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Time-Optimal Asymmetric S-Curve Trajectory Planning of Redundant Manipulators under Kinematic Constraints

SENSORS 2023年第6期23卷 3074页

作者： Liu, Tianyu Cui, Jingkai Li, Yanhui Gao, Siyuan Zhu, Mingchao Chen, Liheng Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys CAS Key Lab On Orbit Mfg & Integrat Space Opt Syst Changchun 130033 Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing 100049 Peoples R China

This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method.

关键词： asymmetric S-curve velocity scheduling trajectory planning redundant manipulators whale optimization algorithm kinematic constraints

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An Optimal Registration on Shearlet domain with Novel Weighted Energy fusion for Multi-Modal Medical Images

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OPTIK 2021年 225卷

作者： Nair, Rekha R. Singh, Tripty Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru India

Medical images usually display various attributes of data on human viscera and abnormal tissue in different modalities. The fusion of images, ensures effective deployment of all relevant information from several modalities into a single image. Finally, the paper leads to a novel multi-modal medical image fusion method based on Non-Subsampled Shearlet Transform (NSST) called Denoised Optimum B-Spline Shearlet Image Fusion(DOBSIF) that is based on real-time and standard radiological datasets. The proposed, novel registration based fusion technique DOBSIF, is compared with existing state-of-art techniques like PCA, DWT, SWT etc., and in comparison the proposed DOBSIF works effectively for both the color and grayscale images. To improve the fusion function, a novel pre-fusion method is carried out with the help of the whale optimization algorithm(WOA), using the ideal B-spline-based registration method and in addition a novel Weighted Energy fusion rule is applied to derive relevant information from the source images. The proposed work initially follows normal pre-processing measures such as Gaussian filtering, edge sharpening, and resizing followed by optimal registration, fusion with novel fusion rule and segmentation of tumor part from the fused image. The visual quality of the fused image was evaluated by expert radiologists and the fused resultant image is robust, subject to the modified Active Contour(ACM) model for lesion identification.

关键词： Multi-modal medical image fusion whale optimization algorithm Optimal B-spline registration Weighted Energy fusion rule Active Contour Model

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Efficient Ranking-Based whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations

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ENERGIES 2021年第13期14卷

作者： Abdel-Basset, Mohamed Mohamed, Reda El-Fergany, Attia Askar, Sameh S. Abouhawwash, Mohamed Zagazig Univ Fac Comp & Informat Dept Comp Sci Zagazig 44519 Egypt Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig 44519 Egypt King Saud Univ Dept Stat & Operat Res Coll Sci Riyadh 11451 Saudi Arabia Mansoura Univ Dept Math Fac Sci Mansoura 35516 Egypt Michigan State Univ Coll Engn Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Efficient and accurate estimations of unidentified parameters of photovoltaic (PV) models are essential to their simulation. This study suggests two new variants of the whale optimization algorithm (WOA) for identifying the nine parameters of the three-diode PV model. The first variant abbreviated as RWOA is based on integrating the WOA with ranking methods under a novel updating scheme to utilize each whale within the population as much as possible during the optimization process. The second variant, namely HWOA, has been based on employing a novel cyclic exploration-exploitation operator with the RWOA to promote its local and global search for averting stagnation into local minima and accelerating the convergence speed in the right direction of the near-optimal solution. Experimentally, RWOA and HWOA are validated on a solar cell (RTC France) and two PV modules (Photowatt-PWP201 and Kyocera KC200GT). Further, these proposed variants are compared with five well-known parameter extraction models in order to demonstrate their notable advantages over the other existing competing algorithms for minimizing the root mean squared error (RMSE) between experimentally measured data and estimated one. The experimental findings show that RWOA is superior in some observed cases and superior in the other cases in terms of final accuracy and convergence speed;yet, HWOA is superior in all cases.

关键词： photovoltaic units three-diode model parameter estimation optimization methods whale optimization algorithm ranking method cyclic exploration-exploitation strategy

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Hybrid random forest-based models for predicting shear strength of structural surfaces based on surface morphology parameters and metaheuristic algorithms

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CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Zhou, Jian Yang, Peixi Li, Chuanqi Du, Kun Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 F-38000 Grenoble France

The prediction of shear strength between soil-structure interactions is of great significance to the stability of geotechnical engineering. In this study, 480 morphological data with seven morphological parameters (deviation of the root mean square value of the profile (Pq), skewness of the height distribution in the profile (Psk), kurtosis of the height distribution of the profiles (Pku), average width of outline elements (PSm), root mean square slope of the profile (Pdq), material ratio of the profile(Pmr), number of peaks (Ppc)) were selected to generate a comprehensive database for predicting the peak interface efficiency (IEp) considering three different soil particle sizes (0.35 mm, 0.53 mm, and 0.80 mm). Three random forest (RF) models optimized using dragonfly algorithm (DA-RF), sparrow search algorithm optimized random forest (SSA-RF), and whale optimization algorithm (WOA-RF) were generated to predict IEp. and compared the predictive performance with extreme learning machine (ELM), support vector regression with radial basis function kernel (SVR-RBF) and initial RF models. The mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of all models. The results showed that the WOA-RF model has achieved the best performance by resulting in MAE of (0.0145, 0.0181, 0.0179 and 0.0210, 0.0273, 0.0216), MAPE of (1.9866, 2.6417, 2.5310 and 2.8924, 4.0294, 3.0816), and RMSE of (0 0178, 0.0237,0.0224 and 0.0252, 0.0362, 0.0276), R2 (0.9473, 0.9262, 0.9352 and 0.9404, 0.8433, 0.9313) in the training and testing phases. The results of significance analysis indicated that Pdq and Pq have more importance than other parameters for predicting IEp.

关键词： Peak interface efficiency Morphological parameters Dragonfly algorithm Sparrow Search algorithm whale optimization algorithm

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Modeling the hardness properties of high-performance concrete via developed RBFNN coupling matheuristic algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 4801-4815页

作者： Zhao, Xiaofang Li, Faming Chen, Biao Li, Xiaofei Lu, Shasha Lanzhou Resources Environm Voc Tech Univ LanZhou Lanzhou 730000 Peoples R China Liaoning Tech Univ Fuxin Liaoning Peoples R China

Examining the properties of High-Performance Concrete (HPC) has been a big challenge due to the highly heterogeneous relationships and coherence among several constituents. The employment of silica fume and fly ash as ecofriendly components in mixtures benefits the concrete to improve its physical features. Although machine learning approaches are utilized broadly in many studies solitarily to estimate the mechanical features of concrete, causing to reduce accuracy and lift the cost and complexities of computational networks. Consequently, current research aims to develop a Radial Basis Function Neural Network (RBFNN) integrating with optimization algorithms in order to precisely model the mechanical characteristics of HPC mixtures including compressive strength (CS) and slump (SL). Feeding the dataset of HPC samples to hybrid models will result to reproduce the given CS and SL factors simultaneously. The results of the models showed that the maximum rate of correlation between estimated values and measured ones was obtained at 98.3% while the minimum rate of RMSE was calculated at 3.684mm (and MPa) in the testing phase. Employing such soft-oriented approaches has been benefiting us to reduce costs and increase the result accuracy.

关键词： High-performance concrete radial basis function neural network compressive strength slump whale optimization algorithm

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Predicting Chinese total retail sales of consumer goods by employing an extended discrete grey polynomial model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷

作者： Liu, Chong Xie, Wanli Wu, Wen-Ze Zhu, Hegui Northeastern Univ Sch Sci Shenyang 110819 Peoples R China Nanjing Normal Univ Inst EduInfo Sci & Engn Nanjing 210097 Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Peoples R China

The total retail sales of consumer goods is not only an important indicator to measure the consumption level of the Chinese people, but also an important indicator of the national economy. Therefore, it is of great significance to analyze the development trend of Chinese total retail sales of consumer goods for the healthy development of Chinese economy. To explore the future development trend of Chinese total retail sales of consumer goods, this paper develops an extensive discrete grey model by the introduction of weighted fractional accumulation and discretization error, which is abbreviated as WFDPGM(1,1,t(alpha)) model. To further enhance the prediction performance of the proposed model, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients. Three real cases are used to verify the effectiveness of the proposed model by comparing with other competing models. Lastly, based on Chinese retail sales of consumer goods from 2005 to 2019, the numerical results show that the proposed model outperforms other benchmarks, and the future development of Chinese retail sales of consumer goods will maintain an increasing trend, reaching 533033.15 x 10(9) yuan in 2025. And some managerial insights are obtained from numerical examples.

关键词： Retail sales of consumer goods Weighted fractional accumulation Discretization technique whale optimization algorithm

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Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm

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ENERGY 2023年 285卷

作者： Wang, Chao Zhang, Xin Yun, Xiang Meng, Xiangfei Fan, Xingming Guilin Univ Elect Technol Coll Elect Engn & Automat Guilin 541004 Peoples R China

Data-driven methods have been widely employed in the field of Lithium-ion battery state-of-charge (SOC) estimation. However, most of these methods do not impose constraints on the rate of change between adjacent output SOC values, which leads to the issue of output SOC fluctuations when the battery current undergoes significant variations. To address this issue, a fusion algorithm is proposed in this study, which combines the multi-kernel Incremental relevance vector machine (MIRVM) and seasonal autoregressive integrated moving average (SARIMA) within an adaptive Kalman filter (AKF) framework, called SARIMA_AKF_MIRVM(SFR) algorithm. Employing the MIRVM algorithm, we aim to construct a data-driven model capturing the intricate relationship between voltage, current, and SOC of batteries under varying operating conditions. Furthermore, we shall leverage the SARIMA algorithm to establish a time series forecasting model for the adjacent SOC values. Subsequently, we shall introduce the AKF algorithm to filter prediction results associated with the MIRVM algorithm. Additionally, we shall integrate the whale optimization algorithm to obtain the optimal parameter combination for SFR algorithm. The experimental results demonstrate that the SFR algorithm exhibits exceptional generalization capabilities and robustness. The RMSE and MAE for four experimental test conditions remain below 0.3 %, with the maximum MAE reaching a mere 0.14 %.

关键词： Relevance vector machine Seasonal autoregressive integrated moving average whale optimization algorithm Adaptive Kalman filter SOC estimation

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Renewable energy source-based multiarea AGC system with integration of EV utilizing cascade controller considering time delay

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第1期29卷

作者： Saha, Arindita Saikia, Lalit Chandra Natl Inst Technol Silchar Dept Elect Engn Silchar India

Automatic generation control of the unequal 3-area system with diversified sources like small hydrothermal units in area-1 and area-2 and split shaft gas turbine thermal units in area-3 with nonlinearity of generation rate constraint is explored. A cascade combination of integer order integral-derivative with filter (IDN) and fractional order proportional derivative (FOPD) is considered as a secondary controller (IDN-FOPD). The controller gains and other parameters are optimized using whale optimization algorithm. The performance of IDN-FOPD controller has an upper hand when compared with some classical controllers like integral, proportional-integral, and proportional-integral-derivative with filter (PIDN). Analyses reveal that the performance of a combination of IDN-FOPD in area-1 and PIDN controllers in other areas of system dynamics provides comparable results with IDN-FOPD controllers in all areas. Thus, this combination of controllers is carried for further analyses. Even eigenvalue analysis has been performed for analyzing system stability. The impact of time delay on system dynamics is also studied. The impact of the inclusion of renewable source like a solar thermal power plant in area-1 and electric vehicles in all areas provides better system dynamics in the presence of nonlinearity generation rate constraint and time delay. Sensitivity analysis reveals that controller gains and other parameters obtained at nominal conditions are robust to changes in variable solar insolation and demands no further reset.

关键词： electric vehicle generation rate constraint load frequency control solar thermal power plant whale optimization algorithm

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Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

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DATA & KNOWLEDGE ENGINEERING 2023年 144卷

作者： VijayKumar, K. Rao, R. Rajeswara CSE Srinivasa Inst Engn & Technol Cheyyeru 533216 Andhra Prades India Univ Coll Engn Vizianagaram JNTUK UCEV CSE Vizianagaram 535003 Andhra Prades India

Speaker diarization is the partitioning of an audio source stream into homogeneous segments according to the speaker's identity. It can improve the readability of an automatic speech transcription by segmenting the audio stream into speaker turns and identifying the speaker's true identity when used in combination with speaker recognition systems. Generally, the automatic speaker diarization is done based on two phases, like the transformation of audio segments into feature representation and the clustering. In this paper, clustering along with a hybrid optimization technique is carried out for performing the speaker diarization. For that, the extracted features from the audio signal is processed under speech activity prediction in order to identify the speak segments. The diarization process is done by Deep Embedded Clustering (DEC) in which the constants are trained by the developed Fractional Anticorona whale optimization algorithm (FrACWOA). The FrACWOA is a hybrid optimization technique, which is designed by adapting the concept of fractional theory, precaution behaviour of COVID19 and hunting performance of whales. DEC performs the diarization, which concurrently learns the representation of features as well as cluster assignments with neural networks. Using a mapping from the information space to a lower-dimensional feature space, DEC repeatedly discovers the most effective solution for a clustering objective. On the basis of testing accuracy, diarization error, false discovery rate (FDR), false negative rate (FNR), and false positive rate (FPR) of 0.902, 0.627, 0.276, 0.117, and 0.118, respectively, the developed FrACWOA+DEC algorithm performed much better with six speakers using the EenaduPrathidwani dataset. Comparing the accuracy of the proposed method to existing approaches such as Active learning, DE+K-means, LSTM, MCGAN, ANN-ABC-LA, and ACWOA+DFC, the accuracy of the proposed method is 12.97%, 10.31%, 9.75%, 7.53%, 4.32%, and 2.106% higher when using 6

关键词： Fractional calculus Deep embedded clustering whale optimization algorithm Anticorona virus optimization algorithm Diarization

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Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Khaleel, Mustafa Ibrahim Univ Sulaimani Coll Sci Dept Comp Sulaymaniyah 46001 Iraq

This paper tackles the challenges of computation offloading in the cloud-edge paradigm. Although many solutions exist for enhancing the server's computational and communication efficiency, they mainly focus on reducing latency and often neglect the impact of overlapping multi-request processing on scheduling reliability. Additionally, these approaches do not account for the preemptive characteristics of applications running in the VMs that lead to higher energy consumption. We propose a novel hybrid integer multi-objective dynamic decision-making approach enhanced with the gravity reference point method. This method determines the proportion of computations executed on cloud servers versus those handled locally on edge servers. Our hybrid approach leverages the gravitational potential reference point and crowding degrees to improve the characteristics of whale populations, addressing the limitations of the traditional whale algorithm, which depends on individual whales' varying foraging behaviors influenced by a random probability number. By evaluating the crowding level around the prey, the foraging behavior of individual whales is adjusted to enhance the algorithm's convergence speed and optimization accuracy, thereby increasing its reliability. The results show that our hybrid computation offloading model significantly improves time latency by 76.45%, energy efficiency by 63.12%, reliability by 82%, quality of service by 83.78%, distributor throughput by 87.31%, asset availability by 73.05%, and guarantee ratio by 89.72% compared to traditional offloading methods.

关键词： whale optimization algorithm DAG-structured workflow Cloud computing Task placement Gravity reference model Offloading computation approach

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