基于CAN通信实现MBD代码下载的DSP Bootloader开发

为了实现DSP嵌入式系统在实际应用中便捷下载MBD(model-based design)代码的需求,文章设计了一种基于CAN通信实现MBD代码下载的Bootloader方案。以TMS320F28335为例,通过对MBD代码的结构进行分析,设计了Boot程序与MBD程序的内存划分方案,确保程序下载的有效性和稳定性;开发了相应的Boot程序和上位机程序,详细介绍了Bootloader的实现流程,并对关键步骤函数进行了分析与解释,利用CAN通信实现了程序的下载。实验结果表明,该方法稳定可靠且具有实用性,为DSP嵌入式系统在实际应用中的MBD代码下载提供了一种可行且高效的解决方案。

建筑行业三维标注(MBD)技术研究和应用展望

随着建筑业数字化、工业化的大力发展,业内对三维标注技术(MBD)的需求日益提升。然而在目前的工作流程中,设计、模拟以及生产的数据一体化框架仍不完善,难以满足实际建筑工程的需要。现阶段BIM技术缺乏关于MBD技术的方法与标准,导致即便设计流程采用全三维设计方式,但最终仍需转换为二维图纸交付生产,其与传统设计流程在本质上并未产生明显区别。本文主要围绕建筑行业MBD技术进行研究与应用展望,包括MBD技术发展历程、相关标注标准的体系构建以及应用的潜在方向,旨在为建筑产品全生命周期管理(PLM)提供基础理论技术支持,也为建筑行业发展提供新的发展思路。

基于DEM-MBD耦合的液压挖掘机动力学仿真分析

在实际工况下,液压挖掘机工作装置受到的载荷较大且具有强时变性,因此工作装置各组成杆件及液压缸较易出现各种形式的失效。针对以上问题,选取某型号液压挖掘机作为研究对象,根据液压挖掘机工作装置实际挖掘物料工况,利用多体动力学软件ADAMS建立液压挖掘机虚拟样机模型并利用离散元软件EDEM建立物料模型。通过DEM-MBD耦合仿真分析方法,对液压挖掘机工作装置进行动力学仿真分析,主要获取挖掘物料过程中铲斗受力变化曲线、不同时刻铲斗受力云图,通过对其分析,能够获取铲斗在挖掘作业过程中受力变化情况,为铲斗的有限元分析、优化设计提供理论依据。

基于DEM-MBD耦合的装载机减阻铲装策略仿真分析

装载机铲装物料过程中,往往受到较大的铲装阻力,使得其作业效率降低、能耗升高。针对此问题,对装载机减阻铲装策略进行研究。对装载机铲装物料过程中受到的铲装阻力进行分析,建立装载机各作业阶段铲装阻力的数学模型,提出装载机减阻铲装策略;利用ADAMS建立装载机虚拟样机模型,对它进行动态仿真,通过EDEM完成铲装物料颗粒模型的建立及相关参数的设置;基于DEM-MBD联合仿真,分别对装载机使用2种铲装物料策略进行铲装过程仿真;最后,获取2种铲装策略中物料运动速度矢量以及铲装阻力变化曲线,进行对比分析。结果表明:装载机使用减阻铲装策略铲装物料相比一次铲装法铲装物料受到的最大铲装阻力值减少11.3%,且使用减阻铲装策略所产生的铲装阻力大部分时间内整体小于一次铲装法所产生的铲装阻力,证明了减阻铲装策略的可行性和合理性。

腺嘌呤灌胃联合高磷饮食诱导CKD-MBD大鼠模型的探讨

目的:观察腺嘌呤灌胃联合高磷饮食方法诱导的慢性肾脏病矿物质和骨代谢异常(CKD-MBD)大鼠模型。方法:34只SD大鼠随机分为对照组及模型组,每组17只。实验第1~4周,每日予250 mg/kg腺嘌呤混悬液灌胃及含1.2%高磷饲料喂养,第5~8周腺嘌呤灌胃改为隔日。对照组和模型组灌胃等体积高纯水。共给药8周。8周后杀检取材,苏木精-伊红(HE)、过碘酸雪夫(PAS)染色、马松(Masson)染色法观察两组大鼠肾脏病理,茜素红、HE染色观察主动脉钙化结节形成情况;HE染色观察胫骨组织形态,显微CT(Micro-CT)与CT-AN软件对股骨进行三维分析,检测皮质骨骨形态计量学参数;全自动生化仪测定血钙(Ca)、血磷(P)、血肌酐(Scr)、血尿素氮(BUN)、血清全段甲状旁腺激素(iPTH)水平。结果:与对照组比较,模型组大鼠的Scr、BUN、P和iPTH水平均显著升高(P<0.01),Ca2+水平明显降低(P<0.01);模型组肾小球结构紊乱,肾小球基底膜增厚,肾小管扩张,管腔内有积水、炎性细胞的浸润和棕褐色结晶沉积,肾脏血管明显减少,肾脏间质纤维化,有炎性细胞浸润;模型组大鼠主动脉中膜明显的呈连续性线性分布的钙化结节,局部血管破裂;模型组大鼠α-平滑肌肌动蛋白(α-SMA)蛋白平均光密度值表达明显降低,骨形态蛋白-2(BMP-2)和Runt相关转录因子2(Runx2)蛋白平均光密度值表达升高,差异有统计学意义(P<0.01);模型组大鼠胫骨近端干骺端的小梁骨网络之间的断开和分离增加,骨小梁表面的成骨细胞和破骨细胞数量增加,骨小梁周围出现大量间质纤维化,模型组皮质骨组织密度显著降低(P<0.01)、皮质孔隙度显著增加(P<0.01),皮质骨体积分数差异无统计学意义(P>0.05)。结论:腺嘌呤灌胃联合高磷饮食是制备CKD-MBD大鼠模型较为快速、可行、准确的方法,值得进一步推广。

基于MBD的产品设计数字化军检研究

针对传统军检技术存在质量监督方式落后的问题,就军事代表在产品设计阶段如何开展基于MBD(model baseddefinition)的数字化军检业务进行分析。梳理产品设计阶段的产出物和相应的军检点,并对军检内容、军检方式和军检对象进行研究。结果表明:在产品设计阶段开展基于MBD的数字化军检技术,可提升军事代表对装备质量的监督能力,适应基于MBD的装备研制体系,是未来军检技术的发展方向。

基于MBD的PLC运动控制系统设计与应用

针对复杂算法在PLC运动控制系统中难以工程实现的问题,该文提出了一种基于MBD(model based design)的系统设计和应用方法。首先,利用MATLAB/Simulink建立控制模型并仿真验证,预留变量接口,正确匹配参数定义和数据类型;然后,基于Simulink PLC Coder将模型转换为符合IEC 61131-3标准的结构化文本ST(structure text)代码,根据集成开发环境CoDeSys定制文件格式;最后,将生成代码以功能块FB(function block)的形式导入CoDeSys,实现算法与应用程序的集成。某型起重机回转控制系统的应用证明了MBD方法可有效解决复杂算法工程实现难的问题。

Consistency between domain wall oscillation modes and spin wave modes in nanostrips

Investigations on domain wall(DW) and spin wave(SW) modes in a series of nanostrips with different widths and thicknesses have been carried out using micromagnetic simulation. The simulation results show that the frequencies of SW modes and the corresponding DW modes are consistent with each other if they have the same node number along the width direction. This consistency is more pronounced in wide and thin nanostrips, favoring the DW motion driven by SWs.Further analysis of the moving behavior of a DW driven by SWs is also carried out. The average DW speed can reach a larger value of ~ 140 m/s under two different SW sources. We argue that this study is very meaningful for the potential application of DW motion driven by SWs.

BIG HANKEL OPERATORS ON HARDY SPACES OF STRONGLY PSEUDOCONVEX DOMAINS

In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f )is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H~2(Ω) of other pseudoconvex domains.In these arguments,Amar's L~p-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H~p(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.

ON THE SOBOLEV DOLBEAULT COHOMOLOGY OF A DOMAIN WITH PSEUDOCONCAVE BOUNDARIES

In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].

MINIMIZERS OF L^(2)-SUBCRITICAL VARIATIONAL PROBLEMS WITH SPATIALLY DECAYING NONLINEARITIES IN BOUNDED DOMAINS

This paper is concerned with the minimizers of L^(2)-subcritical constraint variar tional problems with spatially decaying nonlinearities in a bounded domain Ω of R~N(N≥1).We prove that the problem admits minimizers for any M> 0.Moreover,the limiting behavior of minimizers as M→∞ is also analyzed rigorously.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

基于MBD的三维工艺设计在航天产品中的应用研究

介绍了企业在三维机加及装配工艺设计方面开展的相关研究,通过引入三维工艺设计工具并与数字化设计工艺协同平台(Teamcenter, TC平台)集成,实现了三维加工特征识别、加工方法推理、三维工序模型构建以及三维装配动画生成等,最终输出三维工艺文件并回传至TC平台进行审签及发放至生产车间。以某产品为研究对象,对各功能进行了应用验证。

High patatin like phospholipase domain containing 8 expression as a biomarker for poor prognosis of colorectal cancer

BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).

Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

GATA binding protein 2 mediated ankyrin repeat domain containing 26 high expression in myeloid-derived cell lines

BACKGROUND Thrombocytopenia 2,an autosomal dominant inherited disease characterized by moderate thrombocytopenia,predisposition to myeloid malignancies and normal platelet size and function,can be caused by 5’-untranslated region(UTR)point mutations in ankyrin repeat domain containing 26(ANKRD26).Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1)have been identified as negative regulators of ANKRD26.However,the positive regulators of ANKRD26 are still unknown.AIM To prove the positive regulatory effect of GATA binding protein 2(GATA2)on ANKRD26 transcription.METHODS Human induced pluripotent stem cells derived from bone marrow(hiPSC-BM)INTRODUCTION Ankyrin repeat domain containing protein 26(ANKRD26)acts as a regulator of adipogenesis and is involved in the regulation of feeding behavior[1-3].The ANKRD26 gene is located on chromosome 10 and shares regions of homology with the primate-specific gene family POTE.According to the Human Protein Atlas database,the ANKRD26 protein is localized to the Golgi apparatus and vesicles,and its expression can be detected in nearly all human tissues[4].Moreover,UniProt annotation revealed that ANKRD26 is localized in the centrosome and contains coiled-coil domains formed by spectrin helices and ankyrin repeats[5,6].The most common disease related to ANKRD26 is thrombocytopenia 2(THC2),which is a rare autosomal dominant inherited disease characterized by lifelong mild-to-moderate thrombocytopenia and mild bleeding[7-9].Caused by the variants in the 5’-untranslated region(UTR)of ANKRD26,THC2 is defined by a decrease in the number of platelets in circulating blood and results in increased bleeding and decreased clotting ability[8,10].Due to the point mutations that occur in the 5’-UTR of ANKRD26,its negative transcription factors(TFs),Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1),lose their repression effect[11].The persistent expression of ANKRD26 increases the activity of the mitogen activated protein kinase and extracellular signal regulated kinase 1/2 signaling pathways,which are potentially involved in the regulation of thrombopoietin-dependent signaling and further impair proplatelet formation by megakaryocytes(MKs)[11].However,the positive regulators of ANKRD26,which might be associated with THC2 pathology,are still unknown.