基于MATLAB的语音信号基音周期检测的实现

本文针对语音信号基音周期检测进行分析,并使用matlab软件编程实现了语音信号的基音周期检测.在实现基音周期检测时使用中心削波法,该方法使语音信号基音周期检测更为可靠,并采用了三电平削波法减少基于自相关法的基音周期检测的乘法运算量.     

装备研制中的Bayesian网及其应用

针对装备研制过程中产生的大量试验和调试数据，提出采用Bayesian网挖掘各组成单元间的依赖关系，并对．Bayesian网学习中基于信息论的方法进行了改进，使确定网络拓扑结构的过程更加客观．学习得到Bayesian网后，分析了其在失效源判定和发现设计缺陷等方面的应用．     

一种噪声环境下的语音端点检测方法

利用短时过零率来检测清音,用短时能量来检测浊音,两者相配合便实现了信号信噪比较大情况下的端点检测。但是在信噪比较小的环境下,这两种方法便失去了作用。为了能在噪声环境下准确地检测出语音信号的端点,根据对含噪语音在时频域中的研究,提出了一种基于Matching pursuits时频分解算法的语音端点检测方法。该方法使用Matching pursuits算法对含噪信号进行分解,然后再对信号进行魏格纳变换,可以完全去除信号的魏格纳交叉干扰项,使得语音信号和噪声信号在时频平面上具有较直观明显的魏格纳能量分布,利用这个特点再进行端点检测,实验结果表明,该方法能在信噪比较低的情况下,准确地检测出语音信号的端点。     

基于EFICA的混合语音盲分离时域算法

卷积混合语音进行盲源分离时,不能直接应用独立分量分析（ICA）算法。文中采用一种新的卷积混合语音模型,对多通道混合语音使用近来提出时域EFICA的算法进行盲分离,然后利用聚类和重构算法来恢复源信号。通过真实语音实验表明,文中提出的算法能够有效的分离混合语音信号。     

基于近场声全息的载荷识别技术及试验验证

提出1种通过辐射声测量来识别激励源的基于近场声全息的载荷识别技术。针对所研究的圆筒结构,通过测量激励产生的辐射声场,使用近场声全息的声场理论建立内部声等效源,进而用基于等效源的近场声全息方法重构结构表面振速。将振速换算成法向加速度对激励进行识别,并与由力传感器直接测得的力谱进行比较,结果显示主要峰值频率处激励力的辨识误差均在5 d B以下,满足工程应用要求。     

基于HMM的实时语音识别方法研究

随着计算机技术和通信技术的快速发展，语音识别技术在国民经济中的各个领域得到了广泛的应用，并有相关产品的问世。但为了提高工作效率和节省企业的成本，有许多特定应用要与语音识别进行融合。针对企业报关系统的特点，采用了一种基于HMM模型的二级单字识别方法，解决了系统识别效率与识别稳定性的问题，使得该语音识别方法最终满足了报关系统的应用要求，并扼要介绍了词汇库维护、新人语音训练及建立语音新模型的过程。     

Bootstrap重采样Bayesian方法在白车身制造尺寸不合格率估计中的应用

白车身质量是汽车整车质量控制中的重要环节,针对白车身制造尺寸质量控制中检测数据属于小样本数据、数据处理分析不能采用一般大样本条件下统计分析方法的问题引入Bootstrap重采样Bayesian方法。通过对白车身尺寸质量的不合格率进行定义,分析简单计算、滑动计算、β分布Bayesian计算等3种估计不合格率的方法,引入Bootstrap重采样技术结合Bayesian方法进行不合格率的估计,并通过Matlab软件对4种算法进行仿真比较。仿真结果表明,Bootstrap重采样Bayesian方法的预测精度高于其余3种方法,适用于小样本情况下白车身制造尺寸不合格率的估计。最后通过一个实例演示了Bootstrap重采样Bayesian方法在白车身制造尺寸不合格率估计中的应用流程。     

低信噪比环境下的语音识别方法研究

单通道语音信号在信噪比较大的环境下经过增强后再识别,能表现出较高的识别率。但是在低信噪比环境下,增强后语音信号的识别率急剧下降。针对此种情况,提出了一种用在识别系统前端的语音增强算法,该增强算法将采集到的带噪语音信号先使用对数最小均方误差(Logarithmic Minimum Mean Square Error,Log MMSE)提高其信噪比,然后再利用改进的维纳滤波去除噪声残留并提升语音可懂度,最后用梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCC)和隐马尔科夫模型(Hidden Markov Model,HMM)对增强后的语音信号做特征提取并识别。实验分析结果表明,该方法能有效地抑制背景噪声并减少噪声残留,显著提升低信噪比环境下语音识别的准确性。     

网络处理器和通用处理器相结合的流量识别系统

根据网络处理器(NP)与通用处理器(GPP)的特点,提出了一种结合NP和GPP的流量管理系统结构,基于此系统结构,设计了一种流量识别系统,并描述了系统中核心部分——NP与GPP的通信机制的设计和实现。在这种系统中,NP作为快速数据通信进行数据包收发、流存储等处理,并根据需要将部分数据包通过21555非透明桥传给GPP;GPP作为慢速数据通道使用基于深度包检测的L7-filter模块对流量进行识别,并将识别的结果传给NP,NP根据结果对流量进行控制管理等操作。     

模态识别的Bayesian TDD-FFT法及其应用

针对Bayesian FFT(Fast Fourier Transform)在多自由度下的病态Hessian矩阵求逆和模态参数不确定性评估问题,提出了Bayesian TDD-FFT模态方法。提出Bayesian TDD(Time Domain Decomposition)方法,将多测点多自由度信号转变为单测点单自由度信号;结合Bayesian FFT和蒙特卡罗方法获取模态参数的最佳估计和后验概率分布,进行不确定性分析,并通过数值模拟验证了Bayesian TDD-FFT法的有效性;基于上海中心大厦实测数据,通过对比分析指出Bayesian TDD-FFT法能够获得Fast Bayesian FFT法的结果精度,同时发现频率和阻尼没有相关性,而激励谱密度和预测误差谱密度存在明显的相关性。     

Research on Detection Method of Interest Flooding Attack on Content Centric Network

To improve the attack detection capability of content centric network (CCN), we propose a detection method of interest flooding attack (IFA) making use of the feature of self-similarity of traffic and the information entropy of content name of interest packet. On the one hand, taking advantage of the characteristics of self-similarity is very sensitive to traffic changes, calculating the Hurst index of the traffic, to identify initial IFA attacks. On the other hand, according to the randomness of user requests, calculating the information entropy of content name of the interest packets, to detect the severity of the IFA attack, is. Finally, based on the above two aspects, we use the bilateral detection method based on non-parametric CUSUM algorithm to judge the possible attack behavior in CCN. The experimental results show that flooding attack detection method proposed for CCN can not only detect the attack behavior at the early stage of attack in CCN, but also is more accurate and effective than other methods.     

不同信息融合方法在结构损伤识别上的应用和分析

在工程结构的损伤探测领域,不同的信息融合方法和方式对结构的损伤敏感程度以及计算的复杂程度往往不同,而且适用条件也不同。为了解决以上问题,描述了基于结构损伤识别的功能信息融合模型,并在此基础之上采用了多种融合方法进行了数值仿真和分析。数值仿真结果表明,采用了信息融合技术的结构多损伤位置识别,可以产生比单一信息源更精确、更完全的估计和判决,而且不同的信息融合算法的应用往往取决于研究对象和实际条件的要求。     

Bayes classifiers for imbalanced traffic accidents datasets

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009–2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.     

Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel

Proper definition of certain material properties is a paramount issue for accurate simulation. However, the values of a material parameter are commonly uncertain due to multiple factors in practice. To obtain reliable material parameters, parameter identification via Bayesian theory has become an attractive framework and received more attention recently. Based on this frame, the determination of likelihood function is critical for posterior probability. Unfortunately, it is commonly difficult to be determined directly, especially for complex engineering problems. In this study, Bayesian formulas for material parameter identification are given. To make it feasible for real engineering problems, the least square-support vector regression surrogate and Monte Carlo Simulation are integrated to obtain the maximum likelihood estimation of likelihood function. The uncertainty of parameter identification is quantified via the Bayesian method. In two benchmarks, two cases with single and multiple uncertainty sources are used to propagate and quantify uncertainties in material parameters based on Bayesian approach. Moreover, the proposed method is used to identify the material parameters of advanced high strength steel used in vehicle successfully.     

A review of the effect of traffic and weather characteristics on road safety

Taking into consideration the increasing availability of real-time traffic data and stimulated by the importance of proactive safety management, this paper attempts to provide a review of the effect of traffic and weather characteristics on road safety, identify the gaps and discuss the needs for further research. Despite the existence of generally mixed evidence on the effect of traffic parameters, a few patterns can be observed. For instance, traffic flow seems to have a non-linear relationship with accident rates, even though some studies suggest linear relationship with accidents. On the other hand, increased speed limits have found to have a straightforward positive relationship with accident occurrence. Regarding weather effects, the effect of precipitation is quite consistent and leads generally to increased accident frequency but does not seem to have a consistent effect on severity. The impact of other weather parameters on safety, such as visibility, wind speed and temperature is not found straightforward so far. The increasing use of real-time data not only makes easier to identify the safety impact of traffic and weather characteristics, but most importantly makes possible the identification of their combined effect. The more systematic use of these real-time data may address several of the research gaps identified in this research.     

结构影响线识别：反问题可识别性分析与降维贝叶斯不确定性量化

结构影响线识别是移动荷载下既有结构评估的理论基础,其本质上是基于系统输入-输出含噪数据反向对静力系统指定截面的响应函数进行识别。已有研究虽然取得了进展,但它们在以下两个方面存在局限性：缺乏反问题可识别性分析;缺乏不确定性量化。反问题可识别性分析是为了厘清系统识别的参数的解的情况。不确定性量化是基于测量输入-输出含噪数据估计影响线参数的后验概率密度函数。针对上述两个局限性,该文在贝叶斯概率框架的基础上开展关于影响线识别的反问题可识别性分析与贝叶斯不确定性量化。该文进行基于直接参数化的影响线识别,包括系统输入与输出、反问题可识别性分析、参数最优值。经分析得出：一方面,直接参数化无法保证全局模型可识别;另一方面,现有方法即使是全局模型可识别的情况下也无法进行不确定性量化。为保证反问题是全局模型可识别且同时获取参数后验概率密度函数,该文提出基于降维贝叶斯不确定性量化的影响线后验识别,包括系统输入与输出重构、反问题可识别性分析、后验概率密度函数。该文进行模拟数据下新光大桥吊杆拉力影响线识别,与实测及模拟数据下简支梁桥应变影响线识别,验证提出方法的有效性。     

Estimating the distribution of external causes in hospital data from injury diagnosis

Hospital discharge datasets are a key source for estimating the incidence of non-fatal injuries. While hospital records usually document injury diagnosis (e.g. traumatic brain injury, femur fracture, etc.) accurately, they often contain poor quality information on external causes (e.g. road traffic crashes, falls, fires, etc.), if such data is recorded at all. However, estimating incidence by external causes is essential for designing effective prevention strategies. Thus, we developed a method for estimating the number of hospital admissions due to each external cause based on injury diagnosis. We start with a prior probability distribution of external causes for each case (based on victim age and sex) and use Bayesian inference to update the probabilities based on the victim's injury diagnoses. We validate the method on a trial dataset in which both external causes and injury diagnoses are known and demonstrate application to two problems: redistribution of cases classified to ill-defined external causes in one hospital data system; and, estimation of external causes in another hospital data system that only records nature of injuries. In comparison with age–sex proportional distribution (the method usually employed), we found the Bayesian method to be a significant improvement for generating estimates of incidence for many external causes (e.g. fires, drownings, poisonings). But the method, performed poorly in distinguishing between falls and road traffic injuries, both of which are characterized by similar injury codes in our datasets. While such stop gap methods can help derive additional information, hospitals need to incorporate accurate external cause coding in routine record keeping.     

Multi-level hot zone identification for pedestrian safety

According to the National Highway Traffic Safety Administration (NHTSA), while fatalities from traffic crashes have decreased, the proportion of pedestrian fatalities has steadily increased from 11% to 14% over the past decade. This study aims at identifying two zonal levels factors. The first is to identify hot zones at which pedestrian crashes occurs, while the second are zones where crash-involved pedestrians came from. Bayesian Poisson lognormal simultaneous equation spatial error model (BPLSESEM) was estimated and revealed significant factors for the two target variables. Then, PSIs (potential for safety improvements) were computed using the model. Subsequently, a novel hot zone identification method was suggested to combine both hot zones from where vulnerable pedestrians originated with hot zones where many pedestrian crashes occur. For the former zones, targeted safety education and awareness campaigns can be provided as countermeasures whereas area-wide engineering treatments and enforcement may be effective safety treatments for the latter ones. Thus, it is expected that practitioners are able to suggest appropriate safety treatments for pedestrian crashes using the method and results from this study.     

高速公路高架段桥下反射声识别及改善措施

台湾因为中山高速公路承载量逐年不足,拟通过兴建汐五高架来解决交通拥堵问题。该工程虽然有效地解决了交通拥堵问题,但高架桥的桥腹反而变成了平面路段交通噪声的反射面,使当地原有噪声问题更加严重。文章通过声源识别技术取得现场不同声源的贡献量,其中以反射声为环境噪声的主要增量来源。基于前述声源识别结果,针对桥底反射声设计了不同几何形状的桥底吸声装置(W型以及倒N型吸声装置),两种桥底吸声装置的声学检测结果显示,W型以及倒N型吸声装置的斜入射吸声性能较平面型吸声板好。另外依据实测成果可知,进行大型工程的噪声改善前,通过声源识别技术区分出主要和次要噪声源,并针对噪声源提出最佳的改善方案,可大幅提升改善工程的效益。     

Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics, namely lack of prior information (p (θ)), and reduced parameters (θ). Therefore, this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction. Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion. Finally, the results are extended to evaluate detection, accuracy and false alarm rate of the model against the subject matter expert model, Support Vector Machine (SVM), k nearest neighbor (k-NN) using simulated and ground-truth dataset. The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario. Results have shown that the proposed model consistently outperformed other models.