Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARATIVE AND ANALYTIC DESIGN OF MATLAB MODELLING FOR SOLAR DC MICRO GRID IN RURAL ELECTRIFICATION

DOI Number: https://doi.org/10.30780/IJTRS.V04. I06.004 pg. 27 www.ijtrs. com www.ijtrs. org Paper Id: IJTRS-V4-I5-031 Volume IV Issue VI, June 2019 @2017, IJTRS All Right Reserved COMPARATIVE AND ANALYTIC DESIGN OF MATLAB MODELLING FOR SOLAR DC MICRO GRID IN RURAL ELECTRIFICATION Ravi Kiran K C, Ramesh C H V E-Mail Id: kiranravi9535@gmail.com, venkataramesh.ch@nmit.ac. in M. Tech Student, Renewable Energy, NMIT, Bengaluru, India Assistant Professor Dept. of Electrical & Electronics Engineering NMIT Bengaluru, India Abstract-This paper describes the detailed design, analysis of energy dip in solar PV based centralized DC micro grid in rural electrification. Based on the technical and environmental benefits of the renewable energy related solar PV systems are discussed in this paper comparison between the three types of microgrid systems is presented. In this work the consumption of load is about 4.5kWh including the Home load and water pumping system. The estimated required Components such as solar PV Panel, charge controller, battery, distribution system is considered for DC micro grid. further, the voltage dip and Energy drop calculations are presented. The analyzed three architectures are simulated in MATLAB/SIMULINK for power loss and results are presented. KeywordsDC Micro grid, voltage dip, Power loss, Rural Electrification.
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge

Automatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay generation based solely on the given topics while ignoring massive commonsense knowledge. However, this commonsense knowledge provides additional background information, which can help to generate essays that are more novel and diverse. Towards filling this gap, we propose to integrate commonsense from the external knowledge base into the generator through dynamic memory mechanism. Besides, the adversarial training based on a multi-label discriminator is employed to further improve topic-consistency. We also develop a series of automatic evaluation metrics to comprehensively assess the quality of the generated essay. Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.
Evaluation of word embedding vector averaging functions for biomedical word sense disambiguation

The biomedical lexicon contains a large amount of term ambiguity, which hinders correct identification of concepts and reduces the accuracy of semantic indexing and information retrieval tools. Previous work on biomedical word sense disambiguation has shown that supervised machine learning leads to better results than knowledge-based approaches. However, machine learning approaches require the availability of sufficient training data, and generalization performance behind the test data is not known. Knowledge-based methods on the other hand make use of existing knowledge-bases and are therefore mostly limited to the quality of such sources of information about concepts. In this work, we used word embedding vectors to complement the knowledge-base information. We represent the context of an ambiguous term by the average of the embedding vectors of words around the term, and evaluate the impact of using word distance for weighting this average. We show how this weighting improves the disambiguation accuracy of the knowledge-based approach in a subset of the reference MSH WSD data set from 86% to 88%.
Statistical methods for recurrent event data in the presence of a terminal event and incomplete covariate information

SHANKAR VISWANATHAN: Statistical Methods for Recurrent Event Data in the Presence of a Terminal Event and Incomplete Covariate Information. (Under the direction of Dr. Jianwen Cai.) In many clinical and epidemiological studies, recurrent events such as infections in immunocompromised patients or injuries in athletes often occur. It is of interest to examine the relationship between covariates and recurrent events, however in many situations, some of the covariates collected involve missing information due to various reasons. Under such missingness, a commonly practiced method is to analyze complete cases; this method may be inefficient or result in biased estimates for parameters. In this dissertation, we develop methods to analyze recurrent events data with missing covariate information. These will be useful in reducing the bias and improving the efficiency of parameter estimates. This method is motivated by the need for analyzing recurrent infections in a renal transplant cohort from India in which approximately 19% of patients died and over 13% had missing covariate information. Literature shows that opportunistic infections times and death time may be correlated and need to be adjusted in the estimation process. First, we studied this problem by developing methods using marginal rate models for both recurrent events and terminal events with missing data. We adopted a weighted estimating equation approach with missing data assumed to be missing at random (MAR) for estimating the parameters. Second, we considered a marginal rate model for multiple type recurrent events in the presence of a terminal event. We proposed a weighted estimating equation approach assuming that terminal events preclude further recurrent events. We adjusted for the
Smart Sound Sensor for distress situation and number of presented person

In order to allow older adults to active and healthy ageing with comfort and security we have already de veloped a smart audio sensor being able to recognize everyday life sounds in order to detect activities of daily living (ADL) and distres s situations. In this paper, we propose to add a new functionality by ana lyzing the speech flow in order to detect the number of person in a room. The proposed algorithms are based on speaker diarizatio n methods. This information is useful in order to better detect act ivities of daily life but also to know when the person is home alone. This functionality can also offer more comfort through light, heating and air conditioning adaptation to the number of persons
Customer Review-driven Function Formulation for Design Education

Customer Review-driven Function Formulation for Design Education Dr. Ang Liu, University of New South Wales Dr. Ang Liu is a senior lecturer at the School of Mechanical and Manufacturing Engineering, University of New South Wales, Australia Mr. Yuchen Wang, University of New South Wales Yuchen Wang is an undergraduate Aerospace Engineering student in University of New South Wales,Australia. He assists in engineering design education, meanwhile, his student off-class research focuses on space theme projects such as sun sensor and thermal controller. Dr. Yun Dai, University of Southern California Dr. Yun Dai is a postdoc research fellow in the Viterbi School of Engineering, at the University of Southern California, and also the program manager of the Viterbi iPodia Program (ViP). Her research interest broadly involves engineering education, learning technology, and intercultural education.
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation

A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.
Vehicle trajectory modelling with consideration of distant neighbouring dependencies for destination prediction

ABSTRACT Vehicle trajectory modelling is an essential foundation for urban intelligent services. In this paper, a novel method, Distant Neighbouring Dependencies (DND) model, has been proposed to transform vehicle trajectories into fixed-length vectors which are then applied to predict the final destination. This paper defines the problem of neighbouring and distant dependencies for the first time, and then puts forward a way to learn and memorize these two kinds of dependencies. Next, a destination prediction model is given based on the DND model. Finally, the proposed method is tested on real taxi trajectory datasets. Results show that our method can capture neighbouring and distant dependencies, and achieves a mean error of 1.08 km, which outperforms other existing models in destination prediction significantly.
Orchestration of machine learning workflows on Internet of Things data

Applications empowered by machine learning (ML) and the Internet of Things (IoT) are changing the way people live and impacting a broad range of industries. However, creating and automating ML workflows at scale using real-world IoT data often leads to complex systems integration and production issues. Examples of challenges faced during the development of these ML applications include glue code, hidden dependencies, and data pipeline jungles. This research proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows to perform training and enable inference by ML models on IoT data. In the proposed framework, containerized microservices are used to automate the execution of tasks specified in ML workflows, which are defined through REST APIs. To address the problem of integrating big data tools and machine learning into a unified platform, the proposed framework enables the definition and execution of end-to-end ML workflows on large volumes of IoT data. In addition, to address the challenges of running multiple ML workflows in parallel, the ML4IoT has been designed to use container-based components that provide a convenient mechanism to enable the training and deployment of numerous ML models in parallel. Finally, to address the common production issues faced during the development of ML applications, the proposed framework used microservices architecture to bring flexibility, reusability, and extensibility to the framework. Through the experiments, we demonstrated the feasibility of the (ML4IoT), which managed to train and deploy predictive ML models in two types of IoT data. The obtained results suggested that the proposed framework can manage real-world IoT data, by providing elasticity to execute 32 ML workflows in parallel, which were used to train 128 ML models simultaneously. Also, results demonstrated that in the ML4IoT, the performance of rendering online predictions is not affected when 64 ML models are deployed concurrently to infer new information using online IoT data.
A vehicle's weight‐based prioritized reciprocity MAC

In vehicular ad hoc networks, vehicles compete for channel access through the medium access control protocol to transmit messages. Most traditional medium access methods assign the same probability to all vehicles accessing the channel and ignore the intrinsic attributes of different vehicles. Attributes regarding the position and role of a vehicle are important factors to be considered when a vehicle is competing for the channel. In this paper, we introduce a new notion of a weight factor in a fair medium access game and propose a game model to adjust each node's channel access probability with complete information in the vehicular environment based on the weight factors of vehicles. The weight of a vehicle represents a specific property, such as the distance between vehicles, the vehicle's role, and so on, which can be used to distinguish priorities of different vehicles during channel competition. We also provide instances of relevant applications by using the distance between vehicles and a vehicle's role. Then, we analyze the incomplete information game in which each node cannot acquire the previous channel access probabilities of other nodes. Moreover, we construct a distributed best‐response learning algorithm based on the medium access game model and demonstrate that the game can converge to a unique and stable equilibrium under certain conditions. Furthermore, we investigate some performance indicators such as channel access delay and normalized throughput and perform extensive numerical analyses compared with the IEEE 802.11p protocol and an existing game‐based medium access control algorithm without consideration of weights. The performance comparison of high‐priority nodes and all nodes in a network is also studied. Finally, we analyze the effects of the proportion of high‐priority nodes on the performance of the channel. The performance improvement after introducing the weight factor is verified by these numerical results.
Peripheral Notification That Does Not Bother User's Operations by Peripheral Cognition Technology

Various notifications on a display for e-mail arrival, micro-blog updates, and application updates are becoming increasingly important. Thus many studies have been done to make notifications not to prevent a user to achieve a task. In this paper, we propose a novel notification method, the peripheral notification that uses the human cognitive property, visual field narrowing, that a human does not recognize subtle changes in a peripheral visual field when he/she concentrates on a task and that he/she automatically recognizes the changes when not concentrating on the task. By only setting a notification icon in the area of visual field narrowing, a user automatically and easily accepts the notification only when his/her concentration breaks. We conducted two experiments to investigate a visual field narrowing region in a controlled environment and evaluate the effectiveness of peripheral notifications by comparing with a traditional method in a more realistic environment. As a result, we confirmed the visual field narrowing in a display, and our peripheral notification had advantages.
A First Approach to Comparing Cohesive Traction-Separation Laws for Concrete

In the already vast literature dealing with numerical simulations of fracture of concrete elements or structures, a large number of papers deal with cohesive traction-separation laws. However, in many instances, this appears to be a secondary aspect of the published studies, the principal one being the variant of the numerical algorithm being presented in the research. This makes difficult to compare the various available formulations for the vectorial traction-separation law (t–w law). The present work aims at initiating a systematic comparison between the t–w laws, and this is done comparing a subset of the possible formulations using exactly the same numerical algorithms. The analysis is restricted, in this contribution, to damage-based models in the tensile zone (positive normal components of the traction and the crack displacement vectors). All the models use the same uniaxial softening function and the emphasis is on the vectorial character of the t–w law, namely, on the influence of (1) the lack of coaxiality of t and w; (2) the shape of the damage criterion in the traction space; and (3) the influence of the ratio of fracture energies in pure modes I and II. The simulations have been carried out within the finite element framework COFE (Continuum Oriented Finite Element), which implements elements with an embedded adaptable crack to reproduce fracture of concrete as well as a smeared version that, essentially, unifies the numerical algorithms of both approaches.
Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes

Abstract Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse.
Fpga Implementation Of Bist With Self Feedback Logic To Achieve Complete Fault Coverage

This paper presents an implementation of a deterministic BIST technique on FPGA that can efficiently achieve complete fault coverage without using any storage devices. A novel test structure containing a self feedback logic unit and a circular shift register is proposed and the self feedback logic unit to generate a set of seed patterns based on responses of some pre selected internal nets . This seed patterns are then used to generate all required test patterns through a circular shift register . It is notice that the self feedback logic unit requires large routing area, when internal nets connects to the self feedback logic. For the reduction of routing area we use arbitrary formulae at the internal connections.
Improving the accuracy of Natural Language Dependency Parsing

The aim of this thesis is to improve Natural Language Dependency Parsing. We employ a linear Shift Reduce Dependency parsing algorithm avoiding the increase of computational costs. We start by presenting our experiments results achieved during our participation at the multilingual dependency shared task of Conference on Computational Natural Language (CoNLL) 2007. We perform an accurate error analysis of the best parsers presented at the conference to reveal critical aspects of parsing systems. This will lead us to introduce a new parsing method and a new parser combination algorithm with the purpose of improving the deterministic Shift Reduce parser’s accuracy. The new parsing method, called Reverse Revision Parsing, employs a Left-to-Right Shift Reduce parser that parses the sentence followed by a second Right-to-Left Shift Reduce parser that scans the sentence in reverse using additional features obtained from the prediction of the first parser. The new parser combination algorithm, called QuasiLinear Parser Combination, exploits the fact that its inputs are trees in order to avoid the quadratic cost of algorithms for computing the maximum spanning tree of a graph. We report on our experiments’ results obtained during the participation at CoNLL 2008 evaluation task. These results have been achieved employing the Reverse Revision Parsing and a new combination algorithm presented during the course of this thesis. We then present a number of experiments meant to select a set of features that provides the greatest improvement to a Shift Reduce statistical dependency parser. We report on the accuracy gains that such parser can obtain using features from gold chunks, from chunks produced using a statistical chunker and from approximate chunks obtained by detecting noun phrases through regular expression patterns. A parser exploiting features from approximate chunks is applied to a chunking task and its accuracy in chunking is compared to that of a specialized statistical chunker. Finally, we investigate the performances achieved by parsers when they apply to languages that are characterized by a relatively free word order and by a rich morphology. Thus, we perform a detailed quantitative analysis of distributional language data highlighting the relative contribution of a number of distributed grammatical and semantic factors in parsing. We therefore introduce Animacy, a semantic feature usually not present in available treebanks, and discuss its effect in parsing.
Understanding and Statically Detecting Synchronization Performance Bugs in Distributed Cloud Systems

In such an information society, the Internet of Things (IoT) plays an increasingly important role in our daily lives. With such a huge number of deployed IoT devices, Cyber-Physical System (CPS) calls for powerful distributed infrastructures to supply big data computing, intelligence, and storage services. With the increasingly complex distributed software infrastructures, new intricate bugs continue to manifest, causing huge economic loss. Synchronization performance problems, which means that improper synchronizations may degrade the performance and even lead to service exception, heavily influence the entire distributed cluster, imperiling the reliability of the system. As one kind of performance problems, the synchronization performance problems are acknowledged as difficult to diagnosis and fix. We collect 26 performance issues in three real-world distributed systems: HDFS, Hadoop MapReduce, and HBase, and do analysis on their root cause, fix strategy, and algorithm complexity in order to understand these synchronization performance bugs better. Then, we implement a static detection tool including critical section identifier, loop identifier, inner loop identifier, expensive loop identifier, and pruning component. After that, we evaluate our detection tool on these three distributed systems with sampled bugs. In the evaluation, our detection tool accurately finds out all the target bugs. Besides, it points out more new potential performance problems than the previous works. With the strict performance overhead, our detection tool is proved to be greatly efficient.