• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.231-241
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• 2006

When current cyber attacks to information and communication facilities are examined, technologies such as chase evasion technology and defense deviation technology have been rapidly advanced and many weak systems worldwide are often used as passages. And when newly-developed cyber attack instruments are examined, technologies for prefect crimes such as weakness attack, chase evasion and evidence destruction have been developed and distributed in packages. Therefore, there is a limit to simple prevention technology and according to cases, special procedures such as real-time chase are required to overcome cyber crimes. Further, cyber crimes beyond national boundaries require to be treated in international cooperation and relevant procedural arrangements through which the world can fight against them together. However, in current laws, there are only regulations such as substantial laws including simple regulations on Punishment against violation. In procedure, they are treated based on the same procedure as that of general criminal cases which are offline crimes. In respect to international cooperation system, international criminal private law cooperation is applied based on general criminals, which brings many problems. Therefore, this study speculates the procedural law on cyber crimes and presents actual problems of our country and its countermeasures.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.29-32
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• 2000

Layered video coding schemes allow the video information to be transmitted in multiple video bitstreams to achieve scalability. they are attractive in theory for two reasons. First, they naturally allow for heterogeneity in networks and receivers in terms of client processing capability and network bandwidth. Second, they correspond to optimal utilization of available bandwidth when several video qualify levels are desired. In this paper we propose a scalable video codec architectures with motion estimation, which is suitable for real-time audio and video communication over packet networks. The coding algorithm is compatible with ITU-T recommendation H.263+ and includes various techniques to reduce complexity. Fast motion estimation is Performed at the H.263-compatible base layer and used at higher layers, and perceptual macroblock skipping is performed at all layers before motion estimation. Error propagation from packet loss is avoided by Periodically rebuilding a valid Predictor in Intra mode at each layer.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Journal of KIBIM
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• v.11 no.3
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• pp.45-54
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• 2021

The augmented reality (AR) has recently became an attractive technology in construction industry, which can play a critical role in realizing smart construction concepts. The AR has a great potential to help construction workers access digitalized information about design and construction more flexibly and efficiently. Though several AR applications have been introduced for on-site made to enhance on-site and off-site tasks, few are utilized in actual construction fields. This paper proposes a new AR framework that provides on-site managers with an opportunity to easily access the information about construction resources such as workers and equipment. The framework records videos with the camera installed on a wearable AR device and streams the video in a server equipped with high-performance processors, which runs an object detection algorithm on the streamed video in real time. The detection results are sent back to the AR device so that menu buttons are visualized on the detected objects in the user's view. A user is allowed to access the information about a worker or equipment appeared in one's view, by touching the menu button visualized on the resource. This paper details implementing parts of the framework, which requires the data transmission between the AR device and the server. It also discusses thoroughly about accompanied issues and the feasibility of the proposed framework.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1263-1268
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• 2018

In this paper, a video analysis is proposed to implement video surveillance system with deep learning object detection and probabilistic data association filter for tracking multiple objects, and suggests its implementation using GPU. The proposed video analysis technique involves object detection and object tracking sequentially. The deep learning network architecture uses ResNet for object detection and applies probabilistic data association filter for multiple objects tracking. The proposed video analysis technique can be used to detect intruders illegally trespassing any restricted area or to count the number of people entering a specified area. As a results of simulations and experiments, 48 channels of videos can be analyzed at a speed of about 27 fps and real-time video analysis is possible through RTSP protocol.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.97-106
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• 2019

Today, many smart sensor's measurement instruments are used to check the safety situation of various medium and large bridge structures that should be maintained in the construction facilities, but most of them use the method of measuring and confirming the displacement behavior of the bridge at regular intervals. In order to continuously check the safety situation, various measuring instruments are used, but most of them are not able to measure and measure the displacement and behavior of main construction structures at regular intervals. In this study, GNSS and environment smart sensors and drone's image data are transmitted to wireless network so that risk of many bridge's structures can be detected beforehand. As a result, by diagnosing the fine displacement of the bridge in real time and its condition, reinforcement, repair and disaster prevention measures for the structural parts of the bridges, which are expected to be dangerous, and various disasters and accidents can be prevented, and disaster can be prevented could suggest a new alternative.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.485-488
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• 2016

The level of domestic greenhouse complex environmental control technology is a hardware-oriented automation steps that mechanically control the environments of greenhouse, such as temperature, humidity and $CO_2$ through the technology of cultivation and consulting experts. This automation brings simple effects such as labor saving. However, in order to substantially improve the output and quality of agricultural products, it is essential to track the growth and physiological condition of the plant and accordingly control the environments of greenhouse through a software-based complex environmental control technology for controlling the optimum environment in real time. Therefore, this paper is a part of general methods on the greenhouse complex environmental control technology. and presents a horticulture production forecasting methods using artificial neural networks through the analysis of big data systems of smart farm performed in our country and artificial neural network technology trends.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.179-185
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• 2019

Currently, despite having active movements related to port logistics security, there is lack of awareness, education, and security systems related to port technology. Before implementing port logistics security, a mutual authentication agreement should be reached through the establishment of an integrated network that can share port logistics security information in real time. In order to achieve port competitiveness and strengthen logistics service, establishment of national strategy for logistics security is necessary. However, there is an urgent need to raise the security consciousness among the port logistics organization members and enhance the information security ability which is a crucial feature of the port logistics organization. Therefore, the objective of this study is to analyze the factors affecting the information security capacity of port logistics organization members. Even though the analysis rejected the hypothesis that security regulations affect security awareness, the security activities and security awareness were significantly correlated. It also has a positive impact on the relationship between security norms and security abilities, and security awareness and security abilities.