Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

A dynamic process model for the simulation of nanoparticle fractionation in tubular centrifuges is presented. Established state-of-the-art methods are further developed to incorporate multi-dimensional particle properties (traits). The separation outcome is quantified based on a discrete distribution of particle volume, elongation and flatness. The simulation algorithm solves a mass balance between interconnected compartments which represent the separation zone. Grade efficiencies are calculated by a short-cut model involving material functions and higher dimensional particle trait distributions. For the one dimensional classification of fumed silica nanoparticles, the numerical solution is validated experimentally. A creation and characterization of a virtual particle system provides an additional three dimensional input dataset. Following a three dimensional fractionation case study, the tubular centrifuge model underlines the fact that a precise fractionation according to particle form is extremely difficult. In light of this, the paper discusses particle elongation and flatness as impacting traits during fractionation in tubular centrifuges. Furthermore, communications on separation performance and outcome are possible and facilitated by the three dimensional visualization of grade efficiency data. Future research in nanoparticle characterization will further enhance the models use in real-time separation process simulation

Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches

Doctor of Philosophy (Computer Engineering), 2020Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC. In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

Design of a robotic arm for laboratory simulations of spacecraft proximity navigation and docking

The increasing number of human objects in space has laid the foundation of a novel class of orbital missions for servicing and maintenance. The main goal of this thesis is the development, building and testing of a robotic manipulator for the simulation of orbital maneuvers, with particular attention to Active Debris Removal (ADR) and On-Orbit Servicing (OOS). There are currently very few ways to reproduce microgravity in a non-orbital environment: among the main techniques, it is worth mentioning parabolic flights, pool simulations and robotic facilities. Parabolic flights allow to reproduce orbital conditions quite faithfully, but simulation conditions are very constraining. Pool simulations, on the other hand, have fewer constrictions in terms of cost, but the drag induced by the water negatively affects the simulated microgravity. Robotic facilities, finally, permit to reproduce indirectly (that is, with an appropriate control system) the physics of microgravity. State of the art on 3D robotic simulations is nowadays limited to industrial robots facilities, that bear conspicuous costs, both in terms of hardware and maintenance. This project proposes a viable alternative to these costly structures. Through dedicated algorithms, the system is able to compute in real time the consequences of these contacts in terms of trajectory modifications, which are then fed to the hardware in the loop (HIL) control system. Moreover, the governing software can be commanded to perform active maneuvers and relocations: as a consequence, the manipulator can be used as the testing bench not only for orbital servicing operations but also for attitude control systems, providing a faithful, real-time simulation of the zero-gravity behavior. Furthermore, with the aid of dynamic scaling laws, the potentialities of the facility can be exponentially increased: the simulation environment is not longer bounded to be as big as the robot workspace, but could be several orders of magnitude bigger, allowing for the reproduction of otherwise preposterous scenarios. The thesis describes the detailed mechanical design of the facility, corroborated by structural modeling, static and vibrational finite element verification. A strategy for the simulation of impedance-matched contacts is presented and an analytical control analysis defines the set of allowable inertial properties of the simulated entities. Focusing on the simulation scenarios, an innovative information theoretic approach for simultaneous localization and docking has been designed and applied for the first time to a 3D rendezvous scenario. Finally, in order to instrument the facility’s end effector with a consistent sensor suite, the design and manufacturing of an innovative Sun sensor is proposed

A comparison of multiple techniques for the reconstruction of entry, descent, and landing trajectories and atmospheres

The primary importance of trajectory reconstruction is to assess the accuracy of pre-flight predictions of the entry trajectory. While numerous entry systems have flown, often these systems are not adequately instrumented or the flight team not adequately funded to perform the statistical engineering reconstruction required to quantify performance and feed-forward lessons learned into future missions. As such, entry system performance and reliability levels remain unsubstantiated and improvement in aerothermodynamic and flight dynamics modeling remains data poor. The comparison is done in an effort to quantitatively and qualitatively compare Kalman filtering methods of reconstructing trajectories and atmospheric conditions from entry systems flight data. The first Kalman filter used is the extended Kalman filter. Extended Kalman filtering has been used extensively in trajectory reconstruction both for orbiting spacecraft and for planetary probes. The second Kalman filter is the unscented Kalman filter. Additionally, a technique for using collocation to reconstruct trajectories is formulated, and collocation's usefulness for trajectory simulation is demonstrated for entry, descent, and landing trajectories using a method developed here to deterministically find the state variables of the trajectory without nonlinear programming. Such an approach could allow one to utilize the same collocation trajectory design tools for the subsequent reconstruction.Ph.D.Committee Chair: Braun, Robert; Committee Member: Lisano, Michael; Committee Member: Russell, Ryan; Committee Member: Striepe, Scott; Committee Member: Volovoi, Vital