Pre-Symptom Detector of Root Disease Palm Oil (Ganoderma) Trunk Based on LoRa and IoT

The stem rot disease caused by the Ganoderma boninense is a type of disease that is deadly to oil palm plants and can cause a significant reduction in oil palm productivity. Difficulty in detecting disease infected oil palm plants is cause of the high risk of plan death due to the condition and risk of oil palm plants being affected by disease as early as possible. The system used is Long Range (LoRa) technology which utilizes radio frequencies as signal transmission between transmitter and receiver devices. The transmitter device is equipped with TGS 2611, MQ-138, MS1100 and TGS822 sensors as a tool for detecting ganoderma disease and is also equipped with a GPS sensor which functions to map trees affected by the disease. Meanwhile, the receiver as the recipient of the data that has been sent by the transmitter via LoRa will be forwarded to BIynk Apps via the internet network, thus forming an IoT (Internet of Things) system. This technology helps monitor oil palm plantations more efficiently because it can be monitored in real time on a smartphone application. The research results show that the four sensors can detect levels of volatile organic compounds (VOC) from ganoderma fungi with three classification; healthy, moderate and sick

The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%

Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions

Nutrition Temperature and TDS Control System with Fuzzy Logic on Pak Choy Hydroponics (Brassica rapa subsp. chinensis)

Hydroponics is a method of cultivation that does not use soil as a medium, allowing it to be applied in limited spaces such as urban households. One of the vegetable plants that can be grown using hydroponics is pak choy (Brassica rapa subsp. chinensis). To produce healthy pak choy plants that can efficiently absorb nutrients in a hydroponic system, several factors need to be considered, such as the level of Total Dissolved Solids (TDS) in the nutrient solution, nutrient solution temperature, and air humidity in the hydroponic environment. The ideal nutrient solution temperature for hydroponic plants falls within the range of 25-27C. In this system, a monitoring and control system will be designed to optimize the growth of pak choy plants in a Deep Flow Technique (DFT) hydroponic system. In this system, the nutrient solution temperature will be controlled with a set point of 25C using an on/off control for a peltier device. To maintain the TDS level at a set point of 1200 ppm in the nutrient solution, fuzzy logic control will be employed, generating timer-based control signals for the nutrient pump A, nutrient pump B, and water pump. The monitoring system will be displayed on an Internet of Things (IoT) dashboard platform, such as ThingSpeak

The Development of Javanese Glossary Website as a Form of Language Maintenance and Revitalization

As a vital component of cultural identity, language is under pressure as a result of globalization. This article discusses the creation of a website that provides a dictionary of Javanese phrases to help preserve and revitalize the language. In this study, we collect, categorize, and display Javanese words on electronic resources. In addition, the system usability scale (SUS) was used to conduct usability tests on the investigated websites to determine how user-friendly they actually were. Gathering terms from multiple sources, categorizing them, and developing a user-friendly interface with a search bar are all steps in the process of making a website. Users from all walks of life fill out the SUS questionnaire as part of the usability testing process. The test results reveal how well the website satisfies its users' requirements. Creating a database of Javanese words online and putting it through the SUS test is a great example of how technology can be used to help preserve a language and its heritage. It is believed that by taking this step, more people will become familiar with the Javanese language and become invested in its continued existence in the modern world. The usability testing results demonstrate that the development strategy and interface design effectively fostered a positive user experience. High scores on the SUS questionnaire, with an average rating of 80.25, indicate that users find the website satisfactory and user-friendly

Augmentation of Additional Arabic Dataset for Jawi Writing and Classification Using Deep Learning

This research aims to create an additional dataset containing Arabic characters for writing Jawi script and to train classification models using deep learning architectures such as InceptionV3 and ResNet34. The initial stage of the study involves digital image processing to obtain the additional Arabic character dataset from several sources, including HMBD, AHAWP, and HUCD, encompassing various connected and disconnected forms of Jawi script. Image processing includes steps such as preprocessing to enhance image quality, segmentation to separate Arabic characters from the background, and augmentation to increase dataset variability. Once the dataset is formed, we train the models using appropriate training data for each InceptionV3 and ResNet34 architecture. The classification evaluation results indicate that the model with ResNet34 architecture achieved the best performance with an accuracy of 96%. This model successfully recognizes Jawi script accurately and consistently, even for classes with similar shapes. The main contribution of this research is the availability of the additional Arabic character dataset that can be utilized for Jawi script recognition and performance assessment of various deep learning models. The study also emphasizes the importance of selecting the appropriate architecture for specific character recognition tasks. The research findings affirm that the model with ResNet34 architecture has excellent capability in recognizing the additional Arabic characters for writing Jawi. The results of this research have the potential to support further developments in Jawi character recognition applications and provide valuable insights for researchers in the field of character recognition sourced from Arabic characters. Dataset augmentation results can be accessed at https://singkat.usk.ac.id/g/En0skCKGA

Impact of Segmentation and Popularity-based Cache Replacement Policies on Named Data Networking

The data distribution mechanism of internet protocol (IP) technology is inefficient because it necessitates the user to await a response from the server. Named data networking (NDN) is a cutting-edge technology being assessed for enhancing IP networks, primarily because it incorporates a data packet caching technique on every router. However, the effectiveness of this approach is highly dependent on the router's content capacity, thus requiring the use data replacement mechanism when the router capacity is full. The least recently used (LRU) method is employed for cache replacement policy; yet, it is considered ineffective as it neglects the content's popularity. The LRU algorithm replaces the infrequently requested data, leading to inefficient caching of popular data when multiple users constantly request it. To address this problem, we propose a segmented LRU (SLRU) replacement strategy that considers content popularity. The SLRU will evaluate both popular content and content that has previously been popular in two segment categories, namely the probationary and protected segments. Icarus simulator was used to evaluate multiple comprehensive scenarios. Our experimental results show that the SLRU obtains a better cache hit ratio (CHR) and able to minimize latency and link load compared to existing cache replacement policies such as First In, First Out (FIFO), LRU, and Climb

Real-Time Detection of Power Quality Disturbance Using Fast Fourier Transform and Adaptive Neuro-Fuzzy Inference System

Power quality disturbances cause equipment damage or financial losses. Therefore, the electric power system needs to identify and distinguish any power quality disturbances to reduce problems. This paper proposes hybrid methods combining FFT and ANFIS algorithm for detection of power quality disturbances. There are 11 types of power quality disturbances that can be detected, such as sag, swell, undervoltage, overvoltage, voltage flicker, voltage harmonic, sag + harmonic, swell + harmonic, undervoltage + harmonic, overvoltage + harmonic, and flicker + harmonic. The parameters used to detect disturbances are Vrms, Duration, THDv (Total Harmonic Distortion voltage), and Fluctuation-Count. The detection process starts by sensing voltage and calculating all the parameters, where THDv was obtained by Fast Fourier Transform. All the parameters such as Vrms, Duration, THDv, and Fluctuation-Count are processed by Adaptive Neuro-Fuzzy Inference System, and the result is the type of disturbance. Matlab simulations show that the suggested method performs outstandingly to identify 11 type of Power Quality Disturbances with 99.3% accuracy

IoT-based Monitoring System for Energy Consumption Costs from Battery Supply

oai:jurnal.usk.ac.id:article/35237A battery must be monitored in real-time to ensure it meets its designed lifetime. Additionally, energy costs from the battery supply must be calculated and controlled to enable solar power plant entrepreneurs to profit practically. This project aims to develop an IoT-based monitoring and controlling system for battery conditions, especially energy consumption costs from battery supply. This system uses an ESP32 microcontroller, INA219 sensor, single channel 5 VDC optocoupler relay, and OLED display. The ESP32 processes the current and voltage from the INA219 sensor and then displays on the OLED display. The parameters displayed include consumed energy costs, current, voltage, power, consumed energy, and used battery capacity. Data is also sent to the Blynk website using IoT, allowing these parameters to be monitored in real time. Based on test results, the average error in calculating energy costs is 0.046%, and other measured or calculated parameters are below 1%. This system can also turn the power flow to the load on and off using the Blynk platform. It can be concluded that the system works well, enabling IoT-based monitoring and control of battery parameters

Dual-mode Antenna Tracking System for Rocket Launch Applications

Rocket launches are complex events that require tracking antennas to maintain a communication link. This study introduces a hybrid tracking strategy that combines manual and program modes by utilizing a predetermined trajectory of the rocket. Automatic switching between tracking modes ensures ongoing monitoring, even during unexpected trajectory changes with the monopulse approach. The dual parabolic antenna arrangement enables this switching. The system estimates the monopulse ratio from the signal strength of each antenna, allowing automatic program tracking to shift to manual mode when reception concerns arise. Performance evaluations included manual, programmable, and dual-mode tests. The system responded to human input and automatically aligned the antenna with slight elevation errors during the initial phase. Adjusting the initial elevation reduced the error. The mode transition was examined by measuring the antenna radiation patterns and monopulse ratio. The systems performance was evaluated in rocket launches, with the rocket trajectory input into the graphical user interface. The antenna exhibited an azimuthal movement of up to 10 , and the ratio fluctuation values remained within the antennas field of view. After 8.8 seconds, the mode switched from program to manual, indicating that the functioning of the systems functioning was stable