5 results
Search Results
Now showing 1 - 5 of 5
Article Citation - WoS: 18Citation - Scopus: 24Fusion of Smartphone Sensor Data for Classification of Daily User Activities(Springer, 2021) Sengul, Gokhan; Ozcelik, Erol; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisNew mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.Article Citation - WoS: 23Citation - Scopus: 26Reconstruction of 3d Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3d Models From Shapenetcore Dataset(Mdpi, 2019) Kulikajevas, Audrius; Maskeliunas, Rytis; Damasevicius, Robertas; Misra, SanjayDepth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.Article Citation - WoS: 62Citation - Scopus: 78Hybrid Microgrid for Microfinance Institutions in Rural Areas - a Field Demonstration in West Africa(Elsevier, 2019) Ayodele, Esan; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisWe present a hybrid energy microgrid optimization model for a microbank in a remote rural residential area. The model is based on the use of renewable (wind turbines & solar photovoltaic (PV)) and conventional (gasoline generators) energy sources and battery storage systems. We conducted a detailed assessment of a typical microbank's load, residential loads and energy resources in a village called Ajasse-Ipo in Kwara State, Nigeria. We performed the modeling of a hybrid microgrid system, followed by an economic analysis and sensitivity analysis to optimize the hybrid system design. We performed simulations based on the energy resources available (solar PV, wind, gasoline generator & battery energy storage system) to satisfy the energy demands of the microbank, while the excess energy was supplied to meet the demand of the community loads, i.e. water pumping machine and rural home lighting. The results obtained showed that the hybrid system comprising the solar PV/battery/diesel was most techno-economically viable with a Net Present Cost (NPC) and Cost of Energy (COE) of $468,914 and 0.667$/kWh, respectively. Comparing these results with those obtained using analytical methods, the solar PV, battery and converter sizes obtained were slightly higher than the optimal system configurations as produced by HOMER. The proposed hybrid energy system also allowed to achieve almost 50% reductions in CO2, CO, unburned hydrocarbons, particulate matter, SO2 & NO2. The system can be applicable for other rural regions in the developing countries with similar environmental conditions.Article Citation - WoS: 103Citation - Scopus: 160Cassava Disease Recognition From Low-Quality Images Using Enhanced Data Augmentation Model and Deep Learning(Wiley, 2021) Abayomi-Alli, Olusola Oluwakemi; Damasevicius, Robertas; Misra, Sanjay; Maskeliunas, RytisImprovement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.Article Citation - WoS: 11Citation - Scopus: 18Prospects of Ocean-Based Renewable Energy for West Africa's Sustainable Energy Future(Emerald Group Publishing Ltd, 2021) Adesanya, Ayokunle; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, RobertasPurpose The limited supply of fossil fuels, constant rise in the demand of energy and the importance of reducing greenhouse emissions have brought the adoption of renewable energy sources for generation of electrical power. One of these sources that has the potential to supply the world's energy needs is the ocean. Currently, ocean in West African region is mostly utilized for the extraction of oil and gas from the continental shelf. However, this resource is depleting, and the adaptation of ocean energy could be of major importance. The purpose of this paper is to discuss the possibilities of ocean-based renewable energy (OBRE) and analyze the economic impact of adapting an ocean energy using a thermal gradient (OTEC) approach for energy generation. Design/methodology/approach The analysis is conducted from the perspective of cost, energy security and environmental protection. Findings This study shows that adapting ocean energy in the West Africa region can significantly produce the energy needed to match the rising energy demands for sustainable development of Nigeria. Although the transition toward using OBRE will incur high capital cost at the initial stage, eventually, it will lead to a cost-effective generation, transmission, environmental improvement and stable energy supply to match demand when compared with the conventional mode of generation in West Africa. Originality/value The study will contribute toward analysis of the opportunities for adopting renewable energy sources and increasing energy sustainability for the West Africa coast regions.
