Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination

This paper aims at evaluating the methods of multiclass support vector machines (SVMs) for effective use in distance relay coordination. Also, it describes a strategy of supportive systems to aid the conventional protection philosophy in combating situations where protection systems have maloperated and/or information is missing and provide selective and secure coordinations. SVMs have considerable potential as zone classifiers of distance relay coordination. This typically requires a multiclass SVM classifier to effectively analyze/build the underlying concept between reach of different zones and the apparent impedance trajectory during fault. Several methods have been proposed for multiclass classification where typically several binary SVM classifiers are combined together. Some authors have extended binary SVM classification to one-step single optimization operation considering all classes at once. In this paper, one-step multiclass classification, one-against-all, and one-against-one multiclass methods are compared for their performance with respect to accuracy, number of iterations, number of support vectors, training, and testing time. The performance analysis of these three methods is presented on three data sets belonging to training and testing patterns of three supportive systems for a region and part of a network, which is an equivalent 526-bus system of the practical Indian Western grid.

Subject Teacher Training and Teaching in English

The purpose of this Master s thesis is on one hand to find out how CLIL (Content and Language Integrated Learning) teachers and English teachers perceive English and its use in teaching, and on the other hand, what they consider important in subject teacher education in English that is being planned and piloted in STEP Project at the University of Helsinki Department of Teacher Education. One research question is also what kind of language requirements teachers think CLIL teachers should have. The research results are viewed in light of previous research and literature on CLIL education. Six teachers participate in this study. Two of them are English teachers in the comprehensive school, two are class teachers in bilingual elementary education, and two are subject teachers in bilingual education, one of whom teaches in a lower secondary school and the other in an upper secondary school. One English teacher and one bilingual class teacher have graduated from a pilot class teacher program in English that started at the University of Helsinki in the middle of the 1990 s. The bilingual subject teachers are not trained in English but they have learned English elsewhere, which is a particular focus of interest in this study because it is expected that a great number of CLIL teachers in Finland do not have actual studies in English philology. The research method is interview and this is a qualitative case study. The interviews are recorded and transcribed for the ease of analysis. The English teachers do not always use English in their lessons and they would not feel confident in teaching another subject completely in English. All of the CLIL teachers trust their English skills in teaching, but the bilingual class teachers also use Finnish during lessons either because some teaching material is in Finnish, or they feel that rules and instructions are understood better in mother tongue or students English skills are not strong enough. One of the bilingual subject teachers is the only one who consciously uses only English in teaching and in discussions with students. Although teachers good English skills are generally considered important, only the teachers who have graduated from the class teacher education in English consider it important that CLIL teachers would have studies in English philology. Regarding the subject teacher education program in English, the respondents hope that its teachers will have strong enough English skills and that it will deliver what it promises. Having student teachers of different subjects studying together is considered beneficial. The results of the study show that acquiring teaching material in English continues to be the teachers own responsibility and a huge burden for the teachers, and there has, in fact, not been much progress in the matter since the beginning of CLIL education. The bilingual subject teachers think, however, that using one s own material can give new inspiration to teaching and enable the use of various pedagogical methods. Although it is questionable if the language competence requirements set for CLIL teachers by the Finnish Ministry of Education are not adhered to, it becomes apparent in the study that studies in English philology do not necessarily guarantee strong enough language skills for CLIL teaching, but teachers own personality and self-confidence have significance. Keywords: CLIL, bilingual education, English, subject teacher training, subject teacher education in English, STEP

Training and Voids in Receive Antenna Subset Selection in Time-Varying Channels

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N , we highlight a new issue of ``training voids'', in which the last pilot transmission is not fully exploited by the receiver. We then present new ``void-filling'' methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void-filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed-form equations for the symbol error probability with and without void-filling are also developed.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Novel Transmit Precoding Methods for Rayleigh Fading Multiuser TDD-MIMO Systems With CSIT and No CSIR

In this paper, we present novel precoding methods for multiuser Rayleigh fading multiple-input-multiple-output (MIMO) systems when channel state information (CSI) is available at the transmitter (CSIT) but not at the receiver (CSIR). Such a scenario is relevant, for example, in time-division duplex (TDD) MIMO communications, where, due to channel reciprocity, CSIT can be directly acquired by sending a training sequence from the receiver to the transmitter(s). We propose three transmit precoding schemes that convert the fading MIMO channel into a fixed-gain additive white Gaussian noise (AWGN) channel while satisfying an average power constraint. We also extend one of the precoding schemes to the multiuser Rayleigh fading multiple-access channel (MAC), broadcast channel (BC), and interference channel (IC). The proposed schemes convert the fading MIMO channel into fixed-gain parallel AWGN channels in all three cases. Hence, they achieve an infinite diversity order, which is in sharp contrast to schemes based on perfect CSIR and no CSIT, which, at best, achieve a finite diversity order. Further, we show that a polynomial diversity order is retained, even in the presence of channel estimation errors at the transmitter. Monte Carlo simulations illustrate the bit error rate (BER) performance obtainable from the proposed precoding scheme compared with existing transmit precoding schemes.

Broadland Council Training Services – Winning your e-Learning Spurs with Xerte/Maxos

Broadland Council Training Services have reined in their reliance on traditional learning methods by introducing Xerte/Maxos to their equine-based students. Learners who were once deluged by stacks of paper and unable to utilise an internet connection in a horse yard are now able to access interactive learning exercises using Maxos: Xerte on a memory stick. Students are now more engaged and focused on their studies, teaching methods are much more diverse, and success rates have improved.

Somatisation in primary care: experiences of primary care physicians involved in a training program and in a randomised controlled trial

Background: A new intervention aimed at managing patients with medically unexplained symptoms (MUS) based on a specific set of communication techniques was developed, and tested in a cluster randomised clinical trial. Due to the modest results obtained and in order to improve our intervention we need to know the GPs' attitudes towards patients with MUS, their experience, expectations and the utility of the communication techniques we proposed and the feasibility of implementing them. Physicians who took part in 2 different training programs and in a randomised controlled trial (RCT) for patients with MUS were questioned to ascertain the reasons for the doctors' participation in the trial and the attitudes, experiences and expectations of GPs about the intervention. Methods: A qualitative study based on four focus groups with GPs who took part in a RCT. A content analysis was carried out. Results: Following the RCT patients are perceived as true suffering persons, and the relationship with them has improved in GPs of both groups. GPs mostly valued the fact that it is highly structured, that it made possible a more comfortable relationship and that it could be applied to a broad spectrum of patients with psychosocial problems. Nevertheless, all participants consider that change in patients is necessary; GPs in the intervention group remarked that that is extremely difficult to achieve. Conclusion: GPs positively evaluate the communication techniques and the interventions that help in understanding patient suffering, and express the enormous difficulties in handling change in patients. These findings provide information on the direction in which efforts for improving intervention should be directed.

Effects of 12 weeks muscle hipetrophy training program in a young male athlete: stydy case

PURPOSE: The main goals of the present study were: 1) to review some recommendations about how to increase lean body mass; 2) to analyse whether following scientific sources of current recommendations, visible changes can be shown or not in a participant (body composition, strength and blood analyses). METHODS: One male athlete completed 12 weeks of resistance training program and following a diet protocol. Some test were determined such as, strength 6RM, blood analyses, skindfold measurements, body perimeters and impedance test. Body composition measurements were taken 3 times during the program (before-T1, after 6 weeks of intervention period-T2 and at the end of the program-T3). On the other hand, strength tests and blood analyses were performed twice (before and after the program). RESULTS: Strength was increased in general; blood analyses showed that Creatine kinase was increased a 104% and Triglycerides level was decreased a 22.5%; in the impedance test, body mass (1.6%), lean body mass (3.5%) and Body mass index (1.7%) were increased, whereas fat mass was decreased (15.5%); relaxed and contracted biceps perimeters were also increased. CONCLUSION: A muscle hypertrophy training program mixed with an appropriate diet during 12 weeks leads to interesting adaptations related to increase in body weight, lean body mass, biceps perimeters, strength and creatine kinase levels, and a decrease in fat mass.

Integrated treatment of first episode psychosis with online training (e-learning): study protocol for a randomised controlled trial

Background: The integrated treatment of first episode psychosis has been shown to improve functionality and negative symptoms in previous studies. In this paper, we describe a study of integrated treatment (individual psychoeducation complementary to pharmacotherapy) versus treatment as usual, comparing results at baseline with those at 6-month re-assessment (at the end of the study) for these patients, and online training of professionals to provide this complementary treatment, with the following objectives: 1) to compare the efficacy of individual psychoeducation as add-on treatment versus treatment as usual in improving psychotic and mood symptoms; 2) to compare adherence to medication, functioning, insight, social response, quality of life, and brain-derived neurotrophic factor, between both groups; and 3) to analyse the efficacy of online training of psychotherapists. Methods/design: This is a single-blind randomised clinical trial including patients with first episode psychosis from hospitals across Spain, randomly assigned to either a control group with pharmacotherapy and regular sessions with their psychiatrist (treatment as usual) or an intervention group with integrated care including treatment as usual plus a psychoeducational intervention (14 sessions). Training for professionals involved at each participating centre was provided by the coordinating centre (University Hospital of Alava) through video conferences. Patients are evaluated with an extensive battery of tests assessing clinical and sociodemographic characteristics (Positive and Negative Syndrome Scale, State-Trait Anxiety Inventory, Liebowitz Social Anxiety Scale, Hamilton Rating Scale for Depression, Scale to Assess Unawareness of Mental Disorders, Strauss and Carpenter Prognostic Scale, Global Assessment of Functioning Scale, Morisky Green Adherence Scale, Functioning Assessment Short Test, World Health Organization Quality of Life instrument WHOQOL-BREF (an abbreviated version of the WHOQOL-100), and EuroQoL questionnaire), and brain-derived neurotrophic factor levels are measured in peripheral blood at baseline and at 6 months. The statistical analysis, including bivariate analysis, linear and logistic regression models, will be performed using SPSS. Discussion: This is an innovative study that includes the assessment of an integrated intervention for patients with first episode psychosis provided by professionals who are trained online, potentially making it possible to offer the intervention to more patients.

Operatives training programmes in India

During the last few years great changes have taken place in the fishing industry as a result of which production of fish in the world has increased enormously. From an insignificant trade employing tools and methods of primitive nature, fishing in many countries has become an important industry utilizing complex modern vessels equipped with electronic equipment and operating in the high seas with highly mechanized fishing gear.

Report of the procedures and methods for continuing assessment of the status of the Hilsa resources

The Bangladesh Fisheries Research Institute (BFRI) sampled length frequency data, reviewed historical catch and effort data and sampled water quality to asses the status of hilsa (Tenualosa ilisha) resources. BFRI conducted a training course for BOBLME members. They also prepared awareness building materials for use in workshops for hilsa fishers.

Report of the training on seagrass conservation and monitoring, Myanmar, 26 April -3 May, 2015

Trainers from the region contributed theory and practical training to trainees from government departments, universities and NGOs relevant to conservation of seagrasses and monitoring methods.

Gaussian Process training with input noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.