新型机车信号检测环线安装方法探究

针对目前传统的机车信号测试环线安装在钢轨内侧腰部存在一定的磁屏蔽、安装相对不是很容易、施工钢轨穿孔困难等缺点,结合具体施工等实际因素,设计并实现了一种成本较为低廉、安装方便、削弱构成磁屏蔽的一种机车信号测试环线支架法。该方法采用自主设计,特定加工的环线支架固定在钢轨上,环线通过支架安装在钢轨外侧,高度基本与钢轨高度水平。实践证明,这种环线支架法较之前传统的安装方式使用效果良好、信号检测稳定,设计经过实践检验基本合理,能满足机车信号的正常检测,在一定程度上优化了设计,方便了施工,达到了预期目标。

半径约束遗传算法靶环环线检测方法

针对自动报靶系统环线拟合效果差、准确度低的问题,提出了1种半径约束遗传算法环线检测算法。该算法以胸环靶环线分布特征为基础,以已知半径为约束,首先,基于距离空间聚类法划分靶环区域,采用边界生长法消除环线附近孤立点和短曲线;然后,利用平行线检测方法提取各环线曲线基元;最后,采用遗传算法连接具有最大适应度的共圆基元曲线,完成各环线的最优连接。实验结果表明:与传统的基于Hough变换和最小二乘法的圆拟合方法相比,本算法拟合环线圆形度准确度相对误差最大降低8%,明显提高了环线拟合的精度和准度。

对称感应环线断线检测方法及装置

介绍了应用在FZL200型ATC系统的对称感应环线子系统的环线断线检测方法及装置,重点描述环线断线检测装置的结构和各模块的工作原理。

磁屏蔽对机车信号环线测试的影响及改进方案

针对传统的机车信号测试环线安装在钢轨内侧轨腰,存在磁屏蔽、安装成本高、施工和维护困难等缺点,在分析了磁屏蔽产生的原因后,设计并实现了一种综合成本低廉、安装方便,避免构成磁屏蔽的机车信号环线测试的技术方案。该技术方案在钢轨上或者水泥地面上通过尼龙支架固定测试环线。实践证明,使用尼龙支架固定测试环线的安装方式效果良好、设计合理,能满足机车信号的正常检测,达到了优化的预期目标。

TRIPLET CIRCULAR HOUGH TRANSFORM FOR CIRCLE DETECTION

A new method, triplet circular Hough transform, is proposed for circle detection in image processing and pattern recognition. In the method, a curve in an image is first detected. Next, a sequence of three points on the curve are selected, a sequence of parameters (a,b,r) corresponding to the three points are calculated by solving the circle equation of the curve, and two 2-D accumulators A(a,b) and R(a,b) are accumulated with 1 and r, respectively. Then the parameters {(a, b, r)} of the circles fitting the curve are determined from A(a,b) and R(a,b) by searching for the local maximum over A(a,b). Because no computation loops over center (a, 6) and/or radius r are needed, the method is faster than the basic and directional gradient methods. It needs also much smaller memory for accumulation.

Feasibility investigation on deep ocean compact autonomous Raman spectrometer developed for in-situ detection of acid radical ions

A newly developed Deep Ocean Compact Autonomous Raman Spectrometer （DOCARS） system is introduced and used for in-situ detection of acid radical ions in this paper. To evaluate the feasibility and capability of DOCARS for quantitative analysis of the acid radical ions in the deep ocean, extensive investigations have been carried out both in laboratory and sea trials during the development phase. In the laboratory investigations, Raman spectra of the prepared samples （acid radical ions solutions） were obtained, and analyzed using the method of internal standard normalization in data processing. The Raman signal of acid radical ions was normalized by that of water molecules. The calibration curve showed that the normalized Raman signal intensity of SO4^2-, NO3^-, and HCO^-3 increases linearly as the concentration rises with correlation coefficient R^2 of 0.99, 0.99, and 0.98 respectively. The linear function obtained from the calibration curve was then used for the analysis of the spectra ,data acquired in the sea trial under a simulating chemical field in the deep-sea environment. It was found that the detected concentration of NO3 according to the linear function can reflect the concentration changes of NO~ after the sample was released, and the detection accuracy of the DOCARS system for SO^2-_4 is 8%. All the results showed that the DOCARS system has great potential in quantitative detection of acid radical ions under the deep-sea environment, while the sensitivity of the DOCARS system is expected to be improved.

Cyclostationary Feature Detection Based Spectrum Sensing Algorithm under Complicated Electromagnetic Environment in Cognitive Radio Networks

This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.

Scanning electron microscopy coupled with energydispersive X-ray spectrometry for quick detection of sulfuroxidizing bacteria in environmental water samples

Detection of sulfur-oxidizing bacteria has largely been dependent on targeted gene sequencing technology or traditional cell cultivation, which usually takes from days to months to carry out. This clearly does not meet the requirements of analysis for time-sensitive samples and/or complicated environmental samples. Since energy-dispersive X-ray spectrometry(EDS) can be used to simultaneously detect multiple elements in a sample, including sulfur, with minimal sample treatment, this technology was applied to detect sulfur-oxidizing bacteria using their high sulfur content within the cell. This article describes the application of scanning electron microscopy imaging coupled with EDS mapping for quick detection of sulfur oxidizers in contaminated environmental water samples, with minimal sample handling. Scanning electron microscopy imaging revealed the existence of dense granules within the bacterial cells, while EDS identified large amounts of sulfur within them. EDS mapping localized the sulfur to these granules. Subsequent 16S rRNA gene sequencing showed that the bacteria detected in our samples belonged to the genus Chromatium, which are sulfur oxidizers. Thus, EDS mapping made it possible to identify sulfur oxidizers in environmental samples based on localized sulfur within their cells, within a short time(within 24 h of sampling). This technique has wide ranging applications for detection of sulfur bacteria in environmental water samples.

Approaches to Detecting Gene-Environment Interactions in Human Variation Using Genetic Engineering, Remote Sensing and GIS

Elucidation of the relationships between genetic polymorphisms and environmental exposures can provide insights into the pathways and mechanisms underlying complex traits. A new approach was used to detect G×E （gene-environment） interactions involved in human skin pigmentation variation to better understand the adaptive evolution of skin pigmentation. Specifically, we used genetic engineering, remote UVR （ultraviolet radiation） sensing and GIS （geographic information systems） to integrate the analysis of genetic and environmental factors into a coherent biological framework. Since we expected to generate large datasets for this multidimensional analysis, we used PCA （principal components analysis） as a spatial statistical analysis technique for analyzing the G×E interactions. The results suggest that skin pigmentation may be affected by mutations induced by UVR and support the hypothesis that global variation in skin pigmentation may be the result of localized adaptation to different UVR conditions via natural selection. Analyzing the relationships between heterozygous frequencies for SNP （single nucleotide polymorphism） loci and seasonal UVR levels as the environment changes will help elucidate the selective mechanisms involved in the UVR-induced evolution of skin pigmentation. Skin pigmentation fulfills the criteria for a successful evolutionary G×E interactions model.