A Real-Time Application of Modified Inductor Coupled KY Converter Topology in Speed Control of E-Bikes

Abstract

Congested road network and traffic jam lead to vehicle idling and fuel wastage. An E-bike which is a solution to this is often available with Thumb Throttle Mechanism and single speed operation. A motor controller with multiple output voltage levels and Click Throttle Mechanism is presented in this paper which provides variable speed operation, better power utilization and is highly economical. Proposed modified KY converter utilizes the turns ratio of an On-Line Tap Changer (OLTC) to generate multiple output voltages for variable speed commutation. This possess a stable continuous conduction mode throughout its operation and reduced switching transients when compared to the existing topologies. Mutually independent control variables namely turns ratio and duty ratio, are used here for voltage conversion, which facilitates the flexible transition between multiple output voltage levels. A 15?W prototype of the proposed modified inductor coupled KY converter is realized using Arduino Uno Board.     

A systematic investigation of the integrated effects of gate underlapping,dual work functionality and hetero gate dielectric for improved performance of CP TFETs

This paper presents a comparative analysis of the combined effects of gate underlapping and dual work functionality with hetero gate dielectric engineering for a charge plasma tunnel field-effect transistor (CP TFET). Ultrathin nanoscale devices, despite their size and cost advantage, present serious issues, including doping control, random dopant fluctuation and fabrication complexity. Given these concerns, the concept of charge plasma is introduced to avoid the need for conventional doping for the formation of the source and drain regions, which makes the device resistive to process variation. Conduction for negative gate bias (ambipolarity), excess Miller capacitance (gate-to-drain capacitance) and poor RF performance in TFETs are addressed by the use of gate underlapping from the drain side. In addition, enhanced ON-state current is obtained by work function shifting (dual work functionality). This shift in work function can be accomplished by nitrogen doping of the gate electrode for experimental levels [1]. The combined effects of the underlap and dual work function are seen in the device having a single gate dielectric. However, the ON-state current remains lower in the case of \(\mathrm{SiO}_{2}\) as the gate dielectric. Therefore, a hetero gate dielectric \(\mathrm{SiO}_{2}\) on the drain side and \(\mathrm{HfO}_{2}\) on the source side are considered in order to improve the RF parameters and enhance the ON-current concept, respectively. Finally, the combined effects of gate underlap with work function shift and hetero dielectric are analyzed in CP TFETs. The results show that proper underlap length and gate work function provide a significant improvement in device performance. Therefore, optimization of the underlap length and work function is performed to determine the specific work function that provides overall enhancement of DC and analog/RF performance of the device. In addition, optimization of the dual work function gate length is demonstrated.     

Linearity Distortion Analysis of Junctionless Quadruple Gate MOSFETs for Analog Applications

Silicon - This paper examines a Junctionless quadruple gate (JLQG) MOSFET for analog and linearity distortion performance by numerically calculating transconductance and its higher order...     

Nanostructured poly(3-hexylthiophene-2,5-diyl) films with tunable dimensions through self-assembly with polystyrene

Poly(3-hexylthiophene-2,5-diyl) is among the most widely used conjugated polymers for opto-electronic applications. To enhance its properties, researchers have attempted to nanostructure this polymer using various processes including breath figure arrays, nanolithography and elaborated organic synthesis. We here demonstrate a simple process to nanostructure the conjugated polymer using self-assembly with polystyrene and selective removal of one of the phases. The influence of the molecular weight of each polymer on the thin film morphology was systematically studied by atomic force microscopy. Using this approach, we observe two types of nanostructure, namely, nanoporous and nanoisland structures, of which the dimensions can be tuned by modifying the molecular weight of each polymer in the blend. This simple process introduces a cost-effective alternative to produce thin films of conjugated polymer with average nano-features from 100 nm up to 500 nm which could be used in a wide range of applications.     

A reference based analysis framework for understanding anomaly detection techniques for symbolic sequences

Anomaly detection for symbolic sequence data is a highly important area of research and is relevant in many application domains. While several techniques have been proposed within different domains, understanding of their relative strengths and weaknesses is limited. The key factor for this is that the nature of sequence data varies significantly across domains, and hence while a technique might perform well in its original domain, its performance is not guaranteed in a different domain. In this paper, we aim at establishing this understanding for a wide variety of anomaly detection techniques for symbolic sequences. We present a comparative evaluation of a large number of anomaly detection techniques on a variety of publicly available as well as artificially generated data sets. Many of these are existing techniques while some are slight variants and/or adaptations of traditional anomaly detection techniques to sequence data. The analysis presented in this paper allows relative comparison of the different anomaly detection techniques and highlights their strengths and weaknesses. We extend the reference based analysis (RBA) framework, which was originally proposed to analyze multivariate categorical data, to analyze symbolic sequence data sets. We visualize the symbolic sequences using the characteristics provided by the RBA framework and use the visualization to understand various aspects of the sequence data. We then use the characterization done by RBA to understand the performance of the different techniques. Using the RBA framework, we propose two anomaly detection techniques for symbolic sequences, which show consistently superior performance over the existing techniques across the different data sets.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

Simulation based alternatives for overall process improvement at the cardiac catheterization lab

Cardiac catheterization is one of the critical procedures in patient care. It is pertinent for all process related issues in this department to be handled with due priority. This research is a cross-functional effort with a leading hospital that was in its planning stage to implement an overall process improvement at its cardiac catheterization lab department. It has been determined that significant process delays exist in this department. One of the identified key factors to process improvement was to reduce patient turnaround time. Process mapping was used to study the system and identify potential sources of delays in the system. A set of qualitative recommendations were put forward. These recommendations, include the reduction of wait time of outpatients and inpatients and the reduction of non-value-added times such as transfer time of patients, were evaluated using a discrete event simulation approach. The simulation study shows that the daily operating hours at the catheterization lab could be reduced, aiding to reallocate staff for discharging other duties, as the hospital may require.     

A semantic framework for automatic generation of computational workflows using distributed data and component catalogues

Computational workflows are a powerful paradigm to represent and manage complex applications, particularly in large-scale distributed scientific data analysis. Workflows represent application components that result in individual computations as well as their interdependences in terms of dataflow. Workflow systems use these representations to manage various aspects of workflow creation and execution for users, such as the automatic assignment of execution resources. This article describes an approach to automating a new aspect of the process: the selection of application components and data sources. We present a novel approach that enables users to specify varying degrees of detail and amount of constraints in a workflow request, including the specification of constraints on input, intermediate or output data in the workflow, abstract workflow component classes rather than specific component implementations, and generic reusable workflow templates that express a pre-defined combination of components. The algorithm elaborates the user request into a set of fully ground workflows with specific choices of data sources and codes to be used so that they can be submitted for mapping and execution. The algorithm searches through the space of possible candidate workflows by creating increasingly more specialized versions of the original template and eliminating candidates that violate constraints cumulated in the candidate workflow as components and data sources are selected. A novel feature of our approach is that it assumes a distributed architecture where data and component catalogues are separate from the workflow system. The algorithm explicitly poses queries to external catalogues, and therefore any reasoning regarding data or component properties is not assumed to occur within the workflow system. We describe our implementation of this approach in the Wings workflow system. This implementation uses the W3C Web Ontology Language and associated reasoners to implement the workflow system as well as the data and component catalogues. This research demonstrates the use of artificial intelligence techniques to support the kinds of automation envisioned by the scientific community for large-scale distributed scientific data analysis.     

A Balanced Approach to IT Transformation

The continuous improvement strategies necessary in today's climate of random change mandate that IT organizations transform the way they relate to other business units, deploy new technology, and organize and develop their people. Crafting an individualized transformation program that balances efforts in each of these areas will help IT managers add value to the bottom line while achieving real gains in customer service and productivity.     

Keeping up-to-date with information technology: Testing a model of technological knowledge renewal effectiveness for IT professionals

In a rapidly changing IT environment, IT professionals need to keep abreast of technological knowledge. We examined how well this is achieved by developing a motivational model of “technological knowledge renewal effectiveness.” We hypothesized that (1) renewal effectiveness was influenced by the IT professional's career orientation, perceived IT dynamism, tolerance of ambiguity, delegation; and (2) that this positively affected both intrinsic and extrinsic job satisfaction. Survey data from 126 IT professionals was used to test the hypotheses. The results generally supported the research model. We discussed the implications of these results in both research and practice.