Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)
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Low-Cost Automotive Capacitive Discharge Ignition (CDI) Coil for Low Frequency Ozone Generator
Get PDFThis paper presents an alternative solution for generating ozone using a low-cost automotive Capacitive Discharge Ignition (CDI) coil. High voltage ozone generating theory is implemented using a capacitive discharge circuit that uses ignition coil as its high voltage step-up transformer. A computer simulation has been performed to confirm the validity of the circuit function. By calculation and measurement, the coil has 196,71 voltage amplification factor. Furthermore, it has been implemented at a low frequency of about 10 - 40 Hz. Meanwhile, ozone output is measured using the colorimetric method. From a series of tests, that coil implementation has successfully generated a high voltage on ozone reactor tube at 31.47 kV voltages that essential for ozone production. Change of frequency will change the ozone concentration output linearly. The test was conducted using three different frequency: 10 Hz, 20 Hz, and 40 Hz. The result has shown that the highest ozone yield was 80 mg/hour.This paper presents an alternative solution for generating ozone using a low-cost automotive Capacitive Discharge Ignition (CDI) coil. High voltage ozone generating theory is implemented using a capacitive discharge circuit that uses ignition coil as its high voltage step-up transformer. A computer simulation has been performed to confirm the validity of the circuit function. By calculation and measurement, the coil has 196,71 voltage amplification factor. Furthermore, it has been implemented at a low frequency of about 10 - 40 Hz. Meanwhile, ozone output is measured using the colorimetric method. From a series of tests, that coil implementation has successfully generated a high voltage on ozone reactor tube at 31.47 kV voltages that essential for ozone production. Change of frequency will change the ozone concentration output linearly. The test was conducted using three different frequency: 10 Hz, 20 Hz, and 40 Hz. The result has shown that the highest ozone yield was 80 mg/hour
Estimation of Atmospheric Water Vapor from ANFIS Technique and Its Validation with GPS Data
Get PDFAdaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data.Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data
Propagation of Mobile Communication with Tree Obstacle used OFDM-QAM at 10 GHz
Get PDFThis research focused about mobile communication systems at line communication of road. Frequency communication was used 10 GHz. The tree was obstacle at every node of line communication. That communication was modeled with single diffraction. Single knife edge was used for that diffraction model. The communication transmission that used was Orthogonal Frequency Division Multiplexing. The modulation variation that used was consisted of 16 QAM and 64 QAM. Analysis that used was consisted of modulation variation, transmitter power variation, and coverage area variation. The result showed that SNR was decreased when transmitter power was increased, the value BER 64 QAM lower than BER 16 QAM, and percentage of coverage area that obtained was around 96%.This research focused about mobile communication systems at line communication of road. Frequency communication was used 10 GHz. The tree was obstacle at every node of line communication. That communication was modeled with single diffraction. Single knife edge was used for that diffraction model. The communication transmission that used was Orthogonal Frequency Division Multiplexing. The modulation variation that used was consisted of 16 QAM and 64 QAM. Analysis that used was consisted of modulation variation, transmitter power variation, and coverage area variation. The result showed that SNR was decreased when transmitter power was increased, the value BER 64 QAM lower than BER 16 QAM, and percentage of coverage area that obtained was around 96%
Design Of Bedside Monitor Based On Microcontroller
Get PDFA Bedside monitor is the equipment used to monitor patient condition through some parameters that need sustainable monitoring so that the patient condition is always monitored. This research is monitored by 5 parameters namely heart signal, heart rate, temperature, respiration and SPO2. This research applies quasi experimental design. The free variable is an ECG phantom or human, and the dependent variable is a bedside monitor. The research instruments are a calibration equipment of ECG signal, temperature, and respiration. The result of the heart signal lead 2 is not different from the standard and the result of the heart rate lead has uncertainty (probability) 0 for Lead 2; which is still under the tolerance number (0.5). The results of the temperature measurement of 5 samples with 5 measurements show that there are 3 samples which have standard deviation and 0 (zero) uncertainty, whereas 2 samples have 0.76 (higher than 0.5) uncertainty. This condition is influenced by the patient movements, so the sensor attached on the patient-body does not fit with the standard installation. The respiration measurement results have an accuracy of 98%, while the SPO2 results have a standard deviation and uncertainty below 5% after being compared with the standard calculations. Here are the details: standard deviation 0.894427; 0.547723; 0.44; Probability 0.4; 0.244949; 0.2 and 0.2. Overall, it can be concluded that The Design of Bedside Monitor Based on Microcontroller is feasible and the measurement result of heart signal Lead 2, heart rate, temperature, respiration, SPO2 can be presented on a PC.A Bedside monitor is the equipment used to monitor patient condition through some parameters that need sustainable monitoring so that the patient condition is always monitored. This research is monitored by 5 parameters namely heart signal, heart rate, temperature, respiration and SPO2. This research applies quasi experimental design. The free variable is an ECG phantom or human, and the dependent variable is a bedside monitor. The research instruments are a calibration equipment of ECG signal, temperature, and respiration. The result of the heart signal lead 2 is not different from the standard and the result of the heart rate lead has uncertainty (probability) 0 for Lead 2; which is still under the tolerance number (0.5). The results of the temperature measurement of 5 samples with 5 measurements show that there are 3 samples which have standard deviation and 0 (zero) uncertainty, whereas 2 samples have 0.76 (higher than 0.5) uncertainty. This condition is influenced by the patient movements, so the sensor attached on the patient-body does not fit with the standard installation. The respiration measurement results have an accuracy of 98%, while the SPO2 results have a standard deviation and uncertainty below 5% after being compared with the standard calculations. Here are the details: standard deviation 0.894427; 0.547723; 0.44; Probability 0.4; 0.244949; 0.2 and 0.2. Overall, it can be concluded that The Design of Bedside Monitor Based on Microcontroller is feasible and the measurement result of heart signal Lead 2, heart rate, temperature, respiration, SPO2 can be presented on a PC
Real-Time Object Detection For Wayang Punakawan Identification Using Deep Learning
Get PDFIndonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately.Indonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately
Web of Thing Application for Monitoring Precision Agriculture Using Wireless Sensor Network
Get PDFWireless Sensor Network (WSN) is a technology which can help humans solve problems in daily life for monitoring the environment. This can be done to help farmers in monitoring and making decisions for watering plants. In this study, temperature, humidity and soil moisture sensors were used to help farmers monitor web-based precision agriculture, and the system which be built could make a decision to automatically water plants based on soil conditions. The results of measuring precision agriculture from the sensor node will be sent to the gateway using Zigbee 802.15.4. The data will be stored in the MySQL database provided by the gateway. Then it will be synchronized to the cloud using IoT technology, so users can access it in real time by using web-based application. From the system which is developed, it really helps farmers to complete their work and make innovation in the digital era.Wireless Sensor Network (WSN) is a technology which can help humans solve problems in daily life for monitoring the environment. This can be done to help farmers in monitoring and making decisions for watering plants. In this study, temperature, humidity and soil moisture sensors were used to help farmers monitor web-based precision agriculture, and the system which be built could make a decision to automatically water plants based on soil conditions. The results of measuring precision agriculture from the sensor node will be sent to the gateway using Zigbee 802.15.4. The data will be stored in the MySQL database provided by the gateway. Then it will be synchronized to the cloud using IoT technology, so users can access it in real time by using web-based application. From the system which is developed, it really helps farmers to complete their work and make innovation in the digital era
Optimal Control Design of Active Suspension System Based on Quarter Car Model
Get PDFThe optimal control design of the ground-vehicle active suspension system is presented. The active suspension system is to improve the vehicle ride comfort by isolating vibrations induced by the road profile and vehicle velocity. The vehicle suspension system is approached by a quarter car model. Dynamic equations of the system are derived by applying Newton’s second law. The control law of the active suspension system is designed using linear quadratic regulator (LQR) method. Performance evaluation is done by benchmarking the active suspension system to a passive suspension system. Both suspension systems are simulated in computer. The simulation results show that the active suspension system significantly improves the vehicle ride comfort of the passive suspension system by reducing 50.37% RMS of vertical displacement, 45.29% RMS of vertical velocity, and 1.77% RMS of vertical acceleration.The optimal control design of the ground-vehicle active suspension system is presented. The active suspension system is to improve the vehicle ride comfort by isolating vibrations induced by the road profile and vehicle velocity. The vehicle suspension system is approached by a quarter car model. Dynamic equations of the system are derived by applying Newton’s second law. The control law of the active suspension system is designed using linear quadratic regulator (LQR) method. Performance evaluation is done by benchmarking the active suspension system to a passive suspension system. Both suspension systems are simulated in computer. The simulation results show that the active suspension system significantly improves the vehicle ride comfort of the passive suspension system by reducing 50.37% RMS of vertical displacement, 45.29% RMS of vertical velocity, and 1.77% RMS of vertical acceleration
A Coverage Prediction Technique for Indoor Wireless Campus Network
Get PDFThe placement of an Access Point (AP) is an important key to determine the spread of the signal. To get the optimal spread of signals, a network designer is required to understand how much coverage an AP can generate. A prediction is given to describe the coverage area produced based on AP placement for the wireless campus network, using a coordinate map modeling based on the real size for the indoor environment. The theoretical approach is used to determine the coverage area of an AP device by testing the function of the distance between the AP and the user. The results show that the signal generated by an AP will cover the entire area that is still on the LOS propagation path. The coverage area generated through AP placement in this case study reached 77.5%. The maximum distance between the AP and the user so that it is within the coverage area is 13.851m. There are still areas that are not covered by the AP, especially for the NLOS propagation path because of the obstruction around the AP.The placement of an Access Point (AP) is an important key to determine the spread of the signal. To get the optimal spread of signals, a network designer is required to understand how much coverage an AP can generate. A prediction is given to describe the coverage area produced based on AP placement for the wireless campus network, using a coordinate map modeling based on the real size for the indoor environment. The theoretical approach is used to determine the coverage area of an AP device by testing the function of the distance between the AP and the user. The results show that the signal generated by an AP will cover the entire area that is still on the LOS propagation path. The coverage area generated through AP placement in this case study reached 77.5%. The maximum distance between the AP and the user so that it is within the coverage area is 13.851m. There are still areas that are not covered by the AP, especially for the NLOS propagation path because of the obstruction around the AP
The Performance Improvement of the Low-Cost Ultrasonic Range Finder (HC-SR04) Using Newton's Polynomial Interpolation Algorithm
Get PDFThe ultrasonic range finder sensors are widely used sensor in many applications such as computer applications, general purpose applications, medical applications, automotive applications and industrial grade applications. The ultrasonic range finder sensor has many advantages. The advantages are easy to use, fast in measuring process, non-contact measurement and suitable for air and underwater environment. However, the ultrasonic range finder has deviation especially for low-cost sensor. It affects the accuracy level of the measurement result that performed by its sensor directly. The HC-SR04 categorized as a low-cost ultrasonic range finder sensor. This sensor has significant error level. The improvement of the accuracy level of this low-cost ultrasonic sensor is expected to this research. The Newton's polynomial interpolation algorithm has been used in this research to reduce the error during the measurement process. The implementation of Newton's polynomial interpolation has succeeded to improve the sensor accuracy. The MSE level of 29,96 is obtained without the Newton's Polynomial Interpolation implementation. The implementation of the Newton's Polynomial Interpolation algorithm has succeeded to increase the accuracy level of the sensor by 55,54%. It has been proofed by the decrease of MSE level by 13,32.The ultrasonic range finder sensors are widely used sensor in many applications such as computer applications, general purpose applications, medical applications, automotive applications and industrial grade applications. The ultrasonic range finder sensor has many advantages. The advantages are easy to use, fast in measuring process, non-contact measurement and suitable for air and underwater environment. However, the ultrasonic range finder has deviation especially for low-cost sensor. It affects the accuracy level of the measurement result that performed by its sensor directly. The HC-SR04 categorized as a low-cost ultrasonic range finder sensor. This sensor has significant error level. The improvement of the accuracy level of this low-cost ultrasonic sensor is expected to this research. The Newton's polynomial interpolation algorithm has been used in this research to reduce the error during the measurement process. The implementation of Newton's polynomial interpolation has succeeded to improve the sensor accuracy. The MSE level of 29,96 is obtained without the Newton's Polynomial Interpolation implementation. The implementation of the Newton's Polynomial Interpolation algorithm has succeeded to increase the accuracy level of the sensor by 55,54%. It has been proofed by the decrease of MSE level by 13,32
Maize Leaf Disease Image Classification Using Bag of Features
Get PDFImage classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale, and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%.Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale, and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%