Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.

An improved algorithm of Guided Local Search called objective function adjustment algorithm is proposed for combinatorial optimization problems. The performance of Guided Local Search is improved by objective function adjustment algorithm using multipliers which can be adjusted during the search process. Moreover, the idea of Tabu Search is introduced into the objective function adjustment algorithm to further improve the performance. The simulation results based on some TSPLIB benchmark problems showed that the objective function adjustment algorithm could find better solutions than Local Search, Guided Local Search and Tabu Search.

In this letter, we propose an efficient user selection algorithm aiming to select users with less spatially correlation and meet the user number limit of zero-forcing beamforming in downlink multiuser MIMO systems. This algorithm yields a considerable complexity reduction with only a small loss in performance and it only needs partial users' CSI feedback. Coupled with the algorithm, a null space updating method in O(K2) time and a modified proportional fair scheduling algorithm are also proposed.

The diffraction of a transverse magnetic (TM) plane wave by a perfectly conductive surface made up of a periodic array of rectangular grooves is studied by the modal expansion method. It is found theoretically that the reflection coefficient approaches -1 but no diffraction takes place when the angle of incidence reaches a low grazing limit. Such singular behavior is shown analytically to hold for any finite values of the period, groove depth and groove width and is then demonstrated by numerical examples.

In this paper, we propose a robust statistical framework for extracting scenes from a baseball broadcast video. We apply multi-stream hidden Markov models (HMMs) to control the weights among different features. To achieve a large robustness against new scenes, we used a common simple structure for all the HMMs. In addition, scene segmentation and unsupervised adaptation were applied to achieve greater robustness against differences in environmental conditions among games. The F-measure of scene-extracting experiments for eight types of scene from 4.5 hours of digest data was 77.4% and was increased to 78.7% by applying scene segmentation. Furthermore, the unsupervised adaptation method improved precision by 2.7 points to 81.4%. These results confirm the effectiveness of our framework.

Efficient real-time contents distribution services on the Internet are only possible by suppressing the influence of packet losses. One solution for UDP transmission is the use of Forward Error Correction (FEC) based on Reed-Solomon codes. However, a more efficient method is required since this causes the increase of network traffic and includes the weakness to burst packet losses. In this paper, we propose a data recovery method that generates redundant data with the combination of Reed-Solomon codes and convolution of neighboring blocks. We realize the small amount of redundancy and the high reliability in data transmission compared with using only Reed-Solomon codes in the environment that burst packet losses are occurred frequently. We implement proposal method into the network bridge and confirm its efficiency from the viewpoint of data reconstruction from burst packet losses.

We have been investigating an orthogonal frequency division multiple access (OFDMA) based cellular system that is called "dynamic parameter controlled orthogonal frequency and time division multiple access (DPC-OF/TDMA)" for the development of beyond third generation (B3G) mobile communication systems. Moreover, we have already proposed a time alignment control (TAC) to compensate propagation delays that induce a multiple-access interference (MAI) in the uplink OFDMA. However, that TAC includes a large amount of computations. This means that it is quite difficult for the OFDMA systems to implement TAC into volume-limited hardware devices such as field programmable gate array (FPGA). Thus, we propose a new complexity-reduced TAC (CRTAC) in this paper. CRTAC can be implemented into such devices easily. In this paper, we show some computer simulation results, and then evaluate the error rate performances of DPC-OF/TDMA employing CRTAC. Moreover, we also show the benefit of the reasonable level of the implementation complexity made by CRTAC.

In this paper, we propose a new robust model predictive control (MPC) technique for linear parameter varying (LPV) systems expressed as linear systems with feedback parameters. It is based on the minimization of the upper bound of finite horizon cost function using a new parameter dependent terminal weighting matrix. The proposed parameter dependent terminal weighting matrix for norm-bounded uncertain models provides a less conservative condition for terminal inequality. The optimization problem that satisfies the terminal inequality is solved by semi-definite programming involving linear matrix inequalities (LMIs). A numerical example is included to illustrate the effectiveness of the proposed method.

In this paper, we propose to employ a characteristic function based non-Gaussianity measure as a one-unit contrast function for independent component analysis. This non-Gaussianity measure is a weighted distance between the characteristic function of a random variable and a Gaussian characteristic function at some adequately chosen sample points. Independent component analysis of an observed random vector is performed by optimizing the above mentioned contrast function (for different units) using a fixed-point algorithm. Moreover, in order to obtain a better separation performance, we employ a mechanism to choose appropriate sample points from an initially selected sample vector. Finally, some computer simulations are presented to demonstrate the validity and effectiveness of the proposed method.

For obtaining a higher level of generalization capability in supervised learning, model parameters should be optimized, i.e., they should be determined in such a way that the generalization error is minimized. However, since the generalization error is inaccessible in practice, model parameters are usually determined in such a way that an estimate of the generalization error is minimized. A standard procedure for model parameter optimization is to first prepare a finite set of candidates of model parameter values, estimate the generalization error for each candidate, and then choose the best one from the candidates. If the number of candidates is increased in this procedure, the optimization quality may be improved. However, this in turn increases the computational cost. In this paper, we give methods for analytically finding the optimal model parameter value from a set of infinitely many candidates. This maximally enhances the optimization quality while the computational cost is kept reasonable.

In this paper, a method of detecting overlapping speech segments in meetings is proposed. It is known that the eigenvalue distribution of the spatial correlation matrix calculated from a multiple microphone input reflects information on the number and relative power of sound sources. However, in a reverberant sound field, the feature of the number of sources in the eigenvalue distribution is degraded by the room reverberation. In the Support Vector Machines approach, the eigenvalue distribution is classified into two classes (overlapping speech segments and single speech segments). In the Support Vector Regression approach, the relative power of sound sources is estimated by using the eigenvalue distribution, and overlapping speech segments are detected based on the estimated relative power. The salient feature of this approach is that the sensitivity of detecting overlapping speech segments can be controlled simply by changing the threshold value of the relative power. The proposed method was evaluated using recorded data of an actual meeting.

Over the years, many improvements and refinements to the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algorithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The upper bound of the penalty values is also controlled. The technique is easy to implement and computationally inexpensive. In this study, the new approach is applied to the backpropagation learning algorithm as well as the RPROP learning algorithm. The superiority of the new proposed method is demonstrated though many simulations. By applying the extension, the percentage of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. The behavior of the penalty values during training is also analyzed and their active role within the learning process is confirmed.

We discuss a new design methodology of polynomial neural networks (PNN) in the framework of genetic algorithm (GA). The PNN is based on the ideas of group method of data handling (GMDH). Each node in the network is very flexible and can carry out polynomial type mapping between input and output variables. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables, and the type (order) of the polynomials to each node. In this paper, GA is implemented to better use the optimal inputs and the order of polynomial in each node of PNN. The appropriate inputs and order are evolved accordingly and are tuned gradually throughout the GA iterations. We employ a binary coding for encoding key factors of the PNN into the chromosomes. The chromosomes are made of three sub-chromosomes which represent the order, number of inputs, and input candidates for modeling. To construct model by using significant approximation and generalization, we introduce the fitness function with a weighting factor. Comparisons with other modeling methods and conventional PNN show that the proposed design method offers encouraging advantages and better performance.

Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.

Numerous studies have shown that scaling exponents of internet traffic change over time or scaling ranges. In order to analyze long-range dependent traffic with changing scaling exponents over time scales, we propose a multi-scale traffic model that incorporates the notion of a piecewise self-similar process, a process with spectral changes on its scaling behavior. We can obtain a performance curve smoothened over the range of queue length corresponding to time scales with different scaling exponents by adopting multiple self-similar processes piecewise into different spectra of time scale. The analytical method for the multiscale fractional Brownian motion is discussed as a model for this approach. A comparison of the analytical and simulation results, using traffic data obtained from backbone networks, shows that our model provides a good approximation for Gaussian traffic.

Solving the error correcting code is an important goal with regard to communication theory. To reveal the error correcting code characteristics, several researchers have applied a statistical-mechanical approach to this problem. In our research, we have treated the error correcting code as a Bayes inference framework. Carrying out the inference in practice, we have applied the NMF (naive mean field) approximation to the MPM (maximizer of the posterior marginals) inference, which is a kind of Bayes inference. In the field of artificial neural networks, this approximation is used to reduce computational cost through the substitution of stochastic binary units with the deterministic continuous value units. However, few reports have quantitatively described the performance of this approximation. Therefore, we have analyzed the approximation performance from a theoretical viewpoint, and have compared our results with the computer simulation.

It was previously shown that the number of array elements must exceed the number of sources for multiple target direction of arrival (DOA) tracking. This is clearly not practical for code-division multiple access (CDMA) communications since the number of mobile users is very large. To overcome the restriction, adaptive angle tracking approaches employing the code-matched filters and parallel Kalman/H∞ algorithms are presented in this paper. The proposed approaches are applied to the base station of a mobile communication system. Different from Kalman prediction algorithm which minimize the squared tracking error, the adaptive H∞ filtering algorithm is a worst case optimization. It minimizes the effect of the worst disturbances (including modeling error of direction matrix models and array structure imperfection, process noise, and measurement noise). Hence, the difficult problem of tracking the crossing mobiles can be successfully handled by using the code-matched filters. Computer simulation is provided for illustrating the effectiveness of the adaptive angle tracking approaches.

The main factor determining for both friction and contact resistance is the true contact area in the contact interface. Contact resistance depends on the size of the true contact area and contaminant films interposed between the contact areas of the interface. Moreover, friction force also depends on the true contact area. In particular, the formation of metallic junctions in the true contact area strongly effects the friction force. Therefore, since both electrical contact and friction force are related to the size of the true contact area, the contact resistance and friction force are considered to be interrelated through true contact areas. For electromechanical devices with sliding contacts such as connector and sliding switches, the contact resistance and friction are important characteristics. In order to obtain low contact resistance, contact load should be higher, but the friction force increases. These are opposite-side problems. In this study, as the contact resistance and friction occur in the same true contact area, the relationship between the contact resistance and friction was expressed in an equation. Moreover, this relationship was examined experimentally on a variety of contact surfaces under different surface conditions.

This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.

The effects of rapid thermal annealing (RTA) on bias-stress-induced base leakage were investigated in InGaP/GaAs collector-up heterojunction bipolar transistors (C-up HBTs) fabricated with boron ion implantation. C-up HBTs annealed at 700 for 1 s had negligible leakage, while non-annealed C-up HBTs had leakage (with an activation energy, Ea, of 0.17 eV) that exponentially increased with bias time. Because this Ea is almost the same as that of the hole traps (0.25 eV) observed in the InGaP emitters of non-annealed C-up HBTs, we attribute the leakage to hole tunneling from bases to emitters. By reducing the initial trap density using RTA, we stabilized current gain even after 1,030 h of testing at a junction temperature of 210 and a collector current density of 40 kA/cm2.