Engineering Applications of Artificial Intelligence

Journal: Engineering Applications of Artificial Intelligence

Abbreviation

Eng. Appl. Artif. Intell.

Publisher

Elsevier

ISSN

0952-1976

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Publications 1 - 10 of 17

A novel similarity measure in intuitionistic fuzzy sets and its applications
Pan, Lipeng; Deng, Yong (2022)
Engineering Applications of Artificial Intelligence
Intuitionistic fuzzy set (IFS) is a classical branch of fuzzy set, which has advantage to deal with uncertain problems. In IFS, similarity measure is an important fundamental concept, it is used to measure consistency between different intuitionistic fuzzy sets (IFSs) and becomes a key parameter in fuzzy decision system. However, the previous methods of similarity measure do not take enough account the effect of hesitancy degree on membership degree and non-membership degree, so that produce counterintuitive results when measuring similarity. Hence, in this paper, a new similarity measure of IFS is presented. The effect of hesitancy degree on similarity measure is fully considered in the proposed method and some properties also haven been discussed to prove the reasonable of proposed method. Meanwhile, some numerical examples are analyzed to illustrate characteristics of proposed similarity measure in detail. Further, the experiments of target classification and clustering problem demonstrate effectiveness and superiority of proposed similarity measure in the environment of expert assessments and data set.
A real Shapley value for evidential games with fuzzy characteristic function
Xue, Yige; Deng, Yong (2021)
Engineering Applications of Artificial Intelligence
Game theory is a famous issue of expert decision making. The real Shapley value for cooperative games with fuzzy characteristic function has high performance in deal with cooperative games, which is an effective tool in deal with issues of game theory. The real Shapley value for cooperative games with fuzzy characteristic function is based on the level sets, which is the extent of fuzzy sets. However, the real Shapley value for cooperative games with fuzzy characteristic function cannot solve the evidential games problems. What is the real Shapley value for evidential games with fuzzy characteristic function is still an open problem. This paper proposes the real Shapley value for evidential games with characteristic function, which consists of level sets, the real evidential Shapley value, basic probability assignment function. The real Shapley value for evidential games with fuzzy characteristic function can solve the expert decision making issues under evidential environment, with the aid of basic probability assignment function. Meanwhile, the theorem of the proposed model has been discussed. Numerical examples has been applied to illustrate the effectiveness of the proposed model. The experimental results show that proposed model can obtain the real evidential Shapley value of a given evidential games and address the issues of expert decision making. © 2021 Elsevier Ltd
Efficient safe learning for controller tuning with experimental validation
Zagorowska, Marta; König, Christopher; Yu, Hanlin; et al. (2025)
Engineering Applications of Artificial Intelligence
Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationally inefficient. In this paper, we propose a novel approach to safe learning by formulating a series of optimization problems instead of a grid search. We also develop a method for initializing the optimization problems to guarantee feasibility while using numerical solvers. The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision. Experimental validation on an industrial-grade precision motion system confirms that the proposed algorithm achieves 30% better tracking at sub-micrometer precision as a state-of-the-art safe learning algorithm, improves the default auto-tuning solution, and reduces the computational cost seven times compared to learning algorithms based on exhaustive search.
Goal-oriented graph generation for transmission expansion planning
Varbella, Anna; Gjorgiev, Blazhe; Sartore, Federico; et al. (2025)
Engineering Applications of Artificial Intelligence
The electrification strategies that are being designed to meet sustainability objectives and rising energy demands pose significant challenges for power systems worldwide and require Transmission Expansion Planning (TEP). This study adopts a risk-informed approach to TEP, formulated as a multi-objective optimization problem that concurrently minimizes systemic risks and expansion costs. Given the intractability of this problem with conventional solvers, we turn to artificial intelligence techniques. In particular, we conceptualize power grids as graphs and introduce a goal-oriented graph generation methodology using deep reinforcement learning. We extend welfare-Q learning, a modified variant of Q-learning tailored to yield high rewards across multiple dimensions, by incorporating geometric deep learning for function approximation. This allows us to account for system security while minimizing grid expansion costs. Notably, system risk is evaluated by incorporating a Graph Neural Network (GNN) cascading failure meta-model into the proposed approach. The TEP method is applied to the IEEE 118-bus system, and the efficacy of this novel technique is compared against the state of the art. We conclude that the deep reinforcement learning method can compete with established methods for multi-objective optimization, identifying expansion strategies that improve system security at reduced costs. Furthermore, we test the robustness of the meta-model against topology changes in the transmission network, demonstrating its applicability to novel grid configurations.
Metaheuristic design of feedforward neural networks: A review of two decades of research
Ojha, Varun Kumar; Abraham, Ajith; Snášel, Václav (2017)
Engineering Applications of Artificial Intelligence
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.
Generative adversarial networks enable outlier detection and property monitoring for additive manufacturing of complex structures
Henkes, Alexander; Herrmann, Leon; Wessels, Henning; et al. (2024)
Engineering Applications of Artificial Intelligence
Although additive manufacturing processes have matured in many areas, difficulties with regard to printing accuracy persist. Possible defects are, e.g., the generation of unwanted internal pores or a lack of fusion between layers. In general, defects result in a deviation between as-planned and as-built geometries. Assessing the influence of such deviations on effective (mechanical) properties requires either experimental testing or numerical investigation, both of which are often too complex for being used in time-critical production settings. Previous work with the Finite Cell Method (FCM) has shown that image-based computational homogenization can assist in principle in quality monitoring of produced parts by providing effective mechanical properties of as-built geometries. However, conducting FCM computations online for each and every part of a series production demands a prohibitive amount of computational resources. The paper at hand suggests a remedy to this problem by using generative adversarial networks. This manuscript presents an integrated computational workflow for outlier detection and property monitoring, which in principle consists of four steps: (i) Carry out experiments under varying process conditions and collect respective (micro)-structural images. (ii) Compute effective properties for these images using FCM. (iii) Compose a generative adversarial network (GAN) training dataset from those images that meet desired effective properties. (iv) Use the GAN discriminator to detect outliers in series production. To this end, the discriminator is used as a classifier on as-built parts to judge whether an as-built structure is acceptable or defective. The viability of the approach is demonstrated on additively manufactured lattice structures whose geometry is acquired after production via computed tomography. The methodology is not only applicable for automated property monitoring but potentially also for reliability estimates of neural network-based property predictors.
Potential, challenges and future directions for deep learning in prognostics and health management applications
Fink, Olga; Wang, Qin; Svensén, Markus; et al. (2020)
Engineering Applications of Artificial Intelligence
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.
DBE: Dynamic belief entropy for evidence theory with its application in data fusion
Deng, Jixiang; Deng, Yong (2023)
Engineering Applications of Artificial Intelligence
Belief entropy is an effective uncertainty measurement in Dempster–Shafer evidence theory. However, the weight ratio between discord and non-specificity in the belief entropy is static and cannot be further modified according to different environments. To overcome this issue, this paper proposes dynamic belief entropy (DBE), which is a generalization of belief entropy by introducing a dynamic parameter. Compared with belief entropy, DBE can be flexibly modified based on the dynamic parameter, so as to improve the performance of measuring uncertainty in different environments. Besides, some properties of DBE are presented and illustrated with examples. Also, we design a dynamic data fusion method based on DBE. Compared with the existing methods, the proposed method utilizes DBE-based dynamic techniques, thereby enhancing the classification performance. Moreover, to illustrate the general applicability, the proposed method is verified on classification problems. The experimental results show that the proposed method outperforms the existing methods with a classification accuracy of 95.93% and an F1 score of 96.08%, demonstrating the effectiveness of our method.
A distance of quantum mass function and its application in multi-source information fusion method based on discount coefficient
Pan, Lipeng; Gao, Xiaozhuan; Deng, Yong (2022)
Engineering Applications of Artificial Intelligence
Distance measures provide a novel perspective for measuring the difference or consistency between bodies of evidence, which have been used in a wide range of fields. However, under the framework of quantum mass function, existing distances cannot measure the difference. Hence, this paper formulates a new distance measure, referred to as the distance of the quantum mass functions. The purpose of this distance measure is to quantify the difference between quantum mass functions. It can be demonstrated mathematically that it is a strict distance measure that satisfies the nonnegativity, symmetry, definiteness, triangle inequality. The proposed distance measure is a generalization of the classical evidence distance, and it introduces the concept of Minkowski distance as well. It is therefore not only able to reflects the difference of discord and non-specificity in the mass functions, but it also has the advantage of Minkowski distance, as well as high compatibility. Moreover, A number of numerical examples are also provided to illustrate its properties and advantages. Using the proposed distance measure, we design a new information fusion method based on the discount coefficient within a complex framework. As a further investigation, the proposed fusion method is applied to several data sets experiments and results indicate that compared to other methods, it has a certain potential in the field of multi-source information fusion under the framework of evidence theory.
Neural network-based sliding mode control of electronic throttle
Baric, Miroslav; Petrović, Ivan; Perić, Nedjeljko (2005)
Engineering Applications of Artificial Intelligence

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