Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to analyze inner structures of scanned objects. This article addresses a new dev...
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Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to analyze inner structures of scanned objects. This article addresses a new development of non-destructive testing software platform to efficiently detect inner flaws of space industrial components. As the key of our software, reconstruction algorithms of 2D industrial computed tomography including preprocess of raw data, re-arrange algorithm and filtered back-projection algorithms have been described in detail in this article. Experimental results with four groups of real raw data from China Aerospace Science and Technology Corporation confirmed the accuracy of the reconstruction algorithm in our software. forward algorithm of 2D industrial computed tomography is another important component of our software, which conveniently generates parallel-beam and fan-beam raw data with given parameters.
A novel competitive approach to particle swarm optimization (PSO) algorithms is proposed in this paper. The proposed method uses extrapolation technique with PSO (ePSO) for solving optimization problems. By considerin...
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A novel competitive approach to particle swarm optimization (PSO) algorithms is proposed in this paper. The proposed method uses extrapolation technique with PSO (ePSO) for solving optimization problems. By considering the basics of the PSO algorithm, the current particle position is updated by extrapolating the global best particle position and the current particle positions in the search space. The position equation is formulated with the global best (gbest) position, local best position (pbest) and the current position of the particle. The proposed method is tested with a set of 13 standard optimization benchmark problems and the results are compared with those obtained through two existing PSO algorithms, the canonical PSO (cPSO), the Global-Local best PSO (GLBest PSO). The cPSO includes a time-varying inertia weight (TVIW) and time-varying acceleration co-efficients (TVAC) while the GLBest PSO consists of Global-Local best inertia weight (GLBest IW) with Global-Local best acceleration co-efficient (GLBestAC). The simulation results clearly elucidate that the proposed method produces the near global optimal solution. It is also observed from the comparison of the proposed method with cPSO and GLBest PSO, the ePSO is capable of producing a quality of optimal solution with faster convergence rate. To strengthen the comparison and prove the efficacy of the proposed method a real time application of steel annealing processing (SAP) is also considered. The optimal control objectives of SAP are computed through the above said three PSO algorithms and also through two versions of genetic algorithms (GA), namely, real coded genetic algorithm (RCGA) and hybrid real coded genetic algorithm (HRCGA) and the results are analyzed with the proposed method. From the results obtained through benchmark problems and the real time application of SAP, it is clearly seen that the proposed ePSO method is competitive to the existing PSO algorithms and also to GAs. (C) 2008 Elsevier B.V
The nucleotide sequence of biological databases is growing long terms of quantity, memory and complexity, managing these databases is becoming very complex. In this paper focuses Hidden Markov Model (HMM), has increas...
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ISBN:
(纸本)9781479939756
The nucleotide sequence of biological databases is growing long terms of quantity, memory and complexity, managing these databases is becoming very complex. In this paper focuses Hidden Markov Model (HMM), has increased on the Pattern recognition domain primarily because of its strong mathematical basis and the ability to adapt to unknown of nucleotide sequence of normal and cancer affected liver cells as are pictorially represented by finite state machine. It is a finite automaton with a fixed number of states which are trained to maximize the probability of the observation sequence by using viterbi algorithm and forward algorithm. The work will be focused and analyzed about performance of DNA gene liver cancer database and normal liver cell data set from ncbi DNA data set. Each amino acid can have character variables and also assigned numeric number and its corresponding pair combination of sequence are represented in a graph. The proposed HMM system is validated with two different nucleotide values for analyse the performance and get the simulated output using viterbi and forward algorithms implemented in Mat Lab Tool.
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