• A novel deep learning framework is introduced for joint planning and control optimization.
• The framework is implemented in a stochastic failure-affected production system.
• Deep learning decision-making outperforms the RL-based one ...

Within the context of Industry 4.0, manufacturing plants implement smart technologies, which adopt machine learning and deep learning, to identify manufacturing problems and provide sensible solutions to the experts. In literature, such ...

Recently, the scheduling problem within the context of quality control laboratories has garnered significant attention from researchers, as quality control plays a pivotal role in manufacturing and numerous industries. Given the dynamic nature of ...

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to ...

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• An automated pipeline that extracts food-chemical information from literature.
• A food-chemical knowledge graph with 46 % triplets not in public databases.
• 6 food-chemical potentially novel associations discovered by link ...

Accurate X-ray image defect segmentation is of paramount importance in industrial contexts, as it is the foundation for product quality control and production safety. Deep learning (DL) has demonstrated powerful image scene understanding ...

• A core-profile decomposition network is proposed for X-ray image defect segmentation.
• Core feature learning creates an effective space to extract X-ray image features.
• Elasticity profile refinement uses elasticity scores to enhance ...

Crowdsourcing is an essential data collection method for psychological research. Concerns about the validity and quality of crowdsourced data persist, however. A recent documented increase in the number of invalid responses within crowdsourced ...

• A substantial portion of crowdsourced data will contain responses that are invalid.
• Automated evaluation of a user's IP address can identify a portion of invalid responses.
• Reviewing responses to short answer questions can also ...

• A Shewhart-EWMA chart combining the strengths of np and EWMA charts is proposed.
• It outperforms individual Shewhart and EWMA charts by 499% and 31%, respectively.
• It achieves excellent performance while keeping false alarms at ...

Attribute charts are used widely for monitoring binary events in manufacturing, service, and healthcare processes. While Shewhart type charts are efficacious in detecting large or sudden shifts in a nonconforming rate p, exponentially weighted ...

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of ...

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• A survey of approaches for Uncertainty Quantification in DL-based medical image classification.
• A glimpse into uncertainty techniques allowing estimation at a medically relevant level.
• Discussion on the evaluation protocols used to ...

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• Hyperspectral imaging presents great promise for crop control in agriculture.
• Cannabis chemotypes can be classified through non-invasive analysis.
• Image analysis provided high sensitivity and specificity values in plant ...

The current public acceptance rate towards medical cannabis feasibility has led to a worldwide increase in this plant species production. Nevertheless, the currently transforming legal framework does not prevent the originally unlawful knowledge ...

Defect recognition is an effective quality control measure for the key manufacturing process nodes of industrial products. However, current defect recognition models, which rely on a large amount of supervised data, cannot quickly adapt and ...

Ensuring high product quality is crucial in modern industry, as low-quality products can lead to negative consequences like financial losses, resource wastage, and harm to a company's reputation. Although in high volume production advanced ...

An intermittent sampling model for statistical process control (SPC) is introduced to monitor the quality of output from a production process where the number of defective units in a sample is measured in selected time periods. A Limited Memory I...

• Limited memory influence diagrams (LIMIDs) are applied for attribute statistical process control.
• An intermittent sampling (IS) approach is applied to minimize quality control costs.
• Decision rules based only on the current sample ...

• Sampling systems can be evaluated using variographic analysis of process data.
• Influence of incorrect sampling on assessing bulk materials is investigated.
• A case study of phosphate product lot inspection is given.
• Quick switch ...

Acceptance sampling plans are used in quality control between a producer and a buyer. However, in the literature of acceptance sampling for bulk materials, the term “sampling” refers to obtaining simple random samples in the traditional ...

Big data has brought a profound revolution across industries, redefining data management and decision-making paradigms. The utilization of vast and diverse datasets enables institutions to make more informed risk assessments, identify fraudulent ...

The Smart Applications REFerence Ontology (SAREF) defines a modular set of versioned ontologies that enable semantic interoperability between different Internet of Things (IoT) vendor solutions across various IoT industries. The European ...

Environmental sensor networks produce continuously increasing volumes of raw data that need to be transformed into usable data for monitoring ongoing environmental changes and decision-support. The crucial challenge is providing data in real-time,...

• We present the Python package System for automated Quality Control (SaQC).
• SaQC facilitates the implementation of workflows for automated quality control
• It is designed for domain scientists that manage environmental sensor ...

Segmentation of 2D echocardiography (2DE) images is an important prerequisite for quantifying cardiac function. Although deep learning can automate analysis, variability in image quality and limitations in model generalisability can result in ...

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of ...

Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. ...

Traditional task assignment follows a direct recruitment model in which requesters recruit and select workers to complete tasks. Because of the unclear division of roles and the diversity of each role’s mission, this model is neither efficient ...

• Cooperative unit-based task assignment is effective than direct recruitment models.
• Workers are organized into cooperative units whose proxies bid for requesters’ tasks.
• A two-tier task assignment framework is proposed to produce ...

In this paper, a feature selection (FS) method is proposed to identify key quality features (KQFs) in complex manufacturing processes. We propose a multi-objective binary particle swarm optimization algorithm, called MPBPSO, with three ...

• Quality feature selection is addressed by a multi-objective optimization model.