Abstract

The high rate of groundwater usage has resulted in a rapid decline in groundwater levels, which has necessitated its monitoring. This paper focuses on estimating the groundwater level inside borewells remotely and in real-time using the Internet of Things (IoT). For this, a solution is proposed which is based on string tension and does not require any electrical components to be lowered into the borewell. The solution provides a completely automated, real-time, reliable, and IoT-enabled alternative to existing methods for borewell water monitoring. The proposed approach is compared with an ultrasonic sensor-based approach in a controlled environment as well as in a tank. Additionally, four nodes were deployed in a small educational campus in the Indian city of Hyderabad to ascertain its usability and reliability in practical situations. The observations from the data collected over a month show that the proposed low-cost solution is reliable and has a good performance in the field. Index Terms—Borewell, Real-time, IoT, String tension, Water-level.

Abstract

This study presents an Internet of Things (IoT)- based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas offering a promising solution for optimising water distribution and reducing water wastage.

Abstract

Climate classification plays a significant role in the development of building codes and standards. It guides the design of buildings’ envelope and systems by considering their location’s climate conditions. Various methods, such as ASHRAE Standard 169, Köppen, Trewartha utilize climate parameters such as temperature, humidity, solar radiation, precipitation, etc., to classify climates. When establishing requirements in building codes and standards, it is crucial to validate the classification based on the building’s thermal loads. This paper introduces a novel methodology for classifying cities based on the number of similar days between them. It calculates similarity using daily mean temperature, relative humidity, and solar radiation by applying threshold values. A matrix of similar days is analyzed through agglomerative hierarchical clustering with different thresholds. A scoring system based on building thermal load, where lower scores signify better classification, is employed to select the best method. The method was tested using U.S. weather data, yielding a lower score of 54.5 compared to ASHRAE Standard 169’s score of 63.09. This suggests that the new approach results in less

Abstract

This paper focuses on the direction of arrival (DoA) estimation for two coherent sources using a uniform linear array (ULA). It is demonstrated that the DoA estimation error increases as the angle of the incoming signal moves away from the center in the range (-90°, 90°) for existing schemes. In addition, this paper also focuses on improving the DoA estimation performance in low signal to noise ratio (SNR). Therefore a cascaded neural network (CaNN) is proposed to improve the DoA estimation consisting of two stages of neural networks (NNs). The first NN is the enhanced SNR (ESNR) classifier, which is used to improve performance for different SNRs. The second NN is the angle estimator, which improves the performance for different angle ranges. For overcoming the issue of coherent signals, a spatially smoothened auto covariance matrix is fed to the SNR classifier and angle estimator blocks. A performance comparison with an existing scheme such as spatial smoothing- multiple signal classification (SS-MUSIC) and ESNR CaNN shows that the proposed CaNN for different angles and SNR ranges performs better than the existing schemes.

Abstract

Accidental fires in public and large buildings not only cause property loss but also can lead to loss of lives. During such emergencies, building evacuation depends on a range of factors including floor plans, exits available, obstructions if any, the occupancy levels of the building, and so on. The study here brings together the spatial, temporal, and path planning possibilities to evaluate fire evacuation strategies for 2D building plans. It provides a geospatial framework to assess the impacts of dynamic changes in the building environment and its impact on evacuation outcomes. In this study, occupancy-based path planning using Pgrouting over an IndoorGML formatted data is combined with modeling their interactions over the path toward the exit to assess the outcomes. This computational approach over the time-dependent path provides interesting insights into determining the number of paths and the need for one or more exits during an emergency. The study shows that integrating the floor plan into path generation and people flow can be a powerful tool for assessing the building environment.

Abstract

This study considers simple acceleration sensitive Non-structural elements (NSEs) with fundamental period TS ≤ 0.07seconds, modelled as a rigid cantilever with lumped mass at the top (stick model). Numerical modelling of building and NSE together (complete model) and nonlinear time history analyses using 11 natural ground motions are performed in commercial software PERFORM 3D. Parametric studies of NSEs with lumped mass of 1-10 percent total seismic weight of building (in increments of 1 percent) for each fundamental period of NSE (in increments of 0.01s) is carried out, using spectrally scaled ground motions. Variations in: (a) lateral force demand on NSE, (b) Component Amplification Factor (CAF), and (c) Floor Amplification Factor (FAF), are monitored. Results are compared with the values of above parameters estimated as per EAK (2000), Eurocode 8 (1998 (1):2004), ASCE 07 (2016), NZS 4219 (2009), and NZS 1170 (5) (2004). It is observed that FAF varies nonlinearly across the floors (and significantly in the lower floors), as against linear variation stipulated in the above codes. The average CAF for the ground motions considered ranged between 1 and 2 up to Ts ≤ 0.06s. However, it increased significantly for Ts = 0.07s, thereby complementing ASCE 07 stipulation of 0.06s limit for rigid NSEs. The value of 2 is consistent with NZS 1170(5):2004 and 1 with ASCE 07 and EAK (2000). Further, as the NSE weight increased, the fundamental period of the building increased, and the average CAF marginally decreased. And, seismic force demand imposed on all NSEs during nonlinear time history analysis exceeded code recommended seismic design force under most ground motions.

Abstract

The performance of high-power laser systems based on incoherent beam combining (IBC) depends on laser beam propagation properties in the atmosphere and their orientation on the target. We have designed an IBC system based on three different beam directing mechanisms, viz., collimated, inclined, and focused-inclined. We have mathematically modeled the laser beam propagation in the atmosphere, including turbulence, wandering, jitter, Strehl ratio, and thermal blooming. The beam propagation efficiency is evaluated for the above combinations to evaluate the IBC system performance in the turbulent atmosphere. We report the simulation results of an IBC system of 44 individual laser sources, each of 1 kW combined incoherently at a target. A comparative study of 3 beam-directing mechanisms is performed, and it is observed that the collimated beam IBC system is most affected by the atmospheric parameters due to the maximum wandering and spreading of the beam at the target compared to inclined and focused-inclined IBC. Whereas a focused-inclined IBC system is found to be the most effective for high-power energy applications, with an average intensity of more than 250 W/cm^2 even at the 10 km target location.

Abstract

Civic Crowdfunding (CC) uses the ``power of the crowd" to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride," resulting in the project not being funded. Researchers introduce refunds for single project CC to incentivize agents to contribute, guaranteeing the project's funding. These funding guarantees are applicable only when agents have an unlimited budget. This paper focuses on a combinatorial setting, where multiple projects are available for CC and agents have a limited budget. We study specific conditions where funding can be guaranteed. Naturally, funding the optimal social welfare subset of projects is desirable when every available project cannot be funded due to budget restrictions. We prove the impossibility of achieving optimal welfare at equilibrium for any monotone refund scheme. Further, given the contributions of other agents, we prove that it is NP-Hard for an agent to determine its optimal strategy. That is, while profitable deviations may exist for agents instead of funding the optimal welfare subset, it is computationally hard for an agent to find its optimal deviation. Consequently, we study different heuristics agents can use to contribute to the projects in practice. We demonstrate the heuristics' performance as the average-case trade-off between the welfare obtained and an agent's utility through simulations.

Abstract

Residential District Cooling Systems (DCS) are crucial for maintaining thermal comfort in urban areas, making it imperative to understand occupant cooling behavior as it significantly influences DCS operation. While several studies have investigated cooling behavior within small user groups through on-site measurements or surveys, these often fall short in representing the broader population. In this study we considered 387 homes in Hyderabad, which has a DCS connection for chilled water supply. Operational data spanning three months across different seasons were meticulously selected to assess the residents’ behavior for the cooling demand. In order to better facilitate the operation of the residential DCS, user load profile and system performance were analyzed using real world data for different seasons in the year. Our findings revealed notable disparities in DCS electrical consumption, with summer usage being 1.9 times higher than the monsoon period and 2.7 times higher than the winter period. Furthermore, a strong positive correlation emerged between outdoor temperature and thermal energy usage by the residents. On average, the daily thermal consumption per residence during winter, summer and monsoon is 3.3 kWhth, 35.1 kWhth and 10.4 kWhth respectively. Interestingly, the probability of a residence using AC during the day for the winter, summer and monsoon seasons are 0.07, 0.41 and 0.18 respectively.

Abstract

This study used IoT-based smart plugs and energy meters to collect device-level electricity data from homes for understanding the seasonal load patterns. The electrical energy consumption of five air conditioners (ACs) and five geysers was monitored using smart plugs during summer and winter to understand the power demand for cooling and hot water consumption. The study offered insights into the seasonal power usage patterns of these appliances. Further, a detailed analysis of daily electrical energy consumption in two homes was conducted to understand disaggregated electricity consumption. Five common household appliances (refrigerator, washing machine, television, water purifier, and microwave oven) were monitored using smart plugs in these homes. Along with smart plugs, smart energy meters were installed in these homes to monitor the total household electricity consumption. Their daily electricity consumption in summer was twice that of winter.