Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

Subjects:

(a) Radar chart of 4 algorithms, (b) The sorting diagram of 4 algorithms. by Yu Liu (6938)

Proposed algorithm mean and standard deviation (StDev) versus compared algorithms for the benchmark functions. by El-Sayed M. El-kenawy (14581088)

Information of nodes and pipes of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The topology of the NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The result of Wilcoxon signed-rand test. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The topology of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Information of nodes and pipes of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Comparison of the detection results. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

IMFs component. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

IMFs component. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

BFO-PSO-VMD-R-LMS-BPNN detection pseudocodes. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

Freiberg plus human equivalent circuit model. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

Experimental setup parameters. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

Kolmogorov-Smirnov test data analysis table. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

S1 Data - by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

S2 Data - by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”