Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time–frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.     

Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into (bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). On the evaluation set, an accuracy of 64.0% from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0% baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.      

盆5气田地面集输系统缓蚀剂的优选及评价

新疆准噶尔盆地盆5气田自建成投产以来,随着生产时间的推移,气田产液量也随之增加,因产出水的矿化度较高,加之该气田天然气中含有CO₂气体,因此腐蚀情况较为严重,影响了气田的安全生产。针对该气田的腐蚀情况,通过室内模拟现场腐蚀环境,采用失重法实验研究了不同缓蚀剂对地面集输系统管材（20G钢）的缓蚀效果,筛选出了缓蚀效果较好的缓蚀剂CT2-4,并采用极化曲线和交流阻抗谱图对其做了电化学评价。结果表明：CT2-4浓度在100～175 mg/L时的缓蚀率可超过85%,防腐效果理想。该研究成果为盆5气田的安全生产提供了技术保障。     

基于时频分布的汉语语音关键频率分布研究

为了得到汉语语音关键频率的大体分布情况,提出了应用时频分布的方法来研究。采用了两种途径来分析语音信号:Adobe Audition3.0软件和自制MATLAB软件。两种途径分别得到语音信号的语谱图,通过比较分析,得出结论:绝大部分的汉语语音频率分布范围是0～5 000 Hz。本方法有需要改进的地方,如:能否得到更加细化、更加准确的频率分布情况。     

信号音检测在3G移动终端中的实现

将噪声消除系统应用在3G手机的下行链路时,在消除环境背景噪声的同时,发现下行信号音失真。这是因为噪声消除系统中的VAD算法对功率剧烈变化的信号音敏感造成的。经过详细研究信号音的时频特性,以及网络传输的误差因素,提出了一种新的适用于下行噪声抑制系统的信号音检测算法,这种算法利用每帧信号的频域功率变化和时域信号周期变化来检测信号音,经过理论的评估和实际的验证,该算法在不影响噪声消除系统的功能前提下,能准确检测出信号音信号,从而避免了失真的发生。     

基于小波语谱图分析的语音去噪技术

由于不同环境下噪声特性不同,多种环境下的语音去噪成为研究难点。提出一种基于小波语谱图分析的去噪技术。该方法的特点在于：利用小波变换的多分辨性对带噪语音进行多尺度分析,利用语谱图列自相关函数的特性划分语音段和噪声段,利用点连续检测法去除语音段残留的噪声。实验显示,小波语谱图分析去噪法对多种环境下的宽带噪声,抑制效果显著。     

基于微观角度探讨TPO防水卷材结构与热老化性能的关系

从微观角度探讨TPO防水卷材结构与老化性能的关系,通过傅里叶红外光谱分析(FTIR)、热失重分析(TG)和扫描电子显微镜(SEM)对其热老化失效形式进行了表征及研究。结果表明,TPO本身的结构以及助剂所造成的微观结构的差异对TPO防水卷材热老化性能的影响显著,在生产制造和实际工程应用中必须引起重视。     

气质联用仪在气体分析中的应用

通过实验论述了气质联用在气体分析中的应用,对硫化氢、氧硫化碳气体的线性、灵敏度和数据重复性进行了考察,探讨了气质联用仪用于气体定量分析的可靠性,并给出了其它气体的色谱分析条件及谱图。     

基于复解析子波变换（CAWT）的汉语语音谱图

复解析子波变换（CAWT）紧密地将希尔伯特变换（HT）与子波变换（WT）结合在一起,在信号分析处理方面具有良好的表现。本文将复解析子波复换应用于汉语语的谱分析中,实验结果表明,文中所构造的复子波语音谱图可以清晰地表征汉语语音的动态谱结构,谱图中的语音形态为进一步的语音识别提供了一种新的途径。     

黄粉虫成虫爬行和咬食活动声音采集分析研究

利用带有摄像装置的害虫声音采集平台,使用大震膜电容麦克风在隔音量约28 dB的隔音箱内对黄粉虫成虫的爬行和咬食活动的声音进行了提取,利用虚拟声卡万用仪对提取的声音文件进行了交流电压和频谱两方面的处理分析.测定结果表明:黄粉虫成虫咬食活动的声音脉冲信号的时间带有明显规律性,时间间隔约为0.6 s.爬行和咬食两种活动声音的交流电压有效值均值、变化范围都呈明显的倍数关系,爬行活动声音的交流电压有效值均值为0.0333V,高于咬食活动声音产生的电压有效值均值0.0136 V,表明无论黄粉虫成虫爬行还是咬食活动的声音频率都更趋向集中在低频段,咬食活动的声音频率的主峰值频率(70~93 Hz)低于爬行活动(140~180 Hz).因此,害虫活动具有多样性,相应产生的活动声音也具有其本身的特征和差别,利用本研究的声音检测方法,可以准确地检测这些害虫活动的特征声音,将为研究开发新型的储粮害虫声音检测技术提供有价值的参考数据.