• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1103-1107
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• 2016

Network assets have been protected from malware infection by checking the integrity of mobile devices through network access control systems, vaccines, or mobile device management. However, most of existing systems apply a uniform security policy to all users, and allow even infected mobile devices to log into the network inside for completion of the integrity checking, which makes it possible that the infected devices behave maliciously inside the network. Therefore, this paper proposes a network defense mechanism based on isolated networks. In the proposed mechanism, every mobile device go through the integrity check system implemented in an isolated network, and can get the network access only if it has been validated successfully.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.9-15
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• 2021

Recently, it is increasingly necessary to run high-end neural network applications with huge computation overhead on top of resource-constrained embedded systems, such as wearable devices. While the huge computational overhead can be alleviated by distributed neural networks running on multiple separate devices, existing distributed neural network techniques suffer from a large traffic between the devices; thus are very vulnerable to communication failures. These drawbacks make the distributed neural network techniques inapplicable to wearable devices, which are connected with each other through unstable and low data rate communication medium like human body communication. Therefore, in this paper, we propose a distributed neural network partitioning technique that is resilient to communication failures. Furthermore, we show that the proposed technique also improves the inference accuracy even in case of no communication failure, thanks to the improved network partitioning. We verify through comparative experiments with a real-life neural network application that the proposed technique outperforms the existing state-of-the-art distributed neural network technique in terms of accuracy and resiliency to communication failures.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.449-456
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• 2019

SMART home is one of the most popular applications of Internet-of-Things (IoT) technologies, which is expanding in terms of range of applications. SMART home technology provides convenience at home by connecting household appliances to a single network, control, and management. However, many general home appliances do not support the network functions yet; hence, enjoying such convenient technology could be difficult, and it could be expensive in the beginning to build the framework. In addition, even though products with SMART home technologies are purchased, the control systems could differ from device to device. Thus, in this paper, we propose a SMART home framework, called an S-mote that can operate all the IoT functions in a single application by adding an infrared or radio frequency module to general home appliances. The proposed framework is analyzed using four types of performance tests by five evaluators. The results of the experiment show that the SMART home environment was implemented successfully and that it functions appropriately, without any operational issues, with various home appliances, including the latest IoT devices, and even those equipped with an infrared or radio frequency module.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2097-2100
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• 2003

Many studies have been done on secure operating system using secure kernel that has various access control policies for system security. Secure kernel can protect user or system data from unauthorized and/or illegal accesses by applying various access control policies like DAC(Discretionary Access Control), MAC(Mandatory Access Control), RBAC(Role Based Access Control), and so on. But, even if secure operating system is running under various access control policies, network traffic among these secure operating systems can be captured and exposed easily by network monitoring tools like packet sniffer if there is no protection policy for network traffic among secure operating systems. For this reason, protection for data within network traffic is as important as protection for data within local system. In this paper, we propose a secure operating system trusted channel, SOSTC, as a prototype of a simple secure network protocol that can protect network traffic among secure operating systems and can transfer security information of the subject. It is significant that SOSTC can be used to extend a security range of secure operating system to the network environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.335-341
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• 2018

The KDE (Kernel Density Estimation) technique in GIS (Geographic Information System) has been widely used as a method for determining whether a phenomenon occurring in space forms clusters. Most human-generated events such as traffic accidents and retail stores are distributed according to a road network. Even if events on forward and rear roads have short Euclidean distances, network distances may increase and the correlation between them may be low. Therefore, the NKDE (Network-based KDE) technique has been proposed and applied to the urban space where a road network has been developed. KDE is being studied in the field of business GIS, but there is a limit to the microscopic analysis of economic activity along a road. In this study, the NKDE technique is applied to the analysis of urban phenomena such as the density of shops rather than traffic accidents that occur on roads. The results of the NKDE technique are also compared to pedestrian networks and road centerline networks. The results show that applying NKDE to microscopic trade area analysis can yield relatively accurate results. In addition, it was found that pedestrian network data that can consider the movement of actual pedestrians are necessary for accurate trade area analysis using NKDE.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.767-777
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• 2012

Topology control increases channel efficiency by controlling transmission power of a node, and as a result, network lifetime and throughput are increased. However, reducing transmission range causes a network connectivity problem, especially in mobile networks. When a network loses connectivity, the network topology should be re-configured. However, topology re-configuration consumes lots of energy because every node need to collect neighbor information. As a result, network lifetime may decrease, even though topology control is being used to prolong the network lifetime. Therefore, network connectivity time needs to be increased to expend network lifetime in mobile networks. In this paper, we propose an Adaptive-Redundant Transmission Range (A-RTR) algorithm to address this need. A-RTR uses a redundant transmission range considering a node status and flexibly changes a node's transmission range after a topology control is performed.

• IEMEK Journal of Embedded Systems and Applications
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• v.1 no.2
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• pp.73-81
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• 2006

The home network is generally separated from the Internet, as it is made up of a private network due to security issues and the lack of IPv4 addresses space. Also, a user may want to move from a terminal to another terminal connected in the home network during communicating with people outside the home. In this case, people connected in the Internet, or another home network could not communicate the user at the home. These limitations prevent a SIP-capable device connected in the home network from communicating with another SIP-capable device connected in the Internet or the outside of the home network. To overcome the limitations, This paper proposes the Adaptive SIP Application Server System as a software architecture that a user inside of the home can communicate with people outside of the home when the home is composed of a private IP-based network. Moreover, the proposed architecture provides the session mobility that allows the user to maintain a media session even if changing the terminal inside of the home during the session established. The proposed system was implemented over a home server device which acts functionality as a connection point for transmitting IP packets between a home network and the Internet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4205-4227
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• 2018

Network Coding (NC) is an approach recently investigated for increasing the network throughput and thus enhancing the performance of wireless mesh networks. The benefits of NC can further be improved when routing decisions are made with the awareness of coding capabilities and opportunities. Typically, the goal of such routing is to find and exploit routes with new coding opportunities and thus further increase the network throughput. As shown in this paper, in case of proactive routing the coding awareness along with the information of the measured traffic coding success can also be efficiently used to support the congestion avoidance and enable more encoded packets, thus indirectly further increasing the network throughput. To this end, a new proactive routing procedure called Congestion-Avoidance Network Coding-Aware Routing (CANCAR) is proposed. It detects the currently most highly-loaded node and prevents it from saturation by diverting some of the least coded traffic flows to alternative routes, thus achieving even higher coding gain by the remaining well-coded traffic flows on the node. The simulation results confirm that the proposed proactive routing procedure combined with the well-known COPE NC avoids network congestion and provides higher coding gains, thus achieving significantly higher throughput and enabling higher traffic loads both in a representative regular network topology as well as in two synthetically generated random network topologies.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.157-166
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• 1995

The Electroencephalogram (EEG) and evoked potential (EP) t;ave widely been used for study of brain functions. The EEG and EP signals acquired from multi-channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG). Since these artifact-affected EEG signals degrade EEG mapping, the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. In this paper a neural-network based classification is proposed to replace or to reduce human expert's efforts and time. From experiments, the neural-network based classification performs as good as human experts : variation of decisions between the neural network and human expert appears even smaller than that between human experts.