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Artificial Intelligence is one of the key technologies that can be applied to bring the factory of the future closer to reality.

This paper shows the methods, techniques and codes developed as Artificial Intelligence tools for turboprop maintenance. Diagnostics is the main area of maintenance considered while Expert Systems and Back Propagation Neural Networks are the Artificial Intelligence subjects discussed. ...Diagnostics is the main area of maintenance considered while Expert Systems and Back Propagation Neural Networks are the Artificial Intelligence subjects discussed. The codes allow consideration of the turboprop with different configurations.

It discusses an Artificial Intelligence based strategy for automation of cluster objects teaching, evaluation and illustrates the adoption of the same for ADAS HIL testing and brings out the efficiency and benefits achieved by the proposed strategy.

The methodology, based on visual interactive simulation and artificial intelligence methods, and its application to a Ford engine assembly facility are described.

The evolving nature of supply networks and the underlying information systems as well as advances in technology drive the application of artificial intelligence (AI) in this context. The paper thus aims to harmonize and optimize the combination of classical methods with those of AI in hybrid planning and development systems.

The importance and need for these advances reside in the creation of automated intelligence (to minimize the need for human resources) and in the formation of tools for the identification of chemicals not previously characterized by IMS.

This study compares the model efficacy of Neural Network and Vector Auto Regression. Further it also analyses the impact of predictors controlling for total industry volume. Understanding both the methodologies has their distinctive advantages and disadvantages. Our empirical findings indicate that based on the characteristics of data such as non-stationary, non-linearity and non-normality paves the way for use of machine learning algorithm relative to econometrics technique. Our results suggest that data type and its characteristics are more important in determining the methodology than the methodology itself. In industry, econometrics methodologies are widely used due to their usage simplicity and its ability to explain the relationships in simple terms.

So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. ...But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor.

The detection and evaluation of damage in composite materials components is one of the main concerns for automotive engineers. It is acknowledged that defects appeared in the manufacturing stage or due to the impact and/or fatigue loads can develop along the vehicle riding. To avoid an unexpected failure of structural components, engineers ask for cheap methodologies assessing the health state of composite parts by means of continuous monitoring. Non Destructive Technique (NDT) for the damage assessment of composite structures are nowadays common and accurate, but an on-line monitoring requires properties as low cost, small size and low power that do not belong to common NDT. The presence of a damage in composite materials, either due to fatigue cycling or low-energy impact, leads to progressive degradation of elastic moduli and strengths.

Therefore, we intend to make a step forward with these data-driven artificial intelligence algorithms. We applied four data-driven artificial intelligence algorithms to diagnose three common faults of PEMFC (each fault type has two severity levels, slight and severe). ...With the development of artificial intelligence, performing fault diagnosis with the massive sampling data of the fuel cell system has become a popular research topic. ...But few people have successfully verified the diagnosis performance of these artificial intelligence algorithms on a real high power on-board PEMFC system. Therefore, we intend to make a step forward with these data-driven artificial intelligence algorithms.

Abstract The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed.

On the basis of our successful applications of the UA-QCMD to various important chemical processes of exhaust emission controls and sintering predictions of both precious metals and support of automotive catalysts, we employed in the present study artificial intelligence to determine fundamental parameters from all electron density functional methods and thermodynamic results.

(c) Approach: One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence into plant modelling. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modelling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems.

This paper describes the applicable and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. ...A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in the term of indicated power and emissions in the appearance of CO and NO emissions level. ...The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN). The operating conditions employed to represent the combustion parameters for controlling the injection and ignition event are start of injection (SOI), end of injection (EOI) and spark advance (SA) timing, which affects to the combustion processes, performance as well as emissions formation.

This paper describes the application and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. ...A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in terms of indicated power and emissions in the appearance of CO and NO emissions level. ...The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN). The operating conditions employed to represent the combustion parameters for controlling the injection and ignition event are start of injection (SOI), end of injection (EOI) and spark advance (SA) timing, which affects to the combustion processes, performance as well as emissions formation.

The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.

The model is based on the Artificial Neural Network (ANN) technique and does not introduce any simplifying hypothesis for the physics governing the system.

This paper discusses, from the particular point of view of the control system the advantages of compartmentalized artificial ecosystems. Control requirements are formulated and an attempt is made to translate these requirements into a preliminary design concept for MELISSA (Micro-Ecological Life Support System Alternative).

A set of driving rules determining random behavior and swarm behavior of autonomous vehicles is developed based on swarm intelligence theory. Under control of these rules, each autonomous vehicle follows the same rules, which make it select target vehicle from all the optimal individuals in detection zone according to characteristics of itself, then approach to the target by changing lane, following former car, or accelerating. ...Moreover, it was found that the proposed swarm intelligence based algorithm increases the accessibility of arterial significantly.

This classification is based on certain acoustic descriptors obtained at a specific noise monitoring point, which are used as inputs for a Feed-Forward Artificial Neural Network. As a result, determined classification groups for the evaluated aircraft models are obtained, so that aircrafts of similar size and technology belong to the same group.