• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.4
• /
• pp.959-979
• /
• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Journal of Sensor Science and Technology
• /
• v.12 no.6
• /
• pp.289-297
• /
• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Korean Journal of Construction Engineering and Management
• /
• v.12 no.6
• /
• pp.22-30
• /
• 2011

In the construction operation, foundation work should be done in advance for the building structure to be installed. This foundation work include a number of activities such as excavation, ground water prevention, piling, wale installation, etc. Caution should be taken in the operation because the dynamics of earth movement can cause a significant failure in the temporary structure. The temporary structure, therefore, should be constantly monitored to understand its behavior. This paper introduces the USN-based monitoring system to automatically identify the behavior of the temporary structure in addition to visual inspection. The autonomous capability of the monitoring system can increase the safety in the construction operation by providing the detailed structural changes of temporary structures.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.2
• /
• pp.317-322
• /
• 2015

Recently, due to the variety and complexity of marine electronics communication devices, much research has been done to adopt the novel communication protocol. Among them, NMEA2000 protocol, is adopted as standardized protocol to the next generation ship. In this paper, we design and implement the conversion algorithm for sensor protocol based on NMEA2000, and analog data module which convert data format between NMEA2000, CAN, Ethernet, RS232. The present study was designed to implement user-based data monitoring system by supporting various communication protocols through the development and application of key technologies through NMEA2000.