 Subjects -> ELECTRONICS (Total: 207 journals)
| A B C D E F G H I J K L M N O P Q R S T U V W X Y Z | | The end of the list has been reached or no journals were found for your choice. |
|
|
- Spatial and Spectral EEG Signal Analysis with Case Study of Slogans on
Consumer’s Behaviour
Authors: Hilman Fauzi Tresna; Daulika Pratiwi, Maya Ariyanti, Adryan Fauzi
Abstract: Neuromarketing utilizes neuroscientific techniques to investigate consumer behavior, providing valuable insights beyond traditional research methods such as questionnaires and interviews which may not provide a complete understanding of consumer decision-making processes.Electroencephalography (EEG) has emerged as a promising tool for analyzing consumer responses to marketing stimuli. Nevertheless, the neural processing of slogans and their impact on short-term memory recall using EEG signals remains understudied. This research aims to bridge this gap by examining the neural activity associated with the recall of slogans using EEG analysis. By employing a spatial selection and spectral processingmethod, which involves Butterworth BPF filtering and L2-norm normalization to identify optimal channel combinations, active brain areas involved in slogan processing can be identified. Results reveal prominent activation in the frontal and occipital regions, particularly the F4 channel, indicating active recall and visual processing in individuals who correctly respond to slogans. These findings underscore the significance of slogans as visual marketing stimuli and offer insights for effective branding strategies. Leveraging EEG signals and understanding short-term memory processes enables marketers to optimize the impact of slogans on consumer engagement and brand recognition.
PubDate: Thu, 10 Aug 2023 00:00:00 +000
- Fish swarmed Fuzzy Time Series for Photovoltaic’s Forecasting in
Microgrid
Authors: Fitri; Aripriharta, Yuni Rahmawati
Abstract: Forecasting irradiation and temperature is important for designing photovoltaic systems because these two factors have a significant impact on system performance. Irradiation refers to the amount of solar radiation that reaches the earth's surface, and directly affects the amount of energy that can be generated by a photovoltaic system. Therefore, accurate irradiation forecasting is essential for estimating the amount of energy a photovoltaic system can produce, and can assist in determining the appropriate system size, configuration, and orientation to maximize energy output. Temperature also plays an important role in the performance of a photovoltaic system. With increasing temperature, the efficiency of the solar cell decreases, which means that the energy output of the system also decreases. Therefore, accurate temperature forecasts are essential for estimating system energy output, selecting suitable materials, and designing effective cooling systems to prevent overheating. In summary, forecasting irradiation and temperature is important for designing photovoltaic systems as it helps in determining suitable system size, configuration, orientation, material selection, and cooling system, which ultimately results in higher energy output and better system performance. In recent decades, many forecasting models have been built on the idea of fuzzy time series. There are several forecasting models proposed by integrating fuzzy time series with heuristic or evolutionary algorithms such as genetic algorithms, but the results are not satisfactory. To improve forecasting accuracy, a new hybrid forecasting model combines fish swarm optimization algorithm with fuzzy time series. The results of irradiance prediction / forecasting with the smallest error are using the type of Fuzzy Time Series prediction model optimized with FSOA with RMSE is 0.83832.
PubDate: Thu, 10 Aug 2023 00:00:00 +000
- Design of MPPT for Buck-boost Converter based on GA to Optimize Solar
Power Generation
Authors: Lailis Syafaah; Amrul Faruq, Basri Noor Cahyadi, Khusnul Hidayat, Novendra Setyawan, Merinda Lestandy, Zulfatman
Abstract: Maximizing power in the Solar Power Plant system was something that needs attention. Indonesia was a tropical country that has two seasons, where the changing weather or cloud movements are always erratic, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) is used. Currently the switching system in MPPT is still not optimal with a system efficiency of around 90%. In this study, the installation of MPPT was carried out which aims to maximize power in the solar photovoltaic (PV) system due to fluctuations or instability of sunlight at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this research, modeling of the solar PV system was made using MATLAB software, where the design of the solar PV system consists of a PV module with capacity 240W, DC to DC converter, battery and MPPT. The MPPT based on Genetic Algorithm (GA) has been tested and compared with MPPT based on Particle Swarm Optimization (PSO) and conventional MPPT, where MPPT based on GA works well in finding the maximum power point in the solar photovoltaic system. it was found that MPPT GA produces a maximum power point close to PV power with an efficiency of 92%, while MPPT PSO has an efficiency of 85% and for conventional MPPT it was 79%. In selecting the method for designing MPPT, a method is needed that has a wide range of sample data. This is due to the fluctuating characteristics of the irradiation value received by solar PV.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Design and Performance of Solar-Powered Surveillance Robot for Agriculture
Application
Authors: Tresna Dewi; Ronald Sukwadi, Marsellinus Bachtiar Wahju
Abstract: Agriculture can benefit from robotics technology to overcome the drawback of limited human labor working in this sector. One of the robot applications in agriculture is a surveillance robot to monitor the condition. This paper describes a surveillance robot that is powered by a capacitor bank charged by a mini solar panel. The solar-powered robot is well-suited for deployment in open agricultural areas in Indonesia, where the irradiance is high. This potential is excellent for generating electricity and charging electric vehicles, such as those used in agriculture. The surveillance robot developed and tested in this study has been successfully deployed in an agriculture-like setting with all-terrain contours and the capacity to avoid obstacles. During high irradiance sunny weather, the shortest charging time was 2 hours. Hence, the proposed technology is effective for designing a surveillance robot for agricultural applications.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Neural Network-Based Image Processing for Tomato Harvesting Robot
Authors: Yurni Oktarina; Ronald Sukwadi, Marsellinus Bachtiar Wahju
Abstract: Agriculture is one of the areas that can benefit from robotics technology, as it faces issues such as a shortage of human labor and access to less arid terrain. Harvesting is an important step in agriculture since workers are required to work around the clock. The red ripe tomatoes should go to the nearest market, while the greenest should go to the farthest market. Harvesting robots can benefit from Neural Network-based image processing to ensure robust detection. The vision system should assist the mobility system in moving precisely and at the appropriate speed. The design and implementation of a harvesting robot are described in this study. The efficiency of the proposed strategy is tested by picking red-ripened tomatoes while leaving the yellowish ones out of the experimental test bed. The experiment results demonstrate that the effectiveness of the proposed method in harvesting the right tomatoes is 80%.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Threat Construction for Dynamic Enemy Status in a Platformer Game using
Classical Genetic Algorithm
Authors: Ardiawan Bagus Harisa; Setiawan Nugroho, Liya Umaroh, Yani Parti Astuti
Abstract: Digital game genres such as Action-Platformer is widely popular among buyers on a platform like Steam. The non-playable character enemies in the game are important in action games. Unfortunately, they usually have static attributes like health point, damage, and enemy movement. Using procedural content generation with a classical genetic algorithm, we drive the threat value of a platform to construct the enemy status resulting in more dynamic enemies. We use the threat value as an input parameter calculated from the enemies’ stats in every platform, such as total damage that the enemy might produce, the player’s health point, and the enemy’s movement speed. We conclude that using a classical genetic algorithm may produce dynamic enemy status through desired threat or danger set by the game designer as an input parameter. Moreover, the game designer may limit the generation with constraints.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Enhanced DV-Hop Algorithm for Efficiency Energy and Network Quality in
Wireless Sensor Networks
Authors: Nirwana Haidar Hari; Mokh. Sholihul Hadi, Sujito
Abstract: Wireless Sensor Networks (WSN) are wireless networks with multiple sensor nodes covering a relatively large area. One of the weaknesses of WSNs is the relatively high energy consumption, which affects the network quality of service. Although WSN network routing using the DV-Hop algorithm is widely used because of its simplicity, improvements need to be made to improve energy efficiency, which impacts network lifetime. This article proposes a method to enhance energy efficiency by comparing the original DV-Hop algorithm with the enhanced DV-Hop algorithm. There are three approaches to enhancing the DV-Hop algorithm. Firstly, the CH node selection is based on the distance to the Base Station so that the selected CH node does not have a far distance from the base station. Secondly, the CH node selection must have a minimum number of sensor nodes as neighbors. Lastly, each selected CH node calculates the minimum distance to the previously selected CH node to ensure that the selected CH node is not in a nearby position. The proposed approach obtains better data packet transfer rate, energy efficiency, and network lifetime results using Matlab software simulation that compares the enhanced DV-Hop algorithm with the original DV-Hop algorithm and three other routing algorithms.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Evaluation of Stratified K-Fold Cross Validation for Predicting Bug
Severity in Game Review Classification
Authors: Mustika Kurnia Mayangsari; Iwan Syarif, Aliridho Barakbah
Abstract: Steam review data provided a lot of information for the game development team, either positive or negative review. It contained an essential point that negative and positive reviews provide crucial information and 7% of positive reviews contained bug reports. These bug reports occurred after the game was released, and many reports of common incidents still exist. If players found an issue in the game, they could report it directly through the review feature provided by the online game platform. Nevertheless, the development team took a long time manually analyzing and categorizing the game review. This study proposed a new approach to automatically categorizing game reviews on Steam based on the bug severity level. Hence, to solve this problem, we suggested introducing a solution based on the research background indicated above. For this experiment, we analyzed reviews on two popular game titles namely, FIFA 23 and Apex Legends. We implemented three different classifiers namely, KNN, Decision Tree, and Naïve Bayes, which would be used to train a dataset to classify the bug severity level. Due to an imbalanced dataset, we performed cross-validation to reduce bias in the dataset. Performance in this model would be evaluated using accuracy rate, precision, recall, and F1 score. As a result, the experiment showed that game reviews of different game titles achieved different accuracy scores. The game review classification for FIFA 23 performed better than the game review classification for Apex Legends. The mean accuracy score of FIFA 23 was 72% with Decision Tree and Apex Legend was 64% with KNN.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Designing a Smart Inverter for Compensating the Voltage Sag Caused by
Motor Start-up
Authors: Indra Budi Hermawan; Ashari Mochamad, Dedet Candra Riawan
Abstract: Starting a large induction motor will always follow up with an inrush current as the nature of an induction motor. On a less stiff power system, that inrush current will be causing a Voltage Sag (VS). A big VS can lead to significant disruptions in power quality and reliability. To address this, a Smart Inverter with an Artificial Intelligence (AI) -driven controller installed in a Photovoltaic (PV) farm is proposed for voltage sag recovery. During normal conditions, the PV farm acts as a power source supporting the main grid, but when large induction motors are started, the smart inverter connected to the PV is responsible for power conversion to recover sags caused by the Induction motor inrush current. The controller inside the Inverter ensures optimal operation. The use of AI also compares the effectiveness of using the Fuzzy Logic Controller (FLC) with the Proportional Integral (PI) Controller to assess their performance in reducing current spikes. Based on simulations, the FLC outperformed PI Controller in mitigating the voltage sag and avoiding the Low Voltage Ride-Through (LVRT). Simulation results show that voltage sag can be recovered for up to 97% of the nominal voltage, a significant improvement over the 80% sag recovery without the smart Inverter. At a nominal grid voltage of 6,600 volts, the VS Magnitude was successfully increased from 5,210 volts to 6,368 volts and the VS Duration also decreased from 6.96 s to 4.97 s. The results achieved validate the effectiveness of the approach in improving the power quality.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
- Classification of Coffee Leaves Diseases Using CNN
Authors: Dara Sucia; Auliya Tara Shintya Larasabi , Yufis Azhar, Zamah Sari
Abstract: Indonesia’s coffee is a one of major export and contributes significantly that generate foreign exchange to the country’s economy. The quality and quantity of coffee production depend on various factors such as humidity, rain, and fungus that can cause rust diseases on coffee leaves. This disease can spread quickly and affect other coffee plants quality, leading to decreased production. To address this issue, the CNN method with the VGG-19 architecture model was utilized to identify coffee plant diseases using image data and the python programming language, which in previous studies used MATLAB as their platform. In addition, VGG-19 has a more profound learning feature than the method used in previous studies, AlexNet which makes the structure of VGG-19 more detailed. The dataset that use in this paper is Robusta Coffee Leaf Images Dataset which have three classes and with 1560 images data in total, but only used 100 images in each classes. The VGG-19 model attained a precision level of 90% when the evaluated using the testing data with ratio 80:20, which 80% is training data, and 20% is validation data as testing data. This paper employed 0.0001 learning rate, batch size 15, momentum 0.9, 12 training iteration, and RMSprop optimizer.
PubDate: Mon, 17 Jul 2023 00:00:00 +000
|
Open Access journal
