Modern approaches to preprocessing industrial data

Olariu, Eliza Maria; Tolas, Ramona; Portase, Raluca; Dînşoreanu, Mihaela; Potolea, Rodica

doi:10.1109/iccp51029.2020.9266215

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…The missing values happened due to maintenance, failure of instrumentation, invalid values and manual checkin. Our work uses the Akima [51] method for supplementary values, for time-series data and filling in the missing values for smooth curves. In our work, we validated the proposed method using accuracy, coefficient of determination (R 2 ), mean squared logarithmic error (MSLE) and earned values (EV).…”

Section: Resultsmentioning

confidence: 99%

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

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Section: Resultsmentioning

confidence: 99%

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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“…For example, in the case of washing machines, the task is to clean the clothes. In [37], a method for detecting cycles from raw data recorded from devices with running patterns similar to the ones described above is introduced.…”

Section: Smartlaundry-real-time System Architecturementioning

confidence: 99%

SmartLaundry: A Real-Time System for Public Laundry Allocation in Smart Cities

Portase,

Tolas,

Potolea

2024

Sensors

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Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing the foundation for smart services and addressing diverse urban challenges. A smart system for public laundry management uses artificial intelligence-based solutions to solve the challenges of the inefficient utilization of public laundries, waiting times, overbooking or underutilization of machines, balancing of loads across machines, and implementation of energy-saving features. We propose SmartLaundry, a real-time system design for public laundry smart recommendations to better manage the loads across connected machines. Our system integrates the current status of the connected devices and data-driven forecasted usage to offer the end user connected via a mobile application a list of recommended machines that could be used. We forecast the daily usage of devices using traditional machine learning techniques and deep learning approaches, and we perform a comparative analysis of the results. As a proof of concept, we create a simulation of the interaction with our system.

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“…In most methods, after data preprocessing, feature selection, and segmentation, a criterion or algorithm is defined to determine when a running cycle starts and ends based on the identified peaks or features. In previous work [9], we present a method for detecting cycles from raw data recorded from appliances based on a pair of start and stop markers from the data.…”

Section: Time Seriesmentioning

confidence: 99%

“…Furthermore, time series data can be leveraged to extract features independent of the time domain, which can later be utilized in various processing tasks such as clustering [8]. A particular application of features extracted from time series sensor data implies identifying running cycles [9] and further processing them to compute features.…”

Section: Introductionmentioning

confidence: 99%

From Sensors to Insights: An Original Method for Consumer Behavior Identification in Appliance Usage

Portase,

Tolas,

Potolea

2024

Electronics

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In light of the energy crisis, extensive research is being conducted to enhance load forecasting, optimize the targeting of demand response programs, and advise building occupants on actions to enhance energy performance. Cluster analysis is increasingly applied to usage data across all consumer types. More accurate consumer identification translates to improved resource planning. In the context of Industry 4.0, where comprehensive data are collected across various domains, we propose using existing sensor data from household appliances to extract the usage patterns and characterize the resource demands of consumers from residential households. We propose a general pipeline for extracting features from raw sensor data alongside global features for clustering device usages and classifying them based on extracted time series. We applied the proposed method to real data from three different types of household devices. We propose a strategy to identify the number of existent clusters in real data. We employed the label data obtained from clustering for the classification of consumers based on data recorded on different time ranges and achieved an increase in accuracy of up to 15% when we expanded the time range for the recorded data on the entire dataset, obtaining an accuracy of over 99.89%. We further explore the data meta-features for a minimal dataset by examining the necessary time interval for the recorded data, dataset dimensions, and the feature set. This analysis aims to achieve an effective trade-off between time and performance.

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Modern approaches to preprocessing industrial data

Cited by 9 publications

References 10 publications

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

SmartLaundry: A Real-Time System for Public Laundry Allocation in Smart Cities

From Sensors to Insights: An Original Method for Consumer Behavior Identification in Appliance Usage

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