• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.1
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• pp.49-60
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• 2020

Although the number of child loss in commercial facilities has been growing recently, the prevention method for child loss are still lacking in the environmental aspect. This research examines the causes of lost child in behavioral aspects in order to develop a guideline to prevent child loss in U.E.C. The observational study on the behaviors of guardians and children was conducted in the U.E.C that is visited by many customers accompanied with children. Then the results of the observational study were marked on the behavioral maps. After analyzing the behavioral maps, the causes of child loss were determined by classifying into behaviors by age and behaviors by functional space. As a result, when guardian is unable to pay attention to child by doing something else such as making a purchase, or an inquiry, child may lose guardian by going towards the interesting factors or playing around. Moreover, if the spaces related to children are located at the node with high pedestrian density and open structure or the environment that is hard for the guardians to watch over their children, it will be easy for guardians to be inattentive to their children, and get separated from each other.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.985-992
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• 2020

Edge computing is a technology that can prepare for a new era of cloud computing. Edge computing is not a remote data center where data is processed and computed, but low-latency/high-speed computing is realized by adding computing power and data processing power to the edge side close to an access point such as a terminal device or a gateway. It is possible. The types of edge computing include mobile edge computing, fog computing, and cloudlet computing. In this article, we describes existing open source platforms for implementing edge computing nodes. By presenting and comparing the structure, features of open source edge platforms, it is possible to acquire knowledge required to select the best edge platform for industrial engineers who want to build an edge node using an actual open source edge computing platform.

• Journal of IKEEE
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• v.26 no.2
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• pp.313-318
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• 2022

The feedback voltage detection structure is proposed to alleviate overshoot and undershoot caused by the removal of the existing external output capacitor. Conventional LDO regulators suffer from overshoot and undershoot caused by imbalances in the power supply voltage. Therefore, the proposed LDO is designed to have a more improved transient response to form a new control path while maintaining only the feedback path of the conventional LDO regulator. A new control path detects overshoot and undershoot events in the output stage. Accordingly, the operation speed of the pass element is improved by charging and discharging the current of the gate node of the pass element. LDO regulators with feedback voltage sensing architecture operate over an input voltage range of 3.3V to 4.5V and have a load current of up to 200mA at an output voltage of 3V. According to the simulation result, when the load current is 200mA, it is 73mV under the undershoot condition and 61mV under the overshoot condition.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.6
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• pp.1-11
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• 2021

To develop an efficient procedure related to the flamelet library, the machine learning process based on artificial neural network(ANN) is applied for the gaseous hydrogen/liquid oxygen combustor under a supercritical pressure condition. For hidden layers, 25 combinations based on Rectified Linear Unit(ReLU) and hyperbolic tangent are adopted to find an optimum architecture in terms of the computational efficiency and the training performance. For activation functions, the hyperbolic tangent is proper to get the high learning performance for accurate properties. A transformation learning data is proposed to improve the training performance. When the optimal node is arranged for the 4 hidden layers, it is found to be the most efficient in terms of training performance and computational cost. Compared to the interpolation procedure, the ANN procedure reduces computational time and system memory by 37% and 99.98%, respectively.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.91-96
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• 2009

In this paper, interoperability in heterogeneous wireless mesh network, and mesh nodes for providing efficient IP mobility technique offers multi-hop ARP. Heterogeneous wireless mesh networks to MANETs based on a wireless mesh network backbone and non-MANET architecture is based on a client wireless mesh network and the two mobile networks, combined with a hybrid wireless mesh network are separate. In two different hybrid wireless mesh network routing protocols used to connect the two protocols in the protocol conversion at the gateway to parallel processing problems seriously overload occurs. All of the network reliability and stability are factors that reduce. Therefore, for efficient integration with L3 routing protocols, design techniques to build ARP multi-hop go through the experiment to increase the number of mesh nodes, the packet forwarding rate and an increased hop number of the node was to ensure reliability and stability.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.707-714
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• 2023

The Internet of Things, which is the key factor of the 4th industrial revolution, are apt to apply to many systems. The existing security mechanism cannot be realized with limited resources such as low capacity of devices and sensors. In order to apply IoT system, a new structure and ultra-lightweight encryption is required. In this paper, we analyzed security issues that can operate in Internet-based smart home networks, and to solve the critical issues against these attacks, technologies for device protection between heterogeneous devices. Security requirements are required to protect from attacks. Therefore, we analyzed the demands and requirements for its application by analyzing the security architecture and features in smart home network.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1197-1206
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• 2023

To assess the risk level of a public restroom implemented in virtual reality, we sought to evaluate the spatial elements present in the restroom. To provide the theoretical foundation for the evaluation subjects and criteria, we introduced prior research that proposed a checklist to entance the safety of public restroom. To set up evaluation criteria, we analyzed and established based on the theories of Paul J. Brantingham and Patricia L. Brantingham, focusing on the interaction between space and criminals. Ronald V. Clarke's "Routine Activity Theory" was also introduced and incorporated into the evaluation approach. We analyzed based on the correlation between the criminal, user, and spatial elements of the public restroom in terms of the criminal's actions, the spatial relevance to crime, and user exposure during use. Using these criteria, we developed an algorithm to evaluate th spatial elements of public restroom. Based on this, we created an application, demonstrating the feasibility of developing on evaluation tool.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.515-524
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• 2005

As the number of the mobile nodes (MNs) increases in the networks, the signaling traffic generated by mobility management for MNs will increase explosively, and such a phenomenon will probably affect overall network performance. In this paper, we propose a novel analytical approach using a continuous-time Markov chain model and hierarchical network model for the analysis on the signaling load of representative IPv6 mobility support Protocols such as Mobile IPv6 (MIPv6) and Hierarchical Mobile IPv6 (HMIPv6). According to these analytical modeling, this paper derives the various signaling costs, which are generated by an MN during its average domain residence time when MIPv6 and HMIPv6 are deployed under the same network architecture, respectively. In addition, based on these derived costs, we investigate the effects of various mobility/traffic-related parameters on the signaling costs generated by an MN under MIPv6 and HMIPv6. The analytical results show that as the average moving speed of an MN gets higher and the binding lifetime is set . to the larger value, and as its average packet arrival rate gets lower, the total signaling cost generated during its average domain residence time under HMIPv6 will get relatively lower than that under MIPv6, and that under the reverse conditions, the total signaling cost under MIPv6 will get relatively lower than that under HMIPv6.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.46-56
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• 2012

Recently, when software architecture is designed, the efficiency of reusability is emphasized. The reusability of the design can raise the quality of GIS software, and reduce the cost of maintenance. Because the object oriented GoF design pattern provides the class hierarchy that can represent repetitively, the importance is emphasized more. This method that designs the GIS software can be applied from various application systems. A multiple distributed sensor network system is composed of the complex structure that each node of the sensor network nodes has different functions and sensor nodes and server are designed by the combination of many classes. Furthermore, this sensor network system may be changed into more complex systems according to a particular purpose of software designer. This paper will design the CATL model by applying Facade pattern that can enhance the efficiency of reuse according to attributes and behaviors in classes in order to implement the complicated structure of the multiple distributed sensor network system based on TinyOS. Therefore, our object oriented GIS design pattern model will be expected to utilize efficiently for design, update, or maintenance, etc. of new systems by packing up attributes and behaviors of classes at complex sensor network systems.