Realizing precision pulse TIG welding with arc length control and visual image sensing based weld detection

Methods of arc length control and visual image based weld detection for precision pulse TIG welding were investigated. With a particular all hardware circuit, arc voltage during peak current stage is sampled and integrated to indicate arc length, deviation of arc length and adjusting parameters are calculated and output to drive a step motor directly. According to the features of welding image grabbed with CCD camera, a special algorithm was developed to detect the central line of weld fast and accurately. Then an application system were established, whose static arc length error is ±0.1 mm with 20 A average current and 1 mm given arc length, static detection precision of weld is 0.01 mm , processing time of each image is less than 120 ms . Precision pulse TIG welding of some given thin stainless steel components with complicated curved surface was successfully realized.

RESTRICTION FRAGMENT LENGTH POLYMORPHISMS ASSOCIATED WITH FACTOR Ⅷ CARRIER DETECTION USING DNA RFLPs IN CHINESE

RFLPs for XbaI, BelI and BglI sites of human FⅧwere informative for 48%, 41% and 15% of females studied, respectively. BglI RFLP is different from that reported by Chan et al, a fact suggests Yangtze River region population of China would be at variance with the Southern Chinese population in certain RFLP distribution. TaqI allelic system Ⅰin the DXS52 region also shows the same variance among them, but heterozygous rate 0f 71% for system Ⅰ(alleles 1 to 8) and 49% for system Ⅱ(αand βalleles) were very similar. Using the Bell/XbaI RFLPs, accurate information could be obtained from this study for 56% of women who were at risk for hemophilia A (HA) carriership. The carrier of the remaining 44% could be determined by utilizing the TaqI RFLP. In addition, we report a new intergenie polymorphism (9%) at DXS115 as a marker for detection of heterozygotes in families at risk for HA. The advantage of using the XbaI/KpnI RFLP is that both the intragemie RFLP and the new intergenie RFLP can be evaluated on the same blot at the same time.

Variable-length sequential dynamic features-based malware detection

In order to solve the problem that traditional signature-based malware detection systems are inefficacious in detecting new malware,a practical malware detection system is constructed to find out new malware. Application programming interface( API) call sequence is introduced to capture activities of a program in this system. After that,based on variable-length n-gram,API call order can be extracted from API call sequence as the malicious behavior feature of a software. Compared with traditional methods,which use fixed-length n-gram,the solution can find more new malware. The experimental results show that the presented approach improves the accuracy of malware detection.

Annoyance-type speech emotion detection in working environment

In order to recognize people＇s annoyance emotions in the working environment and evaluate emotional well- being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a Mandarin database with two thousands samples is built. In searching for annoyance-type emotion features, the prosodic feature and the voice quality feature parameters of the emotional statements are extracted first. Then an improved back propagation （BP） neural network based on the shuffled frog leaping algorithm （SFLA） is proposed to recognize the emotion. The recognition capability of the BP, radical basis function （RBF） and the SFLA neural networks are compared experimentally. The results show that the recognition ratio of the SFLA neural network is 4. 7% better than that of the BP neural network and 4. 3% better than that of the RBF neural network. The experimental results demonstrate that the random initial data trained by the SFLA can optimize the connection weights and thresholds of the neural network, speed up the convergence and improve the recognition rate.

Detecting network intrusions by data mining and variable-length sequence pattern matching

Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.

Real Time Power Monitoring Detection Based on Sequence Time Domain Reflectometry Approach

Sequence Time Domain Reflectometry (STDR) have been demonstrated to be a powerful technique for detecting the length of cable or length of open circuit or short circuit cables. Using this method along with using smart meter on the main electrical panel board to monitor consumption if load at each circuit, enable user to monitor power consumption at each node (power outlet) only by operating a smart digital meter and an STDR circuitry on each circuit at the main electrical panel board. This paper introduces this method and examines it on dead-wire and energized wire with a load connected across it. Experimental results are demonstrated for both types. Test result show the potential application of this approach to provide consumption information and potential cost saving via feedback for users.

A fuzzy adaptive smoothing approach to robust endpoint detection based on MDL using sub-band speech

To develop a more robust endpoint detection algorithm, this paper first proposes a fuzzy adaptive smoothing algorithm. The general idea underlying adaptive smoothing is to adapt the short-term sub-band mean of the amplitude to the local attributes of speech on the basis of discontinuity measures. The adaptive smoothing algorithm in this paper utilizes a scale-space framework through the minimal description length （MDL）. We recommend using the fuzzy muhi-attribute decision making approach to select the proper sub-bands where the word boundary can be more reliably detected. The process and simulation of the fuzzy adaptive smoothing algorithm are given. The parameters utilize the mean amplitude of the audible frequency range （300 -3 700 Hz） and the sub-band mean of the amplitude （16 band filter-bank）. We selected the audible band energy because of its usefulness in detecting high-energy regions and making the distinction between speech and noise. Otherwise, the fuzzy adaptive smoothing algorithm is processed in sub-band speech to utilize the full range of frequency information.

Sequential Detection of Digital Watermarking

The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.

Abnormal activity detection for surveillance video synopsis

Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos.

Fundus Lesion Detection Based on Visual Attention Model

Reliable detection of fundus lesion is important for automated screening of diabetic retinopathy. This paper presents a novel method to detect the fundus lesion in retinal fundus image based on a visual attention model. The proposed method intends to model the visual attention mechanism of ophthalmologists during observing fundus images. That is, the abnormal structures, such as the dark and bright lesions in the image, usually attract the most attention of experts, however, the normal structures, such as optic disc and vessels, have been usually selectively ignored. To measure the visual attention for abnormal and normal areas, the incremental coding length is computed in local and global manner respectively. The final saliency map of fundus lesion is a fusion of attention maps computed for the abnormal and normal areas. Experimental results conducted on the publicly DiaRetDB1 dataset show that the proposed method achieved a sensitivity of 0.71 at a specificity of 0.82 and an AUC of 0.76 for fundus lesion detection, and achieved an accuracy of 100% for normal area (optic disc) detection. The proposed method can assist the ophthalmologists in the inspection of fundus lesion.

基于多模型融合的室内行人航迹推算建模与性能分析

针对行人航位推算(pedestrian dead reckoning,PDR)室内信号易受到环境和多径效应干扰的问题,提出一种基于多模型融合的室内PDR优化建模方法.给出多模型融合的室内PDR建模方法系统模型,包括步数检测、步长推算、方向推算以及位置推算4个关键阶段.该方法在步数检测阶段融合了峰值检测算法、局部最大值算法以及提前过零检测算法;在步长推算阶段融合Weinberg方法和Kim方法,并利用卡尔曼滤波算法校正步数检测和步长推算的误差.基于不同场景从步数、步长、方向、位置误差方面与传统算法进行比较.结果表明,该组合模型结合了传统步数检测和步长推算算法的特征识别结果,可实现对步数检测、步长推算过程中信号特征的优化处理;在手持场景下,步数检测识别准确,步长推算中值误差在0.060 m以内,方向推算平均绝对误差最小为3.06°,位置推算平均误差为0.2353 m,取得较好的室内步行状态识别与定位性能.

空间引力波星载望远镜测试与评估技术研究进展

在空间引力波探测任务中,星载望远镜承担太空超长干涉光路双向光束准直的重要作用。空间引力波探测对星载望远镜提出了pm级光程稳定性和低于10^(-10)级后向杂散光水平的极高要求。超高水平指标要求超过了当前测试技术的精度极限,因此,针对星载望远镜发展测试与评估技术并开展系统超高精度测试是空间引力波探测计划成功的重要前提。本文在概述国内外在研的空间引力波探测星载望远镜研制情况的基础上,重点围绕星载望远镜的核心技术指标——光程稳定性和后向杂散光,介绍了在研望远镜的测试技术发展现状和已经取得的部分测试成果,以及各研究单位进一步的测试计划,为我国的空间引力波探测的星载望远镜测试与评估技术发展提供参考。

基于GBK标量散射模型的超光滑光学元件表面特性参数预测方法

在引力波探测系统中,要实现对引力波的高精度测量,超光滑光学元件散射特性至关重要。然而,现有光学元件表面特性参数测量方法难以满足超光滑光学元件的测量需求。针对这一难题,提出了一种基于GBK(Generalized Beckmann-Kirchhoff)标量散射模型的光学元件表面特性参数预测方法。利用GBK标量散射模型和基于光腔衰荡技术的表面散射测量方法,建立了表征超光滑光学元件表面特性参数(表面粗糙度和自相关长度)的方程组,进而利用图解法求解得到超光滑光学元件的表面特性参数。为了验证预测方法的适应性,对不同表面特性参数下的多种待测光学元件进行了预测,获得了不同表面特性参数下元件表面粗糙度和自相关长度预测值的相对误差曲线。结果表明:表面粗糙度在0.1064~1.064 nm范围内时,其预测值相对误差均在1%以内;自相关长度在1064~3192 nm范围内时,其预测值相对误差均在1%以内。因此,在文中提出的表面特性参数范围内,该预测方法能快速准确预测超光滑光学元件的表面特性参数,具有较好的适应性和有效性,可为引力波探测系统中超光滑光学元件散射特性的测量提供参考。

钢桥面板顶板贯穿裂纹长度涡流检测及其影响因素

针对正交异性钢桥面板顶板贯穿型疲劳裂纹隐蔽性强、难以精确检测的问题,提出通过涡流检测方法对裂纹长度进行测量。首先,对试验获取的涡流检测信号进行分析,探究信号特征与裂纹长度之间的关系,据此拟合出裂纹长度的测量公式,并通过有限元模拟对其有效性进行验证;其次,研究线圈参数与扫查速度对检测效果的影响;最后,通过有限元模拟分析了水平裂纹相邻顶点间距、裂纹与线圈中心的偏移距离、裂纹与线圈扫查方向的夹角对检测结果的影响。结果表明:增加圆柱形线圈外径与内径之比并适当减小线圈内阻可提高检测灵敏度;裂纹长度的测量值随着线圈扫查速度的增大而减小;水平裂纹相邻顶点间距越大,越利于精确测量裂纹长度;随着裂纹与线圈中心偏移距离的增加,检测信号的变化幅值逐渐减小,裂纹长度测量值也随之下降;此外,线圈扫查方向与裂纹长度方向的夹角越大,越不利于检测。该钢桥面板疲劳裂纹长度涡流检测方法可实现高效、便捷、精准的无接触式检测,为钢桥的运营维护提供相关技术参考。

基于IMM-KF算法改进的欺骗式干扰检测算法

针对基于接收机基线长度的欺骗干扰检测方法在短基线和低定位精度下检测性能差的问题,提出了一种交互多模型卡尔曼滤波(Interactive Multi-model Kalman Filtering,IMM-KF)算法改进的欺骗式干扰检测算法。该方法通过IMM-KF算法对两个接收机天线的位置信息进行最优估计,提高基线解算精度,从而提升基于基线长度的欺骗干扰检测方法的检测性能。首先,详细分析了基于基线长度的欺骗干扰检测方法的数学原理;其次,建立欺骗检测算法模型,并对其性能进行仿真分析;然后,根据算法应用场景引入IMM-KF算法优化基线长度估计量;最后,针对不同的基线长度和定位精度进行仿真实验,对算法进行性能评估。仿真结果表明,该算法可以在接收机伪距测量精度为0.1 m、基线长度为0.5 m的情况下达到86%的检测成功率,而传统算法在此情况下检测成功率仅为10%。

短包隐蔽通信下抗检测-干扰式攻击策略

为应对潜在的检测-干扰式恶意攻击,提出了一种基于短包隐蔽通信的抗检测-干扰攻击策略,通过隐藏无线传输行为的存在来实现抗干扰。考虑到干扰机采用Shewhart变点检测实时判断源节点的传输行为,当判决源节点正在传输数据包后启动人工干扰。为隐藏无线传输行为,源节点随机选择一个起始时刻以低功率、低速率传输有限长编码数据包。数据包发送功率越小、编码长度越短,隐蔽性越强,抗检测-干扰式攻击性能越好,但发送功率小和编码长度短使得接收端数据包译码错误概率较大。以有效传输速率最大为目标对数据包发送功率和编码长度进行优化设计。结果表明,存在最优的发送功率和编码长度使系统在传输隐蔽性和可靠性之间取得最优折衷。数值仿真结果验证了理论分析,证明了所提方案的有效性以及优化数据包编码长度和发送功率的必要性。

基于旁孔透射法的隧道锚杆长度检测方法

锚杆的锚固长度是隧道围岩支护过程中重要指标,由于锚杆的检测长度长期受到现场施工环境的限制,传统检测方法往往会对锚杆锚固产生损伤,借鉴旁孔投射法原理,利用激振波接收处理器、激振锤、首波接收器、水听器装置组成锚杆检测仪器,通过波传导时间差来计算锚杆的长度,从而对锚杆锚固长度进行检测,并利用数值模拟软件ABAQUS进行数值仿真分析,验证旁孔投射法原理检测锚杆锚固长度的可行性。

心率变异性信号的检测与仿真分析

心率变异性信号易受干扰而导致严重失真,为了提升心率变异性信号检测系统的抗干扰性能和检测效率,更有效地“复原”失真的数据点,设计了基于奇异函数法和马尔可夫链预测法的心率变异性信号联合检测算法,利用样本序列进行了仿真实验,并从样本序列截取数据长度(m)和分类数目(k)两个维度进行分析,仿真结果表明m和k对算法的时间复杂度(O)以及预测准确度(q)有极为重要的影响。在实际工程应用中,可参考仿真结果对系统的参数进行设定,从而实现检测速度和准确率的兼顾与平衡。

长江漫滩区预制桩桩长检测方法研究

预制桩属于地下隐蔽工程,其长度是保证预制桩承载性能、稳定性能和抗震性能的关键指标之一,多节预制桩由于桩长较长,往往难以开挖验证桩长是否符合设计要求。文章以南京长江漫滩区某工程为例,以静载试验与低应变法检测数据为基础,联合磁测井法和孔内摄像法对多节预制桩长度进行检测。检测结果表明,联合磁测井法和孔内摄像法探测多节预制桩长度是可靠的、有效的。

基于DLS曲线的探伤车伤损检测能力评估方法

为研究探伤车检测能力评估方法,提出了钢轨伤损埋藏深度(Depth)、超声波走波距离(Length)和伤损尺寸(Size)的DLS曲线建模方法,结合探伤车判伤最小走波距离,提出了探伤车对不同埋藏深度、不同尺寸钢轨伤损的检测能力的评估方法。以钢轨横孔为研究对象,采用仿真计算方法得到了不同埋藏深度和尺寸的钢轨横孔走波距离与回波幅值,绘制出钢轨横孔DLS曲线。通过比较钢轨伤损的走波距离比与最小走波距离比,可评估钢轨伤损是否被有效检出。