Production Optimization Monitoring System Implementing Artificial Intelligence and Big Data

Massaro, Alessandro; Selicato, Sergio; Miraglia, Roberto; Panarese, Antonio; Calicchio, Angelo; Galiano, Angelo

doi:10.1109/metroind4.0iot48571.2020.9138198

Cited by 13 publications

(6 citation statements)

References 9 publications

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“…Specifically, applications are in precision agriculture, logistics, buildings, lighting, energy harvesting, wiring, etc. [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Application Fieldsmentioning

confidence: 99%

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Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Massaro¹,

Starace²

2022

Sensors

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Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.

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“…Specifically, applications are in precision agriculture, logistics, buildings, lighting, energy harvesting, wiring, etc. [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Application Fieldsmentioning

confidence: 99%

“…Multisensor system based on the reading of electrical power consumption of different production machines [33] Power of production machines…”

Section: Energy Consumption Monitoring In Productionmentioning

confidence: 99%

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Massaro¹,

Starace²

2022

Sensors

Self Cite

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“…It also facilitates the connection of the input data streams with the CEP engine and the output of the latter with the output data streams. All these features, especially the ease of connecting data streams and invocations from various sources and systems, are what have made the ESB a strong candidate for an IoT architecture [12]- [15].…”

Section: B Event-driven Service-oriented Architecturesmentioning

confidence: 99%

“…Some of these proposals benefit from the integration of CEP with an enterprise service bus (ESB). An ESB eases the resolution of conflicts between hardware (e.g., IoT devices) and software (e.g., the CEP engine) [12] and plays a key role in multiple application domains in general [13] and in the IoT [14] and Industrial IoT (IIoT) [15] ones in particular. Although the performance results are satisfactory, we cannot lose sight of the fact that we can find scenarios which call for greater performance, as well as the inevitable growth in the amount and speed of data generated due to the improvement of cyber-physical devices as well as communications.…”

Section: Introductionmentioning

confidence: 99%

Evaluating a Flow-Based Programming Approach as an Alternative for Developing CEP Applications in IoT

Ortiz

Castillo

Garcia-de-Prado

et al. 2022

IEEE Internet Things J.

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One of the main advantages brought by the Internet of Things (IoT) is the possibility of having large amounts of data from several sources that allow us, once analyzed, to make decisions in various domains in real time. This implies the need to be able to process large volumes of data in more or less limited processing times depending on the application domain. In this sense, complex event processing (CEP), used in conjunction with an enterprise service bus (ESB), has proven to be very efficient in multiple domains. In search for greater efficiency, some CEP engines offer the option of using flow-based programming (FBP) rather than their traditional programming using CEP together with an event bus. However, its use, while it may be more efficient, can lead to other limitations. In this article, we analyze and describe the performance and limitations of using a CEP engine with an ESB versus a CEP engine with FBP. This will allow developers to decide which option is more convenient for their IoT system depending on the application domain and its specific needs.Index Terms-Complex event processing (CEP), dataflow, dataflow computing, enterprise service bus (ESB), flow-based programming (FBP), Internet of Things (IoT).

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“…Finally, the research studies [20][21][22] propose different approaches based on an XGBoost technique for predicting, respectively, the onset of diabetes, crude oil prices, and urban fire accidents, which is useful for public safety. Different studies have been performed in Global Distribution System (GDS) and food factories, defining by DM algorithms Key Performance Indicators (KPIs) [23], product facing BI [9], quality processes [24,25], and sales prediction [26]. Concerning sales prediction, different DM algorithms can be adopted [27], including weather effect [28] and social trends [29].…”

Section: Introductionmentioning

confidence: 99%

Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

Massaro¹,

Panarese²,

Giannone³

et al. 2021

Applied Sciences

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The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

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Production Optimization Monitoring System Implementing Artificial Intelligence and Big Data

Cited by 13 publications

References 9 publications

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Evaluating a Flow-Based Programming Approach as an Alternative for Developing CEP Applications in IoT

Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

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