Scopus ID: 85084754766
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Diana Rose T. Rivera, Alvin B. Culaba, ... Wei-Hsin Chen
Conference Paper | Published: November 1, 2019
Abstract
Torrefaction is a thermochemical process for upgrading raw biomass into a more energy-dense fuel. However, the production of torrefied microalgae biochar may include environmental impact as it consumes raw materials and energy. In this study, a life cycle assessment study was conducted to understand and assess the corresponding global warming potential associated with the production of torrefied microalgae biomass, using a cradle-to-gate scope. Using different scale models of torrefied microalgae biomass production, this study identifies the contribution of the torrefaction process to the overall environmental impact. Using the experimental data, the study was able to analyze the impact of the torrefaction process on biomass thermal degradation using the torrefaction severity factor. The inclusion of the torrefaction severity factor shows that there was a strong relationship on the resulted global warming potential. It revealed that the influence of the torrefaction temperature was higher as compared to the torrefaction duration.Result of the study shows that the torrefaction process had a minimal contribution of 1-20% to the resulted overall environmental impacts. The overall impact shows that up-scaling production can result in a negative carbon dioxide emission.
Scopus ID: 85084756999
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Kyle Jericho Grecia, Antoine Albert Luce, ... Ivan Henderson Gue
Conference Paper | Published: November 1, 2019
Abstract
Climate change has drastically affected our production patterns, negatively distressing the yearly agricultural produce. A core process in the industry is the drying of biomass. Drying increases the value and extends the shelf life of the agricultural products. However, modern drying technologies are still reliant on fossil fuels. Solar-based drying technologies are needed to counteract the fossil fuel dependence. Other than reduction of fuel consumption, solar dryers are easily adaptable to rural communities with heavy reliance on the drying process. Alternative designs have been proposed to improve the performance of the solar dryers, notably integrating thermal energy storage (TES) systems. A limiting factor, however, is that the performance is constrained to the heating capacity of the TES. Previous study has examined the integration of TES with air recirculation, indicating an improved performance. Further evaluation of the dryer with another biomass is needed to illustrate the adaptability of the hybrid feature. This study, therefore, evaluates the performance of solar drying with TES and air recirculation for mango drying. Comparisons were made with other design combinations as a benchmark. Results reveal that the hybrid solar dryer can reduce the drying time from 7.17 hours to 5.32 hours.
Scopus ID: 85084754357
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Conference Paper | Published: November 1, 2019
Abstract
Primary and backup relays pairs are protection schemes for power systems which are set in conjunction to one another to ensure that the protection system operates by limiting an abnormality within its zone of protection. Breakpoints are the starting points of all assumptions and calculations done in protection systems. Previous methods of determining breakpoints favor linear graph theory and expert theory system rather than machine learning. In this study, an adaptive neuro-fuzzy inference (ANFIS) approach is used to determine the breakpoint set for directional overcurrent relays of a given 3-bus network. The two most influential input variables from 15 inputs affecting breakpoint set are determined by Exhaustive Search. The reduced inputs are then used to design the Sugeno type ANFIS. Experimental results show promising results in terms of Root Mean Square Error.
Scopus ID: 85084765618
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-4
Conference Paper | Published: November 1, 2019
Abstract
The use of reinforced concrete as a structural material originates from two different components; Reinforcing steel, known as a highly-tensile material, combined with concrete, a highly-compressive material, act together to resist different types of loadings. However, this composite action is dependent upon the transfer of loads between the steel and concrete known as the bond and is concluded to be on a form of continuous stress along the juncture of steel and concrete. This study focused on producing a bond stress model as a function of concrete compressive strength, embedment length and bar diameter, using single pullout test, with the help of multiple regression analysis by using Microsoft Excel. For the compressive strength, 40 MPa, 50MPa and 60MPa were used, with 50mm, 75mm and 100mm embedment length and bar diameters of 12mm, 16mm and 20mm, producing a total of 27 specimens. After subjecting each specimen to pull out test, data were tabulated and analyzed using Multiple Regression producing a model with 97.81% correlation coefficient. The model was then subjected to parametric testing and it was concluded that increasing the compressive strength of concrete would result to an increase in bond stress; however, increasing the embedment length and bar diameter would result to a decrease in bond stress.
Scopus ID: 85084747218
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Conference Paper | Published: November 1, 2019
Abstract
The electroencephalogram (EEG) signals being a recording of the electrical activity of the brain provides valuable information in the analysis of its function and disorder. Epilepsy is a brain disorder characterized by uncontrolled excessive activity. Repeated abnormal disturbance or seizure causes epilepsy. Hence, EEG signals act as a diagnostic tool for epilepsy. An approach based on tree-based pipeline optimization tool (TPOT) is presented for classification of EEG signals as either seizure or normal activity in the brain. A binarized dataset with sampled signal levels and the corresponding class is subjected to a genetic approach for acquiring an optimized predictive model. In TPOT, the tedious process involved in machine learning being repeatedly performed until arriving at the best solution is automated using genetic algorithm, i.e., evaluate-select-crossover-mutate is repeated to tune the pipeline. In the settings used in this paper, there are 90 pipeline configurations for evaluation for which around 450 models are fitted and evaluated against the training data in one grid search. The best pipeline is the one with the highest cross-validation score in the run at 95.94%. The test accuracy is at 95.27% which is just a little lower than the cross-validation score. The predictive model consists of pre-processing steps Maximum Absolute Scaler and Function Transformer which is utilized by a Gaussian Naïve Bayes classifier. The system is trained and tested for epileptic seizure detection using raw EEG signals. The optimized features and predictor obtained via TPOT resulted to a high-performance accuracy for epileptic seizure detection.
Scopus ID: 85084730289
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Conference Paper | Published: November 1, 2019
Abstract
An ideal library makes every last patron feels what it is like to have their own library where one can traverse a world of knowledge, and then build their personal learning environment. Parallel to this conception is the intensification of fine-tuning the conventional librarianship to transform it into a center for new digital learning. As a contribution to this digital revolution, this paper presents an innovative way of renovating the house of dusty books into the center of creativity, research, and partnership through the fusion of traditional librarianship, self-service solutions, and human-computer interaction. The self-service system employs technologies and modalities such as touch screen-assistive technology for the kiosk terminal with the inclusion of a built-in camera, speaker, microphone, and lights, Automatic Speech Recognition, Radio-Frequency Identification and Content-Based Image Retrieval for holdings circulation and monitoring. In conformance with the ISO 9241-210 (Human-centred design for interactive systems), a series of user-centered evaluations were accomplished to obtain early feedback, and to validate that the user requirements have been satisfied at a later stage of the project cycle. To integrate a humanistic approach, the HCSDLC, or Human-Centered Systems Development Life Cycle Methodology, was utilized to complement the four main user-centered design activities specified in the ISO 9241-210 standard. The prototype designs and final self-service library system were assessed in terms of efficiency, effectiveness, and user satisfaction using metrics defined in the ISO/IEC 25022.
Scopus ID: 85084729434
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Juan Miguel H. Villarroel, Jomar B. Calauod, ... Ronald M. Pascual
Conference Paper | Published: November 1, 2019
Abstract
Ideally, instruction is best done one on one. However, due to the scarcity of public school teachers, this ideal remains just that, only an ideal. This ideal, however, can be realized by using a computer-assisted language learning system. One such language that this system can be applied to is the Pangasinense - one of the top ten languages of the Philippines. Using this system, any Filipino can now learn Pangasinense. Creating this involves developing the speech corpus for the Pangasinense language and designing a reading miscue detector (RMD) that employs hidden markov models (HMM) and artificial neural network (ANN). The RMD uses the reference verification (RV) method that compares the input speech to the reference speech found in the Pangasinense speech corpus. The collection of the speech corpus involved 10 native Pangasinense speakers who each recorded a total of 21 phrases and 309 words that were considered as common conversational phrases or words for Pangasinense. The system was initially tested by 10 native Pangasinense speakers, who also speak Filipino, and their scores were set as the reference scores. The system was then put to test by conducting a six-week pilot study participated by 10 Filipino speakers. The system's effectiveness was then evaluated through the progress trends of all learners' scores for each module. All learners' progress curves showed to have a positive slope. In addition, the system's efficiency was determined by its false alarm rate (FAR), misdetection rate (MdR), and accuracy. The system was able to get a FAR of 26.67% and 30%, MdR of 30.0% and 6.67%, and accuracy of 71.66% and 81.67%, for males and females group, respectively.
Scopus ID: 85078065982
2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT), (2019), pp. 199-202
Joie Ann Maghanoy
Conference Paper | Published: October 1, 2019
Abstract
Geographical Information System helps everyone in many industries on a regular basis. “iReportMo” is an android mobile application that uses GIS-based to let a citizen report an incident right from the mobile device. This application will serve as an easy access that caters emergency concern such as fire, accident, crime and barangay incident in Metro manila. The user may provide details such as location, date, time, and images when reporting an incident. There are few existing literatures have focused on providing assistance to any emergency event using the newest technology, but few have not touched on providing a centralized system that caters all the emergency in one application. Thus, this paper automates the efforts of the manual transaction to effectively respond to an emergency hazard in City of Manila thus makes it more efficient to gather important information in real time.
Scopus ID: 85077723193
TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), (2019), pp. 2408-2412
Conference Paper | Published: October 1, 2019
Abstract
Braille is one of the major tools for teaching the visually impaired. Sufficient number of teachers engaged in special education involved in Filipino Braille is not available. One of the possible approaches to address this problem is the use of computers in automation of extracting information in Braille that can facilitate teaching. Other countries have taken their initiative to develop similar technology capable of teaching Braille however the Filipino Braille code including its contractions, and the Filipino language per se has features that are distinct to other languages. This research proposes a system that use machine vision in recognizing one-cell Filipino Braille contractions. Scanned Braille images undergo image processing and HOG feature extraction to train the system classifier thru SVM. Performance evaluation results reflect a high accuracy of recognition.
Scopus ID: 85078365451
2019 International Symposium on Multimedia and Communication Technology (ISMAC), (2019), pp. 1-6
Conference Paper | Published: August 1, 2019
Abstract
This study utilized data mining and geo-mapping methods to develop a fire risk management system for the Bureau of Fire Protection (BFP) in the city of Manila. This system was integrated into a web portal where the BFP personnel can log and view fire incident reports, which are then evaluated and mined for marking fire “hot spots” on a customized map of the city, as well as for producing recommendations based on the risk level assessment of any location for a given date. Based on results of experimentation, the Decision Tree classifier model was selected, with 95.92% accuracy. Geocoding produced 92% output of geographical coordinates from address information in the data set. The system can help the fire agency in raising the fire risk awareness of the community country and in facilitating their fire risk reduction planning.