EMITTER - International Journal of Engineering Technology
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Optimized Graph Search Algorithms for Exploration with Mobile Robot
Full text linkGraph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS) and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known
Series Arc Fault Breaker in Low Voltage Using Microcontroller Based on Fast Fourier Transform
Full text linkSeries Arc Fault is one of the disturbances of arcing jump is caused by gas ionization between two ends of damaged conductors or broken wire forming a gap in the insulator. Series arc fault is the primary driver of electrical fire. However, lack of knowledge of the disturbance of series arc fault causes the problem of electrical fire not be mitigated. Magnitude current is not capable to detect of series arc fault. Therefore, this paper proposes fast fourier transform (FFT) to detect series AC arc fault in low voltage using microcontroller ARM STM32F7NGH in real time. A cheap and high speed of microcontroller ARM STM32F7NGH can be used for FFT computation to transform signal in time domain to frequency domain. Moreover, in this paper, protection of series AC arc fault is proposed in the real time mode. In this experimental process, some various experiments are tested to evaluate the reliability of FFT and protection with various load starts from 1 A, 2 A, 3 A, 4 A in resistive load. The result of this experiment shows that series AC arc fault protection with STM32F7 microcontroller and FFT algorithm can be utilized to ensure series AC arc fault properly
Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method
Full text linkLearning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model
Addressing Communication, Coordination and Cultural Issues in Global Software Development Projects
Full text linkThe field of Global Software Development has been an active area of research for the last two decades due to its enormous benefits such as lower labor cost, faster development and easy access to the skilled labor pool. Apart from these benefits, it faces some challenges like communication, coordination, trust and configuration management etc. These challenges arise primarily due to physical, cultural and time zone differences. The empirical studies highlight that the existing Global Software Development solutions do not fully meet the user needs as there are still several gaps in these solutions. Therefore, to fulfill these gaps, there is a need to develop novel frameworks that address outstanding issues. In this paper, we have attempted to address the aforesaid GSD challenges. The practitioners can benefit from our proposed framework during the execution of GSD projects. The proposed framework mainly focuses on the root causes of the two principal challenges namely the communication and cultural differences. We believe that if the team members of a software project can communicate effectively and show considerations for others by imparting due reverence to the cultural norms, then the other residual issues can easily be reduced and minimized
Mastitis Detection System in Dairy Cow Milk based on Fuzzy Inference System using Electrical Conductivity and Power of Hydrogen Sensor Value
Full text linkThis study build a system for screening method to detect mastitis in dairy cow milk using Electrical Conductivity (EC) and Power of Hydrogen (pH) sensor. The value of EC and pH sensor is analyze using fuzzy logic to clarify the truth value between it. Mastitis in cows can cause loss and decrease milk production and quality in the dairy farmer industry. Currently, detecting mastitis in cow’s milk still done manually by looking at the color change of the milk and analyzing the cow behavior. This paper has designed a mastitis detection system using the Mamdani type fuzzy inference system and the final result will be displayed on an Android-based smartphone. From the test result, it was found that the system has 79.2% detection accuracy value. This system is suitable for alternative screening method that used to detect mastitis in dairy cow milk
Wavelet Transform and Convolutional Neural Network Based Techniques in Combating Sudden Cardiac Death
Full text linkSudden cardiac death (SCD) is a global threat that demands our attention and research. Statistics show that 50% of cardiac deaths are sudden cardiac death. Therefore, early cardiac arrhythmia detection may lead to timely and proper treatment, saving lives. We proposed a less complex, fast, and more efficient algorithm that quickly and accurately detects heart abnormalities. Firstly, we carefully examined 23 ECG signals of the patients who died from SCD to detect their arrhythmias. Then, we trained a deep learning model to auto-detect and distinguish the most lethal arrhythmias in SCD: Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), from Normal Sinus Rhythm (NSR). Our work combined two techniques: Wavelet Transform (WT) and pre-trained Convolutional Neural Network (CNN). WT was used to convert an ECG signal into scalogram and CNN for features extraction and arrhythmias classification. When examined in the MIT-BIH Normal Sinus Rhythm, MIT-BIH Malignant Ventricular Ectopy, and Creighton University Ventricular Tachyarrhythmia databases, the proposed methodology obtained an accuracy of 98.7% and an F-score of 0.9867, despite being less expensive and simple to execute
Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image
Full text linkCoffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983
Hungarian Mechanism based Sectored FFR for Irregular Geometry Multicellular Networks
Full text linkThe growing demands for mobile broadband application services along with the scarcity of the spectrum have triggered the dense utilization of frequency resources in cellular networks. The capacity demands are coped accordingly, however at the detriment of added inter-cell interference (ICI). Fractional Frequency Reuse (FFR) is an effective ICI mitigation approach when adopted in realistic irregular geometry cellular networks. However, in the literature optimized spectrum resources for the individual users are not considered. In this paper Hungarian Mechanism based Sectored Fractional Frequency Reuse (HMS-FFR) scheme is proposed, where the sub-carriers present in the dynamically partitioned spectrum are optimally allocated to each user. Simulation results revealed that the proposed HMS-FFR scheme enhances the system performance in terms of achievable throughput, average sum rate, and achievable throughput with respect to load while considering full traffic
Selection Method of Modulation Index and Frequency ratio for Getting the SPWM Minimum Harmonic of Single Phase Inverter
Full text linkHarmonic content is an important parameter in relation to the power generated by inverter. In power conversion technology of inverter, sinusoidal pulse width modulation (SPWM) is the most popular used by many researchers. The advantages of SPWM inverter operation as a conversion technique compared to other inverter types can be seen from the low harmonic distortion in the output voltage of inverter. Therefore, the SPWM signal generation process becomes a determining factor for the performance of the overall system. This paper present the method for selecting the modulation index (ma) and frequency ratio (mf) using Cubic Spline Interpolation to get minimum harmonic of SPWM inverter that generated. Both parameters controlled with varied values digitally using microcontroller to generate SPWM, then the output of inverter with and without LC filter was investigated. The results show that the use of Cubic Spline Interpolation method in the selection of ma and mf precisely managed to produce SPWM inverter with minimum harmonic content. At the inverter output, the use of LC filter is not only useful for converting SPWM signals to sinusoidal waveforms but can also reduce harmonic content significantly less than 3 %
HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition
Full text linkHuman Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data