Input Selection Methods for Soft Sensor Design: A Survey

Synergizing language learning: SmallTalk AI In industry 4.0 and Education 4.0

Background

As Industry 4.0 debuted roughly a decade ago, it is now necessary to examine how it affects various aspects of the discipline. It is the responsibility of the education sector to guarantee that the next generation is equipped mentally, physically, and cognitively to face unforeseen challenges. Numerous educational institutions are outfitted with Industry 4.0 technology-based learning. Industry 4.0 fosters advancements in learning methodologies, especially for language enhancements. Learners may gain knowledge at their base, providing them an opportunity for independent study. The majority of subjects have been acquired through Industry 4.0. This research chapter explores the intersection of Industry 4.0 and education, specifically focusing on the SmallTalk AI tool. It investigates how technological and digital innovations within the context of Industry 4.0 can serve as powerful tools to enhance language learning outcomes.

Methods

This article presents a comprehensive analysis of statistical data and empirical evidence to support the positive impact of Industry 4.0 technology of SmallTalk on language acquisition particularly speaking. The study also determines the relationship among participants' usage through the technology acceptance model (TAM). Furthermore, it examines the challenges and opportunities associated with integrating these innovations into language learning pedagogies, offering insights for educators and policymakers to harness the potential of Industry 4.0 in fostering language proficiency. The research employs quantitative analysis. The data obtained from educational institutions has been analyzed using the SPSS and AMOS software.

Results

The results indicate that Industry 4.0 has had an important effect on English language acquisition. This self-supported adaptable system of education facilitates effective student learning. This study also suggests that future research into the utility of Industry 4.0 be conducted elsewhere internationally.

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R² values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model's performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

Machine learning based soft sensor model for BOD estimation using intelligence at edge

Real-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.