ABSTRACT
The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side. © 2022 IEEE.
ABSTRACT
By the beginning of 2020, the illness had been named as COVID-19, which had spread due to its extreme severity affecting multiple industries and sectors throughout the world. To protect the public's health and safety, the Philippine government has established a number of quarantine regulations and travel restrictions in reaction to the current COVID-19 outbreak. Nonetheless, the ILO predicted that the pandemic would initially disrupt the economy and labor markets, affecting 11 million employees, or around 25% of the workforce in the Philippines. Therefore, the government continues to urge employers of local companies and enterprises to use alternative work plans, such as a WFH - work-from-home operation in accordance with the established policies. In line with the concept of telework, several studies have already been carried out, though some were declared inconclusive and require additional study. Hence, in this research, a mobile application was created to evaluate the employee's telework capability assessment using a Fuzzy-based model which utilizes Google AppSheet, Apps Script, and Sheets. The developed mobile application is able to provide capacity evaluation utilizing the four key input variables, which are also reasonably characterized for potential telecommuting cost evaluation. © 2022 IEEE.
ABSTRACT
By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.
ABSTRACT
This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.
ABSTRACT
Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people. © 2022 IEEE.
ABSTRACT
Face masks have been widely used since the start of the COVID-19 pandemic. Facial detection and recognition technologies, such as the iPhone's Face ID, heavily rely on seeing the facial features that are now obscured due to wearing a face mask. Currently, the only way to utilize Face ID with a mask on is by having an Apple Watch as well. As such, this paper intends to find initial means of a reliable personal facial recognition system while the user is wearing a face mask without having the need for an Apple Watch. This may also be applicable to other security systems or measures. Through the use of Multi-Task Cascaded Convolutional Networks or MTCNN, a type of neural network which identifies faces and facial landmarks, and FaceNet, a deep neural network utilized for deriving features from a picture of a face, the masked face of the user could be identified and more importantly be recognized. Utilizing MTCNN, detecting the masked faces and automatically cropping them from the raw images are done. The learning phase then takes place wherein the exposed facial features are given emphasis while the masks themselves are excluded as a factor in recognition. Data in the form of images are acquired from taking multiple pictures of a certain individual's face as well as from repositories online for other people's faces. Images used are taken in various settings or modes such as different lighting levels, facial angles, head angles, colors and designs of face masks, and the presence or absence of glasses. The goal is to recognize whether it is the certain individual or not in the image. The training accuracy is 99.966% while the test accuracy is 99.921%. © 2022 IEEE.
ABSTRACT
Some problems of Filipino farmers in Nueva Ecija are irrigation systems and labor shortage. Most of them are unable to work due to old age while others chose to stop because of the COVID-19 pandemic. Meanwhile, irrigation systems have been an issue due to the lack of resources such as continuous water supply and control. Fortunately, there is a progression of smart farming in the country which could assist in optimizing farming processes. This study presents a systematic literature survey on rice farming technologies and challenges. This study also aims to help address these problems by creating a rice irrigation system that introduces a water level control system. The system was comprised of a mobile application, Arduino ESP32 module, and a tank with water level sensors. The mobile application was used to set the desired water level while the proportional- integral-derivative (PID) controller adjusted the water level automatically. When current water level is lower than the setpoint, the valves to the tank will open. Tank specifications were used to come up with a transfer function for the system. The proposed design was simulated in MATLAB Simulink and PID parameters were tuned to enhance system performance. The tuned control system obtained an output response with less overshoot and faster settling time. © 2022 IEEE.
ABSTRACT
The Philippines has consumed over 108,000 and 101,000 GWh of energy in the years 2019 and 2020, respectively. These consumed energy is generated by different providers such as the Manila electric, Visayan electric, and Negros Occidental electric cooperative which mainly provides for the residential, commercial, and industrial sectors. Moreover, the country utilizes four main energy generation types namely, coal, oil-based, natural gas, and renewable energy. As reported in the Department of Energy's 2020 Power Statistics Report, figures show that the recent COVID-19 pandemic resulted in an increase in generated energy for the household sector. Inversely, the generated energy for commercial and industrial sectors dropped. This is due to the lockdown urging people to have a work or study from home set-up rather than face to face. With this shift in energy distribution may come cases of incorrectly measured power consumption that increases electricity bills of each sector and the companies involved. Therefore, this study aims to apply linear programming to optimize the allocation of energy generation and consumption of power plants in the country. The results of the program will then be compared to the results from and several mathematical models, such as the Gauss-Jordan, M-Method, and Two-Phase Methods to verify its results. © 2021 IEEE.
ABSTRACT
The covid-19 pandemic has severely affected the economy of the Philippines. With 90% of the labor force being affected, hundreds of thousands of families turn to their respective local government units for assistance. LGUs have begun distributing food box assistance to every family under the Food Security Program to ease the economic burden. However, such a program having a vast number of recipients will require a large budget. This study presents the optimization of the content of the food packs used for food aid distribution through linear programming using Matlab. The study's goal is to maximize the nutritional content of the food pack while being under the constraint of a limited budget to ensure the best utilization of scarce resources. © 2021 IEEE.
ABSTRACT
The Philippines' Land Transportation Office (LTO) has the task of evaluating and processing citizen's applications for driver's license for professional and non-professional purposes. The office sees many customers each day and the need for an efficient waiting sitting was needed. Several process optimization and productivity improvements will be discussed in this research. Improvements along the lines of robotic process optimization, social media bots, existing efficient and effective processes, and the like, will be considered in developing suggestions for optimizing waiting line management in the LTO. In addition, existing congestion surveillance methods are explored to consider more solutions. The LTO currently implements a multi-channel, multi-phase line management system for all branches in the country. The research aims to evaluate its effectiveness in processing new driver's license applications in the National Capital Region within a set period of days. The system has proven inefficient based on recent data taken between 2019-2020 [1], even considering the decline in applications because of the COVID-19 pandemic. There is an expected spike in citizens applying for driver's licenses once the quarantine restrictions are lifted entirely, and an effective waiting line management system will be needed. A solution can be created by taking inspiration from efficient process systems from the United States and United Kingdom. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic has brought crisis to people from around the world resulting to a transition from face-to-face classes to an online class in the academic sector. Graduation ceremonies also transition into an online ceremony where students passively attend the session. In this study, a Social Robot named 'Gradbot' is developed to help the students participate actively in their online ceremonies. The Body frame was designed using Fusion360. The Gradbot is compose of the Arduino microcontroller, servo motors, Bluetooth module, mounted on a 2WD car chassis and was simulated using Tinkercad and MATLAB. This study also includes the investigation of the degrees of freedom, type of joints, workspace, and the cartesian product of the developed Gradbot. © 2021 IEEE.
ABSTRACT
With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application that utilizes Tiny-YOLOv4 and DeepSORT tracking algorithm to monitor crowd count and social distancing in a top-view camera perspective. The application is able to process video files or live camera feed such as CCTV or surveillance cameras and generate reports indicating people detected per unit time, percentage of social distancing per unit time, detection and social distancing logs as well as color-coded bounding boxes to indicate if the detected people are following social distancing protocols. © 2021 IEEE.
ABSTRACT
Open-source ventilators (OSVs) are considered as an immediate response for the shortages of ventilator equipment in hospitals due to the ongoing global pandemic caused by the Coronavirus disease 2019 (COVID-19). One of the designs explored for OSVs utilizes a bag-valve-mask as a source for mechanical ventilation. Despite its availability for use and being medically accepted, proper calibration must be observed in measuring ventilator scalars such as inspiratory pressure, inspiratory flow, and tidal volume to promote the safe use of the OSV and prevent OSV users to do more harm to the patient. This study discusses different calibration techniques to properly acquire ventilator scalar measurements using an integrated ventilator scalar measurement module. All in all, different calibration setups and bag-valve-mask-based mechanisms were tested and documented to determine an effective means to acquire accurate and precise ventilator scalar measurements.
ABSTRACT
Automation is considered as the driving force of Fourth Industrial Revolution (Industry 4.0) to develop smart and automated devices for existing manufacturing processes. However, the global medical outbreak perpetrated by the Coronavirus Disease 2019 (COVID-19) challenged researchers to explore new concepts and innovate existing technologies whilst resolving the ongoing health crisis. Thus, the demand for utilizing the automation concept in biomedical devices is reasonably high. For this study, the researchers have successfully implemented an industrial-grade programmable logic controller that will control the mechanical ventilation process of a bag-valve-mask-based emergency ventilator. Various mechanisms were observed, and the results have been documented. © 2020 IEEE.
ABSTRACT
In response to COVID-19 pandemic, universities and related institutions around the world came up with various mechanical ventilator designs to help cope with the expected shortages of ventilators as the pandemic rages. Many of these designs are based on automating the manual operation of the Bag Valve Mask (BVM), a ubiquitous resuscitator device used for emergency ventilation or resuscitation of patients with breathing problems. In this paper, the mechanical design and development process for a BVM-based emergency ventilator is discussed. In particular, the evolution of the design from a simple, low-cost device to a more sophisticated system acceptable to pulmonologists and related medical practitioners is documented. © 2020 IEEE.