Advances in Systems Science and Applications

Pretreatment Performance Evaluation of the Seawater Desalination Plant of Beni Saf BWC

2023-07-09T12:48:13+00:00

The BENI SAF Seawater Desalination Plant is one of the largest projects undertaken by the Algerian state for supplying drinking water, with a capacity of 200,000 m3 per day. The plant uses the Reverse Osmosis technique as a desalination process. The performance of such systems requires the production of pretreated, good-quality feed water. Moreover, a number of large-scale experiments have shown that pretreatment of seawater before reverse osmosis (RO) desalination is key to retard fouling in osmosis membranes. This work aims to study the performance of the pretreatment selected by the BENI SAF Desalination Plant. To achieve this, we determined the physico-chemical and bacteriological parameters of the raw seawater used by the Plant. Variation of SDI, which is a key parameter in controlling fouling potential, was monitored during the year 2019. The pretreatment performance was investigated by monitoring the efficiency of each stage of the pretreatment process. The results obtained show the efficiency of the pretreatment adopted by this desalination plant.

An Overview of Multiple Criteria Decision Making Techniques in the Selection of Best Laptop Model

2023-07-09T12:41:42+00:00

This research article presents a detailed study of a laptop selection problem by analyzing through three (Multi-Criteria Decision Making) MCDM techniques namely, (Weighted Sum Model) WSM, (Weighted Product Model) WPM and (Weighted Aggregated Sum Product Assessment Model) WASPAS. The main goal of this study is to propose the best laptop model among a group of seven alternative models based on 10 selection criteria. For this purpose, (Analytic Hierarchy Process) AHP is used to determine the criteria weightages whereas, WSM, WPM, WASPAS techniques are applied individually to determine the best model and to rank the alternatives. The best model is proposed based on the output results of the three MCDM techniques which suggest that alternative 3 and alternative 2 are the optimum and the foulest choice respectively. All the rankings given by the three methodologies are also compared with the previous result which shows that all the three methods are giving the same outcome and the rankings are more or less same with very minor alterations.

Object Recognition by a Minimally Pre-Trained System in the Process of Studying the Environment

2023-07-09T12:42:42+00:00

We refine a method for describing and evaluating a previously proposed process of studying an abstract environment by a system (robot). In the process, we do not model any biological cognition mechanisms and consider the system as an agent (or a group of agents) equipped with an information processor. The robot (agent) makes a move in the environment, consumes information supplied by the environment, and gives out the next move (thus, the process is considered as a game). The robot moves in an unknown environment and should detect new objects located in it and recognize them. In this case, the system should build comprehensive images of visible things and memorize them if necessary (and it should also choose the current goal set). The main problems here are object recognition and the assessment of information reward in the game. Thus, the main novelty of the paper is a new method of evaluating the amount of visual information about the object as the reward. In such a system, we suggest using a minimally pre-trained neural network to be responsible for the recognition: at first, we train the network only for Biederman geons (geometrical primitives). Training sets of geons are generated programmatically and we demonstrate that such a trained network recognizes geons in real objects quite well. Sets of geons connected with objects (schemes) are used as the rewards.We also expect to generate procedurally new objects from geon schemes obtained from the environment in the future and to store them in a database.

‎Stress-Strength Reliability of a Weibull-Standard Normal Distribution Based on‎ ‎Type-II Progressive Censored Samples

2023-07-09T12:50:50+00:00

In this paper, under the Type-II progressive censored scheme, we obtain the point and interval estimates of stress-strength parameter (R), when stress and strength are two independent Weibull-standard normal variables. We study the problem in three cases. First, assuming that stress and strength have the different scale parameters and the common shape parameter, we obtain maximum likelihood estimation, approximation maximum likelihood estimation and two Bayesian approximation estimates due to the lack of explicit forms. Also, we construct the asymptotic and highest posterior density intervals for R. Second, assuming that common shape parameter is known, we derive the maximum likelihood estimation and Bayes estimate and uniformly minimum variance unbiased estimate of R. Third, assuming that all parameters are unknown and different, we achieve the statistical inference of R, namely maximum likelihood estimation, approximation maximum likelihood estimation and Bayesian inference of R. Furthermore, we use the Monte Carlo simulations to compare of the performance of different methods.

A New Approach on the Modelling and Analysis Stability of a Class of Fractional-Order Quasi-Polynomial Systems

2023-07-09T12:48:34+00:00

Stabilization and observation for nonlinear fractional derivative systems remain open problems in automatic due to the fractional nature and nonlinearity of these systems. The present paper studies global stability by the return output for fractional systems. First, we give some definitions of fractional calculus and the quasi-polynomial (QP) and Lotka-Volterra (LV) systems. Then, we analyze their stabilities as well as linear (LMI) and bilinear (BMI) matrix inequalities. In order to solve the controller design problem. The goal of this paper is to investigate the global and local stability of a dynamic fractional order system using the quasi-polynomial and LV representation. Then, we use the LMI to study the stabilization of this fractional system.

Analysis of Consumer Behaviour Related to Electricity Consumption

2023-07-06T09:35:32+00:00

Almost three-quarters of carbon dioxide emissions originate from households' activities and among the major contributors to climate change is the energy sector. Therefore, increased attention is being paid to energy-efficient behaviour in the household segment. Over the past decades, a limited number of studies focused on the in-depth understanding of consumer behavior within energy consumption and on comparison of consumers’ own perceptions towards their effectiveness in energy consumption to the reality substantiated by the precisely measurable amounts of energy they have been consuming. This study extends the authors' previous research on changes in consumer behavior, triggered by access to the personalized simplified and user-friendly data related to the energy consumption of a particular household. The focus is on describing the behavior patterns in electricity consumption related to consumers’ age and size of agglomeration they inhabit. The study includes data on 3 years' consumption of over 30 000 households from Slovakia. It provides conclusions relevant to the area of the Central European region at least, as among the main factors influencing the energy consumption are weather and dwelling conditions.

On Estimating the Characteristics of a Fork-Join Queueing System with Poisson Input and Exponential Service Times

2023-07-12T05:33:29+00:00

The paper studies the classical fork-join queueing system with M|M|1 subsystems. The analysis of this system is still relevant due to the lack of exact solutions for assessing its performance characteristics if the number of subsystems exceeds two. In addition, the fork-join system is a mathematical model of parallel or distributed computing systems that have become widespread as one of the most effective methods for processing Big Data. An approach based on graphical analysis, non-linear regression, and the use of the Nelder-Mead optimization method is proposed to estimate the mathematical expectation and dispersion of the response time of a fork-join system. As a result, the authors managed to modify the known approximations and significantly (many times) improve their approximation quality. The paper also examines the quality of the experimental data of simulation modeling used to estimate the approximation error of the obtained expressions. As a rule, this issue remains outside the scope of ongoing research in the field of this topic due to the complexity of such an analysis. And sometimes, it is due to the underestimation of the importance of this issue. The article proposes an approach to finding confidence intervals for simulation results. It provides an algorithm for their construction and also gives some recommendations.

Evaluating the Efficacy of the Sliding Mode Algorithm for the Active Suspension System

2023-07-06T09:35:32+00:00

The active suspension system is installed in the car to improve the smoothness and comfort while driving. In this paper, a quarter-dynamic model is utilized to describe the car's vibrations. The SM (sliding mode) control algorithm is proposed to control the operation of the suspension system. The problem's input signal is the excitation from the road surface, while the output signal includes values related to the acceleration and displacement of the vehicle body. In addition, the interaction between the wheel and the road surface, which is expressed through the change of dynamic force, is also considered. The numerical simulation is performed with a sinusoidal pavement excitation signal. According to this result, the car body displacement was greatly reduced when using an active suspension controlled by the SM method. Meanwhile, the RMS value of acceleration is only 14.84% compared to the car using a traditional suspension system. The fluctuation of the unsprung mass is insignificant, so it does not affect the car's smoothness. Additionally, the change in dynamic force is small, not exceeding 3.3%, when the vehicle body oscillates. This helps increase wheel stability and holding. The effect that the SM algorithm brings to this study is positive.

Deep Feature Extraction and Weight Updated Tuned Random Forest for Piper Plant Species Recognition

2023-07-09T12:45:50+00:00

Recently, identifying plant species has become a significant research area as it is vital for securing biodiversity. Plants also possess various medicinal applications. Hence, predicting different species of plants is of utmost significance. However, determining plant species through conventional ways is a time-consuming process. That happens due to huge and distinct botanical terms. With the recent evolution of AI (Artificial Intelligence) based algorithms, researchers have undertaken various attempts to predict plant species. However, most studies averted the consideration of piper plant species which holds huge medicinal benefits. Existing research also failed to predict the plant species due to inefficient feature extraction accurately. Considering such a pitfall, this study proposes Deep CNN (Deep Convolutional Neural Network) and Inception V3 to extract features to perform all plant classification. In addition, the study proposes Deep CNN and VGG16 (Visual Geometry Group16) to extract suitable features for performing piper plant classification. Following this, the study considers PCA (Principle Component Analysis) for feature fusion as it can reduce noise in data and select relevant features for affording independent and uncorrelated data features. Finally, the study proposes WUT-RF (Weight Updated Tuned Random Forest) to classify piper and all plant species. In this process, hyperparameters of RF are tuned with convolutional likelihood weight to attain a high prediction rate. Optimal hyperparameter selection and tuning assist in improvising the performance of the proposed classifier. Performance analysis of this system about performance metrics exposes its effectiveness in plant species detection.

Determining SST Aerodynamic Configuration and Power Plant Parameters under Epistemic Uncertainty

2023-07-09T12:46:52+00:00

The paper considers the problem of determining parameters of the aerodynamic configuration and power plant of an advanced supersonic passenger transport (SST) at the stage of preliminary aerodynamic design under epistemic uncertainty associated with incomplete information about the initial data. Optimization models and algorithms based on them are proposed that operate with the designed SST initial parameters generated by experts on the basis of empirical prediction. Such parameters are proposed to be generated within Liu’s uncertainty theory as uncertain quantities expressed by uncertainty distribution functions. The use of uncertainty theory will make it possible to formalize and perform aerodynamic design process by replacing the functions that depend on uncertain quantities with their numerical characteristics. Such numerical characteristics are effectively interpreted by the decision maker, since they have analogues in probability theory – expected value, quantile, variance. The use of uncertainty theory in solving optimization problems under uncertainty provides low computational costs compared to the theory of probability. The paper discusses the use of numerical methods in the proposed algorithms, since, additionally, it is required to solve the black box function optimization problem. This is due to the lack of simple analytical relations between the SST requirements and the SST aerodynamic configuration and power plant parameters. The adequacy of the developed algorithms is demonstrated by the aerodynamic predictions presented by the Pareto fronts of the objective functions, which allow choosing trade-off design solutions.

Pairwise Similarity Estimation for Discrete Optimization Problems

2023-07-09T12:47:34+00:00

In this paper, we propose a new method, called the pairwise similarity method, for assessing the similarity of instances of optimization problems. This method is a generalization of the metric approach proposed earlier for scheduling problems. It relaxes the requirements to the structure of the problem constraints. The method involves a dissimilarity function for the comparison of instances. It identifies “simple” instances that can be solved in polynomial time and uses them to get good approximations for other instances. We apply the pairwise similarity method to two discrete optimization problems: the majority domination problem and the maximum cut problem.

A Combinatorial View on Derivations in Bimodules

2023-07-06T09:35:32+00:00

This paper is devoted to derivations in bimodules over group rings using previously proposed methods which are related to character spaces over groupoids. The theorem describing the arising spaces of derivations is proved. We consider some examples, in particular the case of $(\sigma, \tau)$-derivations.

Nonuniqueness of Equilibrium in Closed Market Model

2023-07-06T09:35:33+00:00

In this paper, we consider a closed market model. In this model the supply and the demand functions are restored by their price elasticities. We obtain sufficient conditions on nonuniqueness of equilibrium in this model. For several special cases of closed market models we get a criteria of equilibrium uniqueness. The obtained results are illustrated with the example of the market with two goods.