No abstract

Electric vehicles (EVs) have the main advantage of using sustainable forms of energy to operate and can be integrated into electrical power grids for better energy management. An essential part of the EV propulsion system is the type of motor used to propel the EV. Permanent magnet synchronous motors (PMSMs) have found extensive use due to various advantages such as high power density, excellent torque-to-weight ratio and smooth speed profile over the entire torque range. The objective of this paper was to improve the dynamic response in the speed profile for different driving conditions essential in EVs. This was done by using the finite control set model predictive control (FCS-MPC) algorithm for PMSM and by comparing and evaluating the control strategies of a PMSM used in an EV by taking two case studies. The classical control, namely field-oriented control (FOC), of PMSMs is slow to adopt the dynamic changes in the system. The proposed FCS-MPC algorithm for PMSMs provides an improved dynamic response and a good steady-state response for the different driving conditions shown in both cases. In addition, the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) is used to evaluate the FCS-MPC-controlled PMSM to depict its superior performance by matching its speed profile. The results are verified in the hardware in the loop strategy using OPAL-RT. Both the results confirm that the FCS-MPC algorithm, when compared with the conventional FOC, is superior in aspects of steady-state and dynamic responses for various torque and speed profiles.

Reasoning is the fundamental capability which requires knowledge. Various graph models have proven to be very valuable in knowledge representation and reasoning. Recently, explosive data generation and accumulation capabilities have paved way for Big Data and Data Intensive Systems. Knowledge Representation and Reasoning with large and growing data is extremely challenging but crucial for businesses to predict trends and support decision making. Any contemporary, reasonably complex knowledge based system will have to consider this onslaught of data, to use appropriate and sufficient reasoning for semantic processing of information by machines. This paper surveys graph based knowledge representation and reasoning, various graph models such as Conceptual Graphs, Concept Graphs, Semantic Networks, Inference Graphs and Causal Bayesian Networks used for representation and reasoning, common and recent research uses of these graph models, typically in Big Data environment, and the near future needs and challenges for graph based KRR in computing systems. Observations are presented in a table, highlighting suitability of the surveyed graph models for contemporary scenarios.

Utility applications demand highly reliable power converters to match market quality needs. The classical reliability prediction methods do not account for the sudden transients involved with the power converter. This work envisages a new reliability prediction procedure for LLC converter which accounts for the input transients’ impact on the failure rate. Experiments are conducted to collect the actual stress values at input transient and fault conditions, which aids to predict the failure rate with more accuracy. The reliability prediction has been performed using the collected experimental data from the component level to the system level at different mission profiles with transient operating conditions. The impact of various faults and transients on the converter failure rate prediction has been clearly projected from the quantitative analysis presented in this paper. To have a clear picture, the effect of reliability with respect to other stress factors like temperature stress, environmental stress, electrical voltage, current, and power stress on failure rates are also compiled and tabulated. Failure rate and Mean Time Between Failures (MTBF) have been calculated for an LLC converter using the experimental data. The proposed reliability model can be used in the design phase to have an optimal design, planning, and operation of a power electronic converter in the field. This enables to reach out power converters with better reliability profile to cater the industrial needs for real time applications. From the results, it is evident that the reliability prediction is more realistic when the input transients are considered using the experimental data.

Topic extraction is a challenging task under Natural Language Processing and Text Mining. Topic extraction is useful in natural language processing tasks such as automated summarization, question answering, and personalized search. In this paper, we propose an unsupervised topic extraction method using semantic similarity, keyword significance, and graph centrality. First, we select semantically similar words from text documents. Next, we perform disambiguation to find the correct senses of selected words. Then, we build a weighted graph using semantic relationships and significance of words in the text. Finally, we identify topic keywords using a novel concurrent local weighted centrality (LWWC) from words represented as nodes in a graph. Using standard annotated CiteULike and standard Brown datasets, we evaluated the results with precision, recall, and F-measure. We show that the proposed method yields results comparable with the state of the art LDA (Latent Dirichlet Allocation) and S-LDA (Sparse-LDA) topic extraction techniques. We also show that the proposed concurrent LWWC algorithm is more effective than the existing generic centrality measures in networks of words. We verified the statistical significance of improved effectiveness of our approach, using one-way analysis of variance (ANOVA) and Tukey-Honest Significant Difference (Tukey-HSD) post-hoc methods.