PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

Mahadeva, Rajesh; Kumar, Mahendra; Patole, Shashikant P.; Manik, Gaurav

doi:10.1109/access.2022.3224127

Cited by 25 publications

(8 citation statements)

References 40 publications

(31 reference statements)

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“…Numerous authors have addressed the segmentation of nuclei using various conventional approaches, including mathematical morphology, pixel classification, level sets, and graphbased segmentation methods [19,20,24,36], due to its crucial role in the automatic interpretation of stained tissue sections. While nucleus identification is often carried out by a spatially restricted convolutional neural network (SC-CNN), a new adjacent ensemble predictor (NEP) that is paired with CNN has been suggested to more reliably predict the class label of cell nuclei [25].…”

Section: Nuclei Segmentationmentioning

confidence: 99%

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Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Patel,

Chaurasia,

Mahadeva

et al. 2023

E3S Web Conf.

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Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms.

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Section: Nuclei Segmentationmentioning

confidence: 99%

“…The nuclei are crucial in and of themselves for assessing and verifying the presence of illness and the speed at which it spreads. A few of the feature detection techniques that have been employed successfully and efficiently are discussed in this study [33][34][35][36][37][38][39][40][41][42][43].…”

Section: Detection Of Featuresmentioning

confidence: 99%

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Patel,

Chaurasia,

Mahadeva

et al. 2023

E3S Web Conf.

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“…Similarly, Ju et al 44 suggested a hybrid strategy of WOA based on nonlinear convergence factor, chaos initialization, and mutation concepts. Further, Chakraborty et al proposed various artificial intelligence models using WOA and their variants for numerous applications, such as for COVID-19 X-ray image segmentation 45 , global optimization 46 , 47 , numerical optimization 48 , and other applications 49 – 52 .…”

Section: Introductionmentioning

confidence: 99%

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

Mahadeva

Kumar

Gupta

et al. 2023

Sci Rep

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In recent decades, nature-inspired optimization methods have played a critical role in helping industrial plant designers to find superior solutions for process parameters. According to the literature, such methods are simple, quick, and indispensable for saving time, money, and energy. In this regard, the Modified Whale Optimization Algorithm (MWOA) hybridized with Artificial Neural Networks (ANN) has been employed in the Reverse Osmosis (RO) desalination plant performance to estimate the permeate flux (0.118‒2.656 L/h m2). The plant’s datasets have been collected from the literature and include four input parameters: feed flow rate (400‒600 L/h), evaporator inlet temperature (60‒80 °C), feed salt concentration (35‒140 g/L) and condenser inlet temperature (20‒30 °C). For this purpose, ten predictive models (MWOA-ANN Model-1 to Model-10) have been proposed, which are capable of predicting more accurate permeate flux (L/h m2) than the existing models (Response Surface Methodology (RSM), ANN and hybrid WOA-ANN models) with minimum errors. Simulation results suggest that the MWOA algorithm demonstrates a stronger optimization capability of finding the correct weights and biases so as to enable superior ANN based modeling without limitation of overfitting. Ten MWOA-ANN models (Model-1 to Model-10) have been proposed to investigate the plant’s performance. Model-6 with a single hidden layer (H = 1), eleven hidden layer nodes (n = 11) and the thirteen search agents (SA = 13) produced most outstanding regression results (R2 = 99.1%) with minimal errors (MSE = 0.005). The residual errors for Model-6 are also found to be within limits (span of − 0.1 to 0.2). Finally, the findings show that the screened MWOA-ANN models are promising for identifying the best process parameters in order to assist industrial plant designers.

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“…PID parameter optimization (or sometimes referred as parameter tuning) is key for effective performance of userdefined control tasks in manipulators [7], magnetic levitation [8], industrial plants [9], higher order systems [10], and other industrial settings. Nonetheless, tuning the parameters of PID control systems is challenging due to expensive evaluations of real-world surrogates and the requirement for fast adaptation to new tasks [6], [11].…”

Section: Introductionmentioning

confidence: 99%

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Parque,

Khalifa

2023

IEEE Access

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Proportional-Integral-Derivative (PID) is a simple and intuitive feedback-based control mechanism being useful to track set points and to reject disturbances. A key question in gradient-free optimization is to ascertain whether the class of optimization algorithms based on the difference of vectors generalize reasonably well to tackle a large class of PID control problems. For generalization and practical purposes, it would be desirable to render algorithms being able to tune PID controllers over a diverse and large set of control problems/tasks with minimal human intervention (self-adaptation features), and under tight computational budgets. In this paper, aiming to fill the above-mentioned gap, we propose and investigate the effectiveness of a new class of algorithm based on the difference of vectors and self-adaptation mechanisms for PID tuning. As such, we introduce a new class of Differential Evolution with successbased Particle Adaptations (DEPA), which unifies the principles of difference of vectors, particle schemes and trial/parameter adaptation through archive (memory) mechanisms. Our computational simulations using a large/relevant set of 25 control problem instances (tracking of linear, nonlinear, continuous, and discontinuous trajectories in motor position control, motor velocity control, magnetic levitation, inverted pendulum, crane stabilization), and the comparisons with a large set of closely related optimization algorithms, and their extended adaptive variants (23 optimization algorithms in total) has shown the outperforming benefits of the proposed approach in convergence performance under tight function evaluation budgets (1000 function evaluations). Also, the experiments on a real-world inverted pendulum device show the potential for transferability of the learned gains to unseen situations during training. Furthermore, we evaluated the algorithmic extension and the generalization towards diverse fitness landscapes in the CEC 2017 benchmark suite, showing the attractive/outperforming performance overall problem instances. In particular, the proposed framework performs better in 358 control instances compared to other algorithms for PID control tasks, and the algorithmic extension for general optimization landscapes performs better than the related algorithms in 182, 215 and 235 problem instances of CEC 2017 benchmark suite for 10, 30 and 50 dimensions, respectively. Our obtained results have the potential to further advance towards developing efficient and self-adaptive optimization algorithms based on the difference of vectors, which may find use in a wider set of optimization and control problems.

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PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

Cited by 25 publications

References 40 publications

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

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