Digital twin-based Optimization on the basis of Grey Wolf Method. A Case Study on Motion Control Systems

Nowadays, digital twins are fostering the development of plug, simulate and optimize behavior in industrial cyber-physical systems. This paper presents a digital twin-based optimization of a motion system on the basis of a grey wolf optimization (GWO) method. The digital twin of the whole ultraprecision motion system with friction and backlash including a P-PI cascade controller is used as a basement to minimize the maximum position error. The simulation study and the real-time experiments in trajectory control are performed to compare the performance of the proposed GWO algorithm and the industrial method called Fine tune (FT) method. The simulation study shows that the digital twin-based optimization using GWO outperformed FT method with improvement of 66.4% in the reduction of the maximum position error. The real-time experimental results obtained show also the advantage of GWO method with 18% of improvement in the maximum peak error and 16% in accuracy

N-Days Tourist Route Recommender System in Yogyakarta Using Genetic Algorithm Method

Tourism is one of the proven solutions for the Indonesian economy. Tourism in certain regions, such as Yogyakarta, can significantly affect the region's economic development, including creating new jobs, creating new business opportunities, and increasing regional income. However, for tourists from outside Yogyakarta, it requires planning a tour before traveling in Yogyakarta, especially if he wants to spend several days on a tour. Many previous studies have developed systems that can recommend tourist routes, but not within a few days of tourist visits. In this study, we propose the use of Genetic Algorithm (GA) for automatically generating optimal travel itinerary for some days visit (n-days tour route). We develop the recommender system by combining GA and the concept of Multi-Attribute Utility Theory (MAUT). This MAUT used for accommodating user needs based some criteria such as rating, cost, and time. Based on our experimental results, GA is optimal in terms of execution time and number of attractions visited in n-days visit. The average execution time obtained is 59.62%, and the average number of attractions visited obtained is 45.95%. These results show that this method can generate tourist routes efficiently

Multi-objective optimization through artificial intelligence for designing of an Agave angustifolia leaf shredder

A neural network and a genetic algorithm were used in a hybrid method to get the optimal design parameters of an Agave angustifolia Haw. green leaf shredder. First, a prototype of an experimental machine was built using the design parameters recommended by the literature and calculated using linear equations. Then, the shredder prototype was subjected to experiments. The defibration data with different blade adjustments were obtained with experimental values. The data was configured and trained with an artificial neural network to establish a correlation between the defibration quality and the design parameters. The multi-objective optimization method based on genetic algorithms determined the optimal design parameters of the shredder’s functional mechanical elements. The best point was obtained from the least number of broken fibers (2.83%) and the most waste (73.15%). The method used proved suitable to optimize the design parameters; this was based on actual data obtained by experiments performed with the prototype and then modeled through artificial intelligence methods such as neural networks to determine an optimal solution using evolutionary genetic algorithm methods

Improving the efficiency of photovoltaic cells embedded in floating buoys

Solar cells are used to power floating buoys, which is one of their applications. Floating buoys are devices that are placed on the sea and ocean surfaces to provide various information to the floats. Because these cells are subjected to varying environmental conditions, modeling and simulating photovoltaic cells enables us to install cells with higher efficiency and performance in them. The parameters of the single diode model are examined in this article so that the I-V, P-V diagrams, and characteristics of the cadmium telluride (CdTe) photovoltaic cell designed with three layers (CdTe, CdS, and SnOx) can be extracted using A solar cell capacitance simulator (SCAPS) software, and we obtain the parameters of the single diode model using the ant colony optimization (ACO) algorithm. In this paper, the objective function is root mean square error (RMSE), and the best value obtained after 30 runs is 5.2217×10-5 in 2.46 seconds per iteration, indicating a good agreement between the simulated model and the real model and outperforms many other algorithms that have been developed thus far. The above optimization with 200 iterations, a population of 30, and 84 points was completed on a server with 32 gigabytes of random-access memory (RAM) and 30 processing cores

Hybridization of Biologically Inspired Algorithms for Discrete Optimisation Problems

In the field of Optimization Algorithms, despite the popularity of hybrid designs, not enough consideration has been given to hybridization strategies. This paper aims to raise awareness of the benefits that such a study can bring. It does this by conducting a systematic review of popular algorithms used for optimization, within the context of Combinatorial Optimization Problems. Then, a comparative analysis is performed between Hybrid and Base versions of the algorithms to demonstrate an increase in optimization performance when hybridization is employed

Heat Flow Process Identification Using ANFIS-GA Model

This paper provides a nonlinear technique that uses a fuzzy inference system and neural networks for the identification purposes of heat flow transfer in the chamber. Firstly, linear models are obtained by transfer functions with delay using Matlab identification tools for heat exchange. Three different transfer functions are provided (for three sensors in different positions along the chamber), and after it has been concluded that the second model has the smallest error, it is tested using different input. In this case, the linear model failed to represent the behaviour of the system precisely, making the error more than 1.5 C in the steady state. This was expected because linear models are trustworthy only around certain operating ranges. In order to make the new model, which will be unique and valid in the whole state space, another identification method using an adaptive neuro-fuzzy inference system (ANFIS) was presented. Furthermore, for the best performance, the ANFIS architecture was found using one of the most famous population-based optimizations: the genetic evolutionary algorithm. With two inputs and 70 parameters found by optimization (40 premises and 30 consequent) ANFIS greatly outperforms standard identification technique in terms of the mean square error. This nonlinear model was also tested on the different input, which was not used in the training process, and it was concluded that the nonlinear model identifies the real object with a neglectable error, which is 45 times smaller than the linear one

Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation

Improper pain management can lead to severe physical or mental consequences, including suffering, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is challenging because different individuals experience pain differently. To overcome this, researchers have employed machine learning models to evaluate pain intensity objectively. However, these efforts have primarily focused on point estimation of pain, disregarding the inherent uncertainty and variability present in the data and model. Consequently, the point estimates provide only partial information for clinical decision-making. This study presents a neural network-based method for objective pain interval estimation, incorporating uncertainty quantification. This work explores three algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results reveal that LossS outperforms the other two by providing a narrower prediction interval. As LossS outperforms, we assessed its performance in three different scenarios for pain assessment: (1) a generalized approach (single model for the entire population), (2) a personalized approach (separate model for each individual), and (3) a hybrid approach (separate model for each cluster of individuals). Our findings demonstrate the hybrid approach's superior performance, with notable practicality in clinical contexts. It has the potential to be a valuable tool for clinicians, enabling objective pain intensity assessment while taking uncertainty into account. This capability is crucial in facilitating effective pain management and reducing the risks associated with improper treatment.Comment: 26 pages, 5 figures, 9 table

Predicting the potential of capacitive deionization for the separation of pH‐dependent organic molecules

One of the main steps in the biotechnological production of chemical building blocks, such as, e.g. bio-based succinic acid which is used for lubricants, cosmetics, food, and pharmaceuticals, is the isolation and purification of the target molecule. A new approach to isolate charged, bio-based chemicals is by electrosorption onto carbon surfaces. In contrast to ion exchange, electrosorption does not require additional chemicals for elution and regeneration. However, while the electrosorption of inorganic salts is well understood and in commercial use, the knowledge about electrosorption of weak organic acids including the strong implications of the pH-dependent dissociation and their affinity towards physical adsorption must be expanded. Here, we show a detailed discussion of the main pH-dependent effects determining the achievable charge efficiencies and capacities. An explicit set of equations allows the fast prediction of the named key figures for constant voltage and constant current operation. The calculated and experimental results obtained for the electrosorption of maleic acid show that the potential-free adsorption of differently protonated forms of the organic acid play a dominating role in the process. At pH 8 and a voltage threshold of 1.3 V, charge efficiencies of 25% and capacities around 40 mmol/kg could be reached for a constant current experiment. While this capacity is clearly below that of ion exchange resins, the required carbon materials are inexpensive and energy costs are only about 0.013 €/mol. Therefore, we anticipate that electrosorption has the potential to become an interesting alternative to conventional unit operations for the isolation of charged target molecules

A Review of Optimization Techniques: Applications and Comparative Analysis

Optimization algorithms exist to find solutions to various problems and then find out the optimal solutions. These algorithms are designed to reach desired goals with high accuracy and low error, as well as improve performance in various fields, including machine learning, operations research, physics, chemistry, and engineering. As technology continues to advance, optimization algorithms are increasingly needed to address complex real-world challenges and drive innovation across all disciplines. Quantitative leaps have been achieved in improving the efficiency of optimization algorithms through the diversity of sources of information feeding these algorithms according to the type of optimization problem, based on scientific and organized foundations. The objectives of this paper are to discuss the most important optimization algorithms, classify the scientific fields involved in their application, and optimize problems involved in this regard, in addition to providing a brief overview for comparison among these algorithms

RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional method