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

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.233-240
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• 2024

In this study, we introduce a system for real-time monitoring of field conditions within an industrial complex using a 5G network UAV (: Unmanned Aerial Vehicle). When a monitoring event occurs in a sensor mounted on a UAV (detection of fire, harmful gas, or industrial disaster type human accident), key information from the sensor is transmitted to the UAS (: Unmanned Aerial System) application server. As a result of this information transmission and processing, managers or operators of the Industrial Complex Corporation were able to secure legal basis data for fatal accidents, fires, and detection of harmful gases at sites within the Industrial Complex Corporation through trigger processing for each accident risk situation.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• Journal of Platform Technology
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• v.12 no.2
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• pp.45-57
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• 2024

Smart cities are the product of modern urban planning that seeks to innovate information and communication technology and improve the quality of urban life. For the efficient operation of smart cities, data collected, stored, and processed in real time is a key resource. Therefore, data from smart cities collected in various fields must be managed safely, and personal information protection is paramount. In this study, a smart city data security model using blockchain technology was proposed to safely manage smart city data. The proposed model integrates IPFS into the blockchain network to distribute and store data to ensure data confidentiality and integrity, and encrypts data using CP-ABE to efficiently control access to data from users. In addition, privacy was guaranteed while enhancing the usability of data by using Homomorphic Encryption with data access control policies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2642-2664
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• 2024

In the process of modern traffic management, information technology has become an important part of intelligent traffic governance. Real-time monitoring can accurately and effectively track and record vehicles, which is of great significance to modern urban traffic management. Existing tracking algorithms are affected by the environment, viewpoint, etc., and often have problems such as false detection, imprecise anchor boxes, and ID switch. Based on the YOLOv5 algorithm, we improve the loss function, propose a new feature extraction module to obtain the receptive field at different scales, and do adaptive fusion with the SGE attention mechanism, so that it can effectively suppress the noise information during feature extraction. The trained model improves the mAP value by 5.7% on the public dataset UA-DETRAC without increasing the amount of calculations. Meanwhile, for vehicle feature recognition, we adaptively adjust the network structure of the DeepSort tracking algorithm. Finally, we tested the tracking algorithm on the public dataset and in a realistic scenario. The results show that the improved algorithm has an increase in the values of MOTA and MT etc., which generally improves the reliability of vehicle tracking.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.57 no.4
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• pp.479-488
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• 2024

In recent years, changes in the fishing ground environment have led to reduced catches by fishermen at traditional fishing spots and increased operational costs related to vessel exploration, fuel, and labor. In this study, we developed a deep learning model to classify the fishing activities of drift gillnet fishing boats using AIS (automatic identification system) trajectory data. The proposed model integrates long short-term memory and 1-dimensional convolutional neural network layers to effectively distinguish between fishing (throwing and hauling) and non-fishing operations. Training on a dataset derived from AIS and validation against a subset of CCTV footage, the model achieved high accuracy, with a classification accuracy of 90% for fishing events. These results show that the model can be used effectively to monitor and manage fishing activities in coastal waters in real time.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.847-859
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• 2022

The development and commercialization of industrial genetically modified (GM) organisms is actively progressing worldwide, highlighting an increased need for improved safety management protocols. We sought to establish an environmental monitoring method, using real-time polymerase chain reaction (PCR) and propidium monoazide (PMA) treatment to develop a quantitative detection protocol for living GM microorganisms. We developed a duplex TaqMan quantitative PCR (qPCR) assay to simultaneously detect the selectable antibiotic gene, ampicillin (AmpR), and the single-copy Escherichia coli taxon-specific gene, D-1-deoxyxylulose 5-phosphate synthase (dxs), using a direct cell suspension culture. We identified viable engineered E. coli cells by performing qPCR on PMA-treated cells. The theoretical cell density (true copy numbers) calculated from mean quantification cycle (Cq) values of PMA-qPCR showed a bias of 7.71% from the colony-forming unit (CFU), which was within ±25% of the acceptance criteria of the European Network of GMO Laboratories (ENGL). PMA-qPCR to detect AmpR and dxs was highly sensitive and was able to detect target genes from a 10,000-fold (10^-4) diluted cell suspension, with a limit of detection at 95% confidence (LOD_95%) of 134 viable E. coli cells. Compared to DNA-based qPCR methods, the cell suspension direct PMA-qPCR analysis provides reliable results and is a quick and accurate method to monitor living GM E. coli cells that can potentially be released into the environment.

• 대한공업교육학회지
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• v.32 no.1
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• pp.134-152
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• 2007

This study deals with the development of computer program which can restrict students to access to the unallowable web sites during the Internet based class. Our suggested program can find the student's access list to the unallowable sites, display it on the teacher's computer screen. Through the limitation of the student's access, teacher can enhance the efficiency of class and fulfill his educational purpose for the class. The use of our results leads to the effective and safe utilization of the Internet as the teaching tools in the class. Meanwhile, the typical method is to turn off the LAN (Local Area Network) power in order to limit the student's access to the unallowable web sites. Our program has been developed on the Linux operating systems in the small network environment. The program includes following five functions: the translation function to change the domain name into the IP(Internet Protocol) address, the search function to find the active students' computers, the packet snoop to capture the ongoing packets and investigate their contents, the comparison function to compare the captured packet contents with the predefined access restriction IP address list, and the restriction function to limit the network access when the destination IP address is equal to the IP address in the access restriction list. Our program can capture all passing packets through the computer laboratory in real time and exactly. In addition, it provides teacher's computer screen with the all relation information of students' access to the unallowable sites. Thus, teacher can limit the student's unallowable access immediately. The proposed program can be applied to the small network of the elementary, junior and senior high school. Our research results make a contribution toward the effective class management and the efficient computer laboratory management. The related researches provides teacher with the packet observation and the access limitation for only one host, but our suggested program provides teacher with those for all active hosts.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.183-199
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• 2012

Previous Earth Science textbooks have mostly lacked the latest astronomical phenomena frequently being reported in mass media such as popular science magazines. One of the main directions in the revision of the 2009 National Curriculum of Korea is to actively include those phenomena. Furthermore, despite a close link between astronomy and physics, the concept of modern physics has not been actively introduced in Earth Science textbooks and at the same time the linkage of physics to astronomy has rarely been studies in physics textbooks. Therefore, the concept of integration among different fields in science is emphasized in the new National Curriculum. Transient phenomena in the high energy astrophysical objects are examples that reflect such issue. The purpose of this study is to introduce transferring a real-time data and making imaging of astronomical observations using e-Science. As a first step, we performed the first experiment for a large data transfer of astronomical observation between Korea and Japan using KOREN, a National Research and Education Test Network. We introduce actively on-going fields of e-Science in observational activities of astronomy and astrophysics, and their close interrelationship with scientific inquiry activities and public outreach activities. We discuss our experiment in the scientific and educational aspects to the primitive e-Science activity in the Korean astronomical society and, in turn, provide a prospective view for its application to the scientific inquiry activities and public outreach activities in the upcoming commercial Gbps-level internet environments.