Department of Electrical and Electronic Engineering
University of Peradeniya
Department of Electrical and Electronic Engineering
University of Peradeniya
Visible light communication (VLC) is a promising solution to the problems of spectrum crunch, bandwidth limitations, and security issues related with radio frequency (RF) communication. In VLC, 380nm - 780nm range of the electromagnetic spectrum which is known as visible light range is used for the communication. For that purpose, a light-emitting diode or laser is used as the transmitter while a photodiode is used as the receiver. Intensity modulation and direct detection techniques are used to achieve the communication. Modern research has shown that VLC systems can be deployed in underwater environments to achieve high performance compared to conventional RF and acoustic communications. To fill the research gaps of VLC, we design and analyse several indoor and underwater VLC systems which are capable of obtaining higher performance gains.
Designed VLC systems are as follows: Parallel Relay Assisted Underwater VLC Systems, Relay-Aided Non-Orthogonal Multiple Access (NOMA) based Underwater VLC Systems, Autonomous Underwater Vehicle (AUV) Placement for Coverage Maximization of Underwater Sensor Network, Cooperative NOMA aided Underwater VLC Systems, Indoor VLC Systems.
To handle random behavior of RES (Renewable Energy Sources), future Power Systems focused on being rich with RES need flexibility in operations more than it is needed now. Flexibility is defined as the ability of a system to deploy its resources to respond to changes in net load, where net load is defined as the remaining system load not served by RES generation. This research is focused to quantify the flexibility of a power system in terms of a risk metric, which is formulated by accounting the uncertainties in Net Load forecast and generation availability. Furthermore, a flexibility constrained unit commitment (UC) problem which utilizes the risk metric is formulated to enhance the flexibility of the next day operation schedule. The overview of this process is given below.
A Probabilistic uncertainty range is defined for the net load based on a desired risk level. With the application of flexibility constrained UC, a flexible operation is achieved by making sure that the flexibility limits of the scheduled operation of the flexible units in the system is adequate enough to match the defined uncertainty range as shown below.
This research is on investigating more efficient, optimal and accurate algorithms for controlling mobile manipulators using vision as extra sensory information. Different visual servoing techniques, either position- or image-based representations or hybrid methods will be investigated.
This will enable to find either centralized or decentralized control of two or more such mobile manipulators in a coordinated manner for successfully completing a given task.
In power systems, storing energy and using that energy when the demand is high is important. As the most common energy storage, Li-Ion batteries are used in many applications. Such batteries are comprised of modules and the modules consist of series connected and parallel connected cells. When the batteries are in frequent operation, the State of Health (SOH) of the cells deteriorate. As a result, the cells will lose their storage capacity and cell voltages will deviate from their nominal values. Therefore, there is a possibility of having degraded cells within a battery. Identifying such damaged cells is important to maintain the battery at high SOH and thus to make sure the battery can be operated to its full expected lifetime, while delivering the expected performance. For that, accurate methods must be available in locating the degraded cells within the battery.
The Li-ion batteries are consisting of number of cells connected in series and parallel to achieve desired voltage and capacity respectively. Since the state of each cell in such battery affects the overall charging and discharging of the battery, it is required to monitor the individual cell always. This type of monitoring is not practical when the number of cells in a battery is increased. Therefore, researches have been conducted to find a method to estimate the state of each cell by just measuring the battery pack voltage and current. Still there is no exact method to find the states of individual cells.
In a degraded condition of a cell in a pack, it will change the electrical parameters of the cell model and thereby it can be identified. From state space modeling, the parallel connected cell module can be modeled to an observable system which has information of each individual cell in the module. By transforming such a system with appropriate circuit techniques, it would be possible to deduce the cell currents by only measuring the end voltage and the total current through the module. This system can be extended to include multiple cells connected in series in a string which is then connected parallel with similar strings. Using this method, it is proposed to find cell parameter changes by measuring total voltage and current.
Printed yagi arrays have both the features of dipole yagi arrays as well as the inherent advantages of printed patch antennas. This study aims at improving the performance of the printed version using different element geometries. The parameters such as element spacing and substrate properties will also be optimized.
Performance improvement of printed log periodic dipole array (PLPDA) antennas using a model-based multi-objective optimization technique is proposed. Multiple antenna parameters will be accounted for during the optimization such that trade-offs are reduced. On successful completion of the research, the resulting antennas are expected to out-perform the conventional PLDPA in terms of size and antenna performance.
Sri Lankan government promotes solar power current instead of direct current in future. The aim of this work is to analyze the CEB LV energy consumption using machine learning techniques to predict the future energy consumption of each customer. Energy consumption of each customer in three kandy areas was collected from 2014 to 2020 august. Using machine learning techniques and big data analytics, a dataset (with more than 100, 000 customer data for six years )was filtered and analysed.
With the vast improvement of the internet technology and their applications, it is a necessity to improve the network security as well. There are varieties of techniques to detect attacks on communication networks. Meanwhile, new attacks are created to destroy these security measures at rapid speed by network intruders. Support Vector Machine (SVM) is a relatively novel classification model that has shown promising performance than other learning methods in many applications.In this paper, SVM model classifier is analysed using NSL dataset based on Intrusion Detection System. Genetic algorithm is used to enhance the overall performance of SVM. As a result, the performance of four different kernels of SVM model is investigated. The results have shown that genetic algorithms are capable of achieving above 99% of detection rate with 17 features. In addition, the false positive and false negative results shown by linear, RBF, polynomial kernels are comparatively very low except in sigmoid kernel.
Image/video processing has been one of the major developments in the recent history with its applications in areas of Road safety, military, medical and agriculture fields. Due to its complexity a generic solution for multiple object detection in extremely crowded scenes remains to be found. Traditional methods of optical flow connected component analysis and image segmentation have been extensively studied in image processing and video processing material. With recent developments of machine learning and numerical optimization techniques the use of deep neural networks are getting frequent in image processing applications. Among such deep learning-based methods commonly used in this context are RCNN variants, Mask RCNN and YOLOv3. An exhaustive comparison of the traditional methods and deep learning-based methods and also deep learning methods are discussed in this paper. This study will be of use in selection of a method for any extremely crowded scene object detection problem.
One of the fundamental problems associated with harnessing wave energy is that the waves carry only a limited amount of energy flux in the longshore direction. To solve this problem, it is possible to concentrate waves into a narrow channel so that the energy density and the energy flux increases. However, narrow rectangular channels cause multiple wave modes to propagate with different wavelengths at a given wave period. Further, there is a critical channel width below which waves become evanescent. Ideally, the channel width is to be set such that there is only one propagating wave mode so that the wave energy convertor (WEC) functions smoothly.
In addition, there are considerable advantages in nearshore implementations such as avoiding heavy storms which could destroy any kind of a device, frequent boat trips to offshore for maintenance, dangers to the lives of the crew involved and the high costs. A nearshore implementation at a depth of few meters would be easily accessible, visible from the shore and safer for the personnel to work bringing down the costs. After harnessing the concentrated wave energy inside the channel, the remaining energy could be dispersed by using a suitable diffuser so that this process results in minimum erosion to the shoreline. The only disadvantage seems to be the reduction in the available wave energy after breaking.
The current research is mainly about two topics.
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