“Inverse Projection Method”Model in DEA to Measure Technological Progress

In view of the problems in measuring technological progress by DEA model, premias for DEA application is analyzed in this paper. An extended model of DEA is also raised for the cases running counter to the premisess. And precision about the results of the two models is analyzed.

The Study of Single Station Inverting the Sea Surface Current by HF Ground Wave Radar Based on Adjoint Assimilation Technology

This paper introduces the assimilation technology in an ocean dynamics model and discusses the feasibility of inverting the sea surface current in the detection zone by assimilating the sea current radial velocity detected by single station HF ground wave radar in ocean dynamics model. Based on the adjoint assimilation and POM model, the paper successfully inverts the sea surface current through single station HF ground wave radar in the Zhoushan sea area. The single station HF radar inversion results are also compared with the bistatic HF radar composite results and the fixed point measured results by Annderaa current meter. The error analysis shows that acquisition of flow velocity and flow direction data from the single station HF radar based on adjoint assimilation and POM model is viable and the data obtained have a high correlation and consistency with the flow field observed by HF radar.

A DEVELOPING TOOL FOR THE EXPERT SYSTEM IN TECHNOLOGY DESIGN AND EXPERT SYSTEM FOR COCOON COOKING

This paper demonstrates the general character of some technology designing processes through their analysisand introduces a developing tool for the expert system in technology design.With this tool a particular domain ex-pert can directly establish a knowledge base so as to form a practical expert system,and thus enhanca correctness ofknowledge experssion and the developing efficiency of the expert system.It also discusses in detail the mech-anism of reasoning,interpreting and knowledge obtaining.The knowledge base consists of three parts:classifyingrules,essential data,and regulating rules.It can be formed by means of the expert dialouge and edition.In its ap-plication,the knowledge base can constantly accumulate successful experience to achieve itsself-study function.The paper shows the way to describe the knowledge in a particular domain and the process of applying this tool ina particular domain. The tool is written in Turb-Prolog language,And an expert system for cocoon cooking isprovided.

A natural chemical technology for reducing fluoridation through chloride contents in muscles of fish

Accumulation trend of fluoride ion was carried out in 25 fish species, collected randomly from fresh water and marine water, Sindh, Pakistan to ensure the level of chloride contents by Selective Ion Electrode （SIE） method. Results showed an inverse relation in between chloride and fluoride concentration in muscles of fish under investigation, It was observed that chloride ions contents of marine water fish were significantly higher as compared with fresh water fish. This may be related with the habitat of marine water fish while no appreciable divergence in fluoride concentration in both regions was observed. This indicated that the deliberation of fluoride is the phenomena of nature of fish and self-determining. Highest concentration of fluoride ion in fresh water fish and marine water fish showed an inverse level of chloride ion in it, reflects water body conditions of both resources. A probable mechanism of accumulation of fluoride with respect to essential chloride ion which may be related with the size of ionic radii of both halides ion is described in relevant section of this article. Where a larger ionic radii of chloride ion believed to play an imperative role in reducing fluoridation in fishes. This may be regarded as natural chemical technology for reducing fluoride in muscle （edible part） of fish of both regions.

Study of Inverse Lithography Approaches based on Deep Learning

Computational lithography(CL)has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography.The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom.However,as the growth of data volume of photomask layouts,computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms.In the past,a number of innovative methods have been developed to improve the computational efficiency of CL algorithms,such as machine learning and deep learning methods.Based on the brief introduction of optical lithography,this paper reviews some recent advances of fast CL approaches based on deep learning.At the end,this paper briefly discusses some potential developments in future work.

Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN

Inverse lithography technology(ILT)is intended to achieve optimal mask design to print a lithography target for a given lithography process.Full chip implementation of rigorous inverse lithography remains a challenging task because of enormous computational resource requirements and long computational time.To achieve full chip ILT solution,attempts have been made by using machine learning techniques based on deep convolution neural network(DCNN).The reported input for such DCNN is the rasterized images of the lithography target;such pure geometrical input requires DCNN to possess considerable number of layers to learn the optical properties of the mask,the nonlinear imaging process,and the rigorous ILT algorithm as well.To alleviate the difficulties,we have proposed the physics based optimal feature vector design for machine learning ILT in our early report.Although physics based feature vector followed by feedforward neural network can provide the solution to machine learning ILT,the feature vector is long and it can consume considerable amount of memory resource in practical implementation.To improve the resource efficiency,we proposed a hybrid approach in this study by combining first few physics based feature maps with a specially designed DCNN structure to learn the rigorous ILT algorithm.Our results show that this approach can make machine learning ILT easy,fast and more accurate.

Three-dimensional gravity inversion based on optimization processing from edge detection

Gravity inversion requires much computation,and inversion results are often non-unique.The first problem is often due to the large number of grid cells.Edge detection method,i.e.,tilt angle method of analytical signal amplitude(TAS),helps to identify the boundaries of underground geological anomalies at different depths,which can be used to optimize the grid and reduce the number of grid cells.The requirement of smooth inversion is that the boundaries of the meshing area should be continuous rather than jagged.In this paper,the optimized meshing strategy is improved,and the optimized meshing region obtained by the TAS is changed to a regular region to facilitate the smooth inversion.For the second problem,certain constraints can be used to improve the accuracy of inversion.The results of analytic signal amplitude(ASA)are used to delineate the central distribution of geological bodies.We propose a new method using the results of ASA to perform local constraints to reduce the non-uniqueness of inversion.The guided fuzzy c-means(FCM)clustering algorithm combined with priori petrophysical information is also used to reduce the non-uniqueness of gravity inversion.The Open Acc technology is carried out to speed up the computation for parallelizing the serial program on GPU.In general,the TAS is used to reduce the number of grid cells.The local weighting and priori petrophysical constraint are used in conjunction with the FCM algorithm during the inversion,which improves the accuracy of inversion.The inversion is accelerated by the Open Acc technology on GPU.The proposed method is validated using synthetic data,and the results show that the efficiency and accuracy of gravity inversion are greatly improved by using the proposed method.

Seamless-merging-oriented parallel inverse lithography technology

Inverse lithography technology （ILT）, a promising resolution enhancement technology （RET） used in next generations of IC manufacture, has the capability to push lithography to its limit. However, the existing methods of ILT are either time-consuming due to the large layout in a single process, or not accurate enough due to simply block merging in the parallel process. The seamless-merging-oriented parallel ILT method proposed in this paper is fast because of the parallel process; and most importantly, convergence enhancement penalty terms （CEPT） introduced in the parallel ILT optimization process take the environment into consideration as well as environmental change through target updating. This method increases the similarity of the overlapped area between guard-bands and work units, makes the merging process approach seamless and hence reduces hot-spots. The experimental results show that seamless-merging-oriented parallel ILT not only accelerates the optimization process, but also significantly improves the quality of ILT.

A new algorithm of inverse lithography technology for mask complexity reduction

A new complexity penalty term called the global wavelet penalty is introduced, which evaluates the highfrequency components of masks more profoundly by applying four distinctive Haar wavelet transforms and choosing the optimal direction on which the highest frequency components of the mask will be removed. Then, a new gradientbased inverse lithography technology （1LT） algorithm is proposed, with the computation of the global wavelet penalty as the emphasis of its first phase for mask complexity reduction. Experiments with three typical 65 nm flash ROM patterns under existing 90 nm lithographic conditions show that compared with the gradient based algorithm, which relies on the socalled local wavelet penalty, the total vertices of the three results created by the proposed algorithm can be reduced by 12.89%, 12.63% and 12.64%, respectively, while the accuracy of the lithography results remains the same.

Mathematical Insight into Moderate Inversion Gate Delay Variability for Ultradeep Submicron Digital Circuit Design

Voltage scaling has been extensively used in industry for decades to reduce power consumption.In recent years,exploring digital circuit operation in moderate inversion has created an interest among researchers due to its immense capability to provide a perfect tradeoff between high performance and low energy operation.But circuits operating in moderate inversion are susceptible to process variations and variability.To compute variability,statistical parameters such as the probability density function(PDF)and cumulative distribution function(CDF)are required.This paper presents an analytical model framework for delay calculations utilizing log skew normal distribution for ultradeep submicron technology nodes up to 22 nm.The CDF of the proposed model is utilized to calculate minimum and maximum delays with 3σ-accuracy providing better accuracy than the conventional methods.The obtained results are also compared with Monte Carlo simulations with errors lying within the acceptable range of 2%-4%.

隧道不良地质识别:方法、现状及智能化发展方向

随着隧道施工对于不良地质识别精度要求的不断提高以及人工智能技术的发展,融合多源信息的不良地质智能化识别已成为发展趋势。本文首先阐述了常见的6种隧道不良地质类型及其地质成因,回顾分析了隧道主要的不良地质识别方法及现状,详细介绍了笔者在不良地质智能化识别方面的探索性研究:基于机器学习利用图像识别技术对隧道围岩岩性与裂隙特征进行智能识别;融合图像和光谱特征进行不良地质识别;将地化分析融入到传统的超前钻探中,融合随钻参数和地化信息进行不良地质随钻识别,既可以发挥超前钻探在感知岩体质量和地层信息变化方面的优势,又可以发挥地化分析在岩性和不良地质异常识别方面的优势;基于地质与物探联合反演进行不良地质识别,旨在实现掌子面前方不良地质体“形”(位置、形态、规模)和“性”(性质和类型)的精确识别。最后,对隧道不良地质智能化识别的发展趋势进行了展望。

多纵模激光器体制下直接多普勒测风技术的实现与仿真

考虑到单纵模激光器作为多普勒测风激光雷达的激励光源需要复杂的种子注入技术和高精度的稳频锁频技术,本文提出了以自由运转的多纵模激光器作为激励光源的直接多普勒测风激光雷达,以降低激励光源的实现难度和复杂性.针对典型Nd:YAG脉冲激光器,研究了不同激光辐射线宽、光学谐振腔长和中心波长条件下多纵模激光激励的大气弹性散射回波光谱的分布模式.为了综合利用大气风场导致的多纵模激光中每个纵模所激励大气弹性散射回波光谱的多普勒频移,利用具有周期性透过率曲线且四个输出通道相位各相差π/2的可调谐四通道马赫-曾德尔干涉仪,实现对多纵模大气弹性散射回波光谱多普勒频移的高精度鉴别.在此基础上,构建了多纵模激光器体制下直接多普勒测风技术大气风场反演的数学模型.仿真结果表明,所提出的多纵模直接多普勒测风激光雷达能够实现对大气风场信息的高精度测量,并且激光中心波长越大,激光光学谐振腔长越短,系统测风范围越大,测风误差越小.

四川固定强震台址场地效应反演研究

区域地震灾害分析是基于对控制地震地面运动的三个关键因素:震源、传播路径和场地条件的深入了解。这些因素可以借助广义反演技术(Generalized Inversion Technique,GIT),通过强震动记录的频谱分解来单独分析。该文使用完全参数化GIT,对2007—2020年四川地区发生的397次地震(3~6.5级)的3338组强震数据进行反演。针对区域场地效应,考虑基岩裂隙或风化作用的影响,确定了四川地区129个固定强震台址的场地放大系数。在介质衰减效应方面,区域几何衰减平均水平被确定为R^(-0.6),S波平均品质因子为150.33f^(0.78)。对于震源参数,区域应力降均值水平为9.25 MPa。该文结果可以为地震动建模或随机S波模拟提供合理的输入参数及数据,从而改进确定性地震危险性评估。

融合VMD算法的多星GNSS-MR雪深反演

针对当前反射信号分离纯度不高,难以准确提取反射信号中地表环境参数的问题,构建了一种融合VMD算法的多星GNSS-MR雪深反演模型。利用VMD算法可有效实现信号的分离,提高反射信号的分离纯度,并与传统的二阶多项式拟合法进行对比分析。下载了美国科罗拉多州NWOT测站2017年172天的GPS卫星信噪比数据进行验证,结果表明:融合VMD算法的多星GNSS-MR反演雪深模型精度优于二阶多项式拟合模型,反演值与实测值的RMSE降低了近50%,决定系数R^(2)高达0.98,在一定程度上有效提高了雪深的反演精度和可靠性。

临河坳陷高效油气勘探综合物探技术及应用

针对临河坳陷主力生烃凹陷不清、构造及地层难以落实、成藏目标不清、突破井位难以落实等长期以来制约勘探的难点问题,提出了适用勘探新区高效勘探的技术路线及关键技术。以基于多种灵活约束机制的中浅层重力多密度界面反演及归一式重力正演剥层为核心,利用深层目标重力异常提取技术重新认识了临河坳陷地质结构。淖西深洼槽是临河坳陷的主力生烃区,临河坳陷南斜坡受黄河断陷槽控制,发育了中央断垒式潜山披覆构造带,具备两侧双源供烃的有利成藏条件,具有巨大的油气勘探潜力;提出并应用基于井、震资料控制的时频电磁模拟退火电阻率约束反演及基于精细地电结构模型的极化率约束反演等时频电磁目标储层油气检测技术,提高了深层目标的电性分辨率及目标储层的油气预测精度,快速锁定JH2x、LH1x及XH1井等靶区目标,为临河坳陷油气勘探的突破发挥了关键先导性作用,为低勘探程度新区、特别是盆地深层油气勘探提供了有效的方法与技术思路。

Effect of endoscopic full-thickness resection assisted by distal serosal turnover with floss traction for gastric submucosal masses

BACKGROUND Complex and high-risk surgical complications pose pressing challenges in the clinical implementation and advancement of endoscopic full-thickness resection(EFTR).Successful perforation repair under endoscopy,thereby avoiding surgical intervention and postoperative complications such as peritonitis,are pivotal for effective EFTR.AIM To investigate the effectiveness and safety of EFTR assisted by distal serosal inversion under floss traction in gastric submucosal tumors.METHODS A retrospective analysis of patients with gastric and duodenal submucosal tumors treated with EFTR assisted by the distal serosa inversion under dental floss traction from January 2023 to January 2024 was conducted.The total operation time,tumor dissection time,wound closure time,intraoperative bleeding volume,length of hospital stay and incidence of complications were analyzed.RESULTS There were 93 patients,aged 55.1±12.1 years.Complete tumor resection was achieved in all cases,resulting in a 100% success rate.The average total operation time was 67.4±27.0 min,with tumor dissection taking 43.6±20.4 min.Wound closure times varied,with gastric body closure time of 24.5±14.1 min and gastric fundus closure time of 16.6±8.7 min,showing a significant difference(P<0.05).Intraoperative blood loss was 2.3±4.0 mL,and average length of hospital stay was 5.7±1.9 d.There was no secondary perforation after suturing in all cases.The incidence of delayed bleeding was 2.2%,and the incidence of abdominal infection was 3.2%.No patient required other surgical intervention during and after the operation.CONCLUSION Distal serosal inversion under dental-floss-assisted EFTR significantly reduced wound closure time and intraoperative blood loss,making it a viable approach for gastric submucosal tumors.

浅析探地雷达技术的发展历程

探地雷达(GPR)具有高效、无损、简便、快捷等诸多优点,被广泛应用在城市管线检测、堤坝查漏探测、道路危害检测等工程地质探测领域。随着软硬件及计算机技术的快速发展,探地雷达技术效率和分辨率不断提高,呈现出更加广阔的应用前景。通过查阅大量的国内外相关文献,就关于探地雷达技术的发展、雷达数据处理的正反演技术以及结合人工智能技术的探地雷达这四方面的发展历程进行了详细地叙述并系统地总结和浅析未来的发展趋势,对今后探地雷达技术相关的工作发展有一定的指导作用和意义。

主动地震波CT反演技术预测冲击地压危险的应用分析

为预防冲击地压事故的发生,亭南煤矿基于PASAT-M型CT微震探测系统,采用主动CT反演技术对309工作面未开采区域进行原位探测。根据探测结果,结合冲击危险性评价模型进行工作面冲击危险区域的预测,以指导后续卸压防冲工作。此次探测区内煤岩层总体处于中等冲击危险等级;采用综合考虑地震波波速异常系数和地震波波速梯度系数的冲击危险性评价模型进行预测,结果与现场实际基本一致。主动地震波CT反演技术与冲击危险性评价模型结合,可以有效探测回采区域内潜在冲击危险区,为下一阶段卸压防冲措施提供精准指导。

环境污染对城市技术创新影响的异质性研究——来自中国城市的证据

环境污染不仅影响社会公众健康和生活质量,而且给城市绿色发展和技术创新带来巨大挑战。鉴于此,环境污染与城市技术创新之间的影响机理一直是学界争论焦点,引起社会各界广泛关注。本文基于中国285个城市2013-2022年面板数据,创新性使用逆温作为缓解环境污染内生性的工具变量,聚焦环境污染是否影响城市技术创新以及它们之间的影响是否存在异质性特征。研究结果显示,环境污染显著抑制了城市技术创新,且通过多种稳健性检验;作用机制表明,产业集聚和财政分权是环境污染抑制城市技术创新的重要传导渠道;异质性分析发现,2018-2022年间环境污染对城市技术创新的抑制程度显著低于2013-2017年间;此外,中国内陆城市环境污染对城市技术创新的抑制作用显著高于沿海城市。

无人机高光谱在城市水质环境监测应用中反演模型构建的研究

高效的城市水质常态化监测是水环境污染防治的基础所在,无人机载高光谱技术既可以降低传统水质监测方法的人力和时间成本,同时可以推动生态环境智能检测网络的建立。聚焦城市中小河道水质监测的要求,探究无人机高光谱技术中反演模型构建中的关键技术。结果表明,无人机高光谱技术对于城市水质监测具有一定的准确性,具有推广和应用价值。