Because of the broad application of multilevel converters in the high-power area, a cascaded multilevel voltage-source inverter with phase-shifted SPWM (PS-SPWM) switching scheme is proposed as a static synchronous compensator (STATCOM). This can eliminate the bulky and weighty transformers and reduce power loss. In addition, the equivalent carrier frequency can be doubled and the output harmonics will be reduced compared with the STATCOM being put into operation. The operating principle and control methods are analyzed in detail and the feasibility is validated by simulation with MATLAB.

Since the launching of the human genome sequencing project in the 1990s, genomic research has already achieved definite results. At the beginning of the present century, the complete genomes of several model organisms have already been sequenced, including a number of prokaryote microorganisms and the eukaryotes yeast (Saccharomyces cerevisiae), nematode (C. elegans), fruit fly (Drosophila melanogaster) and thale cress (Arabidopsis thaliana) as well as the major part of the human genome. These achievements signified that a new era of data mining and analysis on the human genome had commenced. The language of human genetics would gradually be read and understood, and the genetic information underlying metabolism, development, differentiation and evolution would progressively become known to mankind. Large amounts of data are already accumulating, but at present many of the rules that should guide the understanding of this information are yet unknown. Bioinformatics research is thus not only becoming more important, but is also faced with severe challenges as well as great opportunities.

The aims of this study are to develop the color density concept and to propose the color density based color difference formulas. The color density is defined using the metric coefficients that are based on the discrimination ellipses and the locations of the colors in the color space. The ellipse sets are the MacAdam ellipses in the CIE 1931 xy-chromaticity diagram and the chromaticity-discrimination ellipses in the CIELAB space. The latter set was originally used to develop the CIEDE2000 color difference formula. The color difference can be calculated from the color density for the two colors under consideration. As a result, the color density represents the perceived color difference more accurately, and it could be used to characterize a color by a quantity attribute matching better to the perceived color difference from this color. Resulting from this, the color density concept provides simply a correction term for the estimation of the color differences. In the experiments, the line element formula and the CIEDE2000 color difference formula performed better than the color density based difference measures. The reason behind this is in the current modeling of the color density concept. The discrimination ellipses are typically described with three-dimensional data consisting of two axes, the major and the minor, and the inclination angle. The proposed color density is only a one-dimensional corrector for color differences; thus, it cannot capture all the details of the ellipse information. Still, the color density gives clearly more correct estimations to perceived color differences than Euclidean distances using directly the coordinates of the color space.

The article proposes a new algorithm to improve the security of image encryption based on two-dimensional chaotic maps. Chaotic maps are often used in encrypting images. However, the encryption has periodicity, no diffusion, and at the same time, the real keys space of encryption are fewer than the theoretical keys space, which consequently results in potential security problems. Thus, this article puts forward several ways to solve the problems including adding diffusion mechanism, changing the design of keys and developing a composite encryption system. It designs an algorithm for the version B of the discretized baker map, which is one of the most prevalent chaotic maps, based on which a new image encryption is proposed to avoid the above problems. The simulation results show that the new encryption algorithm is valid and the result can be applied to other two-dimensional chaotic maps, such as the cat map.

The present article gives an introduction to information geometry and surveys its applications in the area of machine learning, optimization and statistical inference. Information geometry is explained intuitively by using divergence functions introduced in a manifold of probability distributions and other general manifolds. They give a Riemannian structure together with a pair of dual flatness criteria. Many manifolds are dually flat. When a manifold is dually flat, a generalized Pythagorean theorem and related projection theorem are introduced. They provide useful means for various approximation and optimization problems. We apply them to alternative minimization problems, YingYang machines and belief propagation algorithm in machine learning.

This paper is concerned with two popular and powerful methods in electrical drive applications: field-oriented control (FOC) and space vector modulation (SVM). The proposed FOC-SVM method is incorporated with a predictive current control (PCC)-based technique. The suggested method estimates the desirable electrical torque to track mechanical torque at a fixed speed operation of permanent magnet synchronous motor (PMSM). The estimated torque is used to calculate the reference current based on FOC. In order to improve the performance of the traditional SVM, a PCC method is established as a switching pattern modifier. Therefore, PCC-based SVM is employed to further minimize the torque ripples and transient response. The performance of the controller is evaluated in terms of torque and current ripple and transient response to step variations of the torque command. The proposed method has been verified with MATLAB-Simulink model. Simulation results confirm the ability of this technique in minimizing the torque and speed ripples and fixing switching frequency, simultaneously. However, it is sensitive to parameter changes.

For a long time, store-and-forward had been the transport mode in network communications. In other words, information had been regarded as a commodity that only needs to be routed through the network, possibly with replication at the intermediate nodes. In the late 1990’s, a new concept called network coding fundamentally changed the way a network can be operated. Under the paradigm of network coding, information can be processed within the network for the purpose of transmission. It was demonstrated that compared with store-and-forward, the network throughput can generally be increased by employing network coding. Since then, network coding has made significant impact on different branches of information science. The impact of network coding has gone as far as mathematics, hysics, and biology. This expository work aims to be an introduction to this fast-growing subject with a detailed discussion of the basic theoretical results.

This paper develops a computerized empty glass bottle inspection method. Wavelet transform and morphologic methods were employed to extract features of the bottle body and the finish from images. Fuzzy support vector machine neural network was adopted as classifiers for the extracted features. Experimental results indicated that the accuracy rate can reach up to 97% by using the method developed to inspect empty glass bottles.

The frequency modulated continuous wave (FMCW) radar has the characteristics of low probability of interception, good hidden property and the ability to counter anti-radiation missiles. This paper proposes a new method for high-speed ground moving target detection (GMTD) using triangular modulation FMCW. According to the characteristic of the opposite range shift induced by the upslope and downslope modulation FMCW, the upslope and downslope are imaged, respectively. After compensation of continuous motion of the platform and time difference between upslope and downslope signals for imaging, the moving target can be detected through displaced phase center antenna (DPCA) technology. When the moving target is detected, the moving target image is extracted, and correlation processing is used to obtain the range shift, which can be used to estimate the target radial velocity, and further to find the real position of the target. The effectiveness of this method is verified by the result of computer simulation.

A new mutual coupling compensation method based on a new mutual impedance matrix, as well as its application to dipole arrays, are proposed. This new mutual impedance matrix is deduced by EMF method, based on the current distribution obtained by the characteristic basis function method. It appears in a concise and explicit formulation that facilitates the numerical calculation. The compensation performance is demonstrated and evaluated through its application in DOA estimation. Numerical results show that the proposed method exhibits excellent compensation performance compared with conventional mutual impedance matrix approaches.

To improve system performance and reduce the complexity and cost of receiver hardware, we investigated a new multiple-input multiple-output (MIMO) scheme combining maximal-ratio transmitting and receiver antenna selection (MRT/RAS). In this scheme, a single receiving antenna, which maximizes the signal-to-noise ratio (SNR) at the receiver, is selected for demodulation. The closed-form outage probability and the bit error rate (BER) of the MRT/RAS system are both presented. The simulation demonstrates that the MRT/RAS scheme can achieve a full diversity order as if all the receiving antennas were used. It is shown that the MRT/RAS scheme outperforms some more complex space-time codes of the same spectral efficiency. The analytical results are verified by simulation. In the end, we also analyze the MRT/RAS system based on partial channel information.

Most traditional artificial intelligence (AI) systems of the past decades are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam’s razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse’s thesis of the computer-generated universe. We first briefly review the history of AI since Gödel’s 1931 paper, then discuss recent post-2000 approaches that are currently transforming general AI research into a formal science.

Non-negative matrix factorization (NMF) is a recently popularized technique for learning partsbased, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) is proposed in this paper. We assume that some entries of the data matrix may be arbitrarily corrupted, but the corruption is sparse. RNMF decomposes the non-negative data matrix as the summation of one sparse error matrix and the product of two non-negative matrices. An efficient iterative approach is developed to solve the optimization problem of RNMF. We present experimental results on two face databases to verify the effectiveness of the proposed method.

In this article, we attempt to document a technical overview on modern miniature unmanned rotorcraft systems. We first give a brief review on the historical development of the rotorcraft unmanned aerial vehicles (UAVs), and then move on to present a fairly detailed and general overview on the hardware configuration, software integration, aerodynamic modeling and automatic flight control system involved in constructing the unmanned system. The applications of the emerging technology in the military and civilian domains are also highlighted.

This article studies a third-order trajectory planning method for point-to-point motion. All available instances for third-order trajectory planning are first analyzed. To distinguish those, three criteria are presented relying on trajectory characteristics. Following that, a fast preprocessing approach considering the trajectory as a whole is given based on the criteria constructed and system constraints. Also, the time-optimality of the trajectory is obtained. The relevant formulas are derived with the combination of geometrical symmetry of trajectory and area method. As a result, an accurate algorithm and its implementation procedure are proposed. The experimental results show the effectiveness and precision of the proposed method. The presented algorithm has been applied in semiconductor manufacturing equipment successfully.

Among the separation techniques used in industry, the triboelectric separation of insulating particles using rotary tube is an efficient technology employed in waste recovery and mineral industries. This process, also called free-fall triboelectric separation, is widely used for the sorting and the purification of granular materials resulting from industrial plastic wastes. This paper aims at the achievement of a comprehensive description of a laboratory triboelectric separator built up by the authors and its utilization for an experimental study carried out on granular samples containing particles of polyvinyl chloride (PVC) and polyethylene (PE). Thus, among the variable factors of the process, we analyzed the influence of the most important ones, i.e., the rotational speed of the cylinder n (rpm), the applied high voltage U (kV), the charging time of the particles t (s), the mass of the sample m (g), and the composition percentage of the sample C_p (%).

Evolutionary computation (EC) is one of the fastest growing areas in computer science that solves intractable optimization problems by emulating biologic evolution and organizational behave iors in nature. To design an EC algorithm, one needs to determine a set of algorithmic configurations like operator selections and parameter settings. How to design an effective and efficient adaptation scheme for adjusting the configurations of EC algorithms has become a significant and promising research topic in the EC research community. This paper intends to provide a comprehensive survey on this rapidly growing field. We present a classification of adaptive EC (AEC) algorithms from the perspective of how an adaptation scheme is designed, involving the adaptation objects, adaptation evidences, and adaptation methods. In particular, by analyzing the population distribution characteristics of EC algorithms, we discuss why and how the evolutionary state information of EC can be estimated and utilized for designing effective EC adaptation schemes. Two AEC algorithms using the idea of evolutionary state estimation, including the clustering-based adaptive genetic algorithm and the adaptive particle swarm optimization algorithm are presented in detail. Some potential directions for the research of AECs are also discussed in this paper.

To overcome the defects of the duration modeling in the homogeneous Hidden Markov Model (HMM) for speech recognition, a duration-distribution-based HMM (DDBHMM) is proposed in this paper based on a formalized definition of a left-to-right inhomogeneous Markov model. It has been demonstrated that it can be identically defined by either the state duration or the state transition probability. The speaker-independent continuous speech recognition experiments show that by only modeling the state duration in DDBHMM, a significant improvement (17.8% error rate reduction) can be achieved compared with the classical HMM. The ideal properties of DDBHMM give promise to many aspects of speech modeling, such as the modeling of the state duration, speed variation, speech discontinuity, and interframe correlation.

This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors. An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper. In order to accommodate different situations of masses, the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy. 106 benign and 110 malignant tumors were included in this study. We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns. Then the segmented results were evaluated by the classification accuracy. 42 features including age, intensity, shape and texture were extracted from each segmented mass and support vector machine (SVM) was used as a classifier. The classification accuracy was evaluated using the area (A_z) under the receiver operating characteristic (ROC) curve. It was found that the maximum likelihood analysis achieved an A_z value of 0.835, the maximum gradient analysis got an A_z value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_z was 0.948. In addition, compared with traditional region growing algorithm, our proposed algorithm is more adaptive and provides a better performance for future works.

Genetic algorithm (GA) is utilized to design microstrip patch antenna shapes for broad bandwidth. A new project based on GA and high frequency simulation software (HFSS) is proposed to perform optimization. Reasonable agreement between simulated results and measured results of the GA-optimized design is obtained. The optimized patch design exhibits a three-fold enhancement in bandwidth when contrasted with a standard square microstrip antenna, showing the validity of this project.

This article develops a polytopic linear parameter varying (LPV) model and presents a non-fragile H₂ gain-scheduled control for a flexible air-breathing hypersonic vehicle (FAHV). First, the polytopic LPV model of the FAHV can be obtained by using Jacobian linearization and tensor-product (TP) model transformation approach, simulation verification illustrates that the polytopic LPV model captures the local nonlinearities of the original nonlinear system. Second, based on the developed polytopic LPV model, a non-fragile gainscheduled control method is proposed in order to reduce the fragility encountered in controller implementation, a convex optimisation problem with linear matrix inequalities (LMIs) constraints is formulated for designing a velocity and altitude tracking controller, which guarantees H₂ control performance index. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.

With the rapid development of next generation deep sequencing technologies, sequencing cDNA reverse-transcribed from RNA molecules (RNA-Seq) has become a key approach in studying gene expression and transcriptomes. Because RNA-Seq does not rely on annotation of known genes, it provides the opportunity of discovering transcripts that have not been annotated in current databases. Studying the distribution of RNA-Seq signals and a systematic view on the potential new transcripts revealed from the signals is an important step toward the understanding of transcriptomes.

By introducing strong parallelism of quantum computing into evolutionary algorithm, a novel quantum genetic algorithm (NQGA) is proposed. In NQGA, a novel approach for updating the rotation angles of quantum logic gates and a strategy for enhancing search capability and avoiding premature convergence are adopted. Several typical complex continuous functions are chosen to test the performance of NQGA. Also, NQGA is applied in selecting the best feature subset from a large number of features in radar emitter signal recognition. The testing and experimental results of feature selection show that NQGA presents good search capability, rapid convergence, short computing time, and ability to avoid premature convergence effectively.

An adaptive background model based on maximum statistical probability and a shadow suppression scheme for indoor and outdoor people detection by exploiting hue saturation value (HSV) color information is proposed. To obtain the initial background scene, the frequency of R, G, and B component values for each pixel at the same position in the learning sequence are respectively calculated; the R, G, and B component values with the biggest ratios are incorporated to model the initial background. The background maintenance, or the so-called background re-initiation, is also proposed to adapt to scene changes such as illumination changes and scene geometry changes. Moving cast shadows generally exhibit a challenge for accurate moving target detection. Based on the observation that a shadow cast on a background region lowers its brightness but does not change its chromaticity significantly, we address this problem in the article by exploiting HSV color information. In addition, quantitative metrics is introduced to evaluate the algorithm on a benchmark suite of indoor and outdoor video sequences. The experimental results are given to show the performance of the algorithm.

On the basis of controlled Lagrangians, a controller design is proposed for underactuated mechanical systems with two degrees of freedom. A new kinetic energy equation (K-equation) independent of the gyroscopic forces is found due to the use of their property. As a result, the necessary and sufficient matching condition comprises the new K-equation and the potential energy equation (P-equation) cascaded, the regular condition, and the explicit gyroscopic forces. Further, for two classes of input decoupled systems that cover the main benchmark examples, the new K-equation, respectively, degenerates from a quasilinear partial differential equation (PDE) into an ordinary differential equation (ODE) under some choice and into a homogeneous linear PDE with two kinds of explicit general solutions. Benefiting from one of the general solutions, the obtained smooth state feedback controller for the Acrobots is of a more general form. Specifically, a constant fixed in a related paper by the system parameters is converted into a controller parameter ranging over an open interval along with some new nonlinear terms involved. Unlike what is mentioned in the related paper, some categories of the Acrobots cannot be stabilized with the existing interconnection and damping assignment passivity based control (IDA-PBC) method. As a contribution, the system can be locally asymptotically stabilized by the selection of the new controller parameter except for only one special case.

With the ever increasing complexity of industrial systems, model-based control has encountered difficulties and is facing problems, while the interest in data-based control has been booming. This paper gives an overview of data-based control, which divides it into two subfields, intelligent modeling and direct controller design. In the two subfields, some important methods concerning data-based control are intensively investigated. Within the framework of data-based modeling, main modeling technologies and control strategies are discussed, and then fundamental concepts and various algorithms are presented for the design of a data-based controller. Finally, some remaining challenges are suggested.

With the continuous improvement of transmission rate, high speed network links require good performance of packet capture. The multiprocessor platform has strong computational capabilities, and brings new chance for high rate packet capture. In this paper, we analyze the performance of common packet capture approaches that are based on general-purpose multiprocessor platform. The analysis contains two aspects: one is the maximum packet capture rate and throughput on multiprocessor platform, the other is central processing unit (CPU) load under the maximum capture rate. By comparing and analyzing the experimental result, we give the maximum packet capture rate and throughput of different capture approaches. Furthermore, we analyze the CPU load which is produced by two capture processes run on the multiprocessor platform simultaneously and make a comparison with the single capture process.

Content-based video copy detection becomes an active research field due to requirement of copyright protection, business intelligence, video retrieval, etc. Although it is assumed in the existing methods that reference database consists of original videos, these videos are difficult to be obtained in many practical cases. In this paper, a copy detection method based on sparse representation is proposed to make use of some imperfect prototypes of original videos maintained in the reference database. A query video is represented as a linear combination of all the videos in the database. Then we can determine that whether the query has sibling videos in the database based on distribution of coefficients and find them out based on reconstruction error. The experiments show that even with very limited dimensional feature, this method can achieve high performance.