Engineering Applications of Artificial Intelligence

In this paper we propose a new hybrid heuristic approach that combines the Quantum Particle Swarm Optimization technique with a local search method to solve the Multidimensional Knapsack Problem. The approach also incorporates a heuristic repair ...

This paper presents a methodological approach for the engineering of Multi-Agent Systems using feedback loops as a first class concept in order to identify organizations. Feedback loops are a way for modeling complex systems that expose emergent ...

Holonic and Service-Oriented Architectures have been proposed as solutions for the conception of flexible and reactive systems. The combination of both architectures has been recognized as an attractive solution for the conception of more flexible and ...

In this work ear recognition of a moving person with the help of a single fixed-in-position video camera is investigated, a novel problem undertaken to the best of our knowledge and belief. The challenges associated with this work are that during data ...

In real structural systems, such as a building structure or a mechanical system, due to inherent structural modeling approximations and errors, and changeable and unpredictable environmental loads, the structural response unavoidably involves ...

Inspite of the significance of the requirement of low power consumption, most of the existing techniques on FIR filter design have only concentrated on minimizing the ripples in pass band and stop band. In this regard the present work proposes an ...

Markov decision processes are a powerful framework for nonlinear, possibly stochastic optimal control. We consider two existing optimistic planning algorithms to solve them, which originate in artificial intelligence. These algorithms have provable near-...

Contractor programming relies on a catalog on elementary contractors which need to be as efficient as possible. In this paper, we introduce a new theorem that can be used to build minimal contractors consistent with equations, and another new theorem to ...

Feature selection is an important task in many problems occurring in pattern recognition, bioinformatics, machine learning and data mining applications. The feature selection approach enables us to reduce the computation burden and the falling accuracy ...

This paper suggests a novel three-level model-free hierarchical learning approach that solves the reference trajectory tracking problem for control systems (CSs). The new approach consists of the low level, the intermediate level and the high level, it ...

In this paper we propose a Lyapunov theory based Markov game fuzzy controller which is both safe and stable. We attempt to optimize a reinforcement learning (RL) based controller using Markov games, simultaneously hybridizing it with a Lyapunov theory ...

We design a flexible Evolutionary Framework for solving several classes of the Critical Node Problem (CNP), i.e. the maximal fragmentation of a graph through node deletion, given a measure of connectivity. The algorithm uses greedy rules in order to ...

This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission ...

A novel continuous function prediction model (CFPM) is proposed to resolve prediction problem whose input and output are both continuous functions (CFs). CFPM can simplify sample space reconstruction by using the coefficients of CFs, and use an improved ...

Traumatic brain injury (TBI) is the leading cause of death and disability in children and young adults worldwide. Cognitive rehabilitation (CR) plans consist of a sequence of CR tasks targeting main cognitive functions. There is not enough on-field ...

There are Numerous researches in the fields of social sciences and multi-agent systems that are dedicated to studying the role of diversity vs. individual capabilities in design and performance of human and artificial societies. This paper addresses ...

In parallel with the increasing popularity of commercial social-networking systems, the scales of such systems have grown notably, now with sizes ranging from hundreds of millions to more than a billion users. Besides being large, these systems also ...

Although time delay is an important element in both system identification and control performance assessment, its computation remains elusive. This paper proposes the application of a least squares support vector machines driven approach to the problem ...

Sales forecasting has long been crucial for companies since it is important for financial planning, inventory management, marketing, and customer service. In this study, a novel clustering-based sales forecasting scheme that uses an extreme learning ...

This paper examines how novel cellular system architectures and intelligent spectrum management techniques can be used to play a key role in accommodating the exponentially increasing demand for mobile data capacity in the near future. A significant ...

Systems with a priori unknown and time-varying dynamic behavior pose a significant challenge in the field of Nonlinear Model Predictive Control (NMPC). When both the identification of the nonlinear system and the optimization of control inputs are done ...

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by ...

Grey theory is one of the most common methods for solving uncertain problems using limited data and poor information, due to its high performance in time series prediction. However, the inappropriate background value and initial value are the main ...

We introduce a method to enhance the inference of meaningful dynamic models from observational data by genetic programming (GP). This method incorporates an inheritable epigenetic layer that specifies active and inactive genes for a more effective local ...

This paper describes a robust model-based fault diagnosis approach that enables to enhance the sensitivity analysis of the residuals. A residual is a fault indicator generated from an analytical redundancy relation which is derived from the structural ...

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). ...

Performance monitoring of offshore wind turbines is an essential first step in the condition monitoring process. This paper provides three novelties regarding power curve modeling. The first consists of illustrating that univariate power curve modeling ...

The demand of high quality confocal microscopic images is increasing for critical tasks such as study of living tissues at cellular resolution and disease diagnosis. The results of such tasks are often affected by the blur introduced in microscopic ...

The multi-variable grey model represented by GM(1,N) is an important causal relationship forecasting model. However, since the structure of GM(1,N) is more complicated than that of the single-variable grey forecasting model GM(1,1), it is difficult to ...