复杂环境钻孔爆破振动的安全阈值及预估控制

爆破振动效应已经成为影响爆破作业的重要因素。鉴于爆破安全规程对爆破振动安全控制标准不全面,建议在爆破方案设计中,可根据现场周边环境分别采用爆破安全规程中的标准、保护对象设计抗振指标、地震烈度表中相应的振动速度以及特别敏感地段的人的感觉作为控制标准来确定合理的爆破振动安全阈值。其次,统一预估标准,保守预估爆破振动数值,作业规程中根据前期爆破振动监测数据确定合理的爆破振动预报公式中的参数,确保预报值接近且不小于爆破实测数据。对作业现场控制爆破振动的措施进行了分析探讨。     

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data, especially in the field of Synthetic Aperture Radar (SAR) image recognition. In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model, this paper proposes a semi-supervised learning method of threshold filtering. In the training process, through the two selections of data by the model, unlabeled data outside the category is filtered out to optimize the performance of the model. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and compared with existing several state-of-the-art semi-supervised classification approaches, the superiority of our method was confirmed, especially when the unlabeled data contained a large amount of out-of-category data.     

Centralized Excited States and Fast Radiation Transitions Reduce Laser Threshold in Carbon Dots

As a new type of solution-processed nano-laser material, carbon dots (CDs) have shown considerable potential in optical communication, laser displays, micro/nano processing, and biomedicine. Reducing the laser threshold of the gain material is of considerable significance for further development of CDs’ applications in the field of micro/nano lasers. A series of blue-emissive CDs (B-CDs) are synthesized by changing the molar ratios of the precursors (citric acid (CA): L-Cysteine (L-Cys)). B-CDs have a structure of carbon nanoparticles with their surface being modified with 5-oxo-3,5-dihydro-2Hthiazolo [3,2-a]pyridine-7-carboxylic acid (TPCA). The laser can only be generated when the molar ratio of the precursors is between 1:1 and 2:1. With an increase in this ratio, the laser threshold decreases from 341.6 to 165.5 mJ cm⁻². The decrease in the laser threshold is attributed to the increase in the radiation transition rate and centralized sp³-related excited state levels, which are favorable for light amplification and population inversion. These results will be instructional for the reasonably design of CDs-based laser materials and prompt their potential use in practical photonics.     

变压器故障检测方法的改进--模糊识别阈值原则法

三比值法是目前被普遍接受的判断变压器故障的有效方法,但这一方法仍存在一个不确定的模糊范围,文中根据变压器故障分解气体的相对比率,运用模糊识别阈值原则判断变压器的故障性质,获得了理想的结果。     

一种基于特征增强的自适应阈值R波检测算法

为提高心电信号R波检测准确度,提出了一种基于特征增强的R波检测算法。首先经过小波分析去除心电信号的高频噪声和基线漂移,然后对信号进行差分运算,选取各数据点前后连续10个差分的最大和最小值做乘积运算,达到增强R波特征的目的,最后设定两个自适应阈值,对全部数据完成检测。实验结果:经过MIT-BIH Arrhythmia Database数据验证,R波检测准确度Acc可达99.57%,敏感度Se高达99.76%,真阳性率+P高达99.82%。将得到的结果与已有文献中的方法进行比较,本文算法简单,实时性好,检测准确率高,更符合实际临床应用的需求。     

自适应多特征融合相关滤波目标跟踪

目的 针对现实场景中跟踪目标背景复杂、光照变化、快速运动、旋转等问题,提出自适应多特征融合的相关滤波跟踪算法。方法 提取目标的HOG（histogram of oriented gradients）特征和利用卷积神经网络提取高、低层卷积特征,借助一种自适应阈值分割方法评估每种特征的有效性,得到特征融合的权重比。根据权重系数融合每种特征的响应图,并据此得到目标的新估计位置,利用尺度相关滤波器计算目标尺度,得到目标尺度完成跟踪。结果 在OTB（object tracking benchmark）-2013公开数据集上进行实验,在对多特征融合进行分析的基础上,测试了本文算法在11种不同属性下的跟踪性能,并与当前流行的7种算法进行对比分析。结果表明,本文算法的成功率和精确度均排名第1,相较于基准算法DSST （discriminative scale space tracking）跟踪精确度提高了4%,成功率提高了6%。在复杂场景下比其他主流算法更具有鲁棒性。结论 本文算法以DSST相关滤波跟踪器为基准算法,借助自适应阈值分割方法评估每种特征的有效性,自适应融合两层卷积特征和HOG特征,使得判别性越强的单一特征融合权重越大,较好表达了目标的外观模型,在背景复杂、目标消失、光照变化、快速运动、旋转等场景下表现出较强的跟踪准确性。     

Haar-like特征双阈值Adaboost人脸检测

目的 针对基于Haar-like特征的Adaboost人脸检测算法,在应用于视频流时训练的时间较长,以及检测效率较低的问题,提出了一种基于区间阈值的Adaboost人脸检测算法。方法 通过运行传统的Adaboost算法对人脸图像Haar-like特征值进行提取分析后,对人脸样本与非人脸样本特征值进行比较,发现在某一特定的特征值区间内,人脸和非人脸区域能够得到准确区分,根据此特性,进行分类器的选择,在简化弱分类器计算步骤的同时,降低训练时间,提高对人脸的识别能力。除此之外,弱分类器的增强通过Adaboost算法的放大使得强分类器分类精度提高,与级联结构的配合使用也提升了最终模型检测人脸的准确率。结果 利用MIT（Massachusetts Institute of Technology）标准人脸库对改进Adaboost算法的性能进行验证,通过实验验证结果可知,改进后的Adaboost人脸检测算法训练速度提升为原来的1.44倍,检测率上升到94.93%,虚警率下降到6.03%。并且将改进算法在ORL（Olivetti Research Laboratory）、FERET（face recognition technology）以及CMU Multi-PIE（the CMU Multi-PIE face database）这3种标准人脸库中,分别与SVM（support vector machine）、DL（deep learning）、CNN（convolutional neural networks）以及肤色模型等4种算法进行了人脸检测对比实验,实验结果显示,改进后的Adaboost算法在进行人脸检测时,检测率提升了2.66%,训练所需时间减少至624.45 s,检测效果明显提升。结论 提出的基于区间阈值的Adaboost人脸检测算法,在分类器的训练和人脸检测方面都比传统的Adaboost算法性能更高,能够更好地满足人员较密集处（如球场等地）对多人脸同时检测的实际需求。     

针对网络恶意攻击并行入侵的仿真设计

为了解决传统网络攻击入侵方案在进行网络恶意攻击并行入侵时,存在攻击入侵成功率低、恶意攻击节点失效率高、网络攻击入侵耗时长的问题,提出针对网络恶意攻击并行入侵实验。分析网络恶意攻击中的重要攻击手段—虫洞攻击和并行入侵的网络结构;在抗原信号的基础上,利用树突状细胞算法构建异度方程,确定特征阈值,以确定的特征阈值作为选择入侵路径概率的基础,完成对网络恶意攻击并行入侵的估计。最后通过仿真设计,证明了所提方案相比传统方案的攻击入侵成功率高、恶意攻击节点失效率低、网络攻击入侵耗时短,说明了所提方案的攻击入侵效果更佳,可为今后的网络防御工作提供良好的参考。     

基于自适应阈值投影的双行车牌分割方法

Aiming at the problem that there are few algorithms and low correct segmentation rate of double row license plate number in China, an adaptive projection method for double row license plate segmentation is proposed. Firstly, the image of license plate in HSV (Hue Saturation Value) space is binarized. Secondly, the double line license plate is segmented into a single line license plate by the adaptive projection method. Finally, the characters on the license plate number are segmented. Experiments show that this method can effectively segment the double line license plate with white characters on blue background, red characters on white background, black characters on green background, and black characters on yellow background, and the correct segmentation rate of the double line electric bicycle brand is higher. At the same time, this method overcomes the shortcomings of the deep learning method such as long training time and a large number of data sets. The proposal t is a simple and efficient segmentation method.     

Hybrid threshold strategy‐based adaptive tracking control for nonlinear stochastic systems with full‐state constraints

This article focuses on the adaptive tracking control problem for a class of interconnected nonlinear stochastic systems under full‐state constraints based on the hybrid threshold strategy. Different from the existing works, we propose a novel pre‐constrained tracking control algorithm to deal with the full‐state constraint problem. First, a novel nonlinear transformation function and a new coordinate transformation are developed to constrain state variables, which can directly cope with asymmetric state constraints. Second, the hybrid threshold strategy is constructed to provide a reasonable way in balancing system performance and communication constraints. By the use of dynamic surface control technique and neural network approximate technique, a smooth pre‐constrained tracking controller with adaptive laws is designed for the interconnected nonlinear stochastic systems. Moreover, based on the Lyapunov stability theory, it is proved that all state variables are successfully pre‐constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed control algorithm.