Miracle-dz/UniFAR-FTUs huggingface dataset:
High Efficiency Power Converter for Low Voltage High Power Applications

A new low-leakage-inductance low-resistance design approach to low-voltage high-power isolated boost converters is presented. Very low levels of parasitic circuit inductances are achieved by optimizing transformer design and circuit lay-out. Primary side voltage clamp circuits can be eliminated by the use of power MOSFETs fully rated for repetitive avalanche. Voltage rating of primary switches can now be reduced, significantly reducing switch on-state losses. Finally, silicon carbide rectifying diodes allow fast diode turn-off, further reducing losses. Test results from a 1.5 kW full-bridge boost converter verify theoretical analysis and demonstrate very high efficiency. Worst case efficiency, at minimum input voltage maximum power, is 96.8 percent and maximum efficiency reaches 98 percent. Keywords—Switched-mode power supply, fuel cell system, efficiency, transformer, SiC-device.
Research and Realization of Authentication Technique Based on OTP

With the continuous development of network application, the problem of network security has become more and more important. Authentication is one of the mechanisms in security defence, and the authentication information is used to verify the user's legitimacy. The One_-Time Password(OTP) technique which needs no third authentication and its security flaws are analyzed in this paper. With the advantages of the Kerberos system, presents a new technique which saves user spp in the database after it is encrypted with the server's public key. Timestamp and IP address are also added into seed information, which makes an improvement in OTP technique. And the security of authentication system has been developed.
Physical properties of comet 81P Wild 2

81P/Wild 2 is the STARDUST space mission target. This comet was selected because of its newness as a Jupiter family comet and because of the discovery of a trajectory offering a low encounter velocity of only 6.1 km·s. Observations over a wide range of heliocentric distances since 1987 have enable us the greatly enhance our knowledge of 81P/Wild 2, as required to mount a successful space mission. We want to present four sets of observations of comet 81P/Wild 2 which were made in order to search for rotational period of the nucleus during November 11–12, 1996, June 26–28, 1998, September 26–28, 1998 and August 12–17, 1999 using the University of Hawaii 2.2 m telescope. At the time of the observations, the comet was at heliocentric distances, r = 2.28, 3.61, 4.01 and 4.98 AU. The comet was active for all runs, and during 1996 November the coma extended >100 ′′ (1.65×10 5 km at the distance of the comet) at PA≈262 , and during 1998 June the coma extended >51 ′′ (1.34×10 5 km at the distance of the comet) at PA≈254 , and during 1998 September the coma extended >12 ′′ (3.50×10 5 km at the distance of the comet) at PA≈125 , and during 1999 August the coma extended >8 ′′ (2.90×10 4 km at the distance of the comet) at PA≈203 . Using a phase-dispersion minimization technique,we will try to find a possible rotation period.
FtsZ‐Independent Mechanism of Division Inhibition by the Small Molecule PC190723 in Escherichia coli

While cell division is a critical process in cellular proliferation, very few antibiotics have been identified that target the bacterial cell‐division machinery. Recent studies have shown that the small molecule PC190723 inhibits cell division in several Gram‐positive bacteria, with a hypothesized mechanism of action involving direct targeting of the tubulin homolog FtsZ, which is essential for division in virtually all bacterial species. Here, it is shown that PC190723 also inhibits cell division in the Gram‐negative bacterium Escherichia coli if the outer membrane permeability barrier is compromised genetically or chemically. The results show that the equivalent FtsZ mutations conferring PC190723 resistance in Staphylococcus aureus do not protect E. coli against PC190723, and that suppressors of PC190723 sensitivity in E. coli, which do not generically decrease outer membrane permeability, do not map to FtsZ or other division proteins. These suppressors display a wide range of morphological and growth phenotypes, and one exhibits a death phenotype in the stationary phase similar to that of a mutant with disrupted lipid homeostasis. Finally, a complementing FtsZ–msfGFP fusion is used to show that PC190723 does not affect the Z‐ring structure. Taken together, the findings suggest that PC190723 inhibits growth and division in E. coli without targeting FtsZ. This study highlights the importance of utilizing a combination of genetic, chemical, and single‐cell approaches to dissect the mechanisms of action of new antibiotics, which are not necessarily conserved across bacterial species.
Enzymatic Bromo-ether Cyclization of Laurediols with Bromoperoxidase

Natural (3E,6R,7R)- and (3Z,6S,7S)-laurediols were subjected to enzymatic reaction with the partially purified bromoperoxidase from the red alga, Laurencia nipponica, in the presence of H2O2 and NaBr to afford laurencin and prelaureatin, respectively. The further reaction of these products with bromoperoxidase gave rise to the bicyclic bromo-ethers.
On-orbit Performance Assessment of Beijing.1 Microsatellite Sensors

The Beijing-1 microsatellite,launched on October 27th in 2005,is the first applied earth observing microsat- ellite of China.It is also one of the members of the international Disaster Monitoring Constellation(DMC). Its total mass is about 167kg.The designed minimum operational lifetime is 5 years.Beijing-1 microsatellite applies DMC multispectral cameras with a high resolution panchromatic imager based on the standard of SSTL.After its launch,Beijing-1 had been tested and evaluated for several months.From January to March in 2006,the primary purpose of the testing work was to evaluate the performance of the sensors.This paper introduced the tested items,the experiments,the data sets,the meth- ods and the results.Based on the characteristics of Beijing-1 mierosatellite and other earth observing satellites,a compre- hensive specifications of on-orbit testing and evaluating was proposed firstly by this paper.According to these specifica- tions,four in-flight testing and evaluating experiments were carried out in Beijing,Wuxi,Hefei and in Baihu farm which is located in Anhui province.In the four sites,three simultaneous measurement datasets were acquired respectively:the Beijing-1 images,the in situ surface reflectance and the atmosphere measurements,and the geometric measurements of linear objects and other typical objects.Based on the data sets,the geometric performance,radiometric performance, spectral performance and general quality of Beijing-1 sensors and images were tested and evaluated.The results of some key parameters,such as the resolution of panchromatic camera,the modulation transform function(MTF),the signal-to- noise(SNR),the linearity and dynamic range of panchromatic and multispectral cameras were given in the paper.Judged by results of the four in-flight testing and evaluating experiments,Beijing-lmierosatellite exhibited a good geometric and radiometric performance.More experiments will be taken place,and more parameters will be measured to evaluate Beijing-1 microsatellite comprehensively.
Research and implement of distributed intrusion detection system

With the great development of computer network,the network security problem becomes more serious,the single IDS can not meet the needs of network security,so the DIDS comes forth.A distributed intrusion detection system is brought forward,the inverted connection technology of the rebound Trojan horse is applied to the communication between agents and server,the agents adopt multi-thread technology to capture the packets,and it can detect the Trojan horse by the incorporation of the system process and the port communication.The server can deploy the uniform tactics to the agents and it can monitor the agents in real time,and so the DIDS en-hances the network security.
Exploration in Greedy Best-First Search for Satisficing Planning

This thesis proposes, analyzes and tests different exploration-based techniques in Greedy Best-First Search (GBFS) for satisficing planning. First, we show the potential of exploration-based techniques by combining GBFS and random walk exploration locally. We then conduct deep analysis on how flaws in heuristics impact GBFS’s performance. Uninformative Heuristic Regions (UHRs) and Early Mistakes (EMs) for GBFS are analyzed and illustrated on a number of International Planning Competition (IPC) benchmarks. Corresponding solutions, namely Greedy Best-First Search with Local Exploration (GBFS-LE) and Type-based Greedy BestFirst Search (Type-GBFS), are proposed and shown to outperform GBFS substantially. While this thesis mainly focuses on improving coverage (number of problems solved) with exploration-based techniques, we also introduce the Diverse Anytime Search (DAS) framework, which reduces unproductive time and improves plan quality by randomized exploration. Finally, we integrate these techniques and build the new satisficing planner Jasper, which ranked 4th of 20 planners in the Sequential Satisficing track of IPC-2014 and solved the largest number of problems among non-portfolio-based planners.
Fast image clustering based on convolutional neural network and binary K-means

Visual features used in state-of-the-art image clustering methods lack of learning, which leads to low representational power. Furthermore, the efficiency of traditional clustering methods is low for large image dataset. So, a fast image clustering method based on convolutional neural network and binary K-means is proposed in this paper. Firstly, a large-scale convolutional neural network is employed to learn the intrinsic implications of training images so as to improve the discrimination and representational power of visual features. Secondly, ITQ hash algorithm is applied to map the high dimensional deep features into low-dimensional hamming space, and multi-index hash table is used to index the initial clustering centers so that the nearest center lookup becomes extremely efficient. Finally, image clustering is accomplished efficiently by binary K-means algorithm. Experimental results of ImageNet-1000 datasets indicate that the expression ability of visual features is effectively improved and the image clustering performance is substantially boosted compared with state-of-the-art methods.
Concepts And Theories Of Human Development

Thank you for downloading concepts and theories of human development. As you may know, people have look hundreds times for their favorite novels like this concepts and theories of human development, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some infectious virus inside their desktop computer. concepts and theories of human development is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the concepts and theories of human development is universally compatible with any devices to read.
QoS based Machine Learning Algorithms for Clustering of Cloud Workloads: A Review

Data mining, known as knowledge discovery is a computer-assisted, analytical process of digging through and analyzing large amount of data and extracting knowledge from the data. Data mining technologies has made many industries like marketing, sales, healthcare organization, financial institutions etc. quite successful. It has a lot of benefits in various fields. It helps in the quick analysis of data and has improved the quality of decision making process and is used to turn information into actionable knowledge. In this paper, data mining process and knowledge discovery process is discussed. Three well known data mining classifier algorithms namely ID3, J48 and Naive Bayes are discussed and their performance has been evaluated using different parameters to find the best algorithm. Further, Naive Bayes classifier algorithm is used for classification of workloads based on different Quality of Service (QoS) parameters.
Learning Hierarchical Representations For Video Analysis Using Deep Learning

With the exponential growth of the digital data, video content analysis (e.g., action, event recognition) has been drawing increasing attention from computer vision researchers. Effective modeling of the objects, scenes, and motions is critical for visual understanding. Recently there has been a growing interest in the bio-inspired deep learning models, which has shown impressive results in speech and object recognition. The deep learning models are formed by the composition of multiple non-linear transformations of the data, with the goal of yielding more abstract and ultimately more useful representations. The advantages of the deep models are three fold: 1) They learn the features directly from the raw signal in contrast to the hand-designed features. 2) The learning can be unsupervised, which is suitable for large data where labeling all the data is expensive and unpractical. 3) They learn a hierarchy of features one level at a time and the layerwise stacking of feature extraction, this often yields better representations. However, not many deep learning models have been proposed to solve the problems in video analysis, especially videos ``in a wild''. Most of them are either dealing with simple datasets, or limited to the low-level local spatial-temporal feature descriptors for action recognition. Moreover, as the learning algorithms are unsupervised, the learned features preserve generative properties rather than the discriminative ones which are more favorable in the classification tasks. In this context, the thesis makes two major contributions. First, we propose several formulations and extensions of deep learning methods which learn hierarchical representations for three challenging video analysis tasks, including complex event recognition, object detection in videos and measuring action similarity. The proposed methods are extensively demonstrated for each work on the state-of-the-art challenging datasets. Besides learning the low-level local features, higher level representations are further designed to be learned in the context of applications. The data-driven concept representations and sparse representation of the events are learned for complex event recognition; the representations for object body parts and structures are learned for object detection in videos; and the relational motion features and similarity metrics between video pairs are learned simultaneously for action verification. Second, in order to learn discriminative and compact features, we propose a new feature learning method using a deep neural network based on auto encoders. It differs from the existing unsupervised feature learning methods in two ways: first it optimizes both discriminative and generative properties of the features simultaneously, which gives our features a better discriminative ability. Second, our learned features are more compact, while the unsupervised feature learning methods usually learn a redundant set of over-complete features. Extensive experiments with quantitative and qualitative results on…
Simple and Fast Synthesis of New Axially Chiral Bipyridine N,N′-Dioxides for Highly Enantioselective Allylation of Aldehydes

Unsymmetrically 3,3'-substituted axially chiral bis(tetrahydroisoquinoline) N,N'-dioxides can be prepared in just three steps. They exhibit unique catalytic activity (turnover frequency, enantioselectivity, substrate scope) in the asymmetric allylation of aromatic aldehydes (up to 96% ee). The product of the enantioselective allylation of benzaldehyde served as a building block for the preparation of an intermediate useful in the enantioselective synthesis of diospongines.
Multivalue Learning in ILP

In this paper we present a method to make more expressive the hypotheses searched by ILP algorithms. This method allows to use more than one value in literals. The method we propose constructs new clauses using information obtained analyzing values in literals with mul-tivalue learning. In order to discover such information, we draw from techniques used in inducing trees algorithms.
Research on Time Noun of Discourse Understanding for Nature Language Understanding on Given Domain

According to the characteristic of natural language understanding in discourse understanding on the given domain,based on the knowledge and connotation,discuss and analyze realization method and difficult point for time noun understanding deeply,the purpose is fever to the whole discourse understanding.It preferably solved the technological process of discourse understanding and classify the time noun to construct this system's time noun structures model.Then introduce a method of understanding connotation of time noun.Finally,a method of judging fuzzy equivalency to time noun is given.
Fast Multi-Resolution Colorization of High-Resolution Gray Images

Colorization could be used to increase the visual appeal of old black and white photos and movies.By means of colorization using optimization,one can add colors automatically to a monochrome image or movie after annotating the image with a few color scribbles,based on a simple premise—neighboring pixels in space that have similar intensities should have similar colors.However,the previous method may fail to deal with high-resolution gray images well because of its quite large size of computation.In this paper,the authors utilize features of the image colorization as an optimization problem to overcome this limitation.An image pyramid model with the edge detection is presented to reduce the computation size for the colorization.As a result,the new method can colorize high-resolution gray images to advantage in computing speed and visual effects.
Negation and NegP Developmental Steps in Bilingual Spanish/English Children

Children start innocently speaking about concrete, tangible items. Their cognitive abilities eventually allow for more complex thinking, and thus syntax is born. The question is, how does a child who is born in one environment and at a critical age switched to another language situation able to thrive so smoothly? There are countless aspects which are universal across languages, such as the fact that all languages have categories, principles, and phonemes. Another aspect that all languages share is negative particles. Negation is a complex part of communication because it “has no referent.and is inherently relational in nature” (Pea, 1980, p. 156). Children communicate negation at such an early age, even though the cognitive abilities needed to understand negation are quite abstract. Given these assumptions as the basis of my study, it is important to note that children must all begin with a similar framework for grammar, and thus for negation. In agreement with lead scholars, it is assumed in this study that “children know basic grammatical principles largely by virtue of their innate biological endowment” (Crain & Pietroski, 2002, p. 164).
MANAGING NETWORKED SOFTWARE DEVELOPMENT

Software development is increasingly performed in network organizations. The focus of attention is gradually shifting from the project as a the basic organization towards a more networked type of organization where relationships between the core project and other partners are established, exercised, maintained and dismantled. In this paper we propose a framework intended for software managers to make sense and diagnosis of the network and based on that points to possible action to be taken. The framework comprises two dimensions. In the one dimension the framework is based the theory of social capital that is used to distinguish between the coherent group of co- workers with a well-established community-of- practice where trust prevails and on the other hand a new relationship where formal control replaces trust. In the other dimension the framework is based on the concept of task uncertainty to distinguish between well-defined tasks that call for problem solving and on the other hand ill-defined tasks that call for problem definition. The framework is applied to four cases and is used to illustrate the network management in the four cases and explain some of the processes involved in the management of networked software development.
A Novel Stream Cipher Based on Nondeterministic Finite Automata

The modified Dӧmӧsi's cryptosystem (1) is a stream cipher based on deterministic finite automata without outputs for encoding and decoding. This cryptosystem uses an additional control system to improve the performance to a better linear time without backtracking. This paper, we propose a nondeterministic variant of the cryptosystem, which allows to reduce the dependency of the key automata on a large size and on reversibility of automata.
Approximation algorithms for configuring nonblocking communication networks

To support network applications that require guaranteed throughput, such as interactive audio and video communication, it is desirable to have a network architecture that allows users to reserve bandwidth on paths of links in the network. In such a network, attempts to reserve bandwidth may fail due to insufficient link capacities, i.e., the attempt blocks. In this dissertation, we examine networks with link bandwidths chosen so that the network is nonblocking, subject to some restrictions on the requests that can be made. Most previous work restricts the traffic in the network by specifying the maximum traffic between each pair of nodes. We restrict the traffic by specifying the maximum traffic that may leave or enter each node. With such restrictions, a star network topology often gives a nonblocking network with cost close to optimal, if not exactly optimal. We support this claim by analytical results, and experimental results on randomly generated instances. We also generalize this method of specifying the traffic for larger networks, in which groups of nodes can be organized into clusters with high traffic among nodes within the clusters, but less traffic between nodes in different clusters. This work is applicable to configuring Asynchronous Transfer Mode (ATM) networks, in which multicast connections are possible. We show that if the network nodes are capable of supporting multicast connections without blocking, then the entire network can also.
Algorithm of Estimating Inner Lip Opening Distance Based on Classification of Mouth States

In order to recognize the distance of mouth opening,this article proposed an algorithm of estimating inner lip opening distance based on the classification of mouth states.Firstly,we converted mouth images to YIQ space from RGBspace.Then making use of the number and position of extreme points of Qvalue in tooth,lip and tongue,we classified mouth states into three categories and decided the range of the first difference from different categories.Thus,we could localize the position of the boundary points of inner lip more precisely and compute the opening distance of inner lip in the condition of tooth and tongue.The experimental results show the related coefficient computed by our method is 0.95while method based on contrast enhancement with multi-threshold binarization is 0.90. Our algorithm improves 5percent in related coefficient between actual values and estimated values of inner lip opening.
Research and application of digital shadow-play bones animation

To improve the authenticity and real time of the digital shadow play animation,reduce digital shadow play animation manufacturing difficulty,the digital shadow play model is Abstract and simplified according to the characteristics of digital shadow play animation.The character model is Abstract to skeleton and classified to apply to the digital shadow play animation;Based on cyclic-coordinate descent algorithm,a solution to improve the efficiency of 2D inverse kinematic is proposed.Finally,the experimental results show that this method is effective reduce system cost and achieve real-time control.