29 research outputs found
Modeling and Simulation of Solar Photovoltaic Cell for the Generation of Electricity in UAE
This paper proposes the implementation of a circuit based simulation for a
Solar Photovoltaic (PV) cell in order to get the maximum power output. The
model is established based on the mathematical model of the PV module. As the
PV cell is used to determine the physical and electrical behavior of the cell
corresponding to environmental factors such as temperature and solar
irradiance, this paper evaluates thirty years solar irradiation data in United
Arab Emirates (UAE), also analyzes the performance parameters of PV cell for
several locations. Based on the Shockley diode equation, a solar PV module is
presented. However, to analyze the performance parameters, Solarex MSX 120, a
typical 120W module is selected. The mathematical model for the chosen module
is executed in Matlab. The consequence of this paper reflects the effects of
variation of solar irradiation on PV cell within UAE. Conclusively, this paper
determines the convenient places for implementing the large scale solar PV
modules within UAE.Comment: To be published in 5th International Conference on Advances in
Electrical Engineering (ICAEE-2019
The Integration of Neuromorphic Computing in Autonomous Robotic Systems
Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm is crucial to overcoming the challenges of deep learning in environments where data and energy are limited. Taking inspiration from this natural learning process, recent advancements [6, 7] have been made in implementing associative learning in artificial systems. This work introduces a pioneering approach by integrating associative learning utilizing an Unmanned Ground Vehicle (UGV) in conjunction with neuromorphic hardware, specifically the XyloA2TestBoard from SynSense, to facilitate online learning scenarios. The system simulates standard associative learning, like the spatial and memory learning observed in rats in a T-maze environment, without any pretraining or labeled datasets. The UGV, akin to the rats in a T-maze, autonomously learns the cause-and-effect relationships between different stimuli, such as visual cues and vibration or audio and visual cues, and demonstrates learned responses through movement. The neuromorphic robot in this system, equipped with SynSense’s neuromorphic chip, processes audio signals with a specialized Spiking Neural Network (SNN) and neural assembly, employing the Hebbian learning rule to adjust synaptic weights throughout the learning period. The XyloA2TestBoard uses little power (17.96 µW on average for logic Analog Front End (AFE) and 213.94 µW for IO circuitry), which shows that neuromorphic chips could work well in places with limited energy, offering a promising direction for advancing associative learning in artificial systems
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
The central goal of this paper is to establish two commonly available
dimensionality reduction (DR) methods i.e. t-distributed Stochastic Neighbor
Embedding (t-SNE) and Multidimensional Scaling (MDS) in Matlab and to observe
their application in several datasets. These DR techniques are applied to nine
different datasets namely CNAE9, Segmentation, Seeds, Pima Indians diabetes,
Parkinsons, Movement Libras, Mammographic Masses, Knowledge, and Ionosphere
acquired from UCI machine learning repository. By applying t-SNE and MDS
algorithms, each dataset is transformed to the half of its original dimension
by eliminating unnecessary features from the datasets. Subsequently, these
datasets with reduced dimensions are fed into three supervised classification
algorithms for classification. These classification algorithms are K Nearest
Neighbors (KNN), Extended Nearest Neighbors (ENN), and Support Vector Machine
(SVM). Again, all these algorithms are implemented in Matlab. The training and
test data ratios are maintained as ninety percent: ten percent for each
dataset. Upon accuracy observation, the efficiency for every dimensionality
technique with availed classification algorithms is analyzed and the
performance of each classifier is evaluated.Comment: 2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka,
Banglades
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
The central aim of this paper is to implement Deep Autoencoder and
Neighborhood Components Analysis (NCA) dimensionality reduction methods in
Matlab and to observe the application of these algorithms on nine unlike
datasets from UCI machine learning repository. These datasets are CNAE9,
Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation,
Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of
these datasets has been reduced to fifty percent of their original dimension by
selecting and extracting the most relevant and appropriate features or
attributes using Deep Autoencoder and NCA dimensionality reduction techniques.
Afterward, each dataset is classified applying K-Nearest Neighbors (KNN),
Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM)
classification algorithms. All classification algorithms are developed in the
Matlab environment. In each classification, the training test data ratio is
always set to ninety percent: ten percent. Upon classification, variation
between accuracies is observed and analyzed to find the degree of compatibility
of each dimensionality reduction technique with each classifier and to evaluate
each classifier performance on each dataset.Comment: 2nd International Conference on Innovation in Engineering and
Technology (ICIET
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
Auxetic Yarn: Fundamentals, Influencing Parameters, Application Areas and Challenges
The mechanical behaviour of auxetic materials and structures is the most distinctive characteristic, which differs from that of conventional engineering materials due to the negative Poisson’s ratio. Auxetic materials have the fascinating feature of widening when stretched and contracting when compressed. In recent times, the research of auxetic materials based on textile structures has received a lot of interest. Auxetic effect development at the yarn phase is a new and exciting field of study. Many researchers already developed different types of auxetic yarns, such as the helical auxetic yarn, the plied auxetic yarn, the semi-auxetic yarn etc. The helical auxetic yarn (HAY) is the most commonly mentioned auxetic yarn. It is made up of a rigid wrap and an elastic core yarn. However, it is interesting that auxetic yarns can be produced from conventional non-auxetic fibres through the conventional spinning system as well. The helical auxetic yarn is a new type of yarn with a wide variety of possible applications. Moreover, pore-opening characteristics of auxetic yarns make it a potential candidate in the fields of technical textiles, such as medical textiles, filter application, protective textiles etc. Fabrication of auxetic textiles by utilizing auxetic yarns through simple weaving and knitting technology opens the door to new applications. The aim of this paper is to address the fundamentals of auxetic yarns, such as structure, shortcomings, production techniques, as well as the influencing process parameters. From various research works, it is evident that the wrap helical angle, the core/wrap diameter ratio, and the initial moduli of wrap component are the most vital processing parameters during the production of auxetic yarns. Finally, some potential application areas and challenges of auxetic yarns are also addressed briefly in this paper