Where Have the Litigants Gone?

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.Comment: 22 pages, 10 figure

Artificial intelligence approaches for advanced battery management system in electric vehicle applications : a statistical analysis towards future research opportunities

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs. © 2023 by the authors

Empowering sustainability : Viability analysis of a floating photovoltaic system at Gulshan Lake

The world's increasing energy demands have intensified the need for renewable energy sources. In response to the land constraints posed by ground-mounted solar installations, Floating Photovoltaic (FPV) has emerged as a promising solution. This study focuses on exploring the feasibility of building floating solar panels in Bangladesh, with an experimental investigation conducted on Gulshan Lake, an urban water body located in Dhaka city. The lake is facing mounting pollution and degradation due to rapid urbanization, industrialization, and population growth. Consequently, the pursuit of sustainable development and clean energy has led to a keen interest in renewable energy sources, particularly floating PV systems. The paper thoroughly considers the technical, financial, and environmental aspects involved in the construction of such a system during the feasibility analysis. The design of the plant and tariff are meticulously carried out using the HOMER software, and the study calculates the optimal cost of electricity for the FPV power plant manually, projecting figures for 20 and 25-year periods at BDT 10.92 and BDT 7.23, respectively. The same calculations performed with the HOMER software yield slightly higher values of BDT 12.17 and BDT 11.87 for the same time spans. Moreover, the paper highlights the cost-effectiveness of the designed PV system

Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision

Optimizing photovoltaic arrays : A tested dataset of newly manufactured PV modules for data-driven analysis and algorithm development

This data article presents a comprehensive dataset comprising experimentally tested characteristics of newly manufactured photovoltaic (PV) modules, which have been collected by using a commercial PV testing system from a solar panel manufacturer company. The PV testing system includes an artificial sunlight simulator to generate input light for the PV and the outputs of the PV are tested by a professional IV tracer in a darkroom environment maintaining IEC60904–9 standard. The dataset encompasses modules with power ratings of 10 W, 85 W, and 247 W, each represented by 40 individual module records. The tested and collected characteristics of each module include open circuit voltage, short circuit current, maximum power point voltage, maximum power point current, maximum power point power, and fill factor. The motivation for this dataset lies in addressing the challenges posed by manufacturing defects and a ± 5 % manufacturing tolerance, which can lead to mismatch power losses in newly installed PV arrays. These losses result in lower current in series strings and lower voltage in parallel branches, ultimately decreasing the array's output power. The dataset serves as a valuable resource for academic research, particularly in the domain of PV array optimization. To facilitate optimization efforts, different algorithms have been explored in the literature. This dataset supports the exploration of these optimization algorithms to find solutions that enhance the position of each module within the array, consequently increasing the overall output power and efficiency of the PV system. The objective is to mitigate mismatch power losses, which, if unaddressed, can contribute to increased degradation rates and early aging of PV modules. This dataset lays the groundwork for addressing critical PV array performance and efficiency issues. In future research, this dataset can be reused to explore and implement optimization algorithms, to improve the overall output power and lifespan of newly installed PV arrays. The smart solution proposed in [1], utilizing a genetic algorithm-based module arrangement, demonstrates promising results for maximizing PV array output power using this dataset

Feasibility analysis of floating photovoltaic power plant in Bangladesh: A case study in Hatirjheel Lake, Dhaka

The installation of large-scale photovoltaic (LSPV) power plants is a solution to mitigate the national energy demand in Bangladesh. However, the land crisis is one of the key challenges for the rapid growth of ground-mounted LSPV plants in Bangladesh. The per unit cost of energy from ground-mounted PV systems is rising as a response to numerous difficulties, particularly for large-scale electricity generation. To overcome the issues with land-based PV, the floating photovoltaic (FPV) could be a viable solution. To the aspirations of the Sustainable and Renewable Energy Development Authority (SREDA), this article has investigated the feasibility of constructing a floating solar plant at Hatirjheel Lake in Dhaka, Bangladesh. The lake is an excellent spot to build an FPV plant due to its geographic location and climatic conditions inside the capital city. In this paper, the design of the plant and tariff are carried out using the PVsyst simulator. It is found that the optimum cost of energy for the plant is $ 0.0959/KWh, which is lesser than the currently operational ground-mounted PV plants in Bangladesh. Additionally, the projected 6.7 MW plant can meet 12.5 % of the local energy demand. Furthermore, the FPV plant is capable to cut off 6685 tons of CO2 annually. A reduction in power costs and environmental protection would assist the government of Bangladesh in achieving the sustainable development goals and electricity generation target of 6000 MW from solar photovoltaics by 2041 as well

Harnessing waterbodies in Dhaka : Exploring the feasibility of floating solar PV to alleviate the energy crisis in Bangladesh

Addressing the energy crisis in the 21st century is pivotal for sustainable development. In recent years, Bangladesh has had a significant energy crisis due to the scarcity of fossil fuels as an indirect effect of the economic downturn after the COVID-19 epidemic and the ongoing conflict between Ukraine and Russia. The persistent power outages have detrimentally impacted both economic output and investor confidence. The implementation of floating solar photovoltaic (FSPV) plants has emerged as a feasible solution to address the energy crisis in Bangladesh and alleviate the lack of energy resources. This article thoroughly examined the feasibility of FSPV systems regarding technical, economic, and ecological considerations by conducting PVsyst simulations in several appropriate lakes in the Dhaka Metropolitan region. The study found that by harnessing 5% and 10% of the surface area of the selected lakes, it is possible to achieve a DC capacity of 27.5 MW and 55 MW, resulting in an annual power generation of about 48,282.736 MWh and 93,749.817 MWh respectively. The article investigated the economic viability using the levelized cost of energy metric, revealing an average LCOE of $0.17/kWh. Additionally, the study highlights the potential of FSPV systems to reduce yearly CO2 emissions by 29,355.90349 and 56,999.88874 tons by utilizing 5% and 10% resources respectively. This substantial decrease in emissions significantly contributes to the carbon reduction targets of the country. Overall, the research provides valuable insights into bridging the energy gap and emphasizes the viability of FSPV technology as a practical solution to the energy crisis of Bangladesh in line with SDGs