Force and impulse multi-sensor based on flexible gate dielectric field effect transistor

Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other fields.Two-dimensional material-based filed effect transistor(2D-FET)sensors are competitive with nano-level size,lower power consumption,and accurate response.However,few of them has the capability of impulse detection which is a path function,expressing the cumulative effect of the force on the particle over a period of time.Herein we fabricated the flexible polymethyl methacrylate(PMMA)gate dielectric MoS_(2)-FET for force and impulse sensor application.We systematically investigated the responses of the sensor to constant force and varying forces,and achieved the conversion factors of the drain current signals(I_(ds))to the detected impulse(I).The applied force was detected and recorded by I_(ds)with a low power consumption of~30 nW.The sensitivity of the device can reach~8000%and the 4×1 sensor array is able to detect and locate the normal force applied on it.Moreover,there was almost no performance loss for the device as left in the air for two months.

引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法

目前基于通用YOLO系列的遥感目标检测算法存在并未充分利用图像的全局上下文信息,在特征融合金字塔部分并未充分考虑缩小融合特征之间的语义鸿沟、抑制冗余信息干扰的缺点。在结合YOLO算法优点的基础上提出GUS-YOLO算法,其拥有一个能够充分利用全局上下文信息的骨干网络Global Backbone。除此之外,该算法在融合特征金字塔自顶向下的结构中引入Attention Gate模块,可以突出必要的特征信息,抑制冗余信息。另外,为Attention Gate模块设计了最佳的网络结构,提出了网络的特征融合结构U-Net。最后,为克服ReLU函数可能导致模型梯度不再更新的问题,该算法将Attention Gate模块的激活函数升级为可学习的SMU激活函数,提高模型鲁棒性。在NWPU VHR-10遥感数据集上,该算法相较于YOLOV7算法取得宽松指标mAP^(0.50)1.64个百分点和严格指标mAP^(0.75)9.39个百分点的性能提升。相较于目前主流的七种检测算法,该算法取得较好的检测性能。

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

Current-collapse suppression and leakage-current decrease in AlGaN/GaN HEMT by sputter-TaN gate-dielectric layer

In this paper, we explore the electrical characteristics of high-electron-mobility transistors(HEMTs) using a TaN/AlGaN/GaN metal insulating semiconductor(MIS) structure. The high-resistance tantalum nitride(TaN) film prepared by magnetron sputtering as the gate dielectric layer of the device achieved an effective reduction of electronic states at the TaN/AlGaN interface, and reducing the gate leakage current of the MIS HEMT, its performance was enhanced. The HEMT exhibited a low gate leakage current of 2.15 × 10^(-7) mA/mm and a breakdown voltage of 1180 V. Furthermore, the MIS HEMT displayed exceptional operational stability during dynamic tests, with dynamic resistance remaining only 1.39 times even under 400 V stress.

First demonstration of a self-aligned p-channel GaN back gate injection transistor

In this study,we present the development of self-aligned p-channel Ga N back gate injection transistors(SA-BGITs)that exhibit a high ON-state current.This achievement is primarily attributed to the conductivity modulation effect of the 2-D electron gas(2DEG,the back gate)beneath the 2-D hole gas(2DHG)channel.SA-BGITs with a gate length of 1μm have achieved an impressive peak drain current(I_(D,MAX))of 9.9 m A/mm.The fabricated SA-BGITs also possess a threshold voltage of 0.15 V,an exceptionally minimal threshold hysteresis of 0.2 V,a high switching ratio of 10~7,and a reduced ON-resistance(RON)of 548Ω·mm.Additionally,the SA-BGITs exhibit a steep sub-threshold swing(SS)of 173 mV/dec,further highlighting their suitability for integration into Ga N logic circuits.

Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography

Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

Remote entangling gate between a quantum dot spin and a transmon qubit mediated by microwave photons

Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Study of a Hydraulic Jump in an Asymmetric Trapezoidal Channel with Different Sluice Gates

In this study,the main properties of the hydraulic jump in an asymmetric trapezoidal flume are analyzed experimentally,including the so-called sequent depths,characteristic lengths,and efficiency.In particular,an asymmetric trapezoidal flume with a length of 7 m and a width of 0.304 m is considered,with the bottom of the flume transversely inclined at an angle of m=0.296 and vertical lateral sides.The corresponding inflow Froude number is allowed to range in the interval(1.40<F1<6.11).The properties of this jump are compared to those of hydraulic jumps in channels with other types of cross-sections.A relationship for calculating hydraulic jump efficiency is proposed for the considered flume.For F1>5,the hydraulic jump is found to be more effective than that occurring in triangular and symmetric trapezoidal channels.Also,when■mes>8 and■>5,the hydraulic jump in the asymmetrical trapezoidal channel downstream of a parallelogram sluice gate is completely formed as opposed to the situation where a triangular sluice is considered.

Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack

Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units is proposed.The time memory characteristics of the gated neural unit is fully utilized,thus the nonlinear flow field characterization ability of the learning and training process is enhanced,and the generalization ability of the whole prediction model is improved.The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil.The results show that the model has good adaptability.In the interpolation prediction,the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%,which can basically represent the variation characteristics of the entire flow field.In the construction of extrapolation models,the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction.Furthermore,the error is larger,even exceeding 20%,which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models.Compared with the conventional state space equation model,the proposed method can improve the extrapolation accuracy and efficiency by 78%and 60%,respectively,which demonstrates the applied potential of this method in aerodynamic modeling.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

预测轴承寿命的gate递归单元特征融合域自适应模型

采用现有的数据驱动模型对不同工况下的轴承剩余使用寿命(RUL)进行预测时,精度会大幅下降。针对这一问题,提出了一种基于门控递归单元特征融合领域自适应(GFFDA)模型的轴承RUL预测方法。首先,采用信号分析方法对轴承振动信号进行了特征提取,并采用特征评价的方法选择出了5个最优特征,在最优特征的基础上,采用粒子群算法优化后的支持向量机的方法对轴承的健康阶段进行了划分;然后,选择目标域和源域退化阶段的最优特征子集作为GFFDA模型的输入,采用源域数据对特征提取器和寿命预测模块进行了预训练;最后,更新了目标特征提取器和寿命预测模块,对目标域的RUL进行了预测;并使用西安交通大学的轴承数据集对该GFFDA模型的有效性进行了验证。研究结果表明:相比于现有的数据驱动模型,GFFDA模型具有更好的跨工况分析能力和更出色的信息提取能力;同时,在对变工况的轴承寿命进行预测时,采用GFFDA模型具有更好的性能。

Dual Gate液晶显示屏栅极制程断路缺陷的分析与改善

文章探究了光刻工序的水汽和ITO刻蚀工序的药液结晶对DualGate产品栅极制程的断路影响,通过DOE试验得到影响因子的最佳改善条件,使55寸DualGate产品栅极制程的断路缺陷发生率整体降低36%,为公司带来80.2万元的月度收益,能够对其他高端产品断路缺陷的改善思路、新工艺设备的设计改进,提供参考。

一款基于新型Field Programmable Gate Array芯片的投影仪梯形校正系统研究与实现

投影设备配备的梯形校正普遍存在校正范围小,画面的一些线条和字符边缘会出现毛刺和不平滑现象,矫正效果不理想.如果采用通用的图像处理芯片和复杂的算法,可以解决上述问题,但又会导致成本急剧上升.为了解决上述矛盾,提出一种基于FPGA(Field Programmable Gate Array)芯片的新型梯形校正实现方案,解决了校正范围与锯齿失真的矛盾问题,并为进一步成为芯片级产品铺平了道路.图像处理采用kaiser窗函数和sinc函数相结合的方法进行插值,这样的滤波器改善了旁瓣抑制,具有较好的通带性能.介绍了梯形失真的产生和校正原理,提出了利用FPGA芯片XC3S400作为核心图像处理单元的梯形校正系统的硬件和软件实现,说明了该芯片结构、功能及特性,最后提供了校正的效果图.

基于Noisy-orGate和贝叶斯网络的研发项目风险评估方法

在没有充足样本数据时,为有效地管理各种风险,提出一个全新的研发项目风险评估模型,这个模型主要是基于Noisy-or Gate和贝叶斯网络进行评估。该模型在贝叶斯网络节点满足构成Noisy-or Gate模型的前提下,利用历史数据或专家判断得到有效的网络参数,进而推断出每一项风险因素发生的概率,结合风险影响权重得到综合风险影响值,并对比得到高风险因素,为风险管理提供依据。通过与AHP方法评估结果对比表明,该模型可以准确地评估研发项目的风险,从而提高风险管理的效率。

GATE在核医学成像和放射治疗中的蒙特卡洛模拟

目的:探讨GATE在核医学成像SPECT和PET、光子和质子放射治疗中的蒙特卡洛模拟,并利用GATE平台研究碳纤维床板对光子放疗时剂量的影响。方法:首先模拟运行GATE V6.1提供的三个例子,分别对应于SPECT、PET和RT,其中RT又分为光子治疗和质子治疗。对SPECT和PET模拟中光子的散射情况进行统计分析,详细比较RT模拟中光子束和质子束在水模体中的能量沉积特性。然后在GATE平台上编程模拟了光子治疗束分别在有碳纤维床板和无床板时射入水模体中,比较并分析这两种情况下水模体中的剂量分布差异。结果:GATE V6.1的三个例子模拟中,SPECT中的未散射光子稳定在36%左右,PET中未散射的真符合计数稳定在44.5%左右,RT模拟中质子相比于光子在深度方向上有明显的剂量分布优势,而光子在横向方向的剂量分布稍好于质子。在碳纤维床板对光子放疗时剂量影响的模拟中,有碳纤维床板相对于无床板时,水模体的表层剂量有明显的提高。结论:GATE能够稳定准确的对核医学成像SPECT和PET及放射治疗过程进行蒙特卡洛模拟。它可以为放射治疗剂量验证、临床放射治疗计划以及核医学成像引导放射治疗的研究提供强大帮助。

Gow-Gates法下牙槽神经阻滞麻醉在下颌阻生第三磨牙拔除术中的麻醉效果研究

目的通过临床随机对照试验的方法评价Gow-Gates法下牙槽神经阻滞麻醉在下颌阻生第三磨牙拔除术中的麻醉有效性和安全性。方法使用左右半口设计,32例患者的左右下颌阻生第三磨牙分别随机采用Gow-Gates法和传统注射法进行下牙槽神经阻滞麻醉,并拔除下颌阻生第三磨牙,记录麻醉效果及不良事件。结果所有患者均完成研究。Gow-Gates法的麻醉成功率为96.9%,传统注射法的麻醉成功率为90.6%,二者的麻醉成功率无统计学差异(P=0.317)。在麻醉程度上,Gow-Gates法麻醉程度为A和B级的比率为96.9%,明显好于传统注射法的78.1%(P=0.034)。Gow-Gates法的回抽出血率明显低于传统注射法(P=0.025),2种注射方法均未出现血肿。结论 Gow-Gates法下牙槽神经阻滞麻醉在下颌阻生第三磨牙拔除术中的麻醉效果好且较为安全,可以作为传统注射法的有效补充。

基于GATE/STIR软件包的核医学仿真平台

采用基于Mont Carlo方法的GATE软件包,设计了GE Discovery LS型PET系统的仿真模型。在此基础上,依据Discovery LS的探测环几何,在ROOT环境下进行了相关辅助工具的开发,实现了GATE列表格式原始符合数据到Interfile3.3格式正弦图数据的离线转换,从而完成仿真平台各环节间的数据接口。以原型系统的实验测试数据为参考,通过仿真分析初步确认了所构建仿真系统的近似度与有效性。结果表明,仿真数据与实验数据能够较好地吻合,真实系统的主要特性在核医学仿真平台中得到了再现。

基于GATE的油田信息抽取技术研究

针对目前国内油田信息抽取系统中文信息抽取不准确的现状,提出了更为准确的基于油田的中文信息抽取的解决方案。在GATE框架下,开发中文信息抽取插件,解决了基于油田的中文分词、中文语料词表和中文命名实体识别,并在企业实际中进行了应用,取得了较好的应用效果,并为后续的油田信息抽取技术研究提供了研究基础。