Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks

Qiu, Sen; Hao, Zheng-Dong; Wang, Zhelong; Liu, Long; Liu, Jiayi; Zhao, Hongyu; Fortino, Giancarlo

doi:10.1109/jiot.2021.3102856

Cited by 67 publications

(43 citation statements)

References 35 publications

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“…In future, the proposed SDGBO can also be applied to other problems, including but not limited to kayak cycle phase segmentation [116], engineering optimization problems [117, 118], service ecosystem [119, 120], location‐based services [121, 122], microgrid planning [123], energy storage planning and scheduling [124], information retrieval services [125, 126], time series analysis [127], urban road planning [128], fault detection [129], human motion capture [130], gene signature identification [131], metabolomic data processing [132, 133], and drug target discovery [134, 135].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Compared with others, the simulation data obtained by the new optimization achieved the minimal root mean square error and were extremely close to the measured datasheet in solar cells. Using Thin-film ST40, the proposed method identified unknown parameters for SDM as well as DDM under light intensities of 200 W/m 2 , 400 W/m 2 , 600 W/m 2 , 800 W/m 2 and 1000 W/m 2 at the temperature of 25 In future, the proposed SDGBO can also be applied to other problems, including but not limited to kayak cycle phase segmentation [116], engineering optimization problems [117,118], service ecosystem [119,120], location-based services [121,122], microgrid planning [123], energy storage planning and scheduling [124], information retrieval services [125,126], time series analysis [127], urban road planning [128], fault detection [129], human motion capture [130], gene signature identification [131], metabolomic data processing [132,133], and drug target discovery [134,135].…”

Section: Application In Multi-crystalline Kc200gt Datasheetmentioning

confidence: 99%

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Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

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In this paper, an efficient sine cosine differential gradient‐based optimization method is proposed for identifying unknown parameters of photovoltaic models. In the simulation, parameter identification is formulated as an objective function to be minimized based on the error between the estimated and experimental data. Based on the original gradient‐based optimization method, the proposed method combines the sine cosine method and the mutation crossover of the differential evolution algorithm. Specifically, the crossover operator enables the algorithm to avoid local optima; and meanwhile, the sine cosine strategy encourages the new individual to calculate the worst position. The simulation results demonstrate that the new optimization method can achieve the minimal root mean square error and obtain better optima relative to other algorithms in different photovoltaic cells. Therefore, the proposed optimization method has great potential to be used for estimating photovoltaic model parameters.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Application In Multi-crystalline Kc200gt Datasheetmentioning

confidence: 99%

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

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“…Due to its strong optimization capability, the developed MSMA can also be applied to other optimization problems, such as multi-objective or many optimization problems [75][76][77], big data optimization problems [78], and combination optimization problems [79]. Moreover, it can be applied to tackle the practical problems such as medical diagnosis [80][81][82][83], location-based service [84,85], service ecosystem [86], communication system conversion [87][88][89], kayak cycle phase segmentation [90], image dehazing and retrieval [91,92], information retrieval service [93][94][95], multi-view learning [96], human motion capture [97], green supplier selection [98], scheduling [99][100][101], and microgrid planning [102] problems.…”

Section: Discussionmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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“…After testing with different categories of data sets and more complex and new practical problems, careful implementation and correct setting of the initial parameters and operators of the method used will help obtain better results. In the near future, the proposed SCGWO can also be applied to other problems, such as covert communication system, [131][132][133] service ecosystem, 134,135 image editing, [136][137][138] image dehazing, [139][140][141] large scale network analysis, 142 energy storage planning and scheduling, 143 social recommendation and QOS-aware service composition, [144][145][146] active surveillance, 147 sentiment classification, 148 data-to-text generation, 149 crowd sensing, 150 feature selection, [151][152][153] location-based services, 154,155 kayak cycle phase segmentation, 156 human motion capture, 157 and information retrieval services. [158][159][160]…”

Section: Discussionmentioning

confidence: 99%

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Heidari

Zhang

et al. 2021

Int J of Intelligent Sys

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Grey wolf optimization (GWO) is a widely used metaheuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima,

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Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks

Cited by 67 publications

References 35 publications

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

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