Query: A Single-Stage LED Driver Based on Interleaved Buck–Boost Circuit and LLC Resonant Converter

Relevant passages:
Optimal design methodology for LLC resonant converter <=SEP=> Although LLC resonant converter can achieve wide operation range with high efficiency, lack of design methodology makes it difficult to be implemented. In this paper, based on the theoretical analysis on the operation principles during normal condition and holdup time, the relationship between converter efficiency and operation range with different circuit parameters has be revealed. An optimal design methodology has been developed based on the revealed relationship. A 1MHz, 1kW LLC converter is designed to verify the proposed method.

LLC series resonant converter based LED lamp driver with ZVS <=SEP=> This paper proposes an LLC resonant converter based LED (Light Emitting Diode) lamp driver with high power factor. The proposed circuit uses a boost converter for PFC (Power Factor Correction) which operates in continuous conduction mode (CCM) and a quasi half bridge resonant converter to drive the LED lamp load. The LLC converter is designed such that solid state switches of quasi half bridge are working under zero voltage switching (ZVS) to reduce switching losses. The analysis, design, modeling and simulation of 50 W LED driver are carried out using MATLAB/Simulink tool for universal AC mains. The power quality indices are calculated such as total harmonic distortion of AC mains current (THDi), power factor (PF) and crest factor (CF) to evaluate the performance of proposed LED lamp driver.

Single-stage Flyback converter for constant current output LED driver with power factor correction <=SEP=> This paper presents a LED driver circuit based on single-stage Flyback converter with power factor correction. Voltage following control and Nonlinear carrier control are applied to constant current mode Flyback converter which is combined with constant current driver technique. By designing the outer loop to stabilize the output voltage and the inner loop to shape input current waveform, that makes the system achieve unity power factor. The circuit doesn't need an expensive multiplier as classical multiplier control. To verify the performance, the proposed circuit was confirmed by simulation and experiment. From the results, the proposed system shows high power factor with 0.96 and constant current output.

Approximate analysis of the resonant LCL DC-DC converter <=SEP=> An approximate analysis of LCL converters operating above and below resonance is presented. The study, based on replacing the rectifier and load by an equivalent parallel resistance, applies the fundamental harmonics method and the assumption that the current through the diodes of the output rectifier has a sinusoidal waveform. Equations obtained for the conduction angle of these diodes (less than /spl pi/ in discontinuous and equal to /spl pi/ in continuous conduction modes) are used for developing the output-to-input voltage ratio as a function of three independent parameters. The theoretical and experimental results are in good agreement.

Steady-State Analysis of the Series Resonant Converter <=SEP=> The series resonant converter is analyzed in steady state, and for constant switching frequency the output current and voltage characteristics are found to be ellipses. The converter operating point can then be easily obtained by superimposing a load line on these elliptical characteristics. Peak resonant capacitor voltage and inductor current are also plotted in the output plane and are dependent to first order only on output current. When peak voltage and current are plotted in this manner, the dependence of component stresses on operating point is clearly revealed. The output characteristics are modified to include the effect of transistor and diode voltage drops, and experimental verification is presented.

Culture and Conformity : A Meta-Analysis of Studies Using Asch ' s ( 1952 b , 1956 ) Line Judgment Task <=SEP=> A meta-analysis of conformity studies using an Asch-type line judgment task (1952b, 1956) was conducted to investigate whether the level of conformity has changed over time and whether it is related crogs-culturally to individualism-collectivism. The fiterature search produced 133 studies drawn from 17 countries. An analysis of U.S. studies found that conformity has declined since the 1950s. Results from 3 surveys were used to assess a country's individualism-collectivism, and for each survey the measures were found to be significantly related to conformity. Collectivist countries tended to show higher levels of conformity than individualist countries. Conformity research must attend more to cultural variables and to their role in the processes involved in social influence.

A Survey on Offline Handwriting Recognition Systems <=SEP=> Handwriting recognition is one of the most challenging and fascinating areas in the field of image processing and machine learning. Its various practical applications include digitizing documents, reading handwritten notes and addresses on postcards, reading the amounts on checks and so on. This paper provides an overview of the different techniques used in the different phases of offline handwriting recognition.

Online Tracker Optimization for Multi-Pedestrian Tracking Using a Moving Vehicle Camera <=SEP=> Multi-pedestrian tracking (MPT) on the road is closely related to a reduction in the possibility of pedestrian–vehicle collisions when using advanced driver assistance systems. Therefore, this paper focuses on MPT on real roads using a moving camera. Although convolutional neural network (CNN)-based single object tracking methods have recently been proposed, the online learning of CNN for MPT is a significant burden in terms of real-time processing. However, because the features extracted from CNN are a good representation and achieve a high generalization capability, to reduce the learning time, a shallow pre-trained CNN was applied in the present study as a feature extractor instead of an object tracker As an online tracker, this study uses random ferns (RF) with pre-trained CNN output feature, which can make the problem computationally tractable and robustness. However, a large RF requires significant amount of memory and computational complexity, and is still a burden in terms of online learning in cases of multi-pedestrian tracking. Therefore, we introduce a teacher–student model compression algorithm for selecting a few optimal ferns for each tracker, thereby, reducing the online learning time. The online learning of a student-RF tracker has an advantage in that it can adaptively update the tracker to achieve robustness against various changes, such as in pose and illumination, and partial occlusions within a limited period of time. The proposed algorithm was successfully applied to benchmark video sequences captured from a moving camera that include multiple pedestrians in various poses. Specifically, the proposed algorithm yields a more accurate tracking performance than the other state-of-the-art methods.

SELF-SERV: A Platform for Rapid Composition of Web Services in a Peer-to-Peer Environment <=SEP=> The automation of Web services interoperation is gaining a considerable momentum as a paradigm for effective Business-to-Business collaboration [2]. Established enterprises are continuously discovering new opportunities to form alliances with other enterprises, by offering value-added integrated services. However, the technology to compose Web services in appropriate time-frames has not kept pace with the rapid growth and volatility of available opportunities. Indeed, the development of integrated Web services is often ad-hoc and requires a considerable effort of lowlevel programming. This approach is inadequate given the size and the volatility of the Web. Furthermore, the number of services to be integrated may be large, so that approaches where the development of an integrated service requires the understanding of each of the underlying services are inappropriate. In addition, Web services may need to be composed as part of a short term partnership, and disbanded when the partnership is no longer profitable. Hence, the integration of a large number of Web services requires scalable and flexible techniques, such as those based on declarative languages. Also, the execution of an integrated service in existing approaches is usually centralised, whereas the underlying services are distributed and autonomous. This calls for the investigation of distributed execution paradigms (e.g., peer-to-peer models), that do not suffer of the scalability and availability problems of centralised coordination [3]. Motivated by these concerns, we have developed the SELF-SERV platform for rapid composition of Web

Metal nanogrids , nanowires , and nanofi bers for transparent electrodes <=SEP=> VOLUME 36 • OCTOBER 2011 • www.mrs.org/bulletin © 2011 Materials Research Society Introduction Transparent electrodes are ubiquitously used in optoelectronic devices where light must pass through a section, and current or voltage needs to be applied. These devices include thin-fi lm solar cells (organic, inorganic, hybrid), displays (liquid-crystal display, plasma display, organic light-emitting diode [OLED]), touch screens (resistive, capacitive), electronic books, smart windows, fl exible lighting, and passive devices (such as transparent heating, transparent speakers). 1 The properties of transparent electrodes are extremely important for device performance, and the specifi cations vary based on types of devices. For example, while source roughness and work function are insignifi cant for resistive touch screens, they are crucial for OLEDs and organic solar cells. 1 Moreover, the optical haze of particular transparent electrodes is benefi cial for high-effi ciency solar cells but problematic for certain types of displays. The mechanical properties of the electrode are crucial for fl exible devices but not important for devices on rigid substrates. Across all devices, however, the optical transparency ( T ) and electrical sheet resistance ( Rs ) are the two most important parameters. Unfortunately, these two parameters are in constant competition with one another. Since Rs values are often reported at different transparencies, it is hard to compare two transparent electrodes directly, and hence a fi gure of merit ( σ dc / σ op , where σ dc is the dc conductivity, and σ op is the optical conductivity) Metal nanogrids, nanowires, and nanofi bers for transparent electrodes

Microwave-Based Noninvasive Concentration Measurements for Biomedical Applications <=SEP=> A Debye relaxation model for the permittivity of aqueous glucose solution is given up to 40 GHz. Measurements of aqueous solutions are compared with invasive measurements on over 40 blood samples. Reflection- and transmission-based techniques are explained, concerning the application in biomedical concentration measurements. Wideband measurements on different sensing structures are introduced. Reflection- and transmission-based sensors are applied to a six-port reflectometer, as well as a homodyne vector network analyzer.

Experimental Results of High-Efficiency Resonant Coupling Wireless Power Transfer Using a Variable Coupling Method <=SEP=> We experimentally demonstrate that the efficiency of wireless power transfer utilizing resonant coupling is improved by applying the “matching condition.” The `matching condition' is extracted from an equivalent circuit model of a magnetically coupled wireless power transfer system. This condition is achieved by varying the coupling factor between the source (load) and the internal resonator. Applying this technique results in efficiency improvements of 46.2% and 29.3% at distances of 60 cm and 1 m, respectively. The maximum efficiency is 92.5% at 15 cm. A circuit model based on the extracted parameters of the resonators produces results similar to the experimental data.

Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol. <=SEP=> INTRODUCTION
Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting.


METHODS AND ANALYSIS
We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results.


ETHICS AND DISSEMINATION
This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.

Passivity of a class of sampled-data systems: Application to haptic interfaces <=SEP=> Passivity of systems comprising a continuous time plant and discrete time controller is considered. This topic is motivated by stability considerations arising in the control of robots and force-reflecting human interfaces (“haptic interfaces”). A necessary and sufficient condition for the passivity of a class of sampled-data systems is derived. An example — implementation of a “virtual wall” via a one degree-of-freedom haptic interface — is presented.

Mapping high dimensional features onto Hilbert curve: Applying to fast image retrieval <=SEP=> The use of high-dimension features is unavoidable in many applications of image retrieval and techniques of dimension reductions are not always efficient. The space-filling curve reduces the number of dimensions to one while preserving the neighborhood relation. In this paper, Hilbert curve, the most neighborhood preserving space-filling curve, is used in shape retrieval. The retrieving is accelerated by ordering the shapes in 1-D dynamical data structure, which enables rapid insertions of new images without changing existing data. A proposal of fast mapping facilitate the computing of 1-D Hilbert indexes from high dimensional features.

An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores <=SEP=> This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.

An arousal-based neural model of infant attachment <=SEP=> We develop an arousal-based neural model of infant attachment using a deep learning architecture. We show how our model can differentiate between attachment classifications during strange situation-like separation and reunion episodes, in terms of both signalling behaviour and patterns of autonomic arousal, according to the sensitivity of previous interaction.

Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph <=SEP=> There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolution of language in Twitter. It is thus impractical to use standard supervised machine learning techniques dependent on annotated training examples. We do without such annotations by using label propagation to incorporate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combination with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach rivals a model supervised with in-domain annotated tweets, and it outperforms the noisily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.

Learning Visual Similarity Measures for Comparing Never Seen Objects <=SEP=> In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g., "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees efficiently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistently outperform the state-of-the-art competitive approaches.

Image compression with auto-encoder algorithm using deep neural network (DNN) <=SEP=> An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.

Ecological momentary assessment. <=SEP=> Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.

Social media for the emergency manager in disaster planning and response <=SEP=> This practitioner paper outlines some of the benefits for the use of social media, from the perspective of a locallevel or county-level emergency manager (EM). As compared to state and national level emergency management, because local level EMs have limited manpower and resources, social media can positively or negatively impact the effectiveness of communication before, during and after disaster strikes. Outlined in this paper are six key points where local EMs have specific needs that could be addressed by the effective use of social media and, in the opinion of the authors, represent the top issues that EMs face when considering how to leverage Twitter, Facebook, YouTube, Instagram and other social media platforms. The six needs addressed in this paper include: 1) Best practices for general social media use by EMs, 2) Social media use for internal command and control within the EM group, 3) Developing situation awareness by monitoring social media, especially prior to predicable events, 4) Communicating disaster preparedness messages through social media, 5) Using social media for gathering damage assessment information during, or immediately following a crisis, and 6) Leveraging social media volunteer groups. This short paper picks up where the Federal Emergency Management Agency’s social media training leaves off and attempts to represent these six needs as use cases for researchers and developers to address in future publications and products.

The Use of Alternative Social Networking Sites in Higher Educational Settings: A Case Study of the E-Learning Benefits of Ning in Education <=SEP=> Distance education as a primary means of instruction is expanding significantly at the college and university level. Simultaneously, the growth of social networking sites (SNS) including Facebook, LinkedIn, and MySpace is also rising among today’s college students. An increasing number of higher education instructors are beginning to combine distance education delivery with SNSs. However, there is currently little research detailing the educational benefits associated with the use of SNSs. Non-commercial, education-based SNSs, such as Ning in Education, have been recently shown to build communities of practice and facilitate social presence for students enrolled in distance education courses. In order to evaluate the largely unexplored educational benefits of SNSs, we surveyed graduate students enrolled in distance education courses using Ning in Education, an education-based SNS, based on their attitudes toward SNSs as productive online tools for teaching and learning. The results of our study suggest that education-based SNSs can be used most effectively in distance education courses as a technological tool for improved online communications among students in higher distance education courses. Introduction The use of distance education courses as a primary instructional delivery option, especially in the higher education community, is expanding at an unprecedented rate. The 9.7% growth rate in the number of college and university students enrolled in at least one online class reported by Allen and Seaman (2007) significantly exceeded the 1.5% growth rate in the overall higher education student population during the same period. Simultaneously, the emergence and growth of commercial social networking sites (SNSs) such as Facebook, Friendster, LinkedIn, LiveJournal, and MySpace has been extensive and widespread (Boyd & Ellison, 2007). Facebook, for example, is currently the fastest growing commercial SNS in the world, with more than 300 million active user profiles (Facebook, 2009). Given the rising popularity of both distance education and SNSs, it seems logical to merge these popular two technologies with the goal of improving online teaching and learning (National School Boards Association [NSBA], 2007; University of Minnesota, 2008). Research has shown that distance education courses are often more successful when they develop communities of practice (Barab & Duffy, 2000; Journal of Interactive Online Learning Brady, Holcomb, and Smith 152 DeSchryver, Mishra, Koehler, & Francis, 2009) as well as encourage high levels of online social presence among students (Anderson, 2005). Fostering a sense of community is critically important, especially in an online environment where students often do not get the opportunity to meet face-to-face with other students or the instructor in the course. Since they facilitate the sharing of information—personal and otherwise—the technologies used in SNSs aid discussion and create intimacy among online students, as they have they ability to connect and build community in a socially and educationally constructed network (Educause Learning Initiative [ELI], 2007). In contrast to SNS, course management systems (CMS), such as Blackboard and Moodle, tend to be very focused and lack the personal touch and networking capacity that SNSs offer. For example, instructors using CMS may pose a question in an online discussion board and each student posts a response. However, these student posts are really not interactions at all, but merely question and answer sessions. Using an SNS that is user centered, rather than class centered, such as a CMS, has the potential to increase student engagement. SNSs can actively encourage online community building, extending learning beyond the boundaries of the classroom (Smith, 2009). A comparison of typical SNS and a traditional CMS appears in Table 1.

On battery State of Charge estimation: A new mixed algorithm <=SEP=> A new SoC estimation algorithm is presented. The estimation is performed mixing the Coulomb-Counting and the Model-Based estimation approaches. The algorithm was tested on a brand new lithium-ion cell which use a nanoscale technology able to grant a more powerful and safer lithium-ion battery than the batteries currently on the market. The cell, called A123-M1, was first characterized and then identified. The cell was identified with a equivalent electrical circuits model which is usually called 2th Randle model: the used test-bench and the identification process are described. Then the SoC estimation problem is described. A briefly review of the state of art of SoC estimation is given and the drawbacks of the methods which are usually used are presented. Finally the new mixed estimation algorithm is described: the algorithm scheme and the results are shown.

A modified two-section UWB Wilkinson power divider <=SEP=> A novel Wilkinson power divider with modification of the traditional two-section Wilkinson power divider by adding one open stub on each branch is proposed. This design method is validated by building a divider working in the UWB band (3.1 GHz - 10.6 GHz). It is better in performance, but is about 10% smaller than the traditional three-section power divider and spares one resistor. The simulation and measurement results of the modified divider show equal power split, a good insertion loss of less than 1.1 dB, a good return loss of all ports of less than - 10 dB, and a good isolation is less than -11 dB over the UWB band.

Online EM for Unsupervised Models <=SEP=> The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.

Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings <=SEP=> Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis. For example, master, seasoned and familiar point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semisupervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman’s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.

Semantic annotation in historical documents <=SEP=> This paper presents an approach to proceed with semantic annotation in historical documents from the 19th century that discuss the constitution of the mother language, the Portuguese Language in Brazil. The objective is to generate a group of semantically annotated documents in agreement with a domain ontology. To provide this domain ontology, the Linguistic Instrument Ontology was employed, and it supported the process of automatic semantic annotation. The results obtained with the annotation were analyzed in comparison with the Gold Standard and they presented an elevated level of coincidence, between 0.86 and 1.00 for the F1-score measure. Besides that, it was possible to locate new documents about the discussed domain in a sample of the Revistas Brazileiras. These results prove the efficacy of the approach of automatic semantic annotation.

Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task <=SEP=> This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).

Active current balancing for parallel-connected silicon carbide MOSFETs <=SEP=> In high power applications of silicon carbide (SiC) MOSFETs where parallelism is employed, current unbalance can occur and affect the performance and reliability of the power devices. In this paper, factors which cause current unbalance in these devices are analyzed. Among them, the threshold voltage mismatch is identified as a major factor for dynamic current unbalance. The threshold distribution of SiC MOSFETs is investigated, and its effect on current balance is studied in experiments. Based on these analyses, an active current balancing scheme is proposed. It is able to sense the unbalanced current and eliminate it by actively controlling the gate drive signal to each device. The features of fine time resolution and low complexity make this scheme attractive to a wide variety of wide-band-gap device applications. Experimental and simulation results verify the feasibility and effectiveness of the proposed scheme.