Evolutionary optimization within an intelligent hybrid system for design integration

Summary form only given. There has been substantial progress in both intelligent systems and robotics in recent years. While in the past robots were most successful in traditional industrial environments, new generations of hybrid intelligent robotic systems are being developed which focus on higher cognitive capabilities, including reasoning learning and language communication. In this paper we give an overview of learning neural robots from a perspective of integrative hybrid intelligent systems and illustrate some new developments including also examples under development in the Centre for Hybrid Intelligent Systems.

Advances in artificial intelligence (AI) techniques that can be used for different purposes have enabled it to be used in many different industrial applications. These are mainly used for modeling, identification, optimization, prediction and control of complex systems under the influence of more than one parameter in industrial applications. With the increasing accuracy of AI techniques, it has also obtained a wide application area on friction stir welding (FSW), one of the production methods developed in recent years. In this study, commonly used AI techniques for FSW, results, accuracy and superiority of AI techniques are reviewed and evaluated. In addition, an overview of AI techniques for FSW in different material combinations is provided. Considering the articles examined; It is seen that welding speed, rotational speed, the plunge depth, spindle torque, shoulder design, base material, pin design/profile, tool type are used as input parameters and tensile strength, yield strength, elongation, hardness, wear rate, welding quality, residual stress, fatigue strength are used as output parameters. As can be seen from the studies, it made important contributions in deciding what input parameters should be in order to have the output parameter at the desired value. The most common used materials for FSW are Al, Ti, Mg, Brass, Cu and so on. When FSW studies using artificial intelligence techniques were examined, it was seen that 81% of the most used materials were AL alloys and 23% of them were made with dissimilar materials. The most commonly utilized AI techniques were said to be artificial neural networks (ANN), fuzzy logic, machine learning, meta-heuristic methods and hybrid systems. As a result of the examination, ANN was the most widely used method among these methods. However, in recent years, with the exploration of new hybrid methods it was seen that hybrid systems used with ANN have higher accuracy.

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems.

With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.

Artificial intelligence (AI) is nowadays an upcoming technology. It is a practice of simulating human intelligence for varied applications. When compared with the standard practices, AI is developing at a rapid rate. AI has proved its worth in several areas such as agriculture, automobile industry, banking and finance, space exploration, artificial creativity, etc. Owing to the efficiency, speed, and independence from human operations, AI is now entering the wastewater treatment sector. This technology has been used for monitoring the performance of the water treatment plants in terms of efficiency parameters, Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) determination, elimination of nitrogen and sulphur, prediction of turbidity and hardness, uptake of contaminants, etc., in the wastewater sector. Artificial Neural Networks (ANN), Fuzzy Logic Algorithms (FL), and Genetic Algorithms (GA) are the basic three models under AI predominantly used in the wastewater sector. Studies reveal that the determination coefficient values of 0.99 can be attained for COD, BOD, heavy metals and organics removal using ANN and hybrid intelligent systems. This review paper describes research with all the possible models of AI utilized in the water treatment which have enhanced the pollutant removal percentage accuracy of ranging from 84% to 90% and provided viewpoint on future directions of novel research, in the field with due focus on pollution remediation, cost effectiveness, energy economy, and water management.

Global Positioning System (GPS) and Inertial Navigation System (INS) data can be integrated to provide a reliable navigation. This paper presents an approach of solving GPS/INS data integration problem, without the need of modeling the characteristics of GPS and INS sensors. Use of Artificial Intelligence (AI) techniques for an intelligent navigation system has been developed as an alternative to the conventional Kalman filter approach, in which it is mandatory to model the entire system. Many AI techniques have been implemented for the same, in which the use of ANFIS instead of neural networks and fuzzy logic has been widely implemented. In this paper Memetic optimization on ANFIS (MANFIS) has been proposed which outperforms Genetically optimized ANFIS (GANFIS).

In this study, we examined the ability of Artificial Intelligence (AI) techniques in deriving parameters of NMR log starting from conventional logs. To perform this, it was applied Fuzzy Logic (FL) and Artificial Neural Networks (ANN) techniques separately, forming independent schemes. On the other hand, Genetic Algorithms (GA) and Simple Mean (SM) approaches were used to assign weighting factors to FL and ANN estimates, with the objective to optimize the individual contributions of each one. This methodology made use of, as input data, gamma rays (GR), resistivity laterolog (RLA1), density (RHOB), neutron (NPOR) and sonic (DTCO) logs from two wells drilled through a carbonate reservoirs in Campos Basin - Brazil. The output responses were compared with free fluid porosity (CMFF) and SDR lateral permeability (KSDR - Schlumberger Doll Research), both derived from NMR log of the same wells. The results indicate that ANN performed better when compared with FL, but this last was essential in the success of SM and GA estimates. However, each approach showed a good fit with the parameter curves of NMR log, confirming the utility of the present methodology in the case when there are only conventional logs in the studied well.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

A hybrid stock trading system for intelligent technical analysis-based equivolume charting

Composite materials offer a wide range of advantages in terms of strength, weight, and versatility, making them a popular choice for structural applications. The performance of composite structures depends not only on the material constituents but also on the topology and arrangement of these components. However, designing an optimal configuration that maximizes the desired performance metrics can be a challenging task due to the large design space and complex interactions between the components. It presents a novel approach for optimizing the multi-component topology and material orientation design of composite structures using artificial intelligence (AI) techniques. The objective is to develop an automated and efficient methodology that can identify the most optimal configuration for a given set of performance requirements. The proposed methodology combines AI algorithms, such as genetic algorithms and machine learning, with advanced computational modeling techniques. The genetic algorithms are employed to explore the design space and search for the best combination of topology and material orientations. Concurrently, machine learning techniques are utilized to model the complex relationships between the design variables and performance metrics, enabling the identification of design patterns and accelerating the optimization process.
 To achieve this, a comprehensive framework is established, encompassing the generation of an initial population of candidate designs, evaluation of their performance using numerical simulations or experimental testing, application of genetic algorithms to iteratively evolve the population by selecting the fittest designs and introducing variations, and utilization of machine learning models to predict the performance of new designs and guide the optimization process. The effectiveness of the proposed methodology is demonstrated through a case study involving the design of a composite aerospace structure. The results reveal that the AI-based optimization approach significantly outperforms traditional trial-and-error methods, leading to improved performance metrics such as strength, stiffness, and weight.
 The findings have significant implications for the design and manufacturing of composite structures in various industries, including aerospace, automotive, and civil engineering. The automated optimization process enabled by AI techniques can efficiently explore the design space, identify innovative solutions, and ultimately enhance the overall performance of composite structures.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

In many domains such as management, production and government, established control approaches struggle to address increasing complexity in a timely manner, resulting in a demand for more agile methods. Hybrid intelligence and decision support systems are useful approaches to augment human decision-making through artificial intelligence (AI). Various application of AI methods to estimate production parameters or to provide forecasts are discussed in the literature or already being implemented, however, human decision-making is still required for either deciding whether to follow specific suggestions or for monitoring their respective implementation. But human behavioral research has shown that human decision-making is rather biased than fully rational, leading to unintended consequences in the collaborative work of humans and machines. Subsequently, the research stream of hybrid intelligence has gained interest recently, aiming to study the collaboration between humans and machines. We contribute to this issue by combining a systematic literature review on AI and cognitive biases combined with practical insights from discussions with experts in order to derive first guidelines addressing the human factor in the design of AI-based decision support systems for complex production environments.

In recent times, research efforts have been directed towards hybridization of various intelligent methodologies in order to solve complex industrial problems. At the same time these research efforts have been undertaken to develop a better understanding of the human information processing system. Today, one can find a number of applications involving hybridization of intelligent methodologies like knowledge based systems, fuzzy systems, genetic algorithms, and case based reasoning with artificial neural networks. The central methodology of many hybrid systems has been artificial neural networks. In fact, 2/3rd of the applications involving intelligent hybrid systems use neural networks. Broadly, in these applications neural networks are either used as the primary problem solving entity, or are used in conjunction with other intelligent methodology/ies which have a distinct and separate role to play in the problem solving process. The former are categorized as fusion and/or transformation based approaches, and the later are categorized as combination approaches. However, fusion and/or transformation based approaches center around not only neural networks but also center around the other other methodology, namely, genetic algorithms. Similarly, the combination approaches can involve intelligent methodologies other than neural networks also. The goal of this chapter is to provide an overview to the reader about fusion and transformation based synergies with neural network and genetic algorithms as the primary problem solving entities. In this direction, this chapter looks at the neuro-symbolic systems, neuro-fuzzy systems, genetic-neuro systems, and genetic-fuzzy systems.

According to the thought of intelligent forecasting and hybrid forecasting, an Intelligent Hybrid (IH) model for short-term traffic flow forecasting was presented. The IH model had three sub-models: History Mean (HM) model, Artificial Neural Network (ANN) model and the Fuzzy Combination (FC) model. By means of the good static stabilization character of HM method, the HM model predicted the traffic flow by the Single Exponential Smoothing method based on the historical traffic data. Otherwise, the ANN model was a 1.5-layer feed-forward neural network built by some common S-function neurons. Because of the strong dynamic nonlinear mapping ability of ANN, the ANN model can estimate the actual traffic flow in a very precise and satisfactory sense. The FC model mixed the two individual forecasting results by fuzzy logic and its output was regarded as the final forecasting of the traffic flow. Factual application results show that the IH model, which takes advantage of the unique strength of the HM model and the ANN model, can produce more precise forecasting than that of two individual models. Thus, the IH model can be an efficient method to the short-term traffic flow forecasting.

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.