种探空仪有较差的层间厚度测量稳定性,但两种仪器在层间厚度的测量结果上具有良好的一致性。研究结果可为三亚探空仪换型前后数据的均一化和进一步提高探空观测质量提供支撑。

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

Side Information-Based Stealthy False Data Injection Attacks Against Multi-Sensor Remote Estimation

Dear Editor,This letter investigates a novel stealthy false data injection(FDI)attack scheme based on side information to deteriorate the multi-sensor estimation performance of cyber-physical systems(CPSs).Compared with most existing works depending on the full system knowledge,this attack scheme is only related to attackers'sensor and physical process model.The design principle of the attack signal is derived to diverge the system estimation performance.Next,it is proven that the proposed attack scheme can successfully bypass the residual-based detector.Finally,all theoretical results are verified by numerical simulation.

Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

Dear Editor, This letter focuses on the protocol-based non-fragile state estimation problem for a class of recurrent neural networks(RNNs). With the development of communication technology, the networked systems have received particular attentions. The networked system brings advantages such as easy to implement.

Brain age estimation:premise,promise,and problems

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

基于Midas GTS分析的深基坑降水开挖变形特性研究

基于Midas GTS软件对广州某深基坑开挖过程进行数值模拟,考虑渗流作用的影响,对比分析了两种情况下基坑开挖过程中周边地表沉降变形、围护结构水平位移的变化规律,并与现场地面沉降调查结果进行比较。结果表明,地下水渗流作用在基坑降水开挖过程中有显著影响。

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

基于MIDAS GTS的顶管下穿施工对高铁桥梁影响的数值分析

为研究新建天然气管道下穿施工对京津城际铁路杨村特大桥的影响,以高压天然气管道穿越京津城际铁路防护套管工程为背景,对下穿京津城际铁路杨村特大桥段顶管施工进行设计,通过运用MIDAS GTS有限元软件对顶管施工下穿高铁桥梁进行有限元数值分析,得出了采取顶管下穿高铁桥梁施工,会引起铁路桥墩的扰动和变形,进而引起结构受力的变化。在揭示了施工过程前后地层应力分布变化规律后,提出相关改进建议。

Double-check in Lizard Age Estimation:Use of Phalanx Bone and Keratin Claw Sheath Lamellas

Dear Editor,The problem of age estimation in amphibians and reptiles with annual fluctuations of growth pattern has been considered to be mostly solved since the skeletochronological method was introduced(Kleinenberg and Smirina,1969).This method is based on counting the number of lines of arrested growth(LAGs)—cyclical growth marks that are usually formed annually and characterized by different optical aspects within the tubular bones.

Non-cooperative Space Target Estimation Algorithm Without Prior Information Dependence Based on Temporal Line of Sight Constraint

Under single-satellite observation,the parameter estimation of the boost phase of high-precision space noncooperative targets requires prior information.To improve the accuracy without prior information,we propose a parameter estimation model of the boost phase based on trajectory plane parametric cutting.The use of the plane passing through the geo-center and the cutting sequence line of sight(LOS)generates the trajectory-cutting plane.With the coefficient of the trajectory cutting plane directly used as the parameter to be estimated,a motion parameter estimation model in space non-cooperative targets is established,and the Gauss-Newton iteration method is used to solve the flight parameters.The experimental results show that the estimation algorithm proposed in this paper weakly relies on prior information and has higher estimation accuracy,providing a practical new idea and method for the parameter estimation of space non-cooperative targets under single-satellite warning.

Report for Type 2 Bayes-Fuzzy Estimation in No-Data Problem

It is well known that the system (1 + 1) can be unequal to 2, because this system has both observation error and system error. Furthermore, we must provide our mustered service within our cool head and warm heart, where two states of nature are existing upon us. Any system is regarded as the two-dimensional variable error model. On the other hand, we consider that the fuzziness is existing in this system. Though we can usually obtain the fuzzy number from the possibility theory, it is not fuzzy but possibility, because the possibility function is as same as the likelihood function, and we can obtain the possibility measure by the maximal likelihood method (i.e. max product method proposed by Dr. Hideo Tanaka). Therefore, Fuzzy is regarded as the only one case according to Vague, which has both some state of nature in this world and another state of nature in the other world. Here, we can consider that Type 1 Vague Event in other world can be obtained by mapping and translating from Type 1 fuzzy Event in this world. We named this estimation as Type 1 Bayes-Fuzzy Estimation. When the Vague Events were abnormal (ex. under War), we need to consider that another world could exist around other world. In this case, we call it Type 2 Bayes-Fuzzy Estimation. Where Hori et al. constructed the stochastic different equation upon Type 1 Vague Events, along with the general following probabilistic introduction method from the single regression model, multi-regression model, AR model, Markov (decision) process, to the stochastic different equation. Furthermore, we showed that the system theory approach is Possibility Markov Process, and that the making decision approach is Sequential Bayes Estimation, too. After all, Type 1 Bays-Fuzzy estimation is the special case in Bayes estimation, because the pareto solutions can exist in two stochastic different equations upon Type 2 Vague Events, after we ignore one equation each other (note that this is Type 1 case), we can obtain both its system solution and its decision solution. Here, it is noted that Type 2 Vague estimation can be applied to the shallow abnormal decision problem with possibility reserved judgement. However, it is very important problem that we can have no idea for possibility reserved judgement under the deepest abnormal envelopment (ex. under War). Expect for this deepest abnormal decision problem, Bayes estimation can completely cover fuzzy estimation. In this paper, we explain our flowing study and further research object forward to this deepest abnormal decision problem.