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161	Combined Computational-Experimental Design of High-Temperature, High-Intensity Permanent Magnetic Alloys with Minimal Addition of Rare-Earth Elements Jha, Rajesh 20 May 2016 (has links) AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys. AlNiCo magnets Data-driven Materials Science Composition-property relationship Materials Genome Initiative (MGI-ICME) Meta-model (Response Surfaces) Evolutionary (Genetic) Algorithms Multi-objective Optimization Pareto Optimality Principal Component Analysis (PCA) Heirarchichal Clustering Analysis (HCA) Categorical Data Analysis Design of Experiments and Sample Surveys Metallurgy Other Materials Science and Engineering Statistical Methodology Theory and Algorithms
162	A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features Kaufman, Jaime C. 01 January 2014 (has links) Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity. Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations. In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended. Thesis University of North Florida UNF music collaborative filtering collective intelligence content-based filtering MFCC recommend information retrieval database Pandora Last.fm music recommendation system UNF Databases and Information Systems Software Engineering Theory and Algorithms
163	Characterization of Novel Antimalarials From Compounds Inspired By Natural Products Using Principal Component Analysis (PCA) Balde, Zarina Marie G 01 January 2018 (has links) Malaria is caused by a protozoan parasite, Plasmodium falciparum, which is responsible for over 500,000 deaths per year worldwide. Although malaria medicines are working well in many parts of the world, antimalarial drug resistance has emerged as one of the greatest challenges facing malaria control today. Since the malaria parasites are once again developing widespread resistance to antimalarial drugs, this can cause the spread of malaria to new areas and the re-emergence of malaria in areas where it had already been eradicated. Therefore, the discovery and characterization of novel antimalarials is extremely urgent. A previous drug screen in Dr. Chakrabarti's lab identified several natural products (NPs) with antiplasmodial activities. The focus of this study is to characterize the hit compounds using Principal Component Analysis (PCA) to determine structural uniqueness compared to known antimalarial drugs. This study will compare multiple libraries of different compounds, such as known drugs, kinase inhibitors, macrocycles, and top antimalarial hits discovered in our lab. Prioritizing the hit compounds by their chemical uniqueness will lessen the probability of future drug resistance. This is an important step in drug discovery as this will allow us to increase the interpretability of the datasets by creating new uncorrelated variables that will successively maximize variance. Characterization of the Natural Product inspired compounds will enable us to discover potent, selective, and novel antiplasmodial scaffolds that are unique in the 3-dimensional chemical space and will provide critical information that will serve as advanced starting points for the antimalarial drug discovery pipeline. characterization novel antimalarial principal component analysis PCA principal component analysis drug discovery malaria natural product inspired compounds drugs macrocycles kinase inhibitors Analysis Bioinformatics Medicinal Chemistry and Pharmaceutics Medicinal-Pharmaceutical Chemistry Other Chemicals and Drugs Other Immunology and Infectious Disease Parasitic Diseases Parasitology Polycyclic Compounds Statistical Models Theory and Algorithms
164	Anomaly Detection in RFID Networks Alkadi, Alaa 01 January 2017 (has links) Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing techniques, namely Bayesian blocks and Knuth’s algorithms, over time-tagged events and compares them in the context of rate-based anomaly detection. This was accomplished using data from experimental RFID readings and comparing (1) the event counts versus time if using the smoothed curves versus empirical histograms of the raw data and (2) the threshold-dependent alert-rates based on inter-arrival times obtained if using the smoothed curves versus that of the raw data itself. Results indicate that both algorithms adequately model RFID traffic in which inter-event time statistics are stationary but that Bayesian blocks become superior for traffic in which such statistics experience abrupt changes. Thesis University of North Florida UNF RFID RFID security NFC anomaly detection security Bayesian blocks Bayesian statistics Knuth Knuth's rule RFID traffic piecewise constant models security standards RFID networks ISO standards IEC 29167 standards tag-reader sessions active attacks tag cloning replay attacks piecewise linear models RFID command arrivals traffic characterization intrusion detection Radiofrequency identification Bayes methods Data models mathematical model telecommunication traffic telecommunication security Computational Engineering Information Security Theory and Algorithms

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