Two data-driven approaches based on the Fourier-transform infrared spectroscopy (FTIR) data are presented in this work to predict crude oil properties. The first approach is the combination of the principal component ...
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Two data-driven approaches based on the Fourier-transform infrared spectroscopy (FTIR) data are presented in this work to predict crude oil properties. The first approach is the combination of the principal component analysis (PCA) and the support vector regression (SVR), namely PCA-SVR. In the PCA-SVR, the PCA is employed to extract the high-dimension FTIR data to obtain lower-dimensional data. The lower-dimensional data is utilized as the inputs of the SVR to predict crude oil properties. The second approach is a hybrid model composed of the autoencoder and the SVR, namely Auto-SVR. In the Auto-SVR, the autoencoder is exploited to learn new representations for the dimensionality reduction of the FTIR data. The learned lower-dimensional representations are input into the SVR to predict crude oil properties. The presented data-driven approaches are used to predict fractions of light virgin naphtha (LVN), heavy virgin naphtha (HVN), kerosene (Kero), distillate, vacuum gas oil (VGO), and residual in crude oil. According to the obtained results, the presented methods can achieve accurate predictions with satisfactory prediction accuracy.
汽车电子控制系统(Automotive electronic control system)在汽车领域的重要性日益增强,它让汽车变得更加安全、舒适和节能。现阶段汽车电子控制系统正变得越来越复杂,而汽车电子控制系统中以动力总成模块PCM最为复杂和核心,为了解决PC...
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汽车电子控制系统(Automotive electronic control system)在汽车领域的重要性日益增强,它让汽车变得更加安全、舒适和节能。现阶段汽车电子控制系统正变得越来越复杂,而汽车电子控制系统中以动力总成模块PCM最为复杂和核心,为了解决PCM控制功能复杂化与快速开发之间的矛盾,V模式开发流程开始被各大汽车公司应用到汽车电子控制系统中。HIL是V模式开发流程中非常重要的环节,通过HIL仿真测试系统就可以在实验室对PCM进行测试,极大的缩短了开发的时间周期、降低了开发的成本。对驾驶行为进行定性或定量的建模与评估是无人驾驶、智能交通等领域的重要课题和关键技术,在车辆性能测试、辅助驾驶和异常驾驶行为的诊断中都有十分重要的应用价值。从汽车制造厂商的角度来看,了解汽车驾驶员的驾驶行为偏好对优化汽车的驾驶行为体验和个性化的提升车辆的动力学性能都有十分重要的意义。本文通过建立驾驶行为的标准化模型对驾驶员的驾驶行为偏好进行学习,将不同路段、不同环境的驾驶行为映射到联邦车辆测试曲线中,实现对不同驾驶行为的标准化。本文在充分了解了汽车测试的HIL系统发展现状的基础上,结合自身对PCM的开发需求,设计制作了基于NIPXI平台的硬件系统和上位机管理软件NI Verstand的HIL仿真测试系统。然后用autoencoder算法对不同驾驶风格的驾驶员采集到的驾驶数据进行驾驶行为标准化,最后用经过标准化后的驾驶数据对HIL系统进行整机测试,证明了该系统的有效性和准确性。
Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence to-sequence autoencoder (SA). These vector representations are shown to describe the sequential ph...
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ISBN:
(纸本)9781538646595
Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence to-sequence autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as spoken term detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch the phonetic structure from the audio segments of the target language if the source and target languages are similar. In STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.
Internet of Things (IoT) devices are mass-produced and designed for different applications, ranging from monitoring of the environment to on-demand electrical switches, and so on. These IoT devices are often heterogen...
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Internet of Things (IoT) devices are mass-produced and designed for different applications, ranging from monitoring of the environment to on-demand electrical switches, and so on. These IoT devices are often heterogeneous in nature, only to receive updates at infrequent intervals, and can remain 'out of sight' on a home or office network for extended periods. In other words, security and privacy are two key (research and operational) challenges in IoT systems. In recent years, there have been attempts to design deep learning-based solutions to mitigate limitations associated with detection systems designed for typical operational technology (OT) systems, although a number of challenges remain. This paper proposes a federated-based approach that employs a deep autoencoder to detect botnet attacks using on-device decentralized traffic data. Through the suggested federated solution, privacy is addressed by ensuring the device's data is not transferred or moved off the network edge. Instead, the machine learning computation itself is brought to where the data is born (i.e. the edge layer), with the added benefit of data security. We demonstrate that using our proposed model, we can achieve up to 98% accuracy rate in the anomaly detection when using features such as source IP, MAC-IP, and destination IP, etc., for training. The overall comparative performance analysis between our decentralized proposed approach and a centralized format demonstrates a significant improvement in the accuracy rate of attack detection.
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