Computational Intelligence Algorithms Research Articles

Implementation of Health Monitoring System of Induction Machine Using Computational Intelligence

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.  

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea.

Computational Intelligence Algorithms for UAV Swarm Networking and Collaboration: A Comprehensive Survey and Future Directions

Computational Intelligence Algorithms for UAV Swarm Networking and Collaboration: A Comprehensive Survey and Future Directions

Automated Passive Income from Stock Market Using Machine Learning and Big Data Analytics with Security Aspects

With the evolution of Machine Learning, the Internet of Things (IoT), and Big Data Technologies, digital data has increased exponentially. To handle and process such a large bunch of data, high-performance computers are used across various fields. One of the fields has been the financial capital market of stocks, bonds, commodities, foreign exchange, and cryptocurrencies, where supercomputers essentially trade securities with high computational ability and intelligent algorithms. Large financial institutions with significant capital spend a lot of money on programmers and data analysts to develop the best accuracy trading algorithms to drive the overall market. A novice trader or investor with less to no experience in financial markets feels it difficult to search for good trades or stocks to invest their hard-earned money on a short to long-term time horizon. They rely on expert advice for stock recommendations and sometimes end up making significant losses. This paper focuses on developing a universal trend trading indicator that can analyze and predict the overall future trend of any stock, bond, commodity, forex, or cryptocurrency with the highest possible profitability. The historically traded extensive dataset of stock prices and investment reports of large financial institutions worldwide are gathered. Various machine learning and decision-making models are employed to perform technical and fundamental analysis across multiple securities. The output of the trend trading indicator is displayed on charting platforms, which can provide entry-exit levels at which even novice investors can decide where to invest their money. Multi timeframe analysis is deployed to predict short-term, mediumterm, and long-term overall trends, thus increasing the output accuracy. The indicator is helpful for all kinds of retail traders and investors worldwide who struggle to earn profits from financial markets. Our proposed system was able to achieve a profitability of 86.28% annual returns. The entire system, along with the trading orders, is automated so that anyone can earn extra passive income every month from the stock market.

The impact of Russian Energy Resources on the Economic Growth of the EU: Using Computational Intelligence Algorithms

This study explores the impact of Russian oil and natural gas on the economic growth of the European Union. The Gradient boosting algorithm was relied on to determine this effect because of its high prediction metrics (MSE: 0.002, RMSE: 0.040, MAE: 0.034, R2: 99.9). The study depended on three scenarios. The first scenario is that Russia's exports of both products decline to half the year 2022, then to the quarter of 2023, and this second scenario, then the worst scenario, is to prevent Its exports of both products in 2024. But the result is a decline in the European Union's economic growth in 2022 to (-2.15%), then it turns to 2.85% in 2023, and then to 3.86% in 2024, i.e., in the worst scenario year. The evidence for this is that the economies of these countries reduced their growth rates in 2020 (the Covid-19 crisis) to -5.96%, which turned to positive growth in 2021, amounting to 5.38%. This indicates these economies' ability to adapt in the short term by providing alternatives to the crisis.

Integration of the methods of algebra of algorithms and computational intelligence for automation of program systems design

An approach to the creation of intelligent systems is considered, which combines the ideas of Glushkov’s algebra of algorithms with the well-known neuroevolutionary algorithms of computational intelligence, which can be used to automate the design and synthesis of programs. The method of neuroevolution of augmenting topologies is intended to reduce the dimensionality of the space for searching for neural network parameters in the form of gradual development of its structure in the process of evolution. A software toolkit has been developed to support the approach, which is based on the construction of high-level specifications of algorithms represented in systems of algorithmic algebras, and generation of corresponding programs based on implementation templates in a target programming language. Parameterized algorithms called hyperschemes are used to solve a certain class of problems. Setting specific values of parameters and subsequent interpretation of hyperschemes allows obtaining algorithms adapted to specific application conditions. The developed program design and synthesis toolkit provides step-by-step development of programs, starting from a high-level algebraic-algorithmic specification. At the output of the toolkit, a program is automatically generated in one of the target programming languages (С, С++, Java, Python), that also include a subject-oriented language for designing neural networks. The mentioned language includes operators and conditions for working with populations, configurations, genomes, and fitness function. The work of the toolkit is illustrated by the example of designing a parameterized evaluation algorithm for a binary multiplexer and generation of a program for the single-pole balancing problem

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

Starting from the fact that in sensorless control systems of the Permanent Magnet Synchronous Motor (PMSM), the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, thus increasing ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the PMSM sensorless control system based on the LADRC controller with an ESO-/DO-type observer are presented, as is its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. To optimize the gain value of the DO-type observer, a computational intelligence algorithm such as the Ant Colony Algorithm (ACO) is used. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. In addition, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment.

STRATEGIC COMPETENCE AS A GUARANTEE OF PROFESSIONAL ACTIVITY OF A MODERN IT SPECIALIST

The article examines the current problem of professional training of future IT specialists in the context of forming their strategic competence because it is critical for university graduates in the field of IT to be able to strategically plan actions in different ways for effective implementation in their professional activities. Therefore, the strategy of professional activity is carried out by means of separate tactical actions, which collectively make up strategic activity. Thus, s IT specialists’ shaping strategic competence allows understanding of which actions are the most important for the realization of the goal and the achievement of professional tasks. It has been established that strategic competence of an IT specialist is a personal integrative creation. This represents the unity of professionally significant qualities, knowledge and skills, with the help of which specialists formulate the strategic goal of work, show a value-motivational attitude to strategic changes and flexibility in behaviour depending on the factors of the external environment, make a reasonable choice and implement strategies depending on the situation, while achieving the effectiveness of the professional activity. The importance of shaping strategic competence in three main sections is substantiated including: elemental (a set of components, the interaction between which ensures the implementation of the relevant activity); functional (methodology for adopting strategies); and procedural (technology for developing and implementing a strategy in the existing environment). It has been proved that a high level of IT specialists’ strategic competence ensures the following: determination of the leading goal of professional activity, choosing an effective model of professional behaviour following existing conditions, planning and development of measures for adaptation to conditions of professional activity, implementation of planned professional actions and operations (strategies), etc. The article also presents the main aspects of professional training of future IT specialists. The experience of organizing and implementing university training for future IT specialists shows that a high level of strategic competence makes it possible to choose and prepare the right hardware and software, rational methods for solving professional tasks, allows for the modernization of IT infrastructure, optimization of IT efficiency for innovation. This makes it possible to manage the IT sector, in particular, in the context of IT infrastructure management, IT projects, IT costs, to increase the security and stability of the industry. It is important to define long-term goals, taking into account the phased process of optimizing existing IT systems. A strong emphasis is also placed on the following blocks of scientific knowledge: the use of technologies and algorithms of computational intelligence and intelligent data analysis in the tasks of forecasting, analytics, cluster analysis using software tools to support multidimensional data analysis based on DataMining, TextMining, WebMining technologies, mastering system programming languages and methods of developing online programs, performing parallel and distributed calculations, applying numerical methods and algorithms, etc. The article also provides promising directions for research as a study of the real state of shaping strategic competence of future bachelors in the IT field.

High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids.

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.

Explaining the Walking Through of a Team of Algorithms

We propose the Taguchi method, the diversity, and the average number of evaluations of a solution to support an explanation regarding decisions made by a computational intelligence algorithm. These metrics support uncovering interactions in a cooperative team of computational intelligence algorithms to solve the base station deactivation problem.

A method for efficient classification of microphones based on expert knowledge and computational intelligence

Classification of microphones as photoacoustic detectors is important part of procedure of photoacoustic measurements calibration. The requirements of photoacoustic experiment are accuracy, precision, reliability and work in real time in order to be competitive measurement technique. According to current state, real time is still a problem. This paper suggests improvement of classification method currently in use by dimensionality reduction of input vector considered in the data preprocessing, having consequence in significant simplification of measurements and thus notable decrease of measurement time, so reaching real time calibration procedure. By applying the method presented in the article the number of measurement points will be one, two or three depending on its position on frequency axes which is extremely smaller number than commonly accepted (usually 70-80 for the frequency range 20 Hz-20 000 Hz). The method is based on computational intelligence algorithms and expert knowledge.

Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

The most significant component in the education domain is evaluation. Apart from student evaluation, teacher evaluation plays a vital role in the colleges or universities. The implementation of a scientific and appropriate assessment method for enhancing teaching standards in educational institutions is absolutely essential. Conventional teacher assessment techniques have always been bounded to bias and injustice for single dimensional assessment criteria, biased scoring, and ineffective integration. In this regard,it is crucial to develop a special-ized teacher evaluation assistant (TEA) system that integrates with some computational intelligence algorithms. This research concentrates on using Natural language processing(NLP) based techniques for empirically analysing teaching effectiveness. We develop a model in which a teacher is evaluated based on the content he delivers during a lecture. Two techniques are employed to evaluate teacher effectiveness using topic modelling and text clustering. By the application of topic modelling, an accuracy of 75% is achieved and text clustering achieved an accuracy of 80%. Thus, the method can effectively be deployed to assess and predict the effectiveness of a teacher's teaching.

Virtual Modeling and Immersive Holographic Imaging Technologies, Cloud-based Digital Twin Manufacturing and Visual Perceptive Systems, and Bio-inspired Computational Intelligence and Context Awareness Algorithms in the Industrial Metaverse

Virtual Modeling and Immersive Holographic Imaging Technologies, Cloud-based Digital Twin Manufacturing and Visual Perceptive Systems, and Bio-inspired Computational Intelligence and Context Awareness Algorithms in the Industrial Metaverse

Bio-inspired Computational Intelligence and Deep Learning Algorithms, 3D Modeling and Cognitive Neuro-Engineering Technologies, and Immersive Visualization and Internet of Things-based Decision Support Systems in the Virtual Environment of the Metaverse

Bio-inspired Computational Intelligence and Deep Learning Algorithms, 3D Modeling and Cognitive Neuro-Engineering Technologies, and Immersive Visualization and Internet of Things-based Decision Support Systems in the Virtual Environment of the Metaverse

Research on Computational Intelligence Algorithm in LTE Channel Estimation

Because data traffic is growing at a rapid pace thanks to advancements in the Internet of Things, precise modelling and precisely anticipating Long-Term Evolution (LTE) Channel is critical for a variety of applications like as video streaming, effective bandwidth consumption, and power management. In this research, we propose a model based on a Computational Intelligence (CI) Algorithm that may enhance Channel Estimation based on received signal. Two Algorithms are considered. In contrast to previous work that focused solely on designing models to estimate channel using traditional Minimum Mean Square Error (MMSE) and Least Square (LS) algorithms, we used 1) GA (Genetic Algorithm) and 2) PSO (Particle Swarm Optimization Algorithm) to work on Discrete and Continuous Long-Term Evolution (LTE) drive test data. We're looking at LTE in the 5.8 GHz band in particular. By lowering the mean square error of LS and the complexity of MMSE, the design model attempts to improve channel estimation. Pilots are put at random and sent with data to gather channel information, which aids the receiver in decoding and estimating the channel using LS, MMSE, Taguchi GA, and PSO. The Bit Error Rate (BER), Signal to Noise Ratio, and Mean Square Error of a CI-based model have all been estimated. In comparison to the MMSE and LS algorithms, the proposed model BER achieves the target gain of 2.4 dB and 5.4 dB.

Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning

Starting from the nonlinear operating equations of the permanent magnet synchronous motor (PMSM) and from the global strategy of the field-oriented control (FOC), this article compares the linear and nonlinear control of a PMSM. It presents the linear quadratic regulator (LQR) algorithm as a linear control algorithm, in addition to that obtained through feedback linearization (FL). Naturally, the nonlinear approach through the Lyapunov and Hamiltonian functions leads to results that are superior to those of the linear algorithms. With the particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA), and gray wolf Optimization (GWO) computational intelligence (CI) algorithms, the performance of the PMSM–control system (CS) was optimized by obtaining parameter vectors from the control algorithms by optimizing specific performance indices. Superior performance of the PMSM–CS was also obtained by using reinforcement learning (RL) algorithms, which provided correction command signals (CCSs) after the training stages. Starting from the PMSM–CS performance that was obtained for a benchmark, there were four types of linear and nonlinear control algorithms for the control of a PMSM, together with the means of improving the PMSM–CS performance by using CI algorithms and RL–twin delayed deep deterministic policy gradient (TD3) agent algorithms. The article also presents experimental results that confirm the superiority of PMSM–CS–CI over classical PI-type controllers.

Guest Editorial Special Issue on Computational Intelligence for Perception and Decision-Making of Autonomous Systems

The papers in this special section focus on computational intelligence for perception and decision making of autonomous systems. Due to powerful capabilities in environmental perception, real-time computing, and intelligent decision-making, autonomous systems have demonstrated their great potential to efficiently accomplish a variety of complex tasks that humans cannot. Hence, autonomous systems are able to facilitate the development of almost every walk of life and have attracted increasing attention from both academia and industry. However, given high dimensional, heterogeneous, unstructured, and unpredictable data sampled from different modalities of sensors, autonomous systems with conventional algorithms may fail to acquire the accurate information related to the environment, and make the appropriate decision to complete assigned tasks. Notice that recent advanced computational intelligence algorithms including deep neural networks and evolutionary algorithms have the unique ability to efficiently extract useful information from the multi-source heterogeneous data, and thus have been successfully applied in the fields of computer vision, natural language processing, and so on. Therefore, it is promising to have a thorough and tight integration between computational intelligence and autonomous systems by upgrading advanced and innovative computational intelligence algorithms to ensure high-level environmental perception and decision-making of autonomous systems.

Pharmaceutical Foreign Particle Detection: An Efficient Method Based on Adaptive Convolution and Multiscale Attention

The detection of pharmaceutical product quality is indispensable and crucial in drug manufacture. In particular, the detection of liquid pharmaceutical products is generally performed manually and offline. However, this type of method is inefficient and possesses low precision due to limited personal sensing and eyesight capabilities. Since recent advanced computational intelligence (CI) algorithms have achieved great successes in computer vision, CI (e.g., deep neural networks (DNNs)) is expected to provide efficient and powerful tools that will substantially improve the decision-making of autonomous systems. Moreover, with the current automatic detection methods, it is still a challenge to detect tiny particles efficiently in liquid products. This article proposes an end-to-end deep learning (DL) method with adaptive convolution and multiscale attention to locate and classify foreign particles. First, we present a pixel-adaptive feature extraction (PAFE) method for extracting fine-grained features and reducing the intraclass disparities between particles. Following that, a multiscale attention-based feature fusion (MAFF) method, which effectively fuses the pixel-level and semantic-level information of particles, is proposed. Finally, we use a feature-selective anchor-free detection (FSAD) method to quickly detect foreign particles in liquid pharmaceuticals. To confirm the above initiatives, we validate the proposed method on a liquid pharmaceutical dataset, achieving a missed detection rate of 3.6 percent. The speed of our method is an order of magnitude faster than that of other methods, possibly reaching 15 frames per second (FPS). In addition, we test the model’s transferability on a wine dataset.

Hybrid of deep recurrent network and long short term memory for rear-end collision detection in fog based internet of vehicles

The development and use of intelligent transportation systems as an emerging trend in the application of computational intelligence within the concept of internet of vehicles (IoV) is attracting attention in the academia and industries. The computational intelligence algorithms such as the deep learning is playing a significant role in the IoV. The detection of rear end collision in the IoV is a critical aspect of the IoV because of safety. Previous research applied genetically optimized artificial neural network (ANN) (GA-ANN) for the detection of rear end collision in the IoV. However, GA-ANN has limitations such as stretching of the entire network leading to reduced flexibility, deteriorating performance as data increases to very large size, lack of generalization power resulting from over-fitting of the training data and calling for additional effort for regularization functions to monitor the model’s complexity. In this research, as an alternative to GA-ANN for improving the accuracy of rear end collision detection in IoV, we propose a hybrid of Deep Recurrent Neural Network (DRNN) and Long Short Term Memory (LSTM) (DRNN-LSTM) for rear end collision detection in the IoV. The scenario for the IoV is created and simulated to generate the dataset. The propose DRNN-LSTM is applied to detect the rear end collision in IoV. The performance of the proposed DRNN-LSTM is compared with the constituent algorithms of the DRNN-LSTM, ANN and GA-ANN. The results show that the proposed DRNN-LSTM detects rear end collision in IoV better than the GA-ANN, LSTM, DRNN and ANN. Thus, our propose approach has potential to improve safety, and effectiveness of the overall vehicle mobility in the IoV environment. Keywords: Collision Detection; Internet of Vehicles; Long Short Term Memory; Deep Recurrent Neural Network; Deep Learning Algorithm.