Search results

Computer aided process planning (CAPP) is an effective way to integrate computer aided design and manufacturing (CAD/CAM). There are two key issues with the integration: design input in a feature‐based model and acquisition and representation of process knowledge especially empirical knowledge. This paper presents a state of the art review of research in computer integrated manufacturing using neural network techniques. Neural network‐based methods can eliminate some drawbacks of the conventional approaches, and therefore have attracted research attention particularly in recent years. The four main issues related to the neural network‐based techniques, namely the topology of the neural network, input representation, the training method and the output format are discussed with the current systems. The outcomes of research using neural network techniques are studied, and the limitations and future work are outlined.

The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Determining the performance parameters of the propulsion systems of the aircraft, which is the key product of the aviation industry, plays a critical role in reducing adverse environmental impacts. Therefore, the purpose of this paper is to present a temperature performance template for turbojet engines at the design stage using a neural network model that defines the relationship between the performance parameters obtained from ground tests of a turbojet engine used in unmanned aerial vehicles (UAV).

The main parameters of the flow passing through the engine of the UAV propulsion system, where ground tests were performed, were obtained through the data acquisition system and injected into a neural network model created. Fifteen sensors were mounted on the engine – six temperature sensors, six pressure sensors, two flow meters and one load cell were connected to the data acquisition system to make sense of this physical environment. Subsequently, the artificial neural network (ANN) model as a complement to the approach was used. Thus, the predicted model relationship with the experimental data was created.

Fuel flow and thrust parameters were estimated using these components as inputs in the feed-forward neural network. In the network experiments to estimate fuel flow parameter, r-square and mean absolute error were calculated as 0.994 and 0.02, respectively. Similarly, for thrust parameter, these metrics were calculated as 0.994 and 1.42, respectively. In addition, the correlation between fuel flow, thrust parameters and each input parameters was examined. According to this, air compressor inlet (AC_inlet,temp) and outlet (AC_outlet,temp) temperatures and combustion chamber (CC_inlet,temp, CC_outlet,temp) temperature parameters were determined to affect the output the most. The proposed ANN model is applicable to any turbojet engines to model its behavior.

Today, deep neural networks are the driving force of artificial intelligence studies. In this study, the behavior of a UAV is modeled with neural networks. Neural networks are used here as a regressor. A neural network model has been developed that predicts fuel flow and thrust parameters using the real parameters of a UAV turbojet engine. As a result, satisfactory findings were obtained. In this regard, fuel flow and thrust values of any turbojet engine can be estimated using the neural network hyperparameters proposed in this study. Python codes of the study can be accessed from https://github.com/tekinonlayn/turbojet.

The originality of the study is that it reports the relationships between turbojet engine performance parameters obtained from ground tests using the neural network application with open source Python code. Thus, small-scale unmanned aerial propulsion system provides designers with a template showing the relationship between engine performance parameters.

This research paper aims to discuss the effects of exchange rates on interest rates by using wavelet network methodology, which is a combination of wavelets and neural networks.

The paper employs wavelet networks to analyse the relationships between the financial time series. Empirically, the research examines the effects of foreign exchanges on the interest rates in Turkish financial markets by using daily USD/TRY rates and interest rates in Turkish Lira (TRY).

The results indicate that the wavelet network model is the most successful methodology among the alternatives such as Hodrick‐Prescott filter, feed‐forward neural network, wavelet causality, and wavelet correlation analysis in capturing the non‐linear dynamics between the selected time series.

The research results have both methodological and practical originality. On the theoretical side, the wavelet network is superior in modelling the causal linkages of the financial time series. For practical aims, on the other hand, the results show that the level of the effects of the exchange rates on the interest rates varies on the time‐scale used. Wavelet networks shows that the causality relationship is strong in the short run, while the effect decreases in the mid‐run.

The paper aims to propose an approach to intelligent evaluation of the tensile test. A robotized system is used that performs the fabrics tensile test and estimates the extensibility of the samples using a feed‐forward neural network while trying to imitate the human expert estimation.

The specifications of the tensile test are derived by an extensive observation of the respective experts' estimation performance. The fabric sample size and the experimental conditions are specified. Linguistic values of the term “fabric extensibility” are extracted through a knowledge acquisition process. The tensile test is performed by a robot manipulator with a simple gripper and the experimental measurements (force, strain) are fed online into a neural network. The network is trained according to the extensibility estimations of the experts. The trained network is tested in estimating unknown fabric's extensibility.

The results demonstrate that the system is capable of estimating the extensibility of new fabrics.

This work can be integrated in the robotized sewing process with intelligent control where the fabric's extensibility in terms of linguistic values is necessary. The proposed system initiates a new approach, in which the fabric properties are expressed and used in a way that will facilitate the introduction of the artificial intelligence methods into the clothing industry.

The purpose of this paper is to estimate different air–fuel ratio motor shaft speed and fuel flow rates under the performance parameters depending on the indices of combustion efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model. For this purpose, emissions data obtained experimentally from a T56-A-15 turboprop engine under various loads were used.

The designed multilayer feed forward neural network models consist of two hidden layers. 75% of the experimental data used was allocated as training, 25% as test data and cross-referenced by the k-fold four value. Fuel flow, rotate per minute and air–fuel ratio data were used for the training of emission index input values on the designed models and EI_CO, EI_CO2, EI_NO2 and EI_UHC data were used on the output. In the system trained for combustion efficiency, EI_CO and EI_UHC data were used at the input and fuel combustion efficiency data at the output.

Mean square error, normalized mean square error, absolute mean error functions were used to evaluate the error obtained from the system as a result of the test. As a result of modeling the system, absolute mean error values were 0.1473 for CO, 0.0442 for CO₂, 0.0369 for UHC, 0.0028 for NO₂, success for all exhaust emission data was 0.0266 and 7.6165e-10 for combustion efficiency, respectively.

This study has been added to the literature T56-A-15 turboprop engine for the current machine learning methods to multilayer feed forward neural network methods, exhaust emission and combustion efficiency index value calculation.

The performance of the building envelope of a large-scale public building significantly influences the energy consumption of such a building. This study aims to determine the best strategy for the envelope by examining the engineering design of the building in Nanchang University. The building shape coefficient, sun-shading strategies, window–wall ratio, roof, and walls were studied through a method involving multilayer feed-forward neural network model simulations. Results show that the optimum shape coefficient value is 0.32. The combination of interior and exterior blinds and electrochromic glass is the ideal option to reduce the increase in the energy consumption of the architecture caused by solar radiation. Maintaining the window–wall ratio at 0.4 is ideal. A green roof exerts a minimal effect on building energy consumption decrease (only 0.4%). Applying the strategy of vertical greening to the external wall can reduce cooling energy consumption by as much as 5.4%. Adopting the best envelope strategy combination can further decrease energy consumption by 20.8%. This strategy is also applicable to the middle and lower reaches of Yangtze River in China, which flow through Nanchang and have a climate similar to that of the said area. Future research should be directed toward applying artificial neural networks to quantitatively evaluate the effects of a design strategy and produce the best design strategy combination.

To improve the application neural networks predictors on bearing systems and to investigate the exact neural model of the ball‐bearing system.

A feed forward neural network is designed to model‐bearing system. Two results are compared for finding the exact model of the system.

The results of the proposed neural network predictor gives superior performance for analysing the behaviour of ball bearing undergoing loading deformation.

The results of the proposed neural network exactly follows desired results of the system. Neural network predictor can be employed in practical applications.

As theoretical and practical study is evaluated together, it is hoped that ball‐bearing designers and researchers will obtain significant results in this area.

This paper fulfils an identified research results need and offers practical investigation for an academic career and research. Also, It should be very helpful for industrial application of ball‐bearing systems.

The purpose of this present study is to find a scientific method for the evaluation of environmental impacts according to the requirement 4.3.1.

To realize the objectives, the authors worked with a representative sample from certified ISO 14001 organizations. The data aim to identify and evaluate (according to the organization's methodology) significant environmental impacts. In this study, the authors created two models for the evaluation of environmental impacts based on an artificial neural network applied in the pilot organization and compared the results obtained from these models with those obtained by applying an analytic hierarchy process (AHP) method. AHP is part of an multi‐criteria decision making method and provides good multi‐criteria support for decision making for problems that can be structured hierarchically.

This paper presents a new approach that uses a backpropagation neural network to evaluate environmental impacts regardless of the organization type.

This paper presents a unique approach for the reliable and objective evaluation of environmental impacts.