• Journal of Information Processing Systems
• /
• v.9 no.3
• /
• pp.405-424
• /
• 2013

This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB's (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.3970-3990
• /
• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2167-2175
• /
• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1205-1214
• /
• 2021

Edge Computing is used as a solution to the cost problem and transmission delay problem caused by network bandwidth consumption that occurs when IoT/CPS devices are integrated into the cloud by performing artificial intelligence (AI) in an environment close to the data source. Since edge computing runs on devices that provide high-performance computation and network connectivity located in the real world, it is necessary to consider application integrity so that it is not exploited by cyber terrorism that can cause human and material damage. In this paper, we propose a technique to protect the integrity of edge computing applications implemented in a script language that is vulnerable to tampering, such as Python, which is used for implementing artificial intelligence, as container images and then digitally signed. The proposed method is based on the integrity protection technology (Docker Contents Trust) provided by the open source container technology. The Docker Client was modified and used to utilize the whitelist for container signature information so that only containers allowed on edge computing devices can be operated.

• Korean Journal of Construction Engineering and Management
• /
• v.25 no.2
• /
• pp.36-44
• /
• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.445-466
• /
• 2022

As everything linking to the internet, people can subscribe to various services from a service provider to facilitate their lives through the Internet of Things (IoT). An obligatory thing for the service provider is that they should push the service data safely and timely to multiple IoT terminal devices regularly after the IoT devices accomplishing the service subscription. In order to control the service message received by the legal devices as while as keep the confidentiality of the data, the public key encryption algorithm is utilized. While the existing public encryption algorithms for push service are too complicated for IoT devices, and almost of the current subscription schemes based on push mode are relying on centralized organization which may suffer from centralized entity corruption or single point of failure. To address these issues, we design a secure subscription-push service scheme based on blockchain and edge computing in this article, which is decentralized with secure architecture for the subscription and push of service. Furthermore, inspired by broadcast encryption and multicast encryption, a new encryption algorithm is designed to manage the permissions of IoT devices together with smart contract, and to protect the confidentiality of push messages, which is suitable for IoT devices. The edge computing nodes, in the new system architecture, maintain the blockchain to ensure the impartiality and traceability of service subscriptions and push messages, meanwhile undertake some calculations for IoT devices with limited computing power. The legalities of subscription services are guaranteed by verifying subscription tags on the smart contract. Lastly, the analysis indicates that the scheme is reliable, and the proposed encryption algorithm is safe and efficient.

• Journal of Sensor Science and Technology
• /
• v.14 no.6
• /
• pp.430-437
• /
• 2005

In this paper, we proposed a biologically inspired light-adaptive edge detection circuit based on the human retina. A saturating resistive network was suggested for light adaptation and simulated by using HSPICE. The light adaptation mechanism of the edge detection circuit was quantitatively analyzed by using a simple model of the saturating resistive element. A light-adaptive capability of the edge detection circuit was confirmed by using the one-dimensional array of the 128 pixels with various levels of input light intensity. Experimental data of the saturating resistive element was compared with the simulated results. The entire capability of the edge detection circuit, implemented with the saturating resistive network, was investigated through the two-dimensional array of the $64{\times}64$ pixels

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1243-1244
• /
• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.475-485
• /
• 2008

One of main issues in watermarking is to resolve the strength of watermark for solving the problem of trade-off between fidelity and robustness of watermarking. In the previous research, the strength of watermark has been resolved fixed value generally without considering local image characteristics such as image brightness, contrast, and edge. This paper proposes a new model to predict the strength of watermark considering local image characteristics such as image brightness, contrast, and edge for digital wavelet transform(DWT)-based image watermarking. For the study, psychological experiment was fulfilled to measure the human image perception and regression analysis showed the proposed model was statistically significant at the level of ${\alpha}\;=\;0.01$. Also the model is practically validated on fidelity and robustness of watermarking.

• Industrial Engineering and Management Systems
• /
• v.10 no.4
• /
• pp.238-246
• /
• 2011

Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.