Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.

Real solutions number of the switching angles in the selective harmonic eliminated PWM

With the purpose of solving the real solutions number of the nonlinear transcendental equations in the selective harmonic eliminated PWM (SHEPWM) technology, the nonlinear transcendental equations were transformed to a set of polynomial equations with a set of inequality constraints using the multiple-angle formulas, an analytic method based on semi-algebraic systems machine proving algorithm was proposed to classify the real solution number of the switching angles. The complete classifications of the real solution number and the analytic boundary point of the single phase and three phases SHEPWM inverter with switch points of N=3 and the single phase SHEPWM inverter with switch points of N=4 are obtained. The results indicate that the relationship between the modulation ratio and the real solution number can be demonstrated theoretically by this method, which has great implications for the solution procedure of switching angles and the improvement of harmonic elimination effects of the inverter.

Parameter estimation of one-class SVM on imbalance text classification

Compared with conventional two-class learning schemes, one-class classification simply uses a single class for training purposes. Applying one-class classification to the minorities in an imbalanced data has been shown to achieve better performance than the two-class one. In this paper, in order to make the best use of all the available information during the learning procedure, we propose a general framework which first uses the minority class for training in the one-class classification stage; and then uses both minority and majority class for estimating the generalization performance of the constructed classifier. Based upon this generalization performance measurement, parameter search algorithm selects the best parameter settings for this classifier. Experiments on UCI and Reuters text data show that one-class SVM embedded in this framework achieves much better performance than the standard one-class SVM alone and other learning schemes, such as one-class Naive Bayes, one-class nearest neighbour and neural network.

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.

Image semantic classification by using SVM

There exists an enormous gap between low-level visual feature and high-level semantic information, and the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features. Taking this into consideration, a novel texture and edge descriptor is proposed in this paper, which can be represented with a histogram. Furthermore, with the incorporation of the color, texture and edge histograms searnlessly, the images are grouped into semantic classes using a support vector machine (SVM). Experiment results show that the combination descriptor is more discriminative than other feature descriptors such as Gabor texture.

Putting woman-centered care into practice : a new (ANEW) approach to psychosocial risk assessment during pregnancy

Background: When antenatal care is provided, identification and management of challenging problems, such as depression, domestic violence, child abuse, and substance abuse, are absent from traditional midwifery and medical training. The main objective of this project was to provide an alternative to psychosocial risk screening in pregnancy by offering a training program (ANEW) in advanced communication skills and common psychosocial issues to midwives and doctors, with the aim of improving identification and support of women with psychosocial issues in pregnancy.

Methods: ANEW used a before‐and‐after survey design to evaluate the effects of a 6‐month educational intervention for health professionals. The setting for the project was the Mercy Hospital for Women in Melbourne, Australia. Surveys covered issues, such as perceived competency and comfort in dealing with specific psychosocial issues, self‐rated communication skills, and open‐ended questions about participants' experience of the educational program.

Results: Educational program participants (n = 22/27) completed both surveys. After the educational intervention, participants were more likely to ask directly about domestic violence (p = 0.05), past sexual abuse (p = 0.05), and concerns about caring for the baby (p = 0.03). They were less likely to report that psychosocial issues made them feel overwhelmed (p = 0.01), and they reported significant gains in knowledge of psychosocial issues, and competence in dealing with them. Participants were highly positive about the experience of participating in the program.

Conclusions:The program increased the self‐reported comfort and competency of health professionals to identify and care for women with psychosocial issues.

Getting your life back on track after stroke : a phase II multi-centered, single-blind, randomized, controlled trial of the Stroke Self-Management Program vs. the Stanford Chronic Condition Self-Management Program or standard care in stroke survivors

Background Self-management is seen as a primary mechanism to support the optimization of care for people with chronic diseases such as symptomatic vascular disease. There are no established and evidence-based stroke-specific chronic disease self-management programs. Our aim is to evaluate whether a stroke-specific program is safe and feasible as part of a Phase II randomized-controlled clinical trial.
Methods Stroke survivors are recruited from a variety of sources including: hospital stroke services, local paper advertisements, Stroke South Australia newsletter (volunteer peer support organization), Divisions of General Practice, and community service providers across Adelaide, South Australia. Subjects are invited to participate in a multi-center, single-blind, randomized, controlled trial. Eligible participants are randomized to either;
• standard care,
• standard care plus a six week generic chronic condition self-management group education program, or,
• standard care plus an eight week stroke specific self-management education group program.
Interventions are conducted after discharge from hospital. Participants are assessed at baseline, immediate post intervention and six months.
Study Outcomes The primary outcome measures determine study feasibility and safety, measuring, recruitment, participation, compliance and adverse events.
Secondary outcomes include:
• positive and active engagement in life measured by the Health Education Impact Questionnaire,
• improvements in quality of life measured by the Assessment of Quality of Life instrument,
• improvements in mood measured by the Irritability, Depression and Anxiety Scale,
• health resource utilization measured by a participant held diary and safety.

Conclusion The results of this study will determine whether a definitive Phase III efficacy trial is justified.

Detection of vehicles with monolithic classifier vis-à-vis a boosted cascaded classifier

This paper describes the comparison of accuracy and performance of two machine learning approaches for visual object detection and tracking vehicles, from an on-road image sequence. The first is a neural network based approach. Where an algorithm of multi resolution technique based on Haar basis functions was used to obtain an image with different scales. Thereafter a classification was carried out with the multilayer feed forward neural network. Principle Component Analysis (PCA) technique was used as a dimension reduction technique to make the classification process much more efficient. The second approach is based on boosting which also yields very good detection rates. In general, boosting is one of the most important developments in classification methodology. It works by sequentially applying a classification algorithm to reweighed versions of the training data, followed by taking a weighted majority vote of the sequence of classifiers thus produced. For this work, a strong classifier was trained by the adaboost algorithm. The results of comparing the two methodologies visà-vis shows the effectiveness of the methods that have been used.

User-centered evaluation of a virtual environment training system : utility of user perception measures

This study assessed the utility of measures of Self-efficacy (SelfEfficacy) and Perceived VE efficacy (PVEefficacy) for quantifying how effective VEs are in procedural task training. SelfEfficacy and PVEefficacy have been identified as affective construct potentially underlying VE efficacy that is not evident from user task performance. The motivation for this study is to establish subjective measures of VE efficacy and investigate the relationship between PVEefficacy, SelfEfficacy and User task performance. Results demonstrated different levels of prior experience in manipulating 3D objects in gaming or computer environment (LOE3D) effects on task performance and user perception of VE efficacy. Regression analysis revealed LOE3D, SelfEfficacy,
PVEefficacy explain significant portions of the variance in VE efficacy. Results of the study provide further evidence that task performance may share relationships with PVEefficacy and SelfEfficacy, and that affective constructs, such as PVEefficacy, and SelfEfficacy may serve as alternative, subjective measures of task performance that account for VE efficacy.

Medical image retrieval with query-dependent feature fusion based on one-class SVM

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.

User-centered design and evaluation of an interactive visual-haptic-auditory interface : a user study on assembly

Utilizing user-centred system design and evaluation method has become an increasingly important tool to foster better usability in the field of virtual environments (VEs). In recent years, although it is still the norm that designers and developers are concerning the technological advancement and striving for designing impressive multimodal multisensory interfaces, more and more awareness are aroused among the development team that in order to produce usable and useful interfaces, it is essential to have users in mind during design and validate a new design from users' perspective. In this paper, we describe a user study carried out to validate a newly developed haptically enabled virtual training system. By taking consideration of the complexity of individual differences on human performance, adoption and acceptance of haptic and audio-visual I/O devices, we address how well users learn, perform, adapt to and perceive object assembly training. We also explore user experience and interaction with the system, and discuss how multisensory feedback affects user performance, perception and acceptance. At last, we discuss how to better design VEs that enhance users perception, their interaction and motor activity.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.