Monitoring the waste to energy plant using the latest AI methods and tools

Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring. In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient. Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme

Segmentation of striatal brain structures from high resolution pet images

Dissertation presented at the Faculty of Science and Technology of the New University of Lisbon in fulfillment of the requirements for the Masters degree in Electrical Engineering and ComputersWe propose and evaluate fully automatic segmentation methods for the extraction of striatal brain surfaces (caudate, putamen, ventral striatum and white matter), from high resolution positron emission tomography (PET) images. In the preprocessing steps, both the right and the left striata were segmented from the high resolution PET images. This segmentation was achieved by delineating the brain surface, finding the plane that maximizes the reflective symmetry of the brain (mid-sagittal plane) and, finally, extracting the right and left striata from both hemisphere images. The delineation of the brain surface and the extraction of the striata were achieved using the DSM-OS (Surface Minimization – Outer Surface) algorithm. The segmentation of striatal brain surfaces from the striatal images can be separated into two sub-processes: the construction of a graph (named “voxel affinity matrix”) and the graph clustering. The voxel affinity matrix was built using a set of image features that accurately informs the clustering method on the relationship between image voxels. The features defining the similarity of pairwise voxels were spatial connectivity, intensity values, and Euclidean distances. The clustering process is treated as a graph partition problem using two methods, a spectral (multiway normalized cuts) and a non-spectral (weighted kernel k-means). The normalized cuts algorithm relies on the computation of the graph eigenvalues to partition the graph into connected regions. However, this method fails when applied to high resolution PET images due to the high computational requirements arising from the image size. On the other hand, the weighted kernel k-means classifies iteratively, with the aid of the image features, a given data set into a predefined number of clusters. The weighted kernel k-means and the normalized cuts algorithm are mathematically similar. After finding the optimal initial parameters for the weighted kernel k-means for this type of images, no further tuning is necessary for subsequent images. Our results showed that the putamen and ventral striatum were accurately segmented, while the caudate and white matter appeared to be merged in the same cluster. The putamen was divided in anterior and posterior areas. All the experiments resulted in the same type of segmentation, validating the reproducibility of our results

Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection

Abstract—This paper addresses a spatiotemporal pattern recognition problem. The main purpose of this study is to find a right representation and matching of action video volumes for categorization. A novel method is proposed to measure video-to-video volume similarity by extending Canonical Correlation Analysis (CCA), a principled tool to inspect linear relations between two sets of vectors, to that of two multiway data arrays (or tensors). The proposed method analyzes video volumes as inputs avoiding the difficult problem of explicit motion estimation required in traditional methods and provides a way of spatiotemporal pattern matching that is robust to intraclass variations of actions. The proposed matching is demonstrated for action classification by a simple Nearest Neighbor classifier. We, moreover, propose an automatic action detection method, which performs 3D window search over an input video with action exemplars. The search is speeded up by dynamic learning of subspaces in the proposed CCA. Experiments on a public action data set (KTH) and a self-recorded hand gesture data showed that the proposed method is significantly better than various state-ofthe-art methods with respect to accuracy. Our method has low time complexity and does not require any major tuning parameters. Index Terms—Action categorization, gesture recognition, canonical correlation analysis, tensor, action detection, incremental subspace learning, spatiotemporal pattern classification. Ç

Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors

This thesis presents novel methodological approaches for the automated segmentation of neurons from electron microscopic image volumes using machine learning techniques. New potentials for neural segmentation are revealed by incorporating (high-level) biological prior knowledge. This goes beyond the modeling of neural tissue which has been applied for the purpose of its segmentation, so far. Firstly, the V-Multicut algorithm is introduced which enables the consideration of topological constraints for segmented membranes. In this way, biologically implausible appearances of membranes are corrected. Secondly, this thesis proves that, in addition to local evidence and topological requirements for the detection of neural membranes, the consideration of high-level biological prior knowledge is beneficial. For this task, both the recently proposed Asymmetric Multiway Cut and the introduced Semantic Agglomerative Clustering algorithm are implemented and quantitatively evaluated. To be precise, the spatial separation of dendrites and axons in mammals is exploited to significantly improve the segmentation quality. Additionally, new ways to improve the scalability of the used algorithms are presented. All in all this thesis serves as another step towards fully automated segmentation of neurons and contributes to the field of connectomics