Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Damage Identification of Truss Structures Based on Force Method

An computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro ge-netic algorithm.For this purpose,the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into ac-count,respectively,are formulated.The compatibility equations in terms of forces are explicitly presented using the singular value decomposition(SVD)technique.Then governing equations with unknown reaction forces and initial elongations are derived.Next,the micro genetic algorithm(MGA)is used to properly identify the site and ex-tent of multiple damage cases in truss structures.In order to verify the accuracy and the superiority of the proposed damage detection technique,the numerical solutions are presented for the planar and space truss models.The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.

Structural modal parameter identifi cation and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic condition.Widespread coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each other.The present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesfingerprint-based person identification ineffective.Periocu-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face masks.However,the periocular biometric region is a small area,and extraction of the required feature is the point of con-cern.This paper has proposed adopted multiple features and emphasis on the periocular region.In the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classification.Hence,we extract three types of fea-tures for the classification of periocular regions for biometric.The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel.In order to extract the frequency domain fea-tures,the wavelet transformation is obtained.By learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition results.The proposed approach achieved the highest accuracy rates with the lowest false person identification.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Gemini阳离子/含氟阴离子表面活性剂混合体系的界面润湿调控及解水锁机制研究

为有效解决低渗透致密砂岩气藏开采普遍存在的水锁伤害问题,研究了Gemini阳离子表面活性剂双季铵盐BQ-16和含氟阴离子表面活性剂全氟辛基磺酸钾(PPS)在降低表面张力和润湿调控方面的协同作用,并探究了BQ-16/PPS表面活性剂混合体系的润湿调控及解水锁机制。结果表明,当PPS的摩尔分数(α_(PPS))为0.7时,BQ-16/PPS表面活性剂混合体系的表面张力达到最低,低于任一纯组分,此时BQ-16/PPS表面活性剂混合体系接近电中性;BQ-16和PPS在岩心表面润湿调控方面表现出协同效应,当BQ-16/PPS表面活性剂混合体系总浓度为1×10^(-4)mol·L^(-1)、α_(PPS)为0.7时,盐水滴接触角最大,达到84.23°;BQ-16/PPS表面活性剂混合体系(α_(PPS)=0.7)在石英砂表面的吸附过程符合准二级动力学方程和Freundlich等温吸附模型,低浓度时为单分子层吸附,高浓度时为多分子层吸附;BQ-16/PPS表面活性剂混合体系(α_(PPS)=0.7)可将岩心的液体饱和度降至0.410,并将渗透率恢复值提高至0.78,说明BQ-16/PPS表面活性剂混合体系可有效解决水锁伤害,这主要与混合体系能够显著降低表面张力并通过在岩心表面的吸附改变岩心表面润湿性有关。

Genome-Wide Exploration of the Grape GLR Gene Family and Differential Responses of VvGLR3.1 and VvGLR3.2 to Low Temperature and Salt Stress

Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.

阳离子表面活性剂遮蔽作用导致的酸化缓速研究

为了减轻传统稠化酸在碳酸盐岩多孔介质中的吸附滞留伤害,降低其对于致密碳酸盐岩改造后渗透率的伤害。提出采用阳离子表面活性剂遮蔽在岩石表面进行改性进而缓速的方式,以鄂尔多斯盆地马家沟组碳酸盐岩为研究对象,根据基本性质测试确定不同碳链长度阳离子表面活性剂界面改性能力,并利用分子模拟技术结合原子力显微镜、润湿角观测提出阳离子表面活性剂缓速机理,再通过动静态缓速性能测试和伤害评价实验研究阳离子表面活性剂缓速性能及地层伤害程度。研究结果表明:(1)碳链长度会影响表面活性剂分子吸附形态,进而改变其界面吸附效果,十四烷基三甲基氯化铵可在岩石表面形成直立致密吸附层,润湿改性效果最佳;(2)阳离子表面活性剂以界面吸附作用对岩石表面进行掩蔽覆盖,增加其疏水性能,阻碍H+接触到岩石表面,控制表面反应速率,达到缓速目的;(3)相较于常规酸,阳离子表面活性剂具有良好的动静态缓速性能,各项动力学参数降低50%~60%,减低了对碳酸盐岩孔喉伤害。研究认为:季铵盐阳离子表面活性剂具备分子量低、界面吸附能力强的优势,在保持缓速能力的同时,有效降低稠化酸对地层伤害程度;该项研究有助于认识表面活性剂缓速性能、改进缓速酸体系、提高碳酸盐岩储层改造效果等都具有积极的意义。

Individual automatic detection and identification of big cats with the combination of different body parts

The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.

A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

海洋来源青霉Penicillium sp.的化学成分研究

海洋微生物因生存环境特殊,常能形成结构特异、活性良好的代谢产物。本文对1株海洋沉积物中分离得到的青霉Penicillium sp.进行化学成分研究和抗菌活性评价。结果分离和鉴定了4个化合物,分别为Nuatigenin(1)、吲哚-3-乙酸(2)、胸苷(3)和3-苯基乳酸(4),其中化合物1对金黄色葡萄球菌和白色念珠菌均有一定的抑菌活性,化合物4对白色念珠菌抑菌效果良好,其余化合物对不同的菌均无抑制作用。

阳离子表面活性剂基压裂液的地层伤害性研究

目前清洁压裂液的研究都是基于零伤害,但经过研究发现,它在一定程度上对地层是有伤害的,因为其中的阳离子表面活性剂在地层中会发生吸附滞留现象,引起润湿反转,从而对地层造成伤害。通过测定阳离子表面活性剂在岩石矿物上的吸附量和进行岩心流动性实验证实了压裂液对地层渗透率的损害率达到12.5%。

油基钻井液的推广及循环利用

针对普通水基钻井液在钻进中强、强水敏性油藏时会造成储层污染、产量降低的问题,指出了油基钻井液在钻进该类油藏时的优势和其成本高、对生态环境有污染的缺点。为了大面积推广应用油基钻井液,重点介绍了油基钻井液的特性和稳定性影响因素,并通过室内评价证明了油基钻井液在油层保护中的良好效果,着重阐述了油基钻井液在王庄油田的现场应用情况,并探讨了油基钻井液的循环利用技术。研究结果表明,油基钻井液不仅具有良好的油层保护效果,而且具有较好的社会经济效益。

青西地区复杂深井钻井液技术研究与应用

青西地区地质情况十分复杂,钻井施工中易发生井漏、缩径、井壁坍塌,并引起卡钻事故.用该地区代表井窿8井钻屑进行的室内研究结果表明,全井粘土矿物含量分布呈M形状,1700～3300 m井段地层粘土矿物易发生水化膨胀,下沟组以下的泥岩段基本无水化现象,但裂缝发育,地层应力释放严重.所以中沟组以上地层应采用强抑制性钻井液,下沟组以下地层应使用防硬脆性泥页岩垮塌钻井液,特别注意高温高压滤失量的控制.针对该地区地层岩性特征,通过室内评价,优选了阳离子正电胶钻井液.现场应用表明,该钻井液能够有效抑制泥页岩水化和硬脆性地层的垮塌,具有良好的抗高温、抗钙、抗石膏及抗钻屑污染的能力,且配方简单,维护间隔时间长,起下钻无阻卡现象,满足了青西地区安全快速钻井的需要.30多口应用井的统计结果表明,复杂情况和事故时间由原来的年平均1.69%下降为1.42%,平均钻井周期由原来的182.1 d缩短到133.2 d以内,油层段平均井径扩大率由15.81%下降为9.4%,固井质量优质率在90%以上.

强抑制性钻井完井液研究与应用

胜利油田郑408区块储层埋藏浅,胶结疏松,粘土含量为0.3％～13.2％,粘土矿物以易分散运移的高岭石和易膨胀的伊蒙混层为主,属强、中水敏性储层,而且存在碱敏现象。针对该地层特点,在大量室内实验的基础上,通过使用黑色正电胶和复合无机盐抑制剂,研制了适合强水敏性储层的水基钻井完井液体系,并将pH值控制在7.0～8.5之间。室内评价结果表明,该体系具有很强的抑制包被能力,能有效防止粘土水化膨胀和造成高岭石分散运移,钻井液各种处理剂之间有较好的相容性,滤液与储层内流体有很好的相容性,保护油气层效果与油基钻井液相当;能抑制钻屑分散,有利于发挥固控设备效率和提高钻井速度;具有良好的携岩性、润滑性能、防塌抑制性和造壁性能,摩擦系数小于0.05;可抗130℃的高温。在郑408区块用强抑制性水基钻井完井液代替油基钻井液用于强水敏储层钻进后,保护了油气层,提高了原油采收率,降低了成本,缩短了钻井周期,而且对环境无污染。

小阳离子-聚合醇钻井液技术研究及应用

在探讨聚合醇和小阳离子的作用机理的基础上,室内将小阳离子与聚合醇钻井液结合,优选出了抑制防塌能力更强的小阳离子聚合醇钻井液配方。室内试验表明,该钻井液比聚磺钻井液在抑制性、润滑性、流变性以及抗温、抗盐能力方面都有不同程度的增强。在吐哈地区地质情况相对复杂的玉西、红台构造进行试验表明,该钻井液可大幅度降低井下复杂情况发生几率,较好地满足了钻进等各项施工以及油层保护的要求,收到了良好效果。

基于强度变化特性的随机谱载荷下疲劳寿命预估模型

提出了一种新的随机谱载荷下构件的疲劳寿命评估模型。该模型以结构强度变化特性为基础,考虑低幅载荷强化和损伤的双重效应,既有清晰的物理意义,又更合理地反映了疲劳过程中零件强度值随着载荷的变化而相应地增高和衰减的特性,克服了传统疲劳累积损伤模型忽略低幅载荷强化作用的缺陷。经过两种构件的试验验证表明,此模型与传统的累积损伤模型相比能更准确地预测构件在随机载荷下的寿命,为结构的轻量化设计提供理论与技术依据。

一株猪细小病毒7群的鉴定和分离

猪细小病毒7群(Porcine parvovirus 7,PPV-7)是2016年首次从美国猪群发现和鉴定的新的猪细小病毒。为弄清我国猪群中是否存在PPV-7,本文用PPV-7特异性的实时荧光定量PCR和常规PCR方法对采集的10份猪流产胎儿和10份保育仔猪病料分别进行检测,结果发现其中1份保育仔猪样品的常规PCR和实时荧光定量PCR检测结果均为阳性,常规PCR扩增出的246 bp特异性条带测序和分子遗传演化时发现,该序列与PPV-7参考毒株KU5637332和KY996757的同源性分别为99.6%和98.0%,表明该样品中含有PPV-7,进一步用PK-15细胞分离病毒,连续传代5次后PK-15细胞都没有出现典型的细胞病变,但其上清液用实时荧光定量PCR方法都可以检测到该病毒。本研究结果证实我国猪群中存在PPV-7的感染,且检测到的PPV-7毒株能在PK15细胞中增殖。

基于相关变化检测与面向对象分类技术的多源遥感图像震害信息提取

遥感图像面向对象分类作为空间信息提取的关键技术,在震害信息提取方面发挥着非常重要的作用,然而由于光学遥感影像是正射图像,只能提取建筑物屋顶信息,这使得单一利用震后光学影像进行震害信息提取存在一定的局限性.针对该问题,本文提出了一种基于合成孔径雷达(SAR)相关变化检测的光学影像震害建筑物面向对象提取方法,即在光学影像面向对象提取的数据中融合SAR相关性,对光学影像进行面向对象提取震害建筑物时不仅考虑建筑物的几何、光谱等特征,还加入震前震后变化信息即SAR相关性进行分类.在此基础上,选取2008年汶川MS8.0地震震区都江堰地区作为研究区进行试验.结果表明,本文提出的方法相对于单一使用光学影像进行震害建筑物提取,其准确度有较明显的提高.

永1-平1井四开小井眼高密度钻井液技术

永1-平1井是永进油田的第一口超深水平井,完钻井深为6 172.5 m。该油田西山窑组地层复杂,泥岩易掉块,砂岩渗透性强,易发生压差卡钻;小井眼环空压耗大、高密度钻井液的流变性、润滑性和高温下的稳定性调控难题严重制约工程施工。在钻井施工中采用了阳离子聚磺混油钻井液、高密度流变性和高温稳定性调控技术以及封堵防塌技术,顺利钻至完钻井深。该井投产后,产液量为18.01 t/d、产油量为12.8 t/d、产气量为3 411 m3/d。该井的钻探成功为超深、超压油气藏的勘探开发积累了重要经验。