International Journal on Advanced Science, Engineering and Information Technology
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Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow
Get PDFModeling the unprecedented traffic flow data generated by Intelligent Transportation Systems can boost the innovation-capacity of the transportation management systems to drive informed decision-making. Thus, this paper attempts to simulate traffic forecasting techniques that can be adopted in the Philippines to make fact-based decisions into accurate and effective traffic management schemes. In this research, a schematic framework is introduced organized into three stages (Preprocessing, Model Identification and Estimation, and Model Checking) sequentially arranged to comprehensively estimate the best-appropriate model to forecast traffic flow using ARIMA and GARCH models. The Model Identification and Estimation is the conditional stage in the framework that pre-determines if hybrid modeling is necessary based on the given datasets. Various accuracy metrics are also used to find the “best†model and select the optimal values for ARIMA and GARCH models. The proposed framework is simulated in R Programming using the vehicular traffic flow datasets at North Avenue, EDSA northbound, Manila, Philippines. The resulting models, consist of the best fit ARIMA (1,1,3) and GARCH (1,2), are combined as the hybrid model and compared using its prediction results. Based on the visual simulation data, the prediction accuracy result of the ARIMA model outperforms the combined ARIMA-GARCH model given the actual data. Conclusively, the simulation performance provides proof to suggest that the forecasting models are timely tools to predict future traffic flow and aid in making better traffic inventions and schemes
Ukrainian MOOC: Quantitative and Thematic Analysis of Online Courses
Get PDFThe article is devoted to the quantitative analysis of the courses hosted on Ukrainian mass open online courses (MOOC) platforms. It has been found that MOOC, being an effective mechanism of lifelong learning, provides the development of general and professional competencies; it supports principles of openness and accessibility. We have analyzed the online courses located on such well-known Ukrainian online platforms as Prometheus, EdEra, and the Open University of Maidan. It has been found that Ukrainian online platforms started to provide their services not so long ago; they have been working since 2014. Most of the online courses (55.3% of their total number) can be found on the Prometheus platform. We have also analyzed the dynamics of online course development over the years on each Ukrainian online platform. The average number of advanced online courses on each platform and on all the platforms, in general, has been revealed. The years 2017 and 2019 turned out to be the most productive in the development of courses. The analysis of thematic areas of the developed online courses has been carried out; the identical sections available on each considered platform have been revealed. The largest number of courses is presented among such sections as Education/EIE (External Independent Evaluation) and Law, proving their relevance among users. The largest number of thematic areas is presented on the Prometheus platform. It has been found that Ukrainian MOOCs have a significant difference in the number of courses compared to a number of well-known foreign online platforms
Analysis of the Effects of Fuel Type Selection on the Performance and Fuel Consumption of a Steam Power Plant
Get PDFFossil energy sources are used as fuel in the combustion process at thermal power plants. The reduced supply of fossil fuels often becomes a problem for electricity generators in the production process. This condition can affect the efficiency value of the Power Plant. To maintain the power plant efficiency, it is possible to regulate the fuel type in the combustion process. The difference in the heating value of the fuel can produce different values of combustion. The results of the combustion process can affect the value of cycle thermal efficiency and fuel consumption requirements. Cycle Tempo software is used in the thermodynamic cycle simulation process in power plants. This software is useful to make a model of a particular thermodynamic cycle or energy conversion systems such as a generator and cooling system. This program can analyze the mass and energy flow measures in the system, including thermodynamic properties, the composition of the processed gas, and the mass flow rate. In the simulation process, first, a cycle model is made based on existing piping data. After that, thermodynamic data such as temperature and pressure are input into each apparatus in the simulation. After making some adjustments, the simulation is run until the right results are obtained. Natural gas, Biosolar B30, and MFO (Marine Fuel Oil) fuel this study. Biosolar B30 is the result of mixing fuel between FAME (Fatty Acid Methyl Ester) and diesel. Biosolar B30 is made with a mixing ratio of 70% diesel fuel and 30% of FAME. FAME is oil from oil palm plants that has been processed to become a biofuel. Based on research results, Biosolar B30 produced the highest cycle thermal efficiency of 31.78%. The least fuel consumption is required by natural gas fuels, which is 277 million liters/year. The simulation results show that the lowest heating value of Boiler is generated by the variation of Biosolar B30 fuel, which results in high thermal efficiency. The amount of fuel needed during the combustion process using Biosolar B30 is increased because the heating value of Biosolar B30 is the lowest. The highest heating value of fuel is Natural Gas, requiring less fuel consumption than MFO and Biosolar B30
Sustainable Vacation Industry Mobility Criteria in Heritage Cultural Natural Conserved Areas
Get PDFTransportation and tourism cannot be separated. Both often lack the attention and cooperation of the stakeholders involved. Although each stakeholder has different goals and criteria, they need to work together to achieve the sustainability of tourism destinations. Nature-protected tourism objects and cultural heritage areas have a key role as conservation and tourism areas with specific criteria for preserving natural values, cultural heritage values, and other positive impacts of tourists visiting local communities. Transportation policies as a guideline of tourist mobility must ensure the sustainability of these values. The success of making transportation policy decisions must be supported by all actors involved with their respective goals and criteria. Therefore, multi-criteria are needed to measure transportation policies on tourist mobility in sustainable destinations. This paper examines the criteria for transportation policies that support mobility in sustainable nature tourism and cultural heritage protected areas using a multi-actor participatory method. The criterion rating is determined by the number of stakeholders involved and the score assigned by the stakeholders against the selected criteria. The results show that the highest to lowest criteria rank are as follows: comprehensive planning, transportation system Integration, safety, and security, visitor management, accessibility, various transport systems, supporting local entrepreneurs, supporting cultural events, low-impact transportation, operational efficiency, protection of cultural assets, visitors experience and transport equality
Determination of Effectiveness and Efficiency of Production Factors in Sweet Potato Farming in Lamongan, Indonesia
Get PDFFood crops have a strategic role in agricultural development, covering food security growth, opening up employment opportunities, and income sources in regional and national economies. This research aims to determine the relationship patterns of sweet potato farming production factors. This research utilized a quantitative approach with the survey method. The data were obtained through questionnaires, and the sampling method used a census of 348 respondent farmers in six sweet potato center villages. The Maximum Probability Estimation frontier 4.1 method was employed to calculate the technical efficiency of sweet potato farming. The results showed that the production factors significant at the trust level of 99 percent, 90 percent, and positive value for sweet potato production in Lamongan Regency, Indonesia, were land area production, Urea fertilizer, Phonska fertilizer, ZA fertilizer, and SP36 fertilizer. The technical efficiency attainment level of sweet potato farming was very high, indicating that sweet potato farming in the research was efficient, with an average technical efficiency of 0.90 percent. The achievement level of allocative efficiency on sweet potato farming was relatively low, implying that sweet potato farming was inefficient, with an average localized efficiency of 0.50 percent. The economic efficiency of sweet potato farming was very low, depicting inefficient sweet potato farming, with an average economic efficiency of 0.48 percent. Therefore, sweet potato farmers must optimize land use, ZA, Urea, SP36, and Phonska to produce sweet potatoes optimally to support food security
Lithofacies Classification Using Supervised and Semi-Supervised Machine Learning Approach
Get PDFThe machine learning approach can help Geoscientists do their work in well log analysis to developing the oil and gas field. Prediction categorical or numerical response variable using a set of predictor variables supervises and semi-supervises learning is an important goal of the machine learning approach in classifying lithofacies using well log data. Semi-supervised classification offers the possibility of exploring the structure of the data without entirely external knowledge or guidance in the form of target or class information, and semi-supervised is very rarely research in the field of lithofacies classification.  Well log data in gamma-ray, resistivity, neutrality, and density logs are collected and selected for data processing and transformation. The use of machine learning algorithms such as Naïve Bayes, SVM, and Decision Tree is to find the log pattern or pattern classifications of lithofacies in supervised and semi-supervised to create a model with conditions requiring the change of data and the corresponding requirements. All supervised machine learning algorithms have the best accuracy because algorithms provide useful predictive in classifications based on the target but not if there are no targets given or semi-supervised. This paper compares some of the famous classification algorithms of machine learning, such as Decision tree, SVM, and Naïve Bayes, on classifying lithofacies with supervised and semi-supervised learning. This research found that the semi-supervised learning of Naïve Bayes has performed well in classified lithofacies. In contrast, in supervised learning, Decision Tree and SVM are superior in accuracy and visualization approach based on expert’s interpretation
Music Source Separation Using ASPP Based on Coupled U-Net Model
Get PDFNoise has established itself as one of the factors that interfere with modern human life, and various noise canceling techniques have been studied to prevent noise. While the old era's noise-canceling technique focused on the physical soundproofing technique, multiple studies have been conducted on the active noise canceling technique that removes only the activated noise in the current era. Active noise canceling (ANC) or digital noise-canceling technology is based on the sound source separation method. This leads to sound source separation technology, which refers to the technology to separate individual sound signals from mixture sounds. Most of the source separation technologies focus on improving speech, not noise reduction. This technology makes it possible to obtain desired sound information more accurately and further improves noise-canceling technology by eliminating unwanted sound information. To provide deeper capability and more enhanced sound separation than the existing structure, we are focused on coupled U-Net model and Atrous spatial pyramid pooling technique (ASPP). This paper presents the music source separation method that combined Coupled U-Net structure with Atrous spatial pyramid pooling technique. To prove the proposed source separation method, we compared GNSDR, GSIR, and GSAR using MIR-1K, a data set that can evaluate the performance of the music source separation. Performance results show that the proposed source separation method overcame other methods' disadvantages and strengthened the feature map
Implementation of Ergonomic-Based Work Procedures Reducing Complaints of Postural Stress and Work Fatigue Resulting in Increased Employee Income and Company Profit
Get PDFIn general, the manufacturing industry relies more on machine-based processes. However, in certain activities, human labor is still needed due to the limitations of the machine function, as humans are faster than the machine. However, the activities using human muscle power are at risk of postural stress and early work fatigue. Work fatigue will affect work performance and productivity. The Standard Operating Procedures (SOP), which do not consider work attitude, will result in inconvenience at work and complaints on body parts and decreased performance. Besides, it also causes the inability to meet production targets so that employees cannot reach the maximum wage, and the company suffers losses. The redesign of the Standard Operating Procedures (SOP) is needed to make people as the center of all improvement activities. The design used in this study was treatment by subject design. The results showed that there were differences (p <0.05) between pre- and post-intervention results, Decreased postural stress complaints (moment of compressive force) in posture 1 (7.31%), posture 2 (23.09%), and posture 3 (4.74%). In comparison, the reduction of work fatigue was obtained from 81.26±10.85 to 70.87±3.68 (12.78%). After the implementation of ergonomic-based SOPs, there was an increase in employee income by 25.23% or (IDR 2,068,091/month) and an increase in company profits (IDR 51,702,286/month). The implementation of ergonomic-based SOPs was able to reduce postural stress, fatigue, and to increase employee income and company profits
Hydrolytic and Transglycolation Characteristics of Xanthomonas campestris and Bacillus megaterium in Several Substrates
Get PDFSome microbes can produce hydrolytic enzymes and have transglycosylation capacity at the same time, including Xanthomonas campestris and Bacillus megaterium. The enzyme characteristics of the microbes can be observed from their activity by using several types of substrates. This research aimed to characterize the hydrolytic and transglycosylated CGTase enzyme activity from Xanthomonas campestris and Bacillus megaterium on glucose, tapioca, and corn starch media at several concentrations. Stages of research included bacterial rejuvenation, growth on the substrate, and analysis (hydrolytic activity, carbohydrate concentration, and transglycosylation activity). The research design used was a Completely Randomised Design (CRD). The data from observations were then analyzed using Analysis of Variance (ANOVA) with the Duncan Multiple Range Test (DMRT) at a 5% level to determine the effect of treatment on all observational variables. Data analysis was carried out using the SPSS Statistics 17.0 program. The analysis results showed Xanthomonas campestris and Bacillus megaterium with tapioca and cornstarch substrates of 2%, 4%, and 6%, respectively, had hydrolytic activity. CGTase enzymes produced from Xanthomonas campestris and Bacillus megaterium with glucose, tapioca, and corn starch substrates 2%, 4%, and 6%, had intramolecular transglycosylation activity. The CGTase hydrolytic activity test's analysis showed that the CGTase hydrolytic test's significance value was significantly different between treatments with an incubation time of 24 to 120 hours. The substrate, a carbon source, greatly influences the rate of enzyme production produced by microorganisms
Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network
Get PDFResearch on biometric technology get much attention from researchers who interest in the recognition system. One of the biometric objects that will continue to be developed is the palmprint. The hand palm line has a unique characteristic in each person or may not be the same. The palmprint image is easy to capture because clearly visible, so it does not require a specific sensor. This paper presents the automatic extraction feature with Convolutional Neural Network (CNN) technique to get a unique characteristic of palmprint image and identify a person. CNN will get easier to classify the image database if it has many data. CNN belongs to Supervised Learning, which requires training data to create a knowledge base. In the dataset with little training data, the system must increase the training data using augmentation methods like zoom, shear, and rotate. Still, in the palmprint, that augmentation method can change the original character of the palmprint. Our proposed method is adding training data with an edge detection image from the original image. Edge detection used in our method is Canny and Sobel. The addition of Canny and Sobel edge detection for training data is the best combination scenario for palmprint recognition. The experiment results showed that palmprint recognition using Convolution Neural Network with Canny and Sobel edge detection for training data resulted in an accuracy rate of 96.5% for 200 classes, and the Equal Error Rate (ERR) value is 3.5%. This method has been able to recognize 193 palms of 200 people