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Hepatic CT Image Query Based on Threshold-based Classification Scheme with Gabor Features
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作者 蒋历军 罗永兴 +1 位作者 赵俊 庄天戈 《Journal of Shanghai Jiaotong university(Science)》 EI 2008年第6期753-758,共6页
Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our ... Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme. 展开更多
关键词 content based image retrieval Gabor features threshold-based scheme support vector machines (SVM) hepatic computed tomography (CT) images
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A new detector in EBPSK communication system
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作者 靳一 吴乐南 +1 位作者 王继武 余静 《Journal of Southeast University(English Edition)》 EI CAS 2011年第3期244-247,共4页
In order to raise the detection precision of the extended binary phase shift keying (EBPSK) receiver, a detector based on the improved particle swarm optimization algorithm (IMPSO) and the BP neural network is des... In order to raise the detection precision of the extended binary phase shift keying (EBPSK) receiver, a detector based on the improved particle swarm optimization algorithm (IMPSO) and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors. 展开更多
关键词 extended binary phase shift keying DETECTOR impacting filter logistic chaos disturbance Cauchy mutation adaptive threshold-based decision
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Finite Capacity Service System with Partial Server Breakdown and Recovery Policy:An Economic Perspective
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作者 Shreekant Varshney Suman Kaswan +1 位作者 Mahendra Devanda Chandra Shekhar 《Journal of Systems Science and Systems Engineering》 SCIE EI CSCD 2024年第6期651-681,共31页
Developing a comprehensive service strategy to optimize customer satisfaction presents an ongoing challenge for effective facility provider.The essence of comprehensive systems is selecting the suitable service design... Developing a comprehensive service strategy to optimize customer satisfaction presents an ongoing challenge for effective facility provider.The essence of comprehensive systems is selecting the suitable service design,establishing an effective service delivery process,and building continuous improvement.This research analyzes a finite capacity service system incorporating several realistic customer-server dynamics:customer impatience,server’s partial breakdown,and threshold recovery policy.When the number of customers is more,the server is under pressure to increase the service rate to mitigate the service system’s load.Motivating from this fact,the concept of service pressure condition is also incorporated.For characterization,we evaluate state probabilities derived using the matrix-analytic method and henceforth several performance measures.To address the cost optimization problem involving the developed Chapman-Kolmogorov forward differential-difference equations and determine optimal operational parameters,we employ the recently devised cuckoo search(CS)optimization approach.A comparative analysis is performed with the semi-classical optimizer:quasi-Newton(QN)method,and metaheuristics technique:particle swarm optimization(PSO),to validate the efficacy of results.Lastly,several numerical illustrations are depicted in different tables and graphs to understand essential characteristics quickly. 展开更多
关键词 Customer impatience service pressure condition partial server breakdown threshold-based recovery policy Cuckoo search particle swarm optimization QUASI-NEWTON
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