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This paper presents a reverse mapping based space vector pulse-width modulation strategy for five- and seven-level diode-clamped multilevel inverters. In this method, the space vector is expressed as a sum of a contender vector and an error vector. Here, the actual sector, which contains the tip of the reference space vector, needs not to be found. The vector nearest to the reference space vector is the contender vector. By calculating the vector difference of the contender vector from the reference vector, the error vector is determined. Using translation method, the error vector is translated to the origin of space vector diagram. There will be (n–1) steps in space vector diagram for an n-level inverter. As the error vector amplitude will be less than one step of the space vector diagram, the two-level space vector method can be applied. To obtain a vector that is very close to the reference vector, the determined switching states can be added to the contender vector. This technique can be extended to any n-level multilevel inverter. The performance of five-level and seven-level diode-clamped multilevel inverters is presented using MATLAB/Simulink. The performance of inverters is evaluated by using induction motor load. The experimental results using field programmable gate array are presented for seven-level diode-clamped multilevel inverter. [ABSTRACT FROM PUBLISHER]

Space vector modulation (SVM) is a widely used method for multilevel inverters. Most of the SVM methods are nearest three vector (N3V) based schemes. Therefore, several researchers have proposed advanced implementation algorithms with reduced complexity for N3V methods. However, topologies like neutral point (NP) clamped inverter (NPCI) require some distinctive methods (called as non-N3V methods) to balance NP voltage for high modulation index and low power factor operations. Currently, the existing non-N3V methods exercise highly complex algorithms for duty ratio calculation and subtriangle identification. This paper presents a new approach, suitable for both N3V and non-N3V methods, with reduced implementation complexity. The proposed algorithm is based on an equivalent two-level space vector diagram and does not require any trigonometry, coordinate transformation, or volt-second balance solution. Unlike other carrier-based methods, it can change the switching sequence as well as the duration of redundant vectors. Moreover, the proposed approach does not require extra steps for implementing hybrid modulation and it can also be extended to multilevel topologies. One N3V, two non-N3V, and hybrid methods are developed using the proposed modulation algorithm, and are compared in detail. Simulation and experimentation on a three-level NPCI verify the validity of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Power converters using deterministic switching frequency schemes have switching noise concentrated at harmonic frequencies, resulting in enhanced electromagnetic emission. To suppress the harmonic spikes in three-phase multilevel voltage source converters, a digital control scheme based on dithered sigma–delta modulation (SDM) is proposed in this paper. Introducing a dithered sequence in an SDM varies the switching frequency randomly, resulting in the suppression of the spurious harmonic spikes in the output spectrum even with a regular control input. Although the switching frequency varies randomly, the minimum pulsewidth of the proposed scheme is the sampling time period, avoiding the minimum pulsewidth problem. Instead of a conventional scalar quantizer, the quantizer in the proposed dithered sigma–delta converter uses the principle of space vector quantization. The proposed scheme is experimentally verified on a constant $v/f$ open-loop induction motor drive for a three-level inverter realized by cascading two two-level inverters. The performance of the proposed scheme is compared with different pulsewidth modulation schemes in the entire modulation index, including the overmodulation region. [ABSTRACT FROM PUBLISHER]

An (N, K) codebook is a set of N unit-norm code vectors in a K-dimensional vector space. For its applications, it is desired that the maximum magnitude of inner products between a pair of distinct code vectors should be as small as possible, meeting the Welch bound equality strictly or asymptotically. In this paper, an (N, K) codebook is constructed from a K \times N partial matrix with K < N, where each code vector is equivalent to a column of the matrix. To obtain the K \times N matrix, K rows are selected from a J \times N matrix \bm\Phi , associated with a binary sequence of length J and Hamming weight K, where a set of the selected row indices is equivalent to the index set of nonzero entries of the binary sequence. It is then discovered that the maximum magnitude of inner products between a pair of distinct code vectors is determined by the maximum magnitude of \bm\Phi -transform of the binary sequence. Thus, constructing a codebook with small magnitude of inner products is equivalent to finding a binary sequence where the maximum magnitude of its \bm\Phi -transform is as small as possible. From the discovery, new classes of codebooks with nontrivial bounds on the maximum inner products are constructed from Fourier and Hadamard matrices associated with binary sequences. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive analytical investigation of the four-switch three-phase (B4) voltage source inverter. The double Fourier integral analysis is used as an analytical approach to determine the phase-leg switched voltage spectrum under condition of natural sampling. The performance of two proposed switching sequences are subsequently evaluated when the harmonic copper losses are chosen as performance criterion. For a clear identification of dc-link voltage, the spectrum of dc-link current is characterized by convolution of switching function spectrum and corresponding phase current spectrum, in which fundamental component appears causing offset and fluctuation in dc-link two-capacitor voltages. The offset can be suppressed by certain switching states, whereas the effect of fluctuation must be neutralized through analytical compensation for modulating waveforms. Otherwise, symmetry of three-phase output currents and reliable operation of the system will not be retained. Finally, the analytically calculated spectrum of output voltage and dc-link current are demonstrated by comparing with those obtained by fast Fourier transform (FFT) analysis of simulated waveforms in MATLAB/SIMULINK. The capacitor voltage offset suppression and fluctuation effect neutralization are carried out by simulations and experiments, in which the results confirm the validity of the analytical investigation. [ABSTRACT FROM AUTHOR]

The goal of data anonymization is to allow the release of scientifically useful data in a form that protects the privacy of its subjects. This requires more than simply removing personal identifiers from the data because an attacker can still use auxiliary information to infer sensitive individual information. Additional perturbation is necessary to prevent these inferences, and the challenge is to perturb the data in a way that preserves its analytic utility. No existing anonymization algorithm provides both perfect privacy protection and perfect analytic utility. We make the new observation that anonymization algorithms are not required to operate in the original vector-space basis of the data, and many algorithms can be improved by operating in a judiciously chosen alternate basis. A spectral basis derived from the data's eigenvectors is one that can provide substantial improvement. We introduce the term spectral anonymization to refer to an algorithm that uses a spectral basis for anonymization, and give two illustrative examples. We also propose new measures of privacy protection that are more general and more informative than existing measures, and a principled reference standard with which to define adequate privacy protection. [ABSTRACT FROM AUTHOR]

We present a closed-loop approach to solve the semi-autonomous nonholonomic vehicle-parking assistant problem. The method allows for a priori unknown and potentially high vehicle speeds and dynamic environment variations. The computationally low-cost, memory-efficient algorithm respects constraints on the steering angle and its first two derivatives, allowing for comfortable, safe, and smooth steering motions. A parking situation is captured by an arrangement of precalculated vector fields of feasible nonholonomic paths, by which the vehicle is controlled toward a desired goal configuration inside a parking bay. Using standard onboard environment perception sensors, the system adapts to changes in the environment and avoids collisions whenever possible, without aborting a parking procedure. [ABSTRACT FROM AUTHOR]

A simple and effective space vector pulse width- modulation technique for six-phase voltage-source-inverter-fed split-phase induction motors is presented in this paper. The approach employs dual three-phase space vector modulators equipped with overmodulation schemes that compensate for the voltage gain reduction due to the use of two independently controlled three-phase inverters. It secures full control of the voltage gain up to the maximum achievable gain with negligible low-order harmonics and utilizes a simple classification algorithm for the implementation of the space vector modulation (SVM) in both linear and overmodulation modes. Experimental results are provided to verify the validity of the proposed SVM split-phase approach. [ABSTRACT FROM AUTHOR]

Simplified space vector modulation (SVM) techniques for multilevel converters are being developed to improve factors such as the computational cost, number of commutations, and voltage distortion. The feedforward SVM presented in this paper takes into account the actual dc capacitor voltage unbalance of the multilevel power converter. The resulting technique is a low-cost generalized feedforward 3-D SVM method and is particularized for three-phase multilevel diode-clamped converters. This new modulation technique can be applied to topologies where the gamma component may not be zero. The computational cost of the proposed method is similar to those of comparable methods and it is independent of the number of levels of the power converter. Experimental results using a three-level diode-clamped converter are presented to validate the proposed modulation technique. [ABSTRACT FROM AUTHOR]

Most streamline generation algorithms either provide a particular density of streamlines across the domain or explicitly detect features, such as critical points, and follow customized rules to emphasize those features. However, the former generally includes many redundant streamlines, and the latter requires Boolean decisions on which points are features (and may thus suffer from robustness problems for real-world data). We take a new approach to adaptive streamline placement for steady vector fields in 20 and 3D. We define a metric for local similarity among streamlines and use this metric to grow streamlines from a dense set of candidate seed points. The metric considers not only Euclidean distance, but also a simple statistical measure of shape and directional similarity. Without explicit feature detection, our method produces streamlines that naturally accentuate regions of geometric interest. In conjunction with this method, we also propose a quantitative error metric for evaluating a streamline representation based on how well it preserves the information from the original vector field. This error metric reconstructs a vector field from points on the streamline representation and computes a difference of the reconstruction from the original vector field. [ABSTRACT FROM AUTHOR]

We describe and analyze a discriminative algorithm for learning to align an audio signal with a given sequence of events that tag the signal. We demonstrate the applicability of our method for the tasks of speech-to-phoneme alignment ("forced alignment") and music-to-score alignment. In the first alignment task, the events that tag the speech signal are phonemes while in the music alignment task, the events are musical notes. Our goal is to learn an alignment function whose input is an audio signal along with its accompanying event sequence and its output is a timing sequence representing the actual start time of each event in the audio signal. Generalizing the notion of separation with a margin used in support vector machines for binary classification, we cast the learning task as the problem of finding a vector in an abstract inner-product space. To do so, we devise a mapping of the input signal and the event sequence along with any possible timing sequence into an abstract vector space. Each possible timing sequence therefore corresponds to an instance vector and the predicted timing sequence is the one whose projection onto the learned prediction vector is maximal. We set the prediction vector to be the solution of a minimization problem with a large set of constraints. Each constraint enforces a gap between the projection of the correct target timing sequence and the projection of an alternative, incorrect, timing sequence onto the vector. Though the number of constraints is very large, we describe a simple iterative algorithm for efficiently learning the vector and analyze the formal properties of the resulting learning algorithm. We report experimental results comparing the proposed algorithm to previous studies on speech-to-phoneme and music-to-score alignment, which use hidden Markov models. The results obtained in our experiments using the discriminative alignment algorithm are comparable to results of state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Topic detection (TD) is a fundamental research issue in the Topic Detection and Tracking (TDT) community with practical implications; TD helps analysts to separate the wheat from the chaff among the thousands of incoming news streams. In this paper, we propose a simple and effective topic detection model called the temporal Discriminative Probabilistic Model (DPM), which is shown to be theoretically equivalent to the classic vector space model with feature selection and temporally discriminative weights. We compare DPM to its various probabilistic cousins, ranging from mixture models like von-Mises Fisher (vMF) to mixed membership models like Latent Dirichlet Allocation (LDA). Benchmark results on the TDT3 data set show that sophisticated models, such as vMF and LDA, do not necessarily lead to better results; in the case of LDA, notably worst performance was obtained under variational inference, which is likely due to the significantly large number of LDA model parameters involved for document-level topic detection. On the contrary, using a relatively simple time-aware probabilistic model such as DPM suffices for both offline and online topic detection tasks, making DPM a theoretically elegant and effective model for practical topic detection. [ABSTRACT FROM AUTHOR]