• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.141-149
• /
• 2022

In this paper, we propose a design of CCTV enlcosure record management system based on blockchain. Since CCTV video records are transferred to the control center through enclosure, it is very important to manage the enclosure to prevent modulation and damage of the video records. Recently, a smart enclosure monitoring system with real-time remote monitoring and opening and closing state management functions is used to manage CCTV enclosures, but there is a limitation to securing the safety of CCTV video records. The proposed system detect modulated record and recover the record through hash value comparison by distributed stored record in the blockchain. In addition, the integrity verification API is provided to ensure the integrity of enclosure record received by the management server. In order to verify the effectiveness of the system, the integrity verification accuracy and elapsed time were measured through experiments. As a result, the integrity of enclosure record (accuracy: 100%) was confirmed, and it was confirmed that the elapsed time for verification (average: 73 ms) did not affect monitoring.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.2
• /
• pp.51-58
• /
• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.103-114
• /
• 2022

The Internet of Things (IoT) is one of the fastest technologies that are used in various applications and fields. The concept of IoT will not only be limited to the fields of scientific and technical life but will also gradually spread to become an essential part of our daily life and routine. Before, IoT was a complex term unknown to many, but soon it will become something common. IoT is a natural and indispensable routine in which smart devices and sensors are connected wirelessly or wired over the Internet to exchange and process data. With all the benefits and advantages offered by the IoT, it does not face many security and privacy challenges because the current traditional security protocols are not suitable for IoT technologies. In this paper, we presented a comprehensive survey of the latest studies from 2018 to 2021 related to the security of the IoT and the use of machine learning (ML) and deep learning and their applications in addressing security and privacy in the IoT. A description was initially presented, followed by a comprehensive overview of the IoT and its applications and the basic important safety requirements of confidentiality, integrity, and availability and its application in the IoT. Then we reviewed the attacks and challenges facing the IoT. We also focused on ML and its applications in addressing the security problem on the IoT.

• Journal of Ocean Engineering and Technology
• /
• v.36 no.3
• /
• pp.153-160
• /
• 2022

Moored floating platforms have great potential in ocean engineering applications because a mooring system is necessary to keep the platform in station, which is directly related to the operational efficiency and safety of the platform. This paper briefly introduces the technical and operational details of an optical sensor for measuring the tension of mooring lines of a moored platform in waves. In order to check the performance of optical sensors, an experiment with a moored floating platform in waves is carried out in the wave tank at Changwon National University. The experiment is performed in regular waves and irregular waves with a semi-submersible and triangle platform. The performance of the optical sensor is confirmed by comparing the results of the tension of the mooring lines by the optical sensor and tension gauges. The maximum tension of the mooring lines is estimated to investigate the mooring dynamics due to the effect of the wave direction and wavelength in the regular waves. The significant value of the tension of mooring lines in various wave directions is estimated in the case of irregular waves. The results show that the optical sensor is effective in measuring the tension of the mooring lines.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.63-70
• /
• 2023

Due to the spread of high-definition black boxes and the introduction of mobile applications such as 'Smart Citizens Report' and 'Safety Report', the number of public interest reports for violations of Traffic Law has increased rapidly, resulting in shortage of police personnel to handle them. In this paper, we describe the development of a system that can automatically detect lane violations which account for the largest proportion of public interest reporting videos for violations of traffic laws, using deep learning algorithms. In this study, a method for recognizing a vehicle and a solid line object using a YOLO model and a Lanenet model, a method for tracking an object individually using a deep sort algorithm, and a method for detecting lane change violations by recognizing the overlapping range of a vehicle object's bounding box and a solid line object are described. Using this system, it is expected that the shortage of police personnel in charge will be resolved.

• Journal of Industrial Convergence
• /
• v.20 no.11
• /
• pp.191-196
• /
• 2022

In the fintech field, financial transactions with smart phones are actively conducted. User authentication technology is essential for safe financial transactions. PIN authentication through the existing security keypads is convenient to input but has weaknesses in security and others. The biometric authentication technique is secure, but there is a possibility of false positive and false negative authentication. To compensate for this, two-factor authentication is used. In this paper, we propose the 1.5-factor authentication that can increase convenience and security through PIN input with biometric authentication. It provides the stability of fingerprint authentication and convenience of two or three PIN inputs, and this makes safe financial transaction possible. Since biometric authentication is performed at the same time when entering PIN, while security is required by applying fingerprint authentication to the area touched while entering PIN. The User authentication is performed while ensuring convenience to input through additional PIN input in situations where high safety is required, and Safe financial transactions are possible.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.311-323
• /
• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.4
• /
• pp.78-96
• /
• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.

• Smart Structures and Systems
• /
• v.30 no.4
• /
• pp.339-351
• /
• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.2
• /
• pp.91-96
• /
• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.