Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of a 48V, 10kWh LiFePO4 battery management system for low voltage battery storage applications

The Development of a 48V, 10kWh LiFePO4 Battery Management System for Low Voltage Battery Storage Applications. B.V. Horn Department of Electrical and Electronical Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: MEng (Elec) December 2016 Renewable energy sources are a promising replacement for fossil fuels in future energy generation. To fully replace fossil fuels some form of energy storage is required. Chemical batteries offer an energy storage solution that is flexible and scalable for applications ranging from electric vehicles to residential and even commercial applications. Lithium-ion (Li-ion) battery technologies offers the most promising performance in terms of energy density, power density as well as cycle life. Unfortunately, Li-ion batteries are very sensitive to usage outside of the specified operating range. These specified parameters include the battery operating temperature, overand undervoltage thresholds as well as the maximum charge and discharge current. A Battery Management System (BMS) is thus required to monitor all of the above mentioned parameters and to ensure the battery is operated safely and within the specified range. A BMS’s primary focus is on the safety and protection of the battery, to minimise the risk of sudden failure and to maximise the life of the battery. The secondary function of the BMS is to perform battery diagnostics which could be used for more effective energy management of the battery. The objective of this project was to develop a BMS to be used within a Li-ion battery pack for a micro electric vehicle. The developed BMS was used for battery testing. The battery results were used to estimate the battery parameters off-line according to a specific battery model. The estimated battery parameters can be used as a basis for future energy management purposes. An on-line parameter estimation algorithm was also developed. The algorithm was proven to be successful with a simulation. Future work is required in order to simplify the practical implementation of the algorithm. Another objective of this project was to development a solid state contactor (SSC) that can be used to disconnect the battery from a load. Mechanical contactors, which presents some disadvantages, are typically used for high current applications. The proof ii Stellenbosch University https://scholar.sun.ac.za ABSTRACT iii of concept SSC was proven to be an efficient though costly substitute to replace the mechanical contactor within the BMS design. Stellenbosch University https://scholar.sun.ac.zaiii of concept SSC was proven to be an efficient though costly substitute to replace the mechanical contactor within the BMS design. Stellenbosch University https://scholar.sun.ac.za
An Effcient Algorithm for Mining Association Rules In Massive Datasets

Data mining, also known as Knowledge Discovery in Databases (KDD) is one of the most important and interesting research areas in 21st century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and World Wide Web. Nowadays frequent pattern discovery research focuses on finding co-occurrence relationships between items. Apriori algorithm is a classical algorithm for association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on the basis of Apriori algorithm. Most of the previous algorithms Apriori-like algorithm which generates candidates and improving algorithm strategy and structure but at the same time many of the researchers not concentrate on the structure of database. In this research paper, it has been proposed an improved algorithm for mining frequent patterns in large datasets using transposition of the database with minor modification of the Apriori-like algorithm. The main advantage of the proposed method is the database stores in transposed form and in each iteration database is filtered and reduced by generating the transaction id for each pattern. The proposed method reduces the huge computing time and also decreases the database size. Several experiments on real-life data show that the proposed algorithm is very much faster than existing Apriori-like algorithms. Hence the proposed method is very much suitable for the discovering frequent patterns from large datasets.
The influence of nickel ions on the electric behaviour of P2O5–ZnO–Na2O glassy system

Abstract P2O5–ZnO–Na2O glasses doped with various concentrations of NiO in the range from 0 to 6 mol % are prepared. The measurements of the electric properties of the samples are studied at different temperatures and frequencies. It is found that the dielectric constant decreases with increasing frequency as well as the electric modulus shows relaxation behaviour. The effect of different NiO concentrations and their chemical states in the glass network appears in the analysis of the activation energy, where Ni2+ ions have octahedral and tetrahedral states. From the frequency exponent result, the present samples exhibit correlated barrier hopping (CBH) conduction mechanism. The results show that some samples have great dielectric properties.
Entropy of Simulated Liquids Using Multiscale Cell Correlation

Accurately calculating the entropy of liquids is an important goal, given that many processes take place in the liquid phase. Of almost equal importance is understanding the values obtained. However, there are few methods that can calculate the entropy of such systems, and fewer still to make sense of the values obtained. We present our multiscale cell correlation (MCC) method to calculate the entropy of liquids from molecular dynamics simulations. The method uses forces and torques at the molecule and united-atom levels and probability distributions of molecular coordinations and conformations. The main differences with previous work are the consistent treatment of the mean-field cell approximation to the approriate degrees of freedom, the separation of the force and torque covariance matrices, and the inclusion of conformation correlation for molecules with multiple dihedrals. MCC is applied to a broader set of 56 important industrial liquids modeled using the Generalized AMBER Force Field (GAFF) and Optimized Potentials for Liquid Simulations (OPLS) force fields with 1.14*CM1A charges. Unsigned errors versus experimental entropies are 8.7 J K−1 mol−1 for GAFF and 9.8 J K−1 mol−1 for OPLS. This is significantly better than the 2-Phase Thermodynamics method for the subset of molecules in common, which is the only other method that has been applied to such systems. MCC makes clear why the entropy has the value it does by providing a decomposition in terms of translational and rotational vibrational entropy and topographical entropy at the molecular and united-atom levels.
Cosmological Consequences of a Parametrized Equation of State

We explore the cosmic evolution of the accelerating universe in the framework of dynamical Chern–Simons modified gravity in an interacting scenario by taking the flat homogeneous and isotropic model. For this purpose, we take some parametrizations of the equation of state parameter. This parametrization may be a Taylor series extension in the redshift, a Taylor series extension in the scale factor or any other general parametrization of ω . We analyze the interaction term which calculates the action of interaction between dark matter and dark energy. We explore various cosmological parameters such as deceleration parameter, squared speed of sound, Om-diagnostic and statefinder via graphical behavior.
The Galaxy Morphology-Density Relation at High Redshift with Candels

One of the biggest open questions regarding the evolution of the galaxy population over time, is how their properties (such as their morphologies) are affected by their local environment, e.g. the density of matter in the region where they are found. In the local universe, studies have shown that elliptical galaxies are found predominantly in the central regions of galaxy clusters where densities are higher, while disk galaxies reside in regions of lower densities such as the edges of clusters and the low-density "field". We investigate if this pattern continues to exist at earlier times by using data from the CANDELS collaboration at redshifts up to $z \sim 3$. For this, we make use of photometric redshift probability distributions (photo-z PDFs) for the galaxies observed by CANDELS. This required the development of new statistical methods to improve the quality of the PDFs measured by the CANDELS team, described in the thesis. We have used 3D-HST grism redshifts as well as spectroscopic redshifts where available, to test and optimize techniques for combining PDFs determined from multiple methods. We use morphological catalogs provided by the CANDELS team to select galaxies from three main categories: spheroid, disk, and irregular galaxies. We investigate the relative clustering of these different morphological types by estimating two-point cross correlation functions of each type with the full sample of CANDELS galaxies. Our results show that spheroid galaxies still cluster more strongly than disk galaxies at small separations at higher redshifts, while at larger separations the difference in their clustering amplitudes is not statistically significant. At the highest redshifts studied, clustering measurements are too noisy to detect differences in clustering strength, if any persist.
Transformed Lévy processes as state-dependent wear models

Abstract Many wear processes used for modeling accumulative deterioration in a reliability context are nonhomogeneous Lévy processes and, hence, have independent increments, which may not be suitable in an application context. In this work we consider Lévy processes transformed by monotonous functions to overcome this restriction, and provide a new state-dependent wear model. These transformed Lévy processes are first observed to remain tractable Markov processes. Some distributional properties are derived. We investigate the impact of the current state on the future increment level and on the overall accumulated level from a stochastic monotonicity point of view. We also study positive dependence properties and stochastic monotonicity of increments.
Smoothing techniques and difference of convex functions algorithms for image reconstructions

Abstract In this paper, we study characterizations of differentiability for real-valued functions based on generalized differentiation. These characterizations provide the mathematical foundation for Nesterov's smoothing techniques in infinite dimensions. As an application, we provide a simple approach to image reconstructions based on Nesterov's smoothing and algorithms for minimizing differences of convex (DC) functions that involve the regularization.
Wide-frequency tunable bandpass filter with high-frequency selectivity

ABSTRACT Based on the coupling-compensating technologies, a wide-frequency tunable bandpass filter with high-frequency selectivity has been presented in this paper. The coupling coefficient between a pair of resonator and the external quality can be easily controlled, the constant absolute bandwidth (ABW) and the characteristic of tunable wide-frequency can be achieved by changing the position of varactors. By introducing the source-load cross-coupling circuit, the bandpass filter has two transmission zeros, resulting in high-frequency selectivity for the passband. Moreover, only two varactor diodes have been used in the presented tunable bandpass filter. The measured results show that the tunable filter has an almost constant 3 dB absolute bandwidth of 90 ± 5 MHz from 1.55 GHz to 2.00 GHz. The measured insertion loss of the tunable bandpass filter is between 2.2 dB and 3.3 dB, while the return loss is greater than 10 dB within the tunable center frequencies. The measured results of the fabricated tunable bandpass filter show good agreement with the simulated ones.
Visual question answering using hierarchical dynamic memory networks

Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What’s more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.
An Optimization Compiler Framework Based on Polyhedron Model for GPGPUs

Liu, Lifeng. Ph.D., Department of Computer Science and Engineering, Wright State University, 2017. An Optimization Compiler Framework Based on Polyhedron Model for GPGPUs. General purpose GPU (GPGPU) is an effective many-core architecture that can yield high throughput for many scientific applications with thread-level parallelism. However, several challenges still limit further performance improvements and make GPU programming challenging for programmers who lack the knowledge of GPU hardware architecture. In this dissertation, we describe an Optimization Compiler Framework Based on Polyhedron Model for GPGPUs to bridge the speed gap between the GPU cores and the off-chip memory and improve the overall performance of the GPU systems. The optimization compiler framework includes a detailed data reuse analyzer based on the extended polyhedron model for GPU kernels, a compiler-assisted programmable warp scheduler, a compiler-assisted cooperative thread array (CTA) mapping scheme, a compiler-assisted software-managed cache optimization framework, and a compiler-assisted synchronization optimization framework. The extended polyhedron model is used to detect intra-warp data dependencies, cross-warp data dependencies, and to do data reuse analysis. The compiler-assisted programmable warp scheduler for GPGPUs takes advantage of the inter-warp data locality and intra-warp data locality simultaneously. The compiler-assisted CTA mapping scheme is designed to further improve the performance of the programmable warp scheduler by taking inter thread block data reuses into consideration. The compilerassisted software-managed cache optimization framework is designed to make a better use of the shared memory of the GPU systems and bridge the speed gap between the GPU cores and global off-chip memory. The synchronization optimization framework is developed to automatically insert synchronization statements into GPU kernels at compile time, while simultaneously minimizing the number of inserted synchronization statements. Experiments are designed and conducted to validate our optimization compiler frame-
Sliding mode control subject to rice channel fading

In this study, the sliding mode control problem is investigated for a class of discrete-time systems with fading measurements. The channel fading is modelled as the Rice fading model to characterise the fading phenomenon via wireless sensor network. An integral-like sliding surface involving fading parameters is first constructed. And then, a sliding mode controller is designed by using fading measurements and channel parameters. It is shown that the state trajectories can be driven to the neighbourhood around the specified sliding surface and the stochastic stability with prescribed H ∞ performance can be attained. Finally, a numerical example is provided.
A New Root–Finding Algorithm Using Exponential Series

In this paper, we present a new root-finding algorithm to compute a non-zero real root of the transcendental equations using exponential series. Indeed, the new proposed algorithm is based on the exponential series and in which Secant method is special case. The proposed algorithm produces better approximate root than bisection method, regula-falsi method, Newton-Raphson method and secant method. The implementation of the proposed algorithm in Matlab and Maple also presented. Certain numerical examples are presented to validate the efficiency of the proposed algorithm. This algorithm will help to implement in the commercial package for finding a real root of a given transcendental equation.
Adaptive contrast enhancement based on histogram modification framework

ABSTRACT An adaptive contrast enhancement method is proposed in this paper. The proposed method is based on the contrast limited adaptive histogram equalization (CLAHE) and the histogram modification framework. The predefined clip point for the clipped histogram of each block in original CLAHE may still result in excessive contrast enhancement in homogeneous regions, which gives the enhanced image an unnatural look and creates visual artifacts. By replacing the clipped histogram with a modified histogram, the proposed method achieves success in adaptively enhancing contrast in each block based on its content. In addition to this, a novel mapping function is introduced to further improve the enhanced result of histogram equalization (HE). Experiments are conducted with both visible images and infrared images to evaluate the performance of the proposed method. The results show that the proposed method gets better performance on contrast enhancement and visual quality of the enhanced results.
Evolution of Long-Term Industrial Meta-Models – A Case Study

Meta-models in software engineering are used to define properties of models. Therefore the evolution of the metamodels influences the evolution of the models and the software instantiated from them. The evolution of the meta-models is particularly problematic if the software has to instantiate two versions of the same meta-model - a situation common for longterm software development projects such as car development projects. In this paper, we present a case study of the evolution of the standardized meta-model used in the development of the automotive software systems – the AUTOSAR meta-model – at Volvo Car Corporation. The objective of this study is to assist the automotive software designers in planning long term development projects based on multiple AUTOSAR meta-model versions. We achieve this by visualizing the size and complexity increase between different versions of the AUTOSAR meta-model and by calculating the number of changes which need to be implemented in order to adopt a newer AUTOSAR meta-model version. The analysis is done for each major role in the automotive development process affected by the changes.
Video object segmentation and applications in temporal alignment and aspect learning

Modern computer vision has seen recently significant progress in learning visual concepts from examples. This progress has been fuelled by recent models of visual appearance as well as recently collected large-scale datasets of manually annotated still images. Video is a promising alternative, as it inherently contains much richer information compared to still images. For instance, in video we can observe an object move which allows us to differentiate it from its surroundings, or we can observe a smooth transition between different viewpoints of the same object instance. This richness in information allows us to effectively tackle tasks that would otherwise be very difficult if we only considered still images, or even adress tasks that are video-specific. Our first contribution is a computationally efficient technique for video object segmentation. Our method relies solely on motion in order to rapidly create a rough initial estimate of the foreground object. This rough initial estimate is then refined through an energy formulation to be spatio-temporally smooth. The method is able to handle rapidly moving backgrounds and objects, as well as non-rigid deformations and articulations without having prior knowledge about the objects appearance, size or location. In addition to this class-agnostic method, we present a class-specific method that incorporates additional class-specific appearance cues when the class of the foreground object is known in advance (e.g. a video of a car). For our second contribution, we propose a novel model for temporal video alignment with regard to the viewpoint of the foreground object (i.e., a pair of aligned frames shows the same object viewpoint) Our work relies on our video object segmentation technique to automatically localise the foreground objects and extract appearance measurements solely from them instead of the background. Our model is able to temporally align realistic videos, where events may occur in a different order, or occur only in one of the videos. This is in contrast to previous works that typically assume that the videos show a scripted sequence of events and can simply be aligned by stretching or compressing one of the videos. As a final contribution, we once again use our video object segmentation technique as a basis for automatic visual aspect discovery from videos of an object class. Compared to previous works, we use a broader definition of an aspect that considers four factors of variation: viewpoint, articulated pose, occlusions and cropping by the image border. We pose the aspect discovery task as a clustering problem and provide an extensive experimental exploration on the benefits of object segmentation for this task.
Robust Continuous Quadratic Distance Estimation Using Quantiles for Fitting Continuous Distributions

Quadratic distance estimation making use of the sample quantile function over a continuous range is introduced. It extends previous methods which are based only on a few sample quantiles and it parallels the continuous GMM method. Asymptotic properties are established for the continuous quadratic distance estimators (CQDE) and the implementation of the methods are discussed. The methods appear to be useful for balancing robustness and efficiency and useful for fitting distribution with model quantile function being simpler than its density function or distribution function.