• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.744-747
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• 2017

The Internet of things (IoT) needs to be an event-driven approach for efficient real time response and processing. An IoT gateway is sometimes employed to provide the connection and translation between devices and the cloud. Storing data in the local database, and then forwarding it on the cloud is a task to be relegated to a gateway device In this paper, we propose the design of the IoT gateway with Fog computing for storing data from sensors into a local database. In the procedure of designing storing tasks, we propose to use the interfacing software known as Ardulink MQTT bridge. MQTT is a protocol for sensors to publish data to the clients. When it comes to needing historical data, MQTT connector can push MQTT data into SQL database. We write an MQTT client and based on the message topic insert the values into a SQL Database The design of IoT gateway with Fog computing adds value because it provides processing of the data across multiple devices before it sends to the cloud.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.235-240
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• 2002

This paper shows the desist concept of an ATS(Antenna Tracking Software) to control the movement of the MSC(Multi-Spectral Camera) antenna. The MSC has a two-axes directional X-band antenna for image transmission to KGS(KOMSAT2 Ground Station). The main objective of the ATS is to drive the APM(Antenna Pointing Mechanism) to the required elevation and the azimuth position according to an appropriate TPF(Tracking Parameter File). The ATS is implemented as one task of the SBC(Single Board Computer) software, which uses VxWorks as a real time OS. The ATS has several operational modes such as STANDBY mode, First EL mode, First AZ mode, Normal Operation mode, and so on. The ATS uses two PI controllers fur the velocity and the position loop respectively, to satisfy the requirements specification. In order to show the feasibility of the described design concept, the various simulations and the experiments are performed under specific test configuration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4204-4222
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• 2015

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

• Journal of Fisheries and Marine Sciences Education
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• v.11 no.1
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• pp.43-58
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• 1999

To instruct in accordance with learner's trait & preceding knowledge, letting the learner control the learning activities is the important task of educator & major goal of the Education Department this year. This article intends to provide useful Instructional Model for the teachers in fisheries marine high school, when they design the individualized teaching model using motivation. One of the major reason for the fisheries marine high school students' low learning achievement is due to the neglecting motivation elements in teaching - learning processes. Recently, with assistance of the information communication technology development, various teaching methods such as Individualized Multimedia Mediated Instruction, Internet Instruction, have come to the major method in activating motivation and computer-mediated instruction considering the learner's individual difference is the useful tools for the instructional efficiency. Because current navigation text book of fisheries marine high school have special characteristic considering the spacial context & time series from departing port to entering port, Teachers can maximize learner's learning accomplishment by using individualized multimedia & providing similar situation like a real navigation(simulating), representing this text characteristics. Thus this paper searches for the specifications of Keller's Motivation Model & Sweeter's Tutorial Model to solve instructional efficiency problems in fisheries marine high school & developed an efficient instructional design by integrating two models.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.69-77
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• 2021

This study aims to identify security-based threat information for an organization. This is because protecting the threat for IT systems plays an important role for an corporate's intangible assets. Security monitoring systems determine and consequently respond threats by analyzing them in a real time situation, focusing on events and logs generated by security protection programs. The security monitoring task derives priority by dividing threat information into reputation information and analysis information. Reputation information consisted of Hash, URL, IP, and Domain, while, analysis information consisted of E-mail, CMD-Line, CVE, and attack trend information. As a result, the priority of reputation information was relatively high, and it is meaningful to increase accuracy and responsiveness to the threat information.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.30-30
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• 2018

Research on mapping levees is an important task for assessing levee stability. The drone imagery acquired in river basins is useful for generating real-time levee maps. This research proposes a robust methodology for mapping levees in river basins using the drone imagery. In the first step, the multiple imagery taken in the test bed was acquired by the drone. Then, the orthorectified image and DEM (Digital Elevation Model) were generated by the photogrammetry and image processing process. Finally, the significant features on levee surfaces such as levee tops, levee lines, levee slopes, eroded areas were detected from the generated DEM and orthorectified image by manual labors and automatic methods. In future research, the automatic procedure for identifying the significant levee features from the drone imagery would be proposed.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of Astronomy and Space Sciences
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• v.38 no.2
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• pp.83-92
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• 2021

The ionization degree of hydrogen is crucial in the physics of the plasma in the solar chromosphere. It specifically limits the range of plasma temperatures that can be determined from the Hα line. Given that the chromosphere greatly deviates from the local thermodynamic equilibrium (LTE) condition, precise determinations of hydrogen ionization require the solving of the full set of non-LTE radiative transfer equations throughout the atmosphere, which is usually a formidable task. In many cases, it is still necessary to obtain a quick estimate of hydrogen ionization without having to solve for the non-LTE radiative transfer. Here, we present a simple method to meet this need. We adopt the assumption that the photoionizing radiation field changes little over time, even if physical conditions change locally. With this assumption, the photoionization rate can be obtained from a published atmosphere model and can be used to determine the degree of hydrogen ionization when the temperature and electron density are specified. The application of our method indicates that in the chromospheric environment, plasma features contain more than 10% neutral hydrogen at temperatures lower than 17,000 K but less than 1% neutral hydrogen at temperatures higher than 23,000 K, implying that the hydrogen temperature determined from the Hα line is physically plausible if it is lower than 20,000 K, but may not be real, if it is higher than 25,000 K. We conclude that our method can be readily exploited to obtain a quick estimate of hydrogen ionization in plasma features in the solar chromosphere.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.