Conventional Search Method Research Articles

Optimal design and development of a 2-DOF PKM-based machine tool

This paper presents multiobjective optimization of a typical 2-degree-of-freedom (DOF) parallel kinematic machine (PKM) tool that has only single DOF joints. Nondimensional indices, namely global stiffness index (GSI), global conditioning index (GCI), and workspace index, are considered as the objectives for optimization. The indices GSI and GCI depict the variation of stiffness and dexterity of PKM within the workspace. The leg length and distance between two rails on which actuators slide are treated as design variables as these greatly influence the characteristics of PKM. A multiobjective genetic algorithm (MOGA) approach is implemented in MATLAB to find an efficient solution to this complex optimization problem. Fitness function includes inverse kinematics equations, Jacobian and stiffness matrices to compute and optimize the nondimensional indices. First, the optimal value of each index is obtained by single-objective GA. To further improve the results, a hybrid function PATTERNSEARCH is used. This helps to select appropriate boundary conditions for MOGA. To obtain the optimal values of all the three indices, a multiobjective GA is carried out. The results are compared with a conventional exhaustive search method of optimization. The obtained results show that the use of MOGA enhances the quality of the optimization outcome. Secondly, a prototype has been designed and developed with the optimal dimensions. The actual workspace of the PKM and influence of leg collision on the workspace are studied. Finally, a preliminary experimentation was carried out. A comparison between PKM and the three-axis serial kinematic machine tool is presented.

Global optimisation by simulated annealing for common reflection surface stacking and its application to low-fold marine data in southwest Japan

The common reflection surface (CRS) stack is an alternative method of producing a zero-offset stacked section with a higher signal-to-noise ratio (SNR) than the conventional normal moveout (NMO)/dip moveout (DMO) stack method. Since, however, it is difficult to determine global optimal parameters for the CRS stack method by the conventional three-step search method, especially for complex structures and low-fold data, we investigate the ability of simulated annealing (SA) to optimise our estimation of these parameters. We show a detailed but practical procedure for the application of SA to the CRS stack method. We applied the CRS stack method with SA to numerically modelled seismic reflection data, and to multichannel marine seismic data over complicated geological structures around the Median Tectonic Line (MTL) in Japan. We used the results of the conventional three-step search algorithm as the initial model for the SA search and showed that with this approach SA can estimate CRS parameters accurately within a reasonable number of calculations. The CRS stack method with this approach provided a clearer seismic profile with a higher SNR than either a conventional NMO stack method or a conventional CRS stack method.

A dynamic model selection strategy for support vector machine classifiers

The Support Vector Machine (SVM) is a very powerful technique for general pattern recognition purposes but its efficiency in practice relies on the optimal selection of hyper-parameters. A naïve or ad hoc choice of values for these can lead to poor performance in terms of generalization error and high complexity of the parameterized models obtained in terms of the number of support vectors identified. The task of searching for optimal hyper-parameters with respect to the aforementioned performance measures is the so-called SVM model selection problem. In this paper we propose a strategy to select optimal SVM models in a dynamic fashion in order to address this problem when knowledge about the environment is updated with new observations and previously parameterized models need to be re-evaluated, and in some cases discarded in favor of revised models. This strategy combines the power of swarm intelligence theory with the conventional grid search method in order to progressively identify and sort out potential solutions using dynamically updated training datasets. Experimental results demonstrate that the proposed method outperforms the traditional approaches tested against it, while saving considerable computational time.

Multiobjective Optimization Approach for Sensor Arrangement in A Complex Indoor Environment

Various meta-heuristic search methods have been employed to resolve the sensor arrangement problem, which is a type of NP-hard, combinational problem. However, the difficulty of weight tuning, when formulating a single objective function, is the chief obstacle to the use of the single-objective optimization methods. Although multiobjective optimization methods have been applied recently to avoid the difficulty involved in weight design, the original multiobjective optimization method still requires a greater number of generations for the solutions to converge to the optimal Pareto front. Moreover, unlike in previous works, we deal with four unknowns to define the sensor arrangement problem more practically: 1) The number of sensors is unknown, 2) no candidate is given for installation, 3) the coverage radii of sensors are variable, and 4) sensors cover a wide area in which obstacles exist in complicated arrangements. To improve the search approach for a sensor arrangement with these requirements, we first propose a representation scheme to encode the sensor arrangement problem as a set of chromosomes. Genetic operators and a repair scheme are also properly employed in the proposed encoding method. In addition, two strategies, i.e., the hierarchical fitness assignment strategy and the hybrid optimization strategy, are proposed to improve convergence. We also perform experiments with two commercial sensors to verify the proposed multiobjective optimization approach for sensor arrangement (MOASA). The results show that the proposed MOASA gives better performance than conventional search methods. The effects of the proposed strategies are investigated with additional experiments in terms of the quality of Pareto solutions.

Efficient Capacity-Based Joint Quantized Precoding and Transmit Antenna Selection Using Cross-Entropy Method for Multiuser MIMO Systems

The current paper considers the joint precoding and transmit antenna selection to reduce hardware cost, such as radio-frequency chains, associated with antennas in the downlink of multiuser multiple-input multiple-output systems with limited feedback. The joint precoding and transmit antenna selection algorithm requires an exhaustive search of all possible combinations and permutations to find the optimum solution at the transmitter, thus resulting in extremely high computational complexity. To reduce the computational load while still maximizing channel capacity, the cross-entropy (CE) method is adopted to determine the suboptimum solution. Compared with the conventional genetic algorithm and random search method, the CE method provides better performance under the same computational complexity, as shown by the simulation results.

Combinatorial Optimization Method Based on Proximate Optimality Principle Using Neighborhood Search

This paper proposes a new neighborhood search method for solving combinatorial optimization problems based on Proximate Optimality Principle (POP). The proposed combinatorial optimization method has higher optimality and lower computational complexity than conventional neighborhood search methods. The proposed method is applied to several typical combinatorial optimization problems in order to verify the performance of the proposed method.

A fast algorithm for Huffman decoding based on a recursion Huffman tree

This paper focuses on the time efficiency of Huffman decoding. In this paper, we utilize numerical interpretation to speed up the decoding process. The proposed algorithm firstly transforms the given Huffman tree into a recursion Huffman tree. Then, with the help of the recursion Huffman tree, the algorithm has the possibility to decode more than one symbol at a time if the minimum code length is less than or equal to half of the width of the processing unit. When the minimum code length is larger than the half of the width of the processing unit, the proposed method can still increase the average symbols decoded in one table access (thus speeding up the decoding time). In fact, the experimental results of the test files show that the average number of decoded symbols at one time for the proposed method ranges from 1.91 to 2.13 when the processing unit is 10. The experimental comparisons show that, compared to the conventional binary tree search method and the level-compressed Huffman decoding method, the decoding time of the proposed method is a great improvement.

Updating the Finite Element Model of a Bridge Model Using a Hybrid Optimization Method

Finite element model updating of structures usually ends up with a nonlinear optimization problem. An efficient optimization technique is proposed firstly, which draws together the global searching capability of chaos-based optimization technique and high searching efficiency of trust-region Newton method. This hybrid approach is demonstrated to be more efficient and prone to global minimum than conventional gradient search methods and random search methods by testifying with three test functions. The optimization problem for model updating using modal frequencies and modal shapes is formulated, and a procedure to update the boundary support parameters is presented. A modal test was conducted on a beam structure, and the identified mode frequencies are employed to formulate the optimization problem with the support parameters as the updating parameters. The discrepancy between the mode frequencies of the finite element models before and after updating is greatly reduced, and the updated support condition meet quite well with the insight to the devices that form the supports.

Along-Wind Aero-Elasticity of High-Rise Buildings by Using Indirect Forced Actuation Technique

The frequency-dependent aerodynamic damping and stiffness of high-rise buildings in along-wind motion have been systematically investigated and compared through wind tunnel tests under smooth wind flow. A novel identification scheme based on the indirect forced actuation technique was developed, involving only a simple curve-fitting technique on the frequency response function induced by the actuation. To ensure that global minimization in curve-fitting was achieved, a genetic algorithm and a conventional gradient search method were used in obtaining the final results. An alternative derivation of the frequency response function via the time-domain state space equation is also presented, which has the supporting advantage that the simulation of time history of the structural response becomes possible. To demonstrate the approach, various prism models representing different high-rise buildings with varied aspect ratios and height-width ratios were used in the experimental identification. A total of nine mo...

A heuristic iterated-subspace minimization method with pattern search for unconstrained optimization

Recently, an increasing attention was paid on different procedures for an unconstrained optimization problem when the information of the first derivatives is unavailable or unreliable. In this paper, we consider a heuristic iterated-subspace minimization method with pattern search for solving such unconstrained optimization problems. The proposed method is designed to reduce the total number of function evaluations for the implementation of high-dimensional problems. Meanwhile, it keeps the advantages of general pattern search algorithm, i.e., the information of the derivatives is not needed. At each major iteration of such a method, a low-dimensional manifold, the iterated subspace, is constructed. And an approximate minimizer of the objective function in this manifold is then determined by a pattern search method. Numerical results on some classic test examples are given to show the efficiency of the proposed method in comparison with a conventional pattern search method and a derivative-free method.

SIMPRO: simple protein homology detection method by using indirect signals

Detecting homologous proteins is one of the fundamental problems in computational biology. Many tools to solve this problem have been developed, but development of a simple, effective and generally applicable method is still desirable. We propose a simple but effective information retrieval approach, named SIMPRO, to identify homology relationship between proteins. The key idea of our approach is that by accumulating and comparing indirect signals from conventional homology search methods, the search sensitivity can be increased. We tested the idea on the problem of detecting homology relationship between Pfam families, as well as detecting structural homologs based on SCOP, and found that our method achieved significant improvement. Our results indicate that simple manipulation of conventional homology search outputs by SIMPRO algorithm can remarkably improve homology search accuracy.

Return-Link Code Acquisition for 1-D and 2-D With DS-CDMA for High-Capacity Multiuser Systems

Acquisition of the code timing in a direct-sequence code-division multiple-access (DS-CDMA) system at the base station must take place before signal detection and decoding is possible. Code acquisition under severe multiple-access-interference (MAI) conditions with time-varying codes makes the task even more difficult. Inefficient designs lead to a large number of false alarms and/or missed detections. This requirement is needed for conventional single-antenna (1-D) designs, as well as for multielement antenna (2-D) designs. This paper details a powerful code-acquisition technique for the uplink of DS-CDMA systems under high-loaded situations for both 1-D and 2-D schemes, where the number of users is greater than the processing gain. Under this high-MAI condition, the DS-CDMA acquisition problem becomes very difficult, and conventional search methods simply fail. The method discussed utilizes soft data from a multiuser detector to reduce the interference received by the acquisition unit. Analytical performance is compared to simulation results in terms of the number of users, processing gain, interferer-signal power, cancellation factor, antenna configuration, and noise variance. Numerical results validate performance under realistic conditions with amplitude, phase, and frequency impairments.

Effective Gene Selection Method Using Bayesian Discriminant Based Criterion and Genetic Algorithms

Microarray gene expression data usually consist of a large amount of genes. Among these genes, only a small fraction is informative for performing cancer diagnostic tests. This paper focuses on effective identification of informative genes. A newly developed gene selection criterion using the concept of Bayesian discriminant is used. The criterion measures the classification ability of a feature set. Excellent gene selection results are then made possible. Apart from the cost function, this paper addresses the drawback of conventional sequential forward search (SFS) method. New genetic algorithms based Bayesian discriminant criterion is designed. The proposed strategies have been thoroughly evaluated on three kinds of cancer diagnoses based on the classification results of three typical classifiers which are a multilayer perception model (MLP), a support vector machine model (SVM), and a 3-nearest neighbor rule classifier (3-NN). The obtained results show that the proposed strategies can improve the performance of gene selection substantially. The experimental results also indicate that the proposed methods are very robust under all the investigated cases.

세분화된 탐색 범위에서의 방향 지향적 전영역 고속 탐색 알고리즘

본 논문에서는 비디오 부호화의 움직임 예측에 사용되는 블록 정합 알고리즘의 계산량을 줄이는 고속 전영역 탐색 알고리즘을 제안한다. 블록 정합 알고리즘에서 사용되는 기존의 나선형 탐색 알고리즘은 탐색 영역의 중심에서 시작하여 탐색 지점을 화소 단위로 이동화면서 움직임 예측을 수행하기 때문에 움직임이 적은 영상에 적합하다. 본 논문에서는 움직임이 작은 영상 뿐 아니라 움직임이 많은 영상에서도 효율적인 움직임 예측을 하기 위해 다음과 같은 알고리즘을 제안한다. 먼저 초기 문턱 값을 계산함에 있어서 확장된 예측기를 사용하여 보다 최소값에 근사한 문턱값을 계산한다. 그리고 탐색영역을 블록으로 세분화 한 후 각 영역을 새로운 탐색 순서에 따라 움직임 예측을 수행하고 방향성에 따라 영역을 재분할한다. 재분할된 영역이 가지는 방향성에 따라 방향 지향적인 탐색 순서를 적용한다. 실험 결과에서 제안하는 알고리즘이 기존의 나선형 전영역 탐색 알고리즘에 비해 객관적인 화질의 열화 없이 계산량이 평균적으로 약 94% 감소하는 것을 확인할 수 있다. We propose the fast full search algorithm that reduces the computational load of the block matching algorithm which is used for a motion estimation in the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion picture. However we proposed the efficient motion estimation method which is good for the fast and slow motion picture. Firstly, when finding the initial threshold value, we use the expanded predictor that can approximately calculate minimum threshold value. The proposed algorithm estimates the motion in the new search order after partitioning the search window and adapt the directional search order in the re-divided search window. At the result, we can check that the proposed algorithm reduces the computational load 94% in average compared to the conventional spiral full search algorithm without any loss of image quality.

Wyner–Ziv Video Coding With Universal Prediction

The coding efficiency of a Wyner-Ziv video codec relies significantly on the quality of side information extracted at the decoder. The construction of efficient side information is difficult thanks in part to the fact that the original video sequence is not available at the decoder. Conventional motion search methods are widely used in the Wyner-Ziv video decoder to extract the side information. This substantially increases the Wyner-Ziv video decoding complexity. In this paper, we propose a new method to construct side estimation based on the idea of universal prediction. This method, referred to as Wyner-Ziv video coding with universal prediction (WZUP), does not perform motion search or assume an underlying model of the original input video sequences at the decoder. Instead, WZUP estimates the side information based on its observations on the past reconstructed video data. We show that WZUP can significantly reduce decoding complexity at the decoder and achieve a fair side estimation performance, thus making it possible to design both the video encoder and the decoder with low computational complexity

Externally growing self-organizing maps and its application to e-mail database visualization and exploration

In this paper we present an approach to organize and classify e-mails using self-organizing maps. The aim is on the one hand to provide an intuitive visual profile of the considered mailing lists and on the other hand to offer an intuitive navigation tool, were similar e-mails are located close to each other, so that the user can scan easily for e-mails similar in content. To be able to evaluate this approach we have developed a prototypical software tool that imports messages from a mailing list and arranges/groups these e-mails based on a similarity measure. The tool combines conventional keyword search methods with a visualization of the considered e-mail collection. The prototype was developed based on externally growing self-organizing maps, which solve some problems of conventional self-organizing maps and which are computationally viable. Besides the underlying algorithms we present and discuss some system evaluations in order to show the capabilities of the approach.

An efficient method of Huffman decoding for MPEG-2 AAC and its performance analysis

This paper presents a new method for Huffman decoding specially designed for the MPEG-2 AAC audio. The method significantly enhances the processing efficiency of the conventional Huffman decoding realized with the ordinary binary tree search method. A data structure of one-dimensional array is newly designed based on the numerical interpretation of the incoming bit stream and its utilization for the offset oriented nodes allocation. The Huffman tree implemented with the proposed data structure allows the direct computation of the branching location, eliminating the need for the pipeline-violating compare and jump instructions. The experimental results show the average performance enhancement of 67% and 285%, compared to those of the conventional binary tree search method and the sequential search method, respectively. The proposed method also shows slightly better processing efficiency, while requiring much less memory space, compared even with those up-to-date efficient search methods of Hashemian and its variants.

Myocardial border detection by branch-and-bound dynamic programming in magnetic resonance images

Dynamic programming (DP) is a mathematical technique for making optimal decisions on the sequencing of interrelated problems. It has been used widely to detect borders in magnetic resonance images (MRI). MRI is noninvasive and generates clear images; however, it is impractical for manual measurement of the huge number of images generated by dynamic organs such as those of the cardiovascular system. A fast and effective algorithm is essential for on-line implementation of MRI-based computer aided measurement and diagnosis. In this paper, a branch-and-bound dynamic programming technique is applied to detect the endocardial borders of the left ventricular. The proposed branch-and-bound method drastically reduces the computational time required in conventional exhaustive search methods. Statistical tests are conducted to verify the CPU time performance of the branch-and-bound technique in comparison to the conventional exhaustive search method.

Application of Machine Learning to Improve the Results of High‐Throughput Docking Against the HIV‐1 Protease.

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

Fast Cell Search Algorithm for Overlay System with Cellular and Isolated Cells in Forward Link for OFCDM Broadband Wireless Access

This paper proposes a new cell-specific scrambling code (CSSC) assignment method and a fast cell search algorithm in the forward link for Orthogonal Frequency and Code Division Multiplexing (OFCDM) wireless access that are suitable for a system incorporating coexisting isolated and cellular cells. In the proposed method, one or some CSSC groups and thereby the CSSCs belonging to the CSSC groups are exclusively assigned to isolated cells. By detecting the best CSSC assigned to an isolated cell with higher priority than the cellular cells, the best cell including the isolated cell obtaining the minimum path loss can be detected far faster than by using the conventional cell search method, which employs uniform CSSC assignment. Computer simulation results show that by using the proposed cell search method together with the exclusive CSSC assignment to the isolated cells, the isolated-cell detection probability of approximately 90% is achieved at the cell boundary after the cell search time of 10msec, while corresponding detection probability using conventional CSSC assignment is approximately 80% without notifying the user equipment of the cell type and its CSSC information of the surrounding cells via the broadcast channel, at the average received signal energy per bit-to-noise power spectrum density ratio (E b /N 0 ) of 10dB for the common pilot channel (CPICH) in the cellular cells, when the transmission power ratio of the CPICH to the packet data channel (PDCH) for a one-code channel is R CPICH = 9dB in a 20-cell layout model.