Ontology-style web usage model for semantic Web applications

Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent the non-taxonomic visiting relationship among the visited pages. The output of this model is an ontology-style document which enables the discovered web usage knowledge to be sharable and machine-understandable in semantic Web applications, such as recommender systems. A case study is presented to show how this model is used in conjunction of the web usage mining and web recommendation. Two real-world datasets are used in the case study. © 2010 IEEE

Web-Page Recommendation Based on Web Usage and Domain Knowledge

© 1989-2012 IEEE. Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method

Effects of armature reaction on the performance of a claw pole motor with soft magnetic composite stator by finite-element analysis

We investigated the effects of armature reaction on the performance of a three-phase three-stack claw pole motor with soft magnetic composite stator core by using three-dimensional finite-element analysis (FEA), which is an effective approach to accurately compute the parameters and performance such as the back electromotive force (EMF), core losses, and winding inductance at various saturation levels. The motor is rated as 500 W at 1800 rpm when the stator current is 4.1 A, driven by a sensorless brushless DC scheme. Because of the armature reaction, the back EMF produced by the rotor permanent magnets and the developed torque is reduced by about 3.3% at the rated load, and the core losses increase drastically by 41% from no-load to full-load. The winding inductance is computed with different loads at different rotor angles. © 2007 IEEE

Accurate determination of parameters of a claw-pole motor with SMC stator core by finite-element magnetic-field analysis

Effective and accurate prediction of key motor parameters, such as winding flux, back electromotive force, inductance and core losses, is crucial for design of high-performance motors. Particularly, for electrical machines with new materials and nonconventional topology, traditional design approaches based on the equivalent magnetic circuit, empirical formulas and previous experiences cannot provide correct computation. The paper presents accurate determination of major parameters of a three-phase three-stack claw-pole permanent-magnet motor with a soft magnetic composite (SMC) stator core by finite-element analysis of the magnetic field. The effects of magnetic saturation and armature reaction are considered. The theoretical results by numerical analysis are validated by the experiments on the claw-pole SMC-motor prototype

An object oriented Bayesian network approach for unsafe driving maneuvers prevention system

© 2017 IEEE. As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers

A stacked long short-term memory approach for predictive blood glucose monitoring in women with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes

A practical circuit model of high frequency transformers in power electronic systems

This paper presents a practical circuit model of high frequency transformers in power electronic systems. All types of core losses (ie. the hysteresis, classical eddy current and anomalous losses) are included in the model. the thermal effect on magnetic hysteresis, the skin effect of eddy currents in magnetic cores and the effect of stray capacitances are also considered. This model can therefore accurately predict the performance and core losses of transformers used in high frequency switching circuits. The practical methods for determining the circuit parameters are developed and presented in the paper, providing crucial benefit for practical applications. The developed model has been applied to simulate the performance of a 500 W transformer in a full bridge inverter operated with square waveform voltage excitation of 25 kHz. The simulations are validated by the experimental results. © Institution of Engineers, Australia 2007

Effect of armature reaction of a permanent-magnet claw pole SMC motor

The finite-element method enables an accurate analysis for the study on effects of armature reaction in electromagnetic devices, particularly those with complex structures and three-dimensional (3-D) magnetic flux paths. This paper investigates the effects of armature reaction on the parameters and performance of a permanent-magnet (PM) claw pole motor with soft magnetic composite (SMC) core, based on the magnetic field analysis using the 3-D nonlinear time-stepping finite-element method. The current in the stator winding produces a magnetic field, which interacts with the air gap field generated by the rotor magnets. Consequently, the air gap flux density profile against the rotor position produced by the rotor magnets deviates, and so does the back electromotive force. Since the stator field also changes the local saturation level of the magnetic core, the winding inductance varies with both the rotor position and stator currents. The inclusion of these effects in terms of parameter variations in the motor model is important for accurate performance analysis. On the other hand, the pattern of inductance against the rotor position and stator currents can be employed to effectively predict the rotor position at standstill and low speeds for robust sensorless control. The parameter computations are verified by experimental results on the PM claw pole SMC motor prototype. © 2007 IEEE

Performance analysis of a linear motor with HTS bulk magnets for driving a prototype HTS maglev vehicle

This paper presents the performance analysis of a linear synchronous motor which employs high-temperature superconducting (HTS) bulk magnets on the mover and normal copper windings on the stator. The linear motor is designed to drive a prototype HTS maglev vehicle in which the mover is suspended by the levitation force between HTS bulks on the mover and permanent magnets on the ground. Finite element magnetic field analysis is conducted to calculate the major parameters of the linear motor and an equation is derived to calculate the electromagnetic thrust force. Theoretical calculations are verified by the measured results on the prototype. © (2013) Trans Tech Publications, Switzerland

Medium-frequency-link power conversion for high power density renewable energy systems

Recent advances in solid-state semiconductors and magnetic materials have provided the impetus for medium frequency-link based medium voltage power conversion systems, which would be a possible solution to reducing the weight and volume of renewable power generation systems. To verify this new concept, in this paper, a laboratory prototype of 1.73 kVA medium-frequency-link power conversion system is developed for a scaled down 1 kV grid applications. The design and implementation of the prototyping, test platform, and the experimental results are analyzed and discussed. It is expected that the proposed new technology would have great potential for future renewable and smart grid applications. © 2013 IEEE