Jurnal Rekayasa Elektrika
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The Performance Comparison of Multiloop PID Controller on NCS Temperature Plant Based on UDP and TCP
Full text linkHardware implementations of networked control systems (NCS) are still rarely found in various research publications so that technical issues, such as how to measure random delay and substitute it into the control equation, still need to be studied further. This study is therefore aimed to compare the performance of NCS based on the used protocol in the ethernet network, i.e. user datagram protocol (UDP) or transmission control protocol (TCP), by applying it in room temperature plant. In this study, the controlled plant is influenced by the fan speed, heating, and window position plant. The heating plant is employed as the main control, while the others are set as sub-plants. The three plants use proportionalintegralderivative (PID) controller where they are regulated by fuzzy logic as the master control unit (MCU). The MCU and three PID controllers are located in the master terminal unit (MTU) while the actuators and sensors are located in the three different remote terminal units (RTU). The verification experiment shows that there is no overshoot on TCP-based NCS, while UDP has 0.725%. For the risetime, the response on UDP is faster than TCP (110.22 as compared to 138.18 s). The same thing also happened to the settling time, where the time with UDP was 101.86 s and 128.44 s for TCP
Impact of Segmentation and Popularity-based Cache Replacement Policies on Named Data Networking
Full text linkThe 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
Full text linkPower 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
Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning
Full text linkThis 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
Optimizing Palm Oil Plantation Productivity Using Offline Blockchain and Drone Rover Solutions
Full text linkThe increasing demand for sustainable palm oil production challenges plantations to maintain efficient management and data transparency, particularly in remote areas with limited internet access. This study aims to develop and implement an offline blockchain system integrated with drone rover devices to support data collection and decision-making without internet connectivity. Drone rovers equipped with sensors and cameras are deployed to collect comprehensive data on plant health, pest detection, and environmental conditions across the plantation. The offline blockchain securely stores this data, ensuring integrity and traceability. Additionally, an AI system is utilized to process this data in real-time, enhancing the precision of monitoring plant health and fruit ripeness. Results indicate that this approach optimizes resource management, improves operational transparency, and enables accurate decision-making in palm oil plantation management. By combining offline blockchain technology with AI-driven analysis, this study provides a scalable and effective solution for sustainable agriculture in connectivity-challenged environments
Streamlining Deep Learning Network for Real-time Sea Turtle Detection
Full text linkMonitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. The need for increased model speed in the inference stage has led to many lightweight vision-based detectors. This work proposes a novel turtle detection to localize multiple sea turtles using a deep learning method. A lightweight primary extractor is applied to distinguish crucial features without producing a huge computational. An excited group attention is offered as an enhancement module that can capture essential turtle components in multi-level convolutional patches. A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. High-efficiency benefits models that can be implemented on low-end devices in terms of real-time data processing speed
Pre-Symptom Detector of Root Disease Palm Oil (Ganoderma) Trunk Based on LoRa and IoT
Full text linkThe 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
IoT-based Monitoring System for Energy Consumption Costs from Battery Supply
Full text linkA 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
Improved Histogram of Oriented Gradient (HOG) Feature Extraction for Facial Expressions Classification
Full text linkFacial expression classification system is one of the implementations of machine learning (ML) that takes facial expression datasets, undergoes training, and then utilizes the trained results to recognize facial expressions in new facial images. The recognized facial expressions include anger, contempt, disgust, fear, happy, sadness, and surprise expressions. The method employed for facial feature extraction utilizes histogram-oriented gradient (HOG). This study proposes an enhancement method for HOG feature extraction by reducing the feature dimension into multiple sub-features based on gradient orientation intervals, referred to as HOG channel (HOG-C). Classifier testing techniques are divided into two methods for comparisonsupport vector machines (SVM) with HOG features and SVM with HOG-C features. The testing results demonstrate that SVM with HOG achieves an accuracy of 99.9% with an average training time of 18.03 minutes, while SVM with HOG-C attains a 100% accuracy with an average training time of 18.09 minutes. The testing outcomes reveal that the implementation of SVM with HOG-C successfully enhances accuracy for facial expression classification
The Development of Javanese Glossary Website as a Form of Language Maintenance and Revitalization
Full text linkAs 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