Simulation and Economic Modelling of a Floating Solar Photovoltaic (FSPV) System using PVSyst

This paper examines the economic feasibility of implementing a Floating Solar Photovoltaic (FSPV) system in a Philippine Lake using the PVsyst simulation tool. The study involved a detailed simulation of the FSPV system's performance, considering various environmental and technical parameters. Key aspects such as system configuration, energy yield, and financial metrics including payback period and net present value were analyzed. Results indicate that the FSPV system could significantly contribute to local energy needs while proving to be a financially viable investment due to substantial reductions in CO2 emissions and lower energy costs compared to traditional power sources. According to assessments and simulations performed using PVSyst software, the FSPV system would possess a capacity of 10 kWp, with an expected available energy output of 13,599 kWh per year and an expected energy consumption of 12,940 kWh per year. The economic modeling of the FSPV system revealed a relatively short payback period of 4.8 years, with a net present value of Php 741,732.00 and a substantial return on investment of 227.5%. The Levelized Cost of Energy (LCOE) was estimated at Php 6.18 per kWh. The study underscores the potential of FSPV systems to meet the renewable energy needs of isolated communities by leveraging local water bodies for solar installations

Current Trends, Advancements, and Challenges in Floating Solar Photovoltaic (FSPV) Systems for Off-Grid Applications: A Review

Increasing energy demands and the pursuit of sustainable and clean energy sources have intensified interest in Floating Solar Photovoltaic (FSPV) systems, particularly for off-grid applications. FSPV technology presents a strategic alternative for countries with limited land but ample water bodies, contributing to energy diversification and conservation of arable land. This paper provides a comprehensive technological trends, advancements, and challenges in the deployment of FSPV systems, drawing from an array of highly regarded publications and extensive patent searches via the Derwent Innovation database and various publications. While large-scale FSPV deployments have been successfully integrated with existing hydroelectric power plants and grid systems, the application of FSPV technology for local community use remains underexplored. The paper identifies the lack of comprehensive literature on stand-alone FSPV systems that include battery charging capabilities and integrated monitoring and control systems to mitigate environmental risks. Moreover, the paper discusses the economic, regulatory, technical, cultural, and environmental barriers to FSPV deployment. It suggests that continuous research and development, backed by supportive policy frameworks, are crucial for overcoming these challenges. The aim is to pave the way for resilient, community-centric FSPV installations that can withstand extreme weather and cater to localized energy needs

Smart Region Mobility Framework

A smart city describes an urban setting which aims to effectively apply ICT technologies to help improve the well-being of its citizens and reduce the negative impacts of urbanization. The priority areas considered in the Global Smart City Index (SCI) by the Institute for Management Development’s (IMD) World Competitiveness Centre were key infrastructures and technologies in (1) health and safety, (2) mobility, (3) activities (e.g., recreational spaces), (4) opportunities (work and school), and (5) governance. A smart region is a term used to extend the concept of a smart city into both urban and rural settings to promote a sustainable planning approach at the regional level. A direction that must be considered is the adoption of a “Smart Region Mobility Framework” to effectively transform our urban and rural regional transportation networks. This research study focused on the development of the smart region mobility framework for an island region group in the Philippines. The smart region goal is to integrate intelligent transportation system (ITS) platforms such as advanced public transportation system (APTS), advanced traveler information system (ATIS), and advanced rural transportation system (ARTS) to the local public transportation route plans (LPTRP) of the region. The activities include the data collection, analysis, and evaluation of multimodal regional transportation networks and social services infrastructure. The transportation network modeling process follows the four-step transportation planning process of trip generation, trip distribution, modal-split analysis, and trip assignment. Based on the analysis of 6 provinces, 16 cities, and 114 municipalities included in the study, there are two cities identified as smart city candidates. One of the smart city candidates is designated as the smart city regional center. In the context of a smart region, the available social services (e.g., employment opportunities, education, and health services) in the designated smart cities can also be made accessible to connected cities/municipalities through ease of transportation and mobility services in the region. Lastly, the study presented the implementation of data flow architecture of the smart region mobility framework, and the regional traveler information system using mobile and web application services

Design and development of the intelligent operating architecture for audio-visual breast self-examination multimedia training system

Breast cancer poses a major health care challenge, especially in developing countries. As the incidence of breast cancer continues to rise steadily in the developing world, the lack of awareness of this disease and the absence of breast cancer screening programs make it almost certain that the majority of breast cancers are diagnosed at an advanced stage. Early detection of breast cancer together with proper treatment increases the rate of survival. Studies suggest that the majority of breast cancers could be first detected during breast self-examination (BSE). The use of computer-guided BSE multimedia training system (BSE-MTS) can increase the probabilities of detecting breast abnormalities compared with non-guided regular BSE. This study is in conjunction with the research initiative of the Commission on Higher Education Philippine Higher Education Research Network (CHED-PHERNet) on Addressing the High Breast Cancer Incidence in Bacolod City. De La Salle University (DLSU) Manila is in collaboration with University of St. La Salle (USLS) Bacolod in undertaking this research. The research has several activities which include the development of a tool for breast cancer monitoring and education. The development of an interactive breast self-examination multimedia training system that can be easily used by the local female population in Western Visayas region of the Philippines, particularly in Bacolod city, can help awareness and prevention of this dreaded disease by early detection of any breast malignancies while it is still in its early stage. The interactive BSE multimedia training system aims to improve the efficiency and accuracy in performing BSE and to promote the ease of use of the system. The major goal of this research is to design and develop the intelligent operating architecture (IOA) for audio-visual BSE multimedia training system that will include computer vision (CV), speech recognition (SR), speech synthesis (SS), and audio-visual (AV) feedback systems. The artificial intelligence (AI) named BEA which is an acronym for Breast Examination Assistant, a virtual health care assistant, will run using the IOA to assist the user in performing BSE. This aims to improve the user experience and confidence by deploying BEA in the real time evaluation of BSE. The research has four key areas of study which are computer vision, speech recognition, speech synthesis, and audio-visual feedback systems. The computer vision system will be used for breast region identification, nipple detection and tracking, hand motion detection and tracking, and palpation level detection. The fundamental algorithms for computer vision has already been developed by the Intelligent Systems Laboratory (ISL) of De La Salle University Manila. Most of these algorithms will be used in this system. The speech recognition system will be used to identify speech commands issued by the user for the speech controlled human-computer interface (HCI) of the system. This is an important feature because in performing BSE it is beneficial if the user can indicate the possible tumor locations just by dictating it to the system. This will allow less body movement during BSE performance and thus contributing less error in the computer vision algorithms. Speech recognition for English and Hiligaynon languages will be developed for this system allowing flexibility in user preferences. Speech synthesis system will be used for audio feedback response of the system to the user. It will be developed to provide a synthesized female voice for BEA. The output of speech synthesis will be fed to the AV feedback system. Speech database will be collected from different female users for speech recognition and synthesis training. The audio-visual feedback system will be used for user guidance in performing BSE for better user experience. It will also have the capability of providing information to user queries. The intelligent operating architecture will serve as the integration system that will supervise the over-all operation of the BSE multimedia training system. BSE performance is a simple yet sensitive healthcare practice. This research also aims to increase the technology acceptance of the users of the system by creating BEA to appear like a personal female healthcare assistant. This technology can bridge the gap in the comfort levels of the female users to the overall benefits of the system

Hiligaynon language 5-word vocabulary speech recognition using Mel frequency cepstrum coefficients and genetic algorithm

In the study conducted by the Department of Health National Epidemiology Center, there is a high incidence and mortality rates of breast cancer among Western Visayan women, specifically in Bacolod city, Philippines. The development of breast self-examination (BSE) multimedia training system that can be easily used by the local female population in Western Visayas can help awareness and prevention of this dreaded disease. This system incorporates Hiligaynon speech recognition for motion control commands. Hiligaynon language, popularly known as Ilonggo, is an Austronesian language spoken in the Western Visayas region of the Philippines with approximately 11 million speakers, 7 million of which are native speakers. This study focuses on a 5-word vocabulary Hiligaynon language speech recognition for the BSE multimedia training system with feature extraction using Mel frequency cepstrum coefficients and pattern recognition using genetic algorithm. The genetic algorithm uses Euclidean distance, neighbourhood selection, two point crossover and elitist survival techniques. The system has an adaptive database system which improves the training and classification of the Hiligaynon words. The results showed that the combined Mel frequency cepstrum coefficients and genetic algorithm techniques used together with the adaptive database system can effectively recognized the different Hiligaynon words with 97.50% accuracy. © 2014 IEEE

Design and development of an artificial intelligent system for audio-visual cancer breast self-examination

This paper presents the development of a computer system for breast cancer awareness and education, particularly, in proper breast self-examination (BSE) performance. It includes the design and development of an artificial intelligent system(AIS) for audio-visual BSE which is capable of computer vision (CV), speech recognition (SR), speech synthesis (SS), and audiovisual (AV) feedback response. The AIS is named BEA, an acronym for Breast Examination Assistant, which acts like a virtual health care assistant that can assist a female user in performing proper BSE. BEA is composed of four interdependent modules: perception, memory, intelligence, and execution. Collectively, these modules are part of an intelligent operating architecture (IOA) that runs the BEA system. The methods of development of the individual subsystems (CV, SR, SS, and AV feedback) together with the intelligent integration of these components are discussed in the methodology section. Finally, the authors presented the results of the tests performed in the system

Fuzzy inference system wireless body area network architecture simulation for health monitoring

According to WHO, 22% of the world population, about 2 billion people, will be age 60 years and older in 2050. About 80% of these elderly people will be living in the developing nations. Population ageing are faced with challenges such as increased in the cases of chronic non-communicable diseases (NCDs) like cardiovascular diseases, obstructive pulmonary diseases, cancer, diabetes, musculoskeletal problems, and ageing-associated mental health conditions. The current healthcare infrastructure cannot cope with the projected increase in demands for health care monitoring and assistance of elderly people. These challenges must be met with improvements in the current health care systems and infrastructure. A wireless body area network (WBAN) that uses a fuzzy inference system (FIS) which can determine the condition of a person by employing sensors to monitor the heart rate, respiration rate, blood pressure, body temperature, and oxygen saturation of hemoglobin in the blood (SpO2) is proposed in this study. Remote patient monitoring with increased patient to health care personnel ratio can be achieved using this method. The results showed that body condition, ranging from critical to very good condition, can be determined using this method. © 2015 IEEE

Philippine license plate detection and classification using faster R-CNN and feature pyramid network

The advancement of image and video processing using Artificial Intelligence (AI) have brought more significance to the role of Automatic License Plate Recognition (ALPR) systems in law enforcement and intelligent transport systems (ITS). However, the adaptation of such a system in the Philippines has been a challenge due to the different variations of Philippine license plates. In this paper, a neural network-based model for the detection and classification of different Philippine license plate formats is proposed. The proposed method classifies license plates into four categories - 1981, 2003, 2014, and other series

Intelligent operating architecture for audio-visual breast self-examination multimedia training system

This paper introduces the initiated development of a computerised system called Breast Self-Examination-Multimedia Training System (BSE-MTS) and ongoing research for its development into effective, intelligent, high-tech system. Firstly, it presents the major components of the BSE-MTS and describes its future development into an intelligent system. Then, it outlines the development of the system as an intelligent integration of various modules of heterogeneous information, i.e.: (1) domain knowledge and perception of breast structures, locations, nodules (2) graphic and visual information (3) speech recognition and speech synthesis in the specific domain (4) interactive audio-visual feedback to the users of the BSE-MTS. The authors performed tests on using BSEMTS and present the outcomes. The paper is result of a research study. © 2015 IEEE

Insect detection and monitoring in stored grains using MFCCs and artificial neural network

The variability in grain production makes it necessary to have strategic grain storage plans in order to ensure adequate supplies at all times. However, insects in stored grain products cause infestation and contamination which reduce grain quality and quantity. In order to prevent these problems, early detection and constant monitoring need to be implemented. Acoustic methods have been established in numerous studies as a viable approach for insect detection and monitoring with various sound parameterization and classification techniques. The aim of this study is to further demonstrate the efficacy of acoustic methods in pest management mainly through feature extraction using Mel-frequency cepstral coefficients (MFCCs) and classification using artificial neural network. The study used sounds from the Sitophilus oryzae (L.) or commonly known as rice weevil in larval stage recorded using five different acoustic sensors with the purpose of proving the capability of artificial neural network to recognize insect sounds regardless of the acoustic sensors used. Network models with varying number of nodes for the hidden layer were experimented in search for the highest accuracy that may be obtained. Results show that the network with 25 nodes for the hidden layer provides the best over-all network performance with 94.70% accuracy and the training, validation, and testing are accurate at 95.10%, 94.00%, and 93.60% respectively. Although, difference in accuracy values across all simulations never exceeded 1%. These show that the proposed method is capable of recognizing insect sounds regardless of the acoustic sensors used provided that proper acoustic signal preprocessing, feature extraction, and implementation of the network are performed. © 2017 IEEE