• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.236-238
• /
• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.6
• /
• pp.719-729
• /
• 2017

Recently, the field of agriculture has been gaining a new leap with the integration of ICT technology in agriculture. In particular, smart farms, which incorporate the Internet of Things (IoT) technology in agriculture, are in the spotlight. Smart farm technology collects and analyzes information such as temperature and humidity of the environment where crops are cultivated in real time using sensors to automatically control the devices necessary for harvesting crops in the control device, Environment. Although smart farm technology is paying attention as if it can solve everything, most of the research focuses only on increasing crop yields. This paper focuses on the development of a system architecture that can harvest high quality crops at the optimum stage rather than increase crop yields. In this paper, we have developed an architecture using apple trees as a sample and used the color information and weight information to predict the harvest time of apple trees. The simple board that collects color information and weight information and transmits it to the server side uses Arduino and adopts model-driven development (MDD) as development methodology. We have developed an architecture to provide services to PC users in the form of Web and to provide Smart Phone users with services in the form of hybrid apps. We also developed an architecture that uses beacon technology to provide orchestration information to users in real time.

• Smart Structures and Systems
• /
• v.30 no.5
• /
• pp.521-535
• /
• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.5
• /
• pp.787-799
• /
• 2023

As IoT devices are widely used at home, IoT automation system that is integrate IoT devices for users' demand are gaining populrity. There is automation rule in IoT automation system that is collecting event and command action. But attacker delay the packet and make time that real state is inconsistent with state recongnized by the system. During the time, the system does not work correctly by predefined automation rule. There is proposed some detection method for delay attack, they have limitations for application to IoT systems that are sensitive to traffic volume and battery consumption. This paper proposes a practical packet delay attack detection technique that can be applied to IoT systems. The proposal scheme in this paper can recognize that, for example, when a sensor transmits an message, an broadcast packet notifying the transmission of a message is sent to the Server recognized that event has occurred. For evaluation purposes, an IoT system implemented using Raspberry Pi was configured, and it was demonstrated that the system can detect packet delay attacks within an average of 2.2 sec. The experimental results showed a power consumption Overhead of an average of 2.5 mA per second and a traffic Overhead of 15%. We demonstrate that our method can detect delay attack efficiently compared to preciously proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.1077-1086
• /
• 2023

The searchable encryption model allows to selectively search for encrypted data stored on a remote server. In a real-world scenarios, the model must be able to support multiple search keywords, multiple data owners/users. In this paper, these models are referred to as Multi Ranked Searchable Encryption model. However, at the time this paper was written, the proposed models use fully-trusted trapdoor centers, some of which assume that the connection between the user and the trapdoor center is secure, which is unlikely that such assumptions will be kept in real life. In order to improve the practicality and security of these searchable encryption models, this paper proposes a new Multi Ranked Searchable Encryption model which uses random keywords to protect search words requested by the data downloader from an honest-but-curious trapdoor center with an external attacker without the assumptions. The attacker cannot distinguish whether two different search requests contain the same search keywords. In addition, experiments demonstrate that the proposed model achieves reasonable performance, even considering the overhead caused by adding this protection process.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.93-98
• /
• 2023

In paddy rice farming, water management is a critical task. To suppress weed emergence during the early stages of growth, fields are deeply flooded, and after transplantation, the water level is reduced to promote rooting and stimulate stem generation. Later, water is drained to prevent the production of sterile tillers. The adequacy of water supply is influenced by various factors such as field location, irrigation channels, soil conditions, and weather, requiring farmers to frequently check water levels and control the ingress and egress of water. This effort increases if the fields are scattered in remote locations. Automated irrigation systems have been considered to reduce labor and improve productivity. However, the net income from rice production in 2022 was about KRW 320,000/10a on average, making it financially unfeasible to implement high-cost devices or construct new infrastructure. This study focused on developing an IoT-Based irrigation valve that can be easily integrated into existing agricultural infrastructure without additional construction. The research was carried out in three main areas: Firstly, an irrigation valve was designed for quick and easy installation on existing agricultural pipes. Secondly, a power circuit was developed to connect a low-power Cat M1 communication modem with an Arduino Nano board for remote operation. Thirdly, a cloud-based platform was used to set up a server and database environment and create a web interface that users can easily access.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.6
• /
• pp.193-204
• /
• 2022

In a smart factory environment in a small and medium-sized enterprise (SME) environment, sensors and actuators operating on actual manufacturing lines, programmable logic controllers (PLCs) to manage them, human-machine interface (HMI) to control and manage such PLCs, and consists of operational technology server to manage PLCs and HMI again. PLC and HMI, which are in charge of control automation, perform direct connection with OT servers, application systems for factory operation, robots for on-site automation, and production facilities, so the development of security technology in a smart factory environment is demanded. However, smart factories in the SME environment are often composed of systems that used to operate in closed environments in the past, so there exist a vulnerable part to security in the current environment where they operate in conjunction with the outside through the Internet. In order to achieve the internalization of smart factory security in this SME environment, it is necessary to establish a process according to the IEC 62443-4-1 Secure Product Development Life cycle at the stage of smart factory SW and HW development. In addition, it is necessary to introduce a suitable development methodology that considers IEC 62443-4-2 Component security requirements and IEC 62443-3 System security requirements. Therefore, this paper proposes an application plan for the IEC 62443 based development security process to provide security internalization to smart factories in an SME environment.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.27 no.5
• /
• pp.40-48
• /
• 2023

In this paper, a sensor-based monitoring system was established to analyze the long-term behavioral characteristics of the caisson quay wall, a representative structural type in port facilities. Data was collected over a period of approximately 10 months. Based on existing literature, anomalous behaviors of port facilities were classified, and a measurement system was selected to detect them. Monitoring systems were installed on-site to periodically collect data. The collected data was transmitted and stored on a server through LTE network. Considering the site conditions, inclinometers for measuring slope and crack meters for measuring spacing and settlement were installed. They were attached to two caissons for comparison between different caissons. The correlation among measured data, temperature, and tidal level was examined. The temperature dominated the spacing and settlement data. When the temperature changed by approximately 50 degrees, the spacing changed by 10 mm, the settlement by 2 mm, and the slope by 0.1 degrees. On the other hand, there was no clear relationship with tidal level, indicating a need for more in-depth analysis in the future. Based on the characteristics of these collected database, it will be possible to develop algorithms for detecting abnormal states in gravity-type quay walls. The acquisition and analysis of long-term data enable to evaluate the safety and usability of structures in the event of disasters and emergencies.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.9
• /
• pp.263-272
• /
• 2023

Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

• Phonetics and Speech Sciences
• /
• v.15 no.4
• /
• pp.109-118
• /
• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.