• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.179-186
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• 2008

In proportion as the growth of the wireless sensor network applications, we need for more accuracy wireless channel information. In the case of indoor or outdoor wireless sensor networks, multipath propagation causes severe problems in terms of fading. Therefore, a path-loss model for multipath environment is required to optimize communication systems. This paper deals with log-normal path loss modeling of the indoor 2.4 GHz channel. We measured variation of the received signal strength between the sender and receiver of which separation was increased from 1 to 30m. The path-loss exponent and the standard deviation of wireless channel were determined by fitting of the measured data. By using the PRR(Packet Reception Rate) of this model. Wireless sensor channel is defined CR(Connect Region), DR(Disconnected Region). In order to verify the characteristics of wireless channel, we performed simulations and experiments. We demonstrated that connection ranges are 24m in indoor, and 14m in outdoor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.574-577
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• 2014

Multimedia contents sharing services based on DLNA (Digital Living Network Alliance) technology such as Allshare or Smartshare in wireless home networks is widely adapted in Korea. However, the characteristics of the wireless network - frequently fluctuated bandwidth and signal strength could degrade the quality perceived by users. To minimize the impact of the challenge there are active researches in dynamic adaptive streaming. This paper proposes a dynamic adaptive streaming approach designed in a wireless network taking into account of the specifications of the user device such as resolution and processor. We modify the Kalman filter considering the characteristics of the device and demonstrate that the proposed approach determines Bit Rate using the modified filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5744-5764
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• 2018

In Wireless Sensor Networks (WSNs) for Internet of Things (IoT) environment, fault tolerance is a most fundamental issue due to strict energy constraint of sensor node. In this paper, a robust energy saving data dissemination protocol for IoT-WSNs is proposed. Minimized energy consumption and dissemination delay time based on signal strength play an important role in our scheme. The representative dissemination protocol SPIN (Sensor Protocols for Information via Negotiation) overcomes overlapped data problem of the classical Flooding scheme. However, SPIN never considers distance between nodes, thus the issue of dissemination energy consumption is becoming more important problem. In order to minimize the energy consumption, the shortest path between sensors should be considered to disseminate the data through the entire IoT-WSNs. SPMS (Shortest Path Mined SPIN) scheme creates routing tables using Bellman Ford method and forwards data through a multi-hop manner to optimize power consumption and delay time. Due to these properties, it is very hard to avoid heavy traffic when routing information is updated. Additionally, a node failure of SPMS would be caused by frequently using some sensors on the shortest path, thus network lifetime might be shortened quickly. In contrast, our scheme is resilient to these failures because it employs energy aware concept. The dissemination delay time of the proposed protocol without a routing table is similar to that of shortest path-based SPMS. In addition, our protocol does not require routing table, which needs a lot of control packets, thus it prevents excessive control message generation. Finally, the proposed scheme outperforms previous schemes in terms of data transmission success ratio, therefore our protocol could be appropriate for IoT-WSNs environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.75-80
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.600-608
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• 2020

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3194-3210
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• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.121-136
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• 2022

Automatic Identification System used in ship communication is required for marine control way, including monitoring of vessel operation in coastal and exchanging of information for safety navigation between them. But, it uses a very high frequency band of approximately 160 MHz, and at the same time, due to the curvature of Earth, there is a limit to the communication distance. Several demonstrations were made successfully over satellite, but not much work has been done yet through cube-satellite which has low-orbit at 500 km altitude. Here, we demonstrate a reception test of AIS (automatic identification system) receiver for a cube-satellites using software-defined radio (SDR). We collected AIS data from ship at port of Busan, Korea, using R8202T2 SDR and established to transmit them using Adam-Pluto and Matlab Simulink. The process of weakening the signal strength to a satellite was constructed using attenuator. Through above process, we demonstrated whether AIS data was successfully received from the AIS payload.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1457-1462
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• 2021

Untact order payment has become a new normal since the COVID-19. BLE beacon is a technology that supports low-power wireless communication within 70 meters without Bluetooth pairing and is specialized for indoor positioning. Therefore, it is suitable for building and providing O2O services beyond B2C and B2B services. However, beacons have a lower utilization rate because they have fewer platforms than wifi that support long-distance wireless communication. Therefore, this paper aims to provide more convenient non-face-to-face related services than before by designing and proposing an O2O service platform using BLE beacons. When scanning beacons, not only does it receive advertising data that beacons have, but it also ensures that the actual distance between the user's terminal and beacons is accurately calculated. Through accurate location, the O2O service platform will be able to provide users with store information, such as coupons and discounts, museum exhibits, and traffic information at the right time.

• Journal of The Korean Society of Integrative Medicine
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• v.11 no.1
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• pp.131-140
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• 2023

Purpose : The purpose of this study is to find out which shoulder abduction angle among the three representative angles of lower trapezius strengthening exercises most effectively improves rounded shoulder posture. Methods : Thirty-one people with rounded shoulders, the subjects of this study, were selected. With the use of a random number table, group A was randomly assigned to a Y-type exercise, group B to a T-type exercise, and group C to an MPC exercise. In the prone position, both arms are abducted at the angles suggested for each group (145 °, 90 °, and 45 °, respectively), and the hands are holding dumbbells to assume a functional posture. With the start signal, the thumb raised the arm to the sky and the arm is raised to the level of the ear for 10 seconds and then returned to the original position. Each week, the strength required was increased by changing the resistance weight, number of times, and set, and the exercise was performed three times a week for a total of four weeks. Results : There was a significant difference in the change in shoulder height of each group after four weeks of exercise in comparison to the amount of change in height before and after exercise in all groups (p<.05). However, no significant difference was observed between all groups in the change in muscle activity of the lower trapezius muscle in each group following four weeks of exercise (p>.05). Conclusion : The results of this study showed that the three lower trapezius strengthening exercises have a positive effect on the improvement of rounded shoulders. In consideration of these results, it is therefore proposed that the various lower trapezius strengthening exercises be applied in parallel during physical therapy for patients with rounded shoulders in clinical practice.