scholarly journals Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4080 ◽  
Author(s):  
Marco Maggipinto ◽  
Elena Pesavento ◽  
Fabio Altinier ◽  
Giuliano Zambonin ◽  
Alessandro Beghi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Impact ◽  
General Setting ◽  
Vertical Axis ◽  
Soft Sensor ◽  
Data Driven ◽  
Soft Sensors ◽  
Physical Measurements ◽  
Washing Machines ◽  
Using Data

Embedding household appliances with smart capabilities is becoming common practice among major fabric-care producers that seek competitiveness on the market by providing more efficient and easy-to-use products. In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance. An indication of the load typology composition (cotton, silk, etc.) is typically provided by the user through a physical selector that, unfortunately, is often placed by the user on the most general setting due to the discomfort of manually changing configurations. An automated mechanism to determine such key information would thus provide increased user experience, better washing performance, and reduced consumption; for this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based statistical model of the process. The proposed method is able to work in a resource-constrained environment such as the firmware of a VA-WM.

scholarly journals Predicting the state of charge and health of batteries using data-driven machine learning

2020 ◽  
Vol 2 (3) ◽  
pp. 161-170 ◽  
Author(s):  
Man-Fai Ng ◽  
Jin Zhao ◽  
Qingyu Yan ◽  
Gareth J. Conduit ◽  
Zhi Wei Seh
Keyword(s):  
Machine Learning ◽  
The State ◽  
State Of Charge ◽  
Data Driven ◽  
Using Data

scholarly journals Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors

2018 ◽  
Author(s):  
Mariane Yvonne Schneider ◽  
Juan Pablo Carbajal ◽  
Viviane Furrer ◽  
Bettina Sterkele ◽  
Max Maurer ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Reduction Potential ◽  
Batch Reactor ◽  
Soft Sensor ◽  
Small Scale ◽  
Ammonium Oxidation ◽  
Soft Sensors ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
Using Data

Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained pH, dissolved oxygen (DO), and oxidation-reduction potential (ORP) sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. We found that the pH soft sensors are able to reliably identify the completion of ammonium oxidation in the SBR’s effluent even without sensor maintenance for over a year. In contrast, the DO soft sensor using data from a maintained sensor showed slightly better detection performance than that using data from unmaintained sensors, as the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors’ type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, we provide soft sensors that allow the performance of unstaffed small-scale SBRs to be monitored with unmaintained sensors and therefore the maintenance and reliability of OST systems to be optimised.

scholarly journals Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

2015 ◽  
Vol 12 (7) ◽  
pp. 6327-6350
Author(s):  
C. A. Sanchez ◽  
B. L. Ruddell ◽  
R. Schiesser ◽  
V. Merwade
Keyword(s):  
Positive Impact ◽  
Data Driven ◽  
Student Perspective ◽  
Use Of Data ◽  
Professional Perspective ◽  
Simulation Group ◽  
Lower Division ◽  
Job Duties ◽  
Using Data ◽  
Data Driven Modeling

Abstract. Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

scholarly journals AERIAL PHOTOGRAMMETRY AND MACHINE LEARNING BASED REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR AN EARTHQUAKE PRONE AREA IN TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 713-720
Author(s):  
G. Karakas ◽  
S. Kocaman ◽  
C. Gokceoglu
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Landslide Susceptibility ◽  
Data Driven ◽  
Natural Phenomenon ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Aerial Photogrammetry ◽  
Prone Area ◽  
Using Data

Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.

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