scholarly journals Monitoring and forecasting water consumption and detecting leakage using an IoT system

2020 ◽  
Vol 20 (3) ◽  
pp. 1103-1113
Author(s):  
Jyoti Gautam ◽  
Amlan Chakrabarti ◽  
Shruti Agarwal ◽  
Anushka Singh ◽  
Shweta Gupta ◽  
...  
Keyword(s):  
Water Consumption ◽  
Daily Basis ◽  
Urban Housing ◽  
Support Vector ◽  
Water Usage ◽  
Monitoring And Forecasting ◽  
Average Water ◽  
The Difference ◽  
Housing Complex ◽  
Smart Technologies

Abstract Water is an important resource for life and its existence and, unfortunately, large quantities of water are being wasted on a daily basis. Monitoring the consumption of water can control water usage, and smart technologies can play a useful role. In this paper, a smart system based on Internet of Things (IoT) has been proposed to monitor the water consumption in an urban housing complex. An ultrasonic sensor, together with Arduino, continuously monitors the water level of water tanks on rooftops and sends these data to a server through a Wi-Fi module. Using the data collected from the IoT system, the daily and weekly average water requirement of households can be calculated. Support vector machines (SVM) are used to forecast water consumption. The observed readings are divided into training and testing datasets. Water consumption is predicted for each day for a user. Error is recorded as the difference between the actual consumption and the predicted value, and it decreases as the number of days increase. An algorithm to monitor leakage of water in the tanks has also been proposed. A web interface allows the user to visualize the water usage, monitor their consumption, and detect any leakage and leakage rate in the system.

scholarly journals Water Consumption Trend among Students in a University’s Residential Hall

2020 ◽  
Vol 5 (9) ◽  
pp. 56-62
Author(s):  
Faezzah Mohd Daud ◽  
Suhaida Abdullah
Keyword(s):  
Water Consumption ◽  
Male Student ◽  
Statistical Hypothesis ◽  
Water Usage ◽  
Average Water ◽  
Daily Water ◽  
The Difference ◽  
The University ◽  
Daily Water Consumption ◽  
Water Meter

In a university, student can be considered as the largest proportion of the campus residents. A university has to allocate high costs to cover student facilities with a very restricted fund.  It is important to understand how the student used these facilities.  Hence, in this study, a trend of water consumption among student was investigated. The objective is to identify the amount of water usage per person and the difference between genders. To measure the water consumption among student, water meter reading (in litres) was done by observing every block of student’s hostels at randomly selected days within five weeks. The collected water meter reading (in litres) was analysed using descriptive and some statistical hypothesis tests. From the analysis, it was found that the average daily water consumption of students in the residential halls is exceed average water demand which is 250 litres/student that provided by Suruhanjaya Perkhidmatan Air Negara (SPAN). In addition, female student found to consume more water than male student. These outcomes showed that the university should take some initiatives to enhance student awareness on the importance of saving their daily water usage. 

KARAKTERISTIK POLA PEMAKAIAN DAN PELAYANAN AIR BERSIH DI WILAYAH USAHA PAM PT. TIRTAARTHA BUANAMULIA

1970 ◽  
Author(s):  
I B. Suryadmaja ◽  
I N. Norken ◽  
I G.B. Sila Dharma
Keyword(s):  
Water Consumption ◽  
Water Demand ◽  
Research Needs ◽  
Water Usage ◽  
Educational Facilities ◽  
Domestic Water ◽  
Water Requirements ◽  
Water Users ◽  
Qualitative Descriptive ◽  
Average Water

Abstract : The purpose of this study is to determine how the pattern of usage, behavior and water services in the areas of business of  PAM PT.Tirtaartha Buanamulia (PT.TB) using qualitative descriptive methods such as survey research instrument (observation) and questionnaires of 337 samples, consisting of domestic water consumption and non- domestic . The result of the analysis showed that the average water consumption in some parts of sub-district of Kuta (Kedonganan village, Tuban village and Kuta village), the business area of PAM PT. TB, amounts to 243.49 liters /person /day. Average water use in the District of South Kuta (Pecatu village, Ungasan village, Kutuh village, Benoa vilage, Tanjung Benoa village and Jimbaran village) amounts 168.01 liters/person/day. Calculation of water demand for star hotels based on the results of the study on average is 726.84 liters /room/ day and non-star hotels 43.85 liters/room/day, the need of water for the restaurant and the restaurant is based on the research of 18.85 liters/seat/day, water usage for educational facilities based on the results of the study amounted to 9.99 liters/person/day, the water requirements for health facilities based on the research needs 562.13 liters /bed/day. The amount of non- domestic water needs based on research results which was 72.69 % of the domestic water needs, is the basis for the provision of water by PAM PT. TB at this time and in the future. The analysis of the results showed that the community of water users in the area of business PAM PAM PT. TB had a good perception of the PAM service; this is evidenced by the level of customer satisfaction to achieve 73.07 % PAM services for domestic and non- domestic 100 %. This study also showed the willingness of subscribers received 10 % increase in the tariff.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Using a Process Approach to Pandemic Planning: A Case Study

2021 ◽  
Vol 11 (9) ◽  
pp. 4121
Author(s):  
Hana Tomaskova ◽  
Erfan Babaee Tirkolaee
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Process Approach ◽  
Research Topic ◽  
Future Research ◽  
Critical Situation ◽  
Pandemic Planning ◽  
The Difference ◽  
Smart Technologies

The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic.

Analyzing the Effect of Mandatory Water Restrictions on Water Usage

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Trey Dronyk-Trosper ◽  
Brandli Stitzel
Keyword(s):  
Water Use ◽  
Water Consumption ◽  
Water Rights ◽  
Water Usage ◽  
Total Water ◽  
Water Restriction ◽  
Way Of Life ◽  
Total Water Consumption ◽  
Water Restrictions

AbstractAs water rights and water usage become an ever more important part of municipalities’ and states’ way of life, it becomes important to understand what policies can be effective for encouraging conservation of water. One method that has been employed at various times and throughout numerous communities is to limit outdoor watering days. We use a dataset with over 3 million property-month observations during the 2007–2015 period in Norman, Oklahoma, to identify whether the periodic implementation of mandatory water restrictions reduces water usage. Our data allow us to exploit variance in the timing of these water restriction programs. Our findings indicate that this policy reduces water consumption by 0.7 % of total water consumption. Additionally, we use home assessment prices to identify heterogeneity in this response, finding that high priced homes are more responsive to water use restrictions.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

2021 ◽  
Vol 13 (15) ◽  
pp. 2981
Author(s):  
Jeanné le Roux ◽  
Sundar Christopher ◽  
Manil Maskey
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
Spectral Response ◽  
Fine Particulate Matter ◽  
Daily Basis ◽  
Land Cover Types ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

scholarly journals Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 419 ◽  
Author(s):  
Dongdong Du ◽  
Jun Wang ◽  
Bo Wang ◽  
Luyi Zhu ◽  
Xuezhen Hong
Keyword(s):  
Prediction Models ◽  
Extraction Methods ◽  
Oxide Semiconductor ◽  
Soluble Solids ◽  
Support Vector ◽  
Least Squares Regression ◽  
Accuracy Rate ◽  
Linear Discriminant ◽  
The Difference ◽  
Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

Water consumption in public schools for the city of Bologna, Italy

2013 ◽  
Vol 13 (2) ◽  
pp. 257-264 ◽  
Author(s):  
Marco Farina ◽  
Marco Maglionico ◽  
Marco Pollastri ◽  
Irena Stojkov
Keyword(s):  
Public Schools ◽  
Water Consumption ◽  
Residential Buildings ◽  
Secondary School Student ◽  
Educational Interventions ◽  
Daily Basis ◽  
Published Data ◽  
Older Students ◽  
Consumption Data ◽  
The City

For most buildings considered to be of a public non-residential type there are insufficient published data to establish and compare the theoretical standards with actual consumption data. Therefore, water consumption per user in non-residential buildings is still a very complicated issue for engineers and designers involved in analysing water demand and water management. This is why linking water consumption and school occupancy is the goal of this paper, trying to set the basis for further design, conservation and educational interventions on this topic. This research integrates quantitative data of water consumption, through water metering and analysis, and historical data about users in buildings. We focused on consumptions for four types of schools: nurseries (0–3-year-old children), kindergartens (3–6 years), elementary schools (6–11 years) and secondary 1st grade schools (11–14 years). The results are that the rational basic demand for water is estimated as 48.8 l per pre-school student per day and 18.7 l per elementary/secondary school student per day. Therefore we found that younger children use more water on a daily basis than older ones, probably because they need more services, such as laundries and kitchens, whereas older students consume water mainly in restrooms.

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