World Congress on Engineering and Technology; Innovation and its Sustainability 2018

Patient’s satisfaction and comfort are the priorities of every hospital. With the traditional appointment system, patients have been experiencing long waiting time, which causes dissatisfaction. This study designed a new web-based appointment scheduling system the “InstaSked” which could reduce the waiting time experienced by patients. It is designed for patients (booking their appointment), medical secretaries (managing patient list), doctors, and management (monitoring patients). The system used an integration of the Six Sigma methodology, DMADV (define, measure, analyze, design, and verify), and BPM (business process management).

Sustained modern trends in industrial development are increasing the quality requirements of all types of products. The two-dimensional case of the mathematical modeling of metal filling powders in an arbitrary bunker taking into account the physical characteristics of a material is considered. The practice of producing new porous materials on the fundamental aspects of metal powders shows that the implementation in full volume of their strength and exploitation characteristics requires a significant increase in the prediction accuracy of physical and mechanical properties of materials and the development of new methods of modeling, which includes complex analysis of the processes of formation materials.

The study focused on creating a low-cost, automated, rapid composting, improving the speed of the composting process of biodegradable wastes with the help of monitoring and maintaining proper temperature range and monitoring proper moisture content. The system includes input of password and mobile number, applying of water and enzyme mixture for moisture content, heating of compost material using blower, driving of mixer using DC motor to promote aeration, sensing of temperature and moisture and then displaying it on LCD, and sending SMS notification after completion of the process. Result showed that monitoring to maintain temperature range and moisture content speed up the bio composting process from a span of more than 1 month to 1–3 days, which makes it a rapid bio composting process.

The study was a descriptive type of research. Independent sample t-test and a cost-benefit analysis were used to determine the viability of adopting appropriate coffee post-harvest and processing technologies. Based from the independent sample t-test done, it could be concluded that the farmers could both prefer and utilize selective picking and stripping during the picking stage of the post-harvest process of the coffee beans. In the drying stage of the coffee beans, sun drying is the preferred technique. In the hulling, sorting, roasting, and grinding stage of the post-harvest process, the use of the machineries will result to higher income and profit for the farmers. The results revealed that using machineries or adopting a technology generates a return greater than the required return and it has a big impact on the projects net present value considering the process that requires machineries. Technology management system and framework on the growth of coffee growing industry in the Philippines and the appropriate post-harvest technology management systems were developed to maximize the profit of the coffee farmers and provide higher-quality coffee products. The proposed post-harvest and processing technology management framework could lead to a profitable and sustainable coffee industry that could provide benefits to farmers, processor, traders, and exporters, alleviate poverty, and could compete in the global arena.

This work is devoted to improving the technology involved in producing long-length powder permeable materials (PPM) by the dry radial-isostatic pressing (DRIP) method in order to obtain products with a uniform density distribution by volume. Based on the study of density distributions in long-length PPM, we proposed new technological schemes and technical solutions for equipment and tools for DRIP long-length powder products of simple and complex forms. The obtained multilayer PPM have significantly higher permeability, pollutant capacity, and service life compared to existing analogs. The developed technology of step-by-step DRIP of powder materials allows to obtain high-performance PPM with uniform density distributions by volume, homogeneous porous structure, and stable operational characteristics.

Advances in sensing technologies have resulted in a broadening of their application areas. One typical application is sensing human status, mental as well as physical aspects. Almost all systems proposed to date need sensors to be attached to the subject’s body, an invasive requirement that increases the user’s burden. Therefore, we need noncontact and noninvasive methods. This paper verifies, experimentally, the possibility of detecting human emotion from facial thermal images taken by an infrared camera. In the experiment, two types of emotion, a somewhat positive feeling and a negative one, are verified by observing subjects performing two different tasks. Based on a preliminary experiment, we focus on the change in temperature of the nose. Thirteen young healthy males/females participated in the experiment. The results showed that the change in temperature depended on the task performed. The positive task yielded an increase (average of all subjects) of +0.52 °C, while the negative task triggered a −0.08 °C decrease. These results imply the method might be feasible. However, subjects exhibiting the cool finger effect showed a very small temperature variance. For noncool finger subjects, the measured temperature differences were +0.95 °C for the positive task and −0.36 °C for the negative task, which show that the method is feasible. Future work includes verifying the method with many subjects and using multiple parts of the face to increase recognition performance.

This study aimed to create a prototype that can detect level of calcium and pH to analyse whether the urine is positive of hypercalciuria – a condition of having high amount of calcium, uric acid crystallization (pH < 5.8), and calcium oxalate formation (pH > 6.8) in urine. Five subjects were tested for their pH level and calcium level with three trials each. Results from paired t-test showed that there are no significant differences in the mean values of the prototype-tested and laboratory-tested pH level (p = 0.310) and calcium level (n = 15. p = 0.781). This indicates that values obtained from two different methods such as using prototype sensor and laboratory testing imply closely related values provided by almost zero mean differences of the two parameters used.

This paper presents the development of a test paper checker that will recognize a handwritten text using Intelligent Character Recognition (ICR) for Alphanumeric Characters. An examination can be conducted in two ways—digital and manual—and each way has a different approach in checking. In this study, the main objective is to recognize alphanumeric handwritten characters accurately using intelligent character recognition. OpenCV is used in the Python programming language and Support Vector Machine as a tool in machine learning for ICR. Answer sheet was designed with 120 items for MCQ and problem-solving questions. Item analysis and printing of results are included in the device. Experiments were conducted by giving an actual examination from the 131 participants in Technological University of the Philippines for testing the accuracy of the device. The results obtained from comparing manual and machine checking had an accuracy of 93.0769%. Thus, the proposed method is applicable for the development of handwritten character recognition.

The study presented a prototype design for “Automated Water Quality Monitoring and Control for Milkfish Pond” in Rock Fin Fish Farm. It aimed to develop a prototype for monitoring quality of water for enhancing the productivity of milkfish, which is one of the main sources of protein consumed by Filipinos. The system can detect threshold values (pH, Dissolved Oxygen, Temperature, Ammonia, and turbidity level), which are some basic parameters needed in monitoring the health of water for fish pond. Additionally, the prototype was designed to control/maintain optimum values of each measured parameters. Since drastic changes or intolerable amount of such measured parameters may cause fish kills or low growth rate, which can lead to loss of income. Laboratory test was compared to measured values from sensors. It was found out that there is no significant difference between laboratory results and prototype sensors’ results, which mean that the prototype design is reliable and accurate with percent error of less than 5% in three measurable acquired data.

Structural damage detection, based on global dynamic parameters, has received considerable attention from civil engineering and even by the local communities. The former sector is facing problems on providing structural integrity to its actual bridge construction due to climate change. Changes in the physical properties of structure such as boundary conditions, stiffness, and mass with respect to modal frequency are customarily studied; however, the unobservable factors such as wind force, humidity and, the most important, temperature must be given weight on analysis. In this study, the suitability of combined approach of supervised machine learning principal component analysis (PCA) and the metaheuristic genetic algorithm (GA) in generating the optimum condition for a reinforced concrete bridge was determined. The parameters that were optimized are the bridge and environment temperatures. These parameters were some of the essential bridge structural health parameters as they have impact on the boundary conditions and properties of materials. This entails that the developed model involves eigenfrequencies as function of temperatures only, which is a minimal parameter approach. The system selected the 50 fittest individuals based on the fitness score and then proceeded to the recombination process. The mutation with rate of 0.01 was applied to test if the solution is the global one. When the iterations had reached the required numbers of generation, the system stopped and gave the optimum condition for a bridge. The GA results showed that the optimum condition for a reinforced concrete bridge needs bridge temperature of 9.578 °C and environment temperature of −8.571 °C. Aside from these temperature values, the bridge is vulnerable to breakage or any damage condition.

Preempting mental fatigue may cause decrease in the quality of life and the worst accidents. A system of electrocardiography and electromyography signals can enhance the detection of alertness and mental fatigue. This study determines the suitability of some computational intelligence, namely, artificial neural network (ANN), fuzzy logic system, and a Sugeno adaptive neuro-fuzzy inference system (ANFIS), in detecting mental alertness and fatigue of a person using neurophysiological signals of electrocardiogram (ECG) and electromyogram (EMG) only instead of using higher-dimensional array of physiological data. The usage of these neurophysiological signals was tested if it correlates with high detection rate as to the usual observable physiological parameters. Muscle contraction was also studied in parallel with varying heart rates. Moreover, a power-efficient off-body access network (oBAN) was materialized using Arduino microcontroller with Bluetooth wireless transmission medium. The system is composed of two major parts: the development of BAN and the implementation of soft algorithms. The data set was extracted from 20 university students of differing ages, genders, and sleep hours. Provided with the same training set, the system detection accuracy for ANN, FIS, and ANFIS is 97.800%, 99.529%, and 99.604%, respectively. An identical testing set was also employed to ANN, FIS, and ANFIS, yielding 71.000%, 99.553%, and 99.556% detection accuracy. Hence, with this physiological data set and purposive classification, ANFIS provides the paramount accuracy.