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The impact of sparsity and entropy criteria on neural network performance
We explore the impact of adding entropy and sparsity criteria to a standard neural network cost function, by considering a variety of network types...
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Neural-network-based automatic trajectory adaptation for quality characteristics control in powder compaction
Future manufacturing systems will have to become more intelligent to be able to guarantee a constantly high quality of products while simultaneously...
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State-of-the-art review of neural network applications in pharmaceutical manufacturing: current state and future directions
Neural network applications, as an emerging machine learning technology, are increasingly being integrated into pharmaceutical manufacturing...
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Learning the manufacturing capabilities of machining and finishing processes using a deep neural network model
In this work, we present a deep neural network model to automatically learn the capabilities of discrete manufacturing processes such as machining...
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Addressing the economic and demographic complexity via a neural network approach: risk measures for reverse mortgages
The study deals with the application of a neural network algorithm for fronting and solving problems connected with the riskiness in financial...
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Efficient quality variable prediction of industrial process via fuzzy neural network with lightweight structure
Quality Variables of industrial processes generally require to be obtained as fast as possible. In this paper, a correlation-wise self-organizing...
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Link-level performance abstraction for mimo receivers using artificial neural network
This paper presents a novel framework for link-level performance abstraction for multiple input multiple output (MIMO) receivers using a neural...
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Lightweight convolutional neural network for fast visual perception of storage location status in stereo warehouse
Accurate storage location status data is an important input for location assignment in the inbound stage. Traditional Internet of Things (IoT)...
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Dynamic pricing of differentiated products under competition with reference price effects using a neural network-based approach
In this paper, we analyze the dynamic-pricing decisions of differentiated products for retailers operating in a competitive environment, for a...
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Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster
During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related...
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Blind recognition algorithm of modulation mode and space–time block code via convolutional neural network
Concentrating on the joint recognition problem of modulation and space–time coding, algorithm for blind identification of modulation and space–time...
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Accurate and energy efficient ad-hoc neural network for wafer map classification
Yield is key to profitability in semiconductor manufacturing and controlling the fabrication process is therefore a key duty for engineers in silicon...
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Evaluation of process planning in manufacturing by a neural network based on an energy definition of hopfield nets
During the planning stages of new factories for the Body-In-White assembly, the processes used per production system need to be defined. Each...
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A reliability prediction model for a multistate cloud/edge-based network based on a deep neural network
Network reliability, named multistate stochastic cloud/edge-based network (MCEN) reliability afterwards, is defined as the probability that demands...
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Knowledge graph-based graph neural network models for multi-perspective modeling of group preferences
The purpose of group recommendation is to recommend items that all users in a group may like; therefore, the modeling target of group recommendation...
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Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing
Virtual metrology (VM) in semiconductor manufacturing is to predict product physical quality measurements using the processing information at a wafer...
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An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection
Artificial intelligence (AI)-based systems are normally data driven applications, where the model is trained to think on its own based on the...
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A layer-wise neural network for multi-item single-output quality estimation
A layer-wise neural network architecture is proposed for classification and regression of time series data where multiple instances have a single...
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Acoustic emission reflection signal classification of PVDF-type AE sensor using convolutional neural network-transfer learning
This study proposes a polyvinylidene fluoride (PVDF)-type AE sensor to demonstrate the feasibility of replacing conventional acoustic emissions (AE)...
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Multi-stage few-shot micro-defect detection of patterned OLED panel using defect inpainting and multi-scale Siamese neural network
Automatic micro-defect detection is crucial for promoting efficiency in the production lines of patterned OLED panels. Recently, deep learning...
