Browsing by Author "Herath, M"

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Embargo
Computer Vision Based Navigation Robot
(IEEE, 2022-12-26) Haputhanthri, M; Himasha, C; Balasooriya, H; Herath, M; Rajapaksha, S; Harshanath, S.M.B.
The majority of industrial environments and homewares need help when exploring unknown locations owing to a lack of understanding about the building structure and the various impediments that may be faced while transporting products from one spot to another. This is because there is a lack of knowledge about the building structure and the potential obstacles that may be encountered. This paper provides “Computer Vision-Based Navigation Robot” as a strategy for indoor navigation with optimal accessibility, usability, and security, decreasing issues that the user may encounter when traveling through indoor and outdoor areas with real-time monitoring of the most up to date IoT technology. The article is titled “Indoor Navigation with Optimal Accessibility, Usability, and Security.” This article proposes “Computer Vision-Based Navigation Robot” as a solution for interior navigation that provides optimum accessibility, usability, and security. This is done in order to tackle the issue that was presented before. Since the readers of this post include people who work in industry as well as physically challenged people who live alone, CVBN Robot takes object-based inputs from its surroundings. This is because the audience for this essay includes both groups of people. This study also covers a variety of methods for localization, sensors for the detection of obstacles, and a protocol for an Internet of Things connection between the server and the robot. This connection enables real-time position and status updates for the robot as it navigates a known but unknown interior environment. In addition, this study covers a variety of methods for localization, sensors for the detection of obstacles, and a protocol for an Internet of Things connection between the server and the robot.
Open Access
Deep Machine Learning-Based Water Level Prediction Model for Colombo Flood Detention Area
(MDPI, 2023-02-08) Herath, M; Jayathilaka, T; Hoshino, Y; Rathnayake, U
Machine learning has already been proven as a powerful state-of-the-art technique for many non-linear applications, including environmental changes and climate predictions. Wetlands are among some of the most challenging and complex ecosystems for water level predictions. Wetland water level prediction is vital, as wetlands have their own permissible water levels. Exceeding these water levels can cause flooding and other severe environmental damage. On the other hand, the biodiversity of the wetlands is threatened by the sudden fluctuation of water levels. Hence, early prediction of water levels benefits in mitigating most of such environmental damage. However, monitoring and predicting the water levels in wetlands worldwide have been limited owing to various constraints. This study presents the first-ever application of deep machine-learning techniques (deep neural networks) to predict the water level in an urban wetland in Sri Lanka located in its capital. Moreover, for the first time in water level prediction, it investigates two types of relationships: the traditional relationship between water levels and environmental factors, including temperature, humidity, wind speed, and evaporation, and the temporal relationship between daily water levels. Two types of low load artificial neural networks (ANNs) were developed and employed to analyze two relationships which are feed forward neural networks (FFNN) and long short-term memory (LSTM) neural networks, to conduct the comparison on an unbiased common ground. The LSTM has outperformed FFNN and confirmed that the temporal relationship is much more robust in predicting wetland water levels than the traditional relationship. Further, the study identified interesting relationships between prediction accuracy, data volume, ANN type, and degree of information extraction embedded in wetland data. The LSTM neural networks (NN) has achieved substantial performance, including R2 of 0.8786, mean squared error (MSE) of 0.0004, and mean absolute error (MAE) of 0.0155 compared to existing studies.
Open Access
Development of Real-Time, Self-Learning Artificial Intelligence-Based Algorithms for Non-Intrusive Energy Disaggregation in a Multi-Appliance Environment
(Faculty of Engineering Sri Lanka Institute of Information Technology, 2023-12) Herath, M
Electricity serves as a cornerstone in modern economies, with demand in residential and commercial sectors rapidly increasing in recent years. Enabling real-time monitoring of individual appliance-wise energy consumption and delivering user feedback is essential for future energy conservation initiatives. Energy disaggregation becomes imperative in furnishing consumption statistics for individual appliances. The acquisition of appliance-specific energy consumption in a non-intrusive manner, without the need for sensors on each device but by utilizing readings from the main household energy meter, highlights Non-Intrusive Load Monitoring (NILM) as a promising solution. NILM, leveraging the capabilities of smart meters and advancements in computational power, gains popularity for its effectiveness in disaggregating and analyzing energy consumption patterns. This study introduces an Artificial Intelligence (AI)-based NILM solution capable of disaggregating the energy consumption of multiple appliances while adapting to new appliances and their evolving behaviors. Among various NILM approaches, Neural Network (NN)-based models demonstrate promising disaggregation capabilities. However, the selection of the most suitable NN type or architecture poses a challenge due to the multitude of approaches in literature. To address this issue, the study standardizes and compares different NNs, with results showing that the Convolutional Neural Network (CNN) exhibits superior prediction accuracy and speed. This study also investigates the impact of different appliances and their consumption profiles on disaggregation performance, rigorously testing parameters such as NN architecture, input-output mapping topologies, data preprocessing, and hyperparameters. This leads to the development of guidelines for future NILM studies. Additionally, the study introduces a hierarchical plug-and-play modular-based model for appliance anomaly detection, extending the application of NILM and overcoming limitations in anomaly detection literature. This study investigates two-dimensional (2D) input-based NILM solutions for predicting appliance energy consumption profiles and classifying appliances. Unlike conventional NN-based models using 1D signals, representing the aggregate energy signal as a 2D image improves performance by leveraging feature extraction capabilities of NNs and preserving vital temporal information and signal amplitude relationships. Various TSS to 2D image conversion methods for NILM were tested, including Gramin Angular Summation Field (GASF), Gramin Angular Difference Field (GADF), Recurrent Plot (RP), and Markov Transition Field (MTF), with GADF outperforming other methods. In addition, the study introduces a simple yet powerful 2D input mechanism for time series data, specifically energy consumption data. This mechanism will be integrated into a CNN-based energy disaggregation model for the first time in the NILM domain, with the aim of improving overall performance. While the proposed method excels over 1D input-based models in training, it is observed that the novel 2D input method requires augmentation in training data volume, data mixing, NN depth, and hyperparameter tuning to achieve superior generalization capabilities. Furthermore, aggregate energy signal-based Voltage-Current (V-I) trajectory plots were investigated for fully non-intrusive appliance classification, demonstrating high accuracy. v The study proposes a single NN architecture named "One-Shot." This model exhibits the capability to simultaneously disaggregate multiple appliances, offering a more efficient alternative to the intricate and computationally demanding existing NN-based NILM models that necessitate separate NNs for each appliance. The efficacy of this approach is evaluated across multiple input-output mapping configurations, with the multi-point multi-bin model proving superior. To address challenges associated with manual model re-training for new appliances and adapting to evolving consumption patterns, a self-learning module is incorporated, enhancing the performance of the OneShot model. To overcome issues related to excessive hyperparameter tuning and insufficient training data, the study presents an unsupervised model based on Blind Source Separation (BSS), utilizing Independent Component Analysis (ICA) to separate appliance energy signals from the aggregate signal. Developing more reliable disaggregation models in local environments requires a local energy dataset. For this purpose, the study creates a local energy dataset from households using a custom-designed data logger, capturing both low and high-frequency energy data at appliance, circuit, and main energy meter levels. This dataset is verified using the One-Shot model developed in this study. In summary, this study advances the field of NILM by introducing AI-based solutions, innovative approaches, and comprehensive guidelines. Ultimately, these contributions aim to foster energy conservation and enhance efficiency in residential and commercial settings globally.
Open Access
Enhancement of Images Under Low Light Conditions Using Artificial Intelligence
(Sri Lanka Institute of Information Technology, 2023-03-25) Marzook, M; Herath, M; Liyanage, M; Thilakanayake, T
Images taken in low light conditions do not contain all the information well-lit images contain. Various features including the colours of objects, details and the quality are lost. Extracting these features from images is very important for any kind of application of it. This study proposes a model to enhance the features of the image taken under low light conditions, by delivering a solution which improves the quality of the image through Artificial Intelligence. Through the proposed method, the clarity of the image is improved, making it closer to a well-lit image equivalent. Both Image Processing and Deep Learning based techniques are explored, including Convolutional Neural Network (CNN) based generative models. The Generative models considered are Autoencoders (AE) and Generative Adversarial Networks (GANs). The study has been carried out by using several datasets combined together, which include image pairs of well-lit and low light images. A comparison between the two CNN-based generative models is carried out. Through the study, it is quantitatively found, by the Structural Similarity Index and supported by the Peak Signal to Noise Ratio, that the proposed CNNbased Autoencoder model overrides the proposed CNN-based GAN model. This is further supported by qualitative observations of the image results. Both models, however, greatly enhance the low light images, bringing to light features that were not visible beforehand, and also provide results with good colour accuracy. Through this research study, the methods and solutions to enhance low light images have been addressed, as well as providing a comparison between two suitable models, Autoencoders and GANs. The proposed solution is able to address many of the limitations existing in the extent literature.
Embargo
An Image Based Approach of Energy Signal Disaggregation Using Artificial Intelligence
(IEEE, 2021-12-09) Senarathna, M; Herath, M; Thilakanayake, H. D; Liyanage, M. H; Angammana, C. J
Non-Intrusive Load Monitoring (NILM) is the real-time monitoring of energy consumption data of individual appliances through the decomposition of composite energy signal captured at the household smart energy meter. Most of the existing NILM techniques utilize one-dimensional (1D) time-series signal analysis to predict the individual appliance energy signals. The utilization of image-based methods for the disaggregation of energy signals is a relatively new approach in the NILM domain. This paper presents a study of a novel computer vision-based Artificial Intelligence (AI) approach when compared to the traditional time series-based NILM methods. Gramian Angular Fields (GAF) and Recurrence Plots (RP) have been widely used in recent literature to encode time series signals as images. Novel image classification techniques with the use of Convolutional Neural Networks (CNN) simplify the extraction of nuclear load features from encoded two-dimensional (2D) images. The results considered the indices validation accuracy and validation loss in comparing the performance of different vision-based AI approaches. The results reveal that Gramian Angular Difference Field (GADF) outperforms both Gramian Angular Summation Field (GASF) and RP with a training accuracy of 97.9% and a validation accuracy of 94.2%. A comprehensive analysis and comparison are presented with an in-depth evaluation using multi-state appliances and it was concluded that GADF is the most suitable 1D to 2D conversion method for the representation of time series energy data for disaggregation purposes.
Open Access
Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka
(MDPI, 2023-04-02) Herath, M; Jayathilaka, T; Azamathulla, H.M; Mandala, V; Rathnayake, N; Rathnayake, U
Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall–nighttime relative humidity, rainfall–evaporation, daytime relative humidity–evaporation, and rainfall–nighttime relative humidity–evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.