基于支持向量机的高速公路事件检测

针对交通领域中的事件检测(无事件模式和事件模式)模式识别问题,描述了支持向量机(SVM)支持的基本方法,建立了基于线性(linear function)、多项式(polynomial function)和径向基(radial basis function)3种核函数的事件检测SVM模型。采用高速公路路段I-880线圈数据集和事件数据集验证模型,结果发现:无论对于向北、向南或混合方向的事件检测,在3个SVM模型中,SVM(P)检测效果最好,SVM(L)最差。SVM算法具有避免局部最小,实现全局最优化,更好的泛化效果的优点,是高速公路事件检测的一种很有潜力的算法。     

小波分析在语音识别系统中的研究与应用

在语音识别系统的不同处理阶段,大部分可以使用小波分析技术进行实现,并且这种实现方法比传统的方法具有更好的效果.本文重点探讨了在端点检测、噪声去除以及参数提取过程中使用小波分析技术的原理及方法,并提出现阶段所存在的问题及解决方案.     

网络攻击检测中的机器学习方法综述

在详细介绍网络攻击检测机器学习原理的基础上,对现有的各种方法进行了评述,并结合网络攻击检测系统的应用需求,阐述了网络攻击检测系统机器学习技术的发展方向。     

Kullback-Leibler distance-based enhanced detection of incipient anomalies

Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters.     

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.     

超高效液相色谱串联质谱法同时测定叶菜中13种抗生素

建立了超高效液相色谱串联三重四极杆质谱(UPLC-MS/MS)同时检测叶菜类蔬菜中磺胺类、喹诺酮类、大环内酯类13种抗生素的分析方法.优化蔬菜基质中抗生素的提取液种类、净化方式、洗脱液种类,以及探讨环境变化和实验条件对实验结果的影响后,确定最优方案为称取500 mg的蔬菜样品,加入20 mL甲醇-Mcllvaine-Na_2EDTA溶液,超声和离心提取3次后,旋转蒸发至20 mL过HLB固相萃取柱.以6 m L甲醇洗脱,收集洗脱液,氮吹近干,用乙腈∶水(体积比2∶8)混合溶液复溶,离心、过滤后采用UPLC-MS/MS进行检测.流动相A相为1‰的甲酸水溶液,B相为乙腈,梯度洗脱.结果显示,小白菜在300ng·g~(-1)加标时,13种抗生素的加标回收率为38. 05%～96. 97%,150 ng·g~(-1)时,加标回收率为34. 52%～111. 10%,50 ng·g~(-1)时,加标回收率为41. 75%～107. 13%,相对偏差RSD值均小于8. 68%.检出限为0. 4～1 ng·g~(-1)、定量限为1. 5～3 ng·g~(-1),在不同种类的叶菜上也表现出较好的提取和回收效率,具有较好的分析应用效果.实际的样品检测发现,4种叶菜不同程度地检测出了抗生素的残留.总的含量范围(以干重计)为1. 59～32. 01 ng·g~(-1).小白菜所含抗生素含量最高,其次为白菜、生菜和芫荽.磺胺二甲基嘧啶为叶菜中含量最高的抗生素种类.各抗生素检测出的含量虽很低,但仍不能忽略长期食用带来的潜在健康风险.     

Electrochemical detection and degradation of ibuprofen from water on multi-walled carbon nanotubes-epoxy composite electrode

This work describes the electrochemical behaviour of ibuprofen on two types of multi-walled carbon nanotubes based composite electrodes,i.e.,multi-walled carbon nanotubes-epoxy(MWCNT) and silver-modified zeolite-multi-walled carbon nanotubes-epoxy(AgZMWCNT) composites electrodes.The composite electrodes were obtained using two-roll mill procedure.SEM images of surfaces of the composites revealed a homogeneous distribution of the composite components within the epoxy matrix.AgZMWCNT composite electrode exhibited the better electrical conductivity and larger electroactive surface area.The electrochemical determination of ibuprofen(IBP) was achieved using AgZMWCNT by cyclic voltammetry,differential-pulsed voltammetry,square-wave voltammetry and chronoamperometry.The IBP degradation occurred on both composite electrodes under controlled electrolysis at 1.2 and 1.75 V vs.Ag/AgCl,and IBP concentration was determined comparatively by differential-pulsed voltammetry,under optimized conditions using AgZMWCNT electrode and UV-Vis spectrophotometry methods to determine the IBP degradation performance for each electrode.AgZMWCNT electrode exhibited a dual character allowing a double application in IBP degradation process and its control.     

芳香化合物受体报道基因检测环境二噁英污染

以建立环境二噁英类污染物的筛查方法为目的,考察酵母基因报告系统方法在水、土壤和动物血清样品检测中的准确性和灵敏性。利用该系统,对TCDD标准品和已知毒性当量的环境污染二英标准品进行测试,建立和标准化测定的方法;对各种水样、土壤和动物TCDD暴露模型血清样品进行测定。结果显示报道系统能够较为准确地反映标准样品的毒性当量浓度,可检测到TCDD污染物的下限浓度为0.01 ng/L;可疑河涌水的二噁英类TEQ污染水平在可检测范围内,经过治理后污染程度下降;土壤检测到最高污染水平为(23.06±1.75)ng/kg;TCDD模型小鼠血清含量明显高出正常对照组(p<0.05,p<0.01和p<0.01)。结论:实验证明该报道系统可用于多种实际环境污染样品二噁英类TEQ检测,灵敏度可达ng级。由于方法具有简单、快速的特点,特别适合于环境可疑较高二噁英类污染物的快速筛查,在实际中有一定的应用价值。     

基于静态压降法量化表征某型贮箱气密性方法研究

目的 弥补运载火箭靶场测试中仅采用静态压降法定性判定贮箱气密性的不足,进一步提高贮箱气检结果的可信度。方法 提出基于静态压降法量化表征某型贮箱气密性的方法。首先,依据气体理想状态方程,得出基于静态压降法表征贮箱气密性的量化方法,并对计算结果进行验证。其次,综合考虑箱压变化、温度、大气压力、稳压时间和测量时间等影响因素,对上述量化方法进行修正,得到基于静态压降法表征某型贮箱气密性改进型量化计算方法。结果 改进后的计算方法精度更高。在此基础上,通过对限制改进型量化计算方法精度的影响因素进行分析,提出了进一步提高改进型量化计算方法精度的优化措施。结论 提出的基于静态压降法量化表征某型贮箱气密性方法可行,能够为后续更好开展靶场气检工作提供有力支撑。     

Fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method

Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential, which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.