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Glycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid chromatography used in electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS), can be used to separate stereoisomeric lipids. Generating LC-ESI-DMS-MS/MS methods for lipidomic analyses is exceedingly difficult demanding intensive manual optimization of DMS parameters that depend on the availability of synthetic lipid standards. Where synthetic standards do not exist, method development is not possible. To address this challenge, I developed a supervised in silico machine learning approach to accelerate method development for ion mobility-based quantification of lipid stereoisomers. I hypothesized that supervised neural network models could be used to learn the relationships between lipid structural characteristics and optimal DMS machine parameter values thereby reducing the total number of empirical experiments required to develop a DMS method and enabling users to “predict” DMS parameters for analytes that lack synthetic standards. Specifically, this thesis describes a supervised learning approach that learns the relationship between two DMS machine parameter values (separation voltage and compensation voltage) and two lipid structural features (N-Acyl chain length and degree of unsaturation). I describe here, iDMS, an algorithm that was trained on 17 lipid species, and can further simulate results of DMS manual method development and suggest optimal parameter values for 47 lipid species. This approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples.

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With the advancement of autonomous driving research, 3D detection based on LiDAR point cloud has gradually become one of the top research topics in the field of artificial intelligence. Compared with RGB cameras, LiDAR point cloud can provide depth information, while RGB images can provide denser resolution. Features from LiDAR and cameras are considered to be complementary. However, due to the sparsity of the LiDAR point clouds, a dense and accurate RGB/3D projective relationship is difficult to establish especially for distant scene points. Recent works try to solve this problem by designing a network that learns missing points or dense point density distribution to compensate for the sparsity of the LiDAR point cloud. During the master’s exploration, we consider addressing this problem from two aspects. The first is to design a GAN(Generative Adversarial Network)-based module to reconstruct point clouds, and the second is to apply regional point cloud enhancement based on motion maps. In the first aspect, we propose to use an imagine-and-locate process, called UYI. The objective of this module is to improve the point cloud quality and is independent of the detection stage used for inference. We accomplish this task through a GAN-based cross-modality module that uses image as input to infer a dense LiDAR shape. In another aspect, inspired by the attention mechanism of human eyes, we use motion maps to perform random augmentation on point clouds in a targeted manner named motion map-assisted enhancement, MAE. Boosted by our UYI and MAE module, our experiments show a significant performance improvement in all tested baseline models. In fact, benefiting from the plug-and-play characteristics of our module, we were able to push the performance of the existing state-of-the-art model to a new height. Our method not only has made great progress in the detection performance of vehicle objects but also achieved an even bigger leap forward in the pedestrian category. In future research, we will continue to explore the feasibility of spatio-temporal correlation methods in 3D detection, and 3D detection related to motion information extraction could be a promising direction.

Artificial neural network (ANN) is a machine learning approach where parameters, i.e., frequency parameters and amplitude parameters, are learnt during the training process. Random features model is a special case of ANN that the structure of random features model is as same as ANN’s but the parameters’ learning processes are different. For random features model, the amplitude parameters are learnt during the training process but the frequency parameters are sampled from some distributions. If the frequency distribution of the random features model is well-chosen, both models can approximate data well. Adaptive random Fourier features with Metropolis sampling is an enhanced random Fourier features model which can select appropriate frequency distribution adaptively. This thesis studies Rectified Linear Unit and sigmoid features and combines them with the adaptive idea to generate another two adaptive random features models. The results show that using the particular set of hyper-parameters, adaptive random Rectified Linear Unit features model can also approximate the data relatively well, though the adaptive random Fourier features model performs slightly better. / I artificiella neurala nätverk (ANN), som används inom maskininlärning, behöver parametrar, kallade frekvensparametrar och amplitudparametrar, hittasgenom en så kallad träningsprocess. Random feature-modeller är ett specialfall av ANN där träningen sker på ett annat sätt. I dessa modeller tränasamplitudparametrarna medan frekvensparametrarna samplas från någon sannolikhetstäthet. Om denna sannolikhetstäthet valts med omsorg kommer båda träningsmodellerna att ge god approximation av givna data. Metoden Adaptiv random Fourier feature[1] uppdaterar frekvensfördelningen adaptivt. Denna uppsats studerar aktiveringsfunktionerna ReLU och sigmoid och kombinerar dem med den adaptiva iden i [1] för att generera två ytterligare Random feature-modeller. Resultaten visar att om samma hyperparametrar som i [1] används så kan den adaptiva ReLU features-modellen approximera data relativt väl, även om Fourier features-modellen ger något bättre resultat.

Every year there are thousands of aviation accidents along with hundreds of human deaths that happen around the world. While the data is sparse, it is well documented that many of these happen from emergency landings gone awry. While pilots can generally make great landings in clear daytime conditions, they are significantly handicapped when it comes to landing at night or amongst poor visibility conditions. Due to the nature of this problem and some of the large scale advances in software technology we propose a solution that provides a significant improvement from the status quo. Using transfer learning on neural networks to classify satellite images along with terrain elevation data from the USGS we are able to recreate maps that can readily direct pilots to locations that are relatively flat and lack structures or vegetation. Using San Luis Obispo as our data set we confirmed that we could correctly classify at least 93\% of landable terrain and then identified areas within that area that could safely be used to land a plane. We then transfer this data into a 3D rendering program that allows us to visualize what is happening. In additional to visualizing where the landing paths are we also create a landing algorithm that demonstrates how the plane will maintain its current glide path and navigate to a successful landing while avoiding obstacles.

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<p>Roller bearings are the common components used in the mechanical systems for mechanical processing and production. The running state of roller bearings often determines the machining accuracy and productivity on a manufacturing line. Roller bearing failure may lead to the shutdown of production lines, resulting in serious economic losses. Therefore, the research on roller bearing fault diagnosis has a great value. This thesis research first proposes a method of signal frequency spectral resampling to tackle the problem of bearing fault detection at different rotating speeds using a single speed dataset for training the network such as the one dimensional convolutional neural network (1D CNN). Second, this research work proposes a technique to connect the graph structures constructed from spectral components of the different bearing fault frequency bands into a sparse graph structure, so that the fault identification can be carried out effectively through a graph neural network in terms of the computation load and classification rate. Finally, the frequency spectral resampling method for feature extraction is validated using our self-collected datasets. The performance of the graph neural network with our proposed sparse graph structure is validated using the Case Western Reserve University (CWRU) dataset as well as our self-collected datasets. The results show that our proposed method achieves higher bearing fault classification accuracy than those recently proposed by other researchers using machine learning approaches and neural networks.</p>