• 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.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.392-394
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• 2019

Tunnels and subterranean roads, which are representative evacuation facilities for national or regional disasters, have radio relay broadcasting facilities for legal reasons and convenience reasons. However, this system is limited to radio broadcasting and it is impossible to effectively communicate disaster or emergency situations that occur locally due to broadcasting of national broadcasting without DMB broadcasting. In order to improve this, we construct a remote disaster broadcasting system at each regional disaster station and implement a system to transmit it to each tunnel using internet or LTE network. The system in the tunnel transmits the emergency broadcasting signal through the existing relay equipment through the modulator which decodes the SMS, the media file, the real time broadcasting or the image received by the digital signal and converts it into FM and DMB frequency. The method proposed and implemented in this study can be used to provide efficient information and remote field control in case of emergency in tunnel and underground roadway.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.195-201
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• 2019

This paper proposes a blockchain system to share Wireless Local Area Network (WLAN) that recently suffers from mutual interference among increasing devices using unlicensed bands. Blockchain technology can induce cooperation from users by incentivizing them with cryptocurrency like shown in Bitcoin example. In this paper, we describe Blockchain based access mechanism in WLAN instead of conventional authentication based access. Here, users can access any WLAN access point by paying through smart contract while they also receive payment from others. In order to support real-time transaction, we apply proof-of-authority that is realized by Byzantine fault tolerant protocol instead of well-known proof-of-work that requires huge computing power and delay.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1353-1358
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• 2021

IoT (Internet of Things) emerges from various technologies such as communications, micro processors and embedded system and so on. The IoT has also been used to UAV (Unmanned Aerial Vehicle) system. In manned aircraft, a pilot and co-pilot should control FCS (Flight Control System) with FBW(Fly By Wire) system for flight operation. In contrast, the flight operation in UAV system is remotely and fully managed by GCS (Ground Control System) almost in real time. To make it possible the communication channel should be necessary between the UAV and the GCS. There are many protocols between two systems. Amongst them, MAVLink (Macro Air Vehicle Link) protocol is representatively used due to its open architecture. MAVLink does not define any securities itself, which results in high vulnerability from external attacks. This paper proposes the method to enhance data security in GCS network by applying cryptographic methods to the MAVLink messages in order to support safe UAV operations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.879-884
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• 2020

In the conventional video file system, videos are stored in a high performance server which has mass storage hard disks or disk arrays. For 3D internet broadcasting, real time operations are required to transmit video files to many clients. This paper describes the design of the 3D internet broadcasting system which can provide realtime streaming service to many users in the 5G environment. In reality, unicast is used to transmit multimedia contents over the internet rather than IP multicast since IP multicast has its own drawbacks in deployment, security, maintenance and so on. In addition, multimedia broadcasting service system like VoD has difficulties in applying to 3D internet broadcasting system since it requires a large amount of system and network resources. In this work, we develop a 3D internet broadcasting system which can construct effective data delivery by minimizing performance-degrading factors.