• Journal of Information Processing Systems
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• v.11 no.3
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.8
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• pp.93-97
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• 2004

Since powerline for powerline communications (PLC) is designed for supplying electric power using 60 Hz wave, they will have different electrical behaviors for high data rate PLC whose bandwidth is allocated between 1 MHz and 30 MHz. Thus, it is necessary to investigate the different properties in this frequency bandwidth for the high data rate PLC. In this paper, low voltage (220V) powerline impedance for indoor high data rate PLC in between 1 MHz and 30 MHz is measured. For measurement a low voltage coupling unit is made and a vector network analyzer is used. A T-equivalent circuit of the low voltage coupling unit is obtained and then powerline impedance is derived by measuring the reflection coefficient of the total powerline network. With the method proposed, impedance is measured in case of a general korean apartment and its property is analyzed. Measurement shows that the average impedance is about 100Ω.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.526-534
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• 2019

Localization methods using radio signals should obtain range measurements from three or more anchors. However, a typical building consists of narrow, long hallways and corners, making it difficult to secure more than three light of sight anchors. The result is a multi-modal solution that makes it difficult to estimate the user's location. In order to overcome this problem, this paper proposes a method for estimating the location using artificial neural networks. Using the artificial neural network, even if a multi-modal solution occurs, the position can be estimated by acquiring user movement pattern information based on accumulated range measurements. The method does not require any additional equipment or sensors, and only anchor-based range measurements can estimate the user's location. In order to verify the proposed method, location estimation tests were performed in situations where the multi-modal solution occurred by installing an insufficient number of anchors in a building. As a result, it was confirmed that the location can be estimated even when the number of anchors is insufficient.

• Journal of the Society of Disaster Information
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• v.14 no.3
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• pp.315-324
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• 2018

Purpose: This paper propose a temporary indoor positioning scheme with devices of internet of things (IoT) for disastrous situations in places without the infrastructure of networks. Method: The proposed scheme is based on the weighted centroid localization scheme that can estimate the position of a target with simple computation. Results: It also is implemented with the IoT devices at the underground parking lot, where the network is not installed, of general office building. According to the experiment results, the positioning error was around 10m without a priori calibration process at $82.5m{\times}56.4m$ underground space. Conclusion: The proposed scheme can be deployed many places without the infrastructure of networks, such as parking lots, warehouses, factory, etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.355-357
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• 2022

In this paper, as a positioning technology for predicting the movement path of a moving object using a recurrent neural network (RNN) model, which is a deep learning network, in an indoor environment, continuous location information is used to predict the path of a moving vehicle within a local path. We propose a movement path generation technique that can reduce decision errors. In the case of an indoor environment where GPS information is not available, the data set must be continuous and sequential in order to apply the RNN model. However, Wi-Fi radio fingerprint data cannot be used as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, we propose a movement path generation technique for a vehicle moving a local path in an indoor environment by giving the necessary sequential location continuity to the RNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.264-266
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• 2022

In order to apply the RNN model to the radio fingerprint-based indoor path generation technology, the data set must be continuous and sequential. However, Wi-Fi radio fingerprint data is not suitable as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, continuity information of sequential positions should be given. For this purpose, clustering is possible through classification of each region based on signal data. At this time, the continuity information between the clusters does not contain information on whether actual movement is possible due to the limitation of radio signals. Therefore, correlation information on whether movement between adjacent clusters is possible is required. In this paper, a deep learning network, a recurrent neural network (RNN) model, is used to predict the path of a moving object, and it reduces errors that may occur when predicting the path of an object by generating continuous location information for path generation in an indoor environment. We propose a method of giving correlation between clustering for generating an improved moving path that can avoid erroneous path prediction that cannot move on the predicted path.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.676-683
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• 2021

Wi-Fi Round-trip timing (RTT) based location estimation technology estimates the distance between the user and the AP based on the transmission and reception time of the signal. This is because reception instability and signal distortion are greater than that of a Received Signal Strength Indicator (RSSI) based fingerprint in an indoor NLOS environment, resulting in a large position error due to multipath fading. To solve this problem, in this paper, we propose an end-to-end based WiFi Trilateration Net (WTN) that combines neural network-based RTT correction and trilateral positioning network, respectively. The proposed WTN is composed of an RNN-based correction network to improve the RTT distance accuracy and a neural network-based trilateral positioning network for real-time positioning implemented in an end-to-end structure. The proposed network improves learning efficiency by changing the trilateral positioning algorithm, which cannot be learned through differentiation due to mathematical operations, to a neural network. In addition, in order to increase the stability of the TOA based RTT, a correction network is applied in the scanning step to collect reliable distance estimation values from each RTT AP.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.549-551
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• 2002

A Wireless Local Area Network (WLAN) is a flexible data communication system implemented as an extension to, or as an alternative for, a wired LAN with in a building or campus. However, communications in an indoor environment present problems not encountered in outdoor wireless communication systems. Since cellular type systems are interference limited, the indoor environment is more hostile than the outdoor environment due to the lower propagation constant. In this paper, the equation for the signal to interference ratio in a multistory building will be derived. Knowing the S/I ratio, the floor frequency reuse can be determined. Finally, the simulation in this research is designed to study the performance (BER) of WLAN system in the multistory environment by applying the 8-PSK Trellis Coded modulation technique. The procedure allows a quick evaluation of BER in Wireless LAN system due to the co-channel interference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.794-799
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• 2015

To manage cell outage problem in indoor wireless communication systems, we should resolve the problem of abrupt network failure quickly. In this paper, we propose a near-optimal cell outage management (NoCOM) scheme to support seamless services to users. In consideration of system throughput, user fairness, and the guarantee of QoS simultaneously, the NoCOM scheme finds the solution of subchannel and power allocations using a non-convex optimization technique and allocates radio resources to users iteratively. Through intensive simulations, we verify the outstanding performances of the proposed NoCOM scheme with respect to the average cell capacity, user fairness, and computational complexity.

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.207-220
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• 2009

A large number of phone calls and data services will take place in indoor environments. In Long Term Evolution (LTE), femtocell, as a home base station for indoor coverage extension and wideband data service, has recently gained significant interests from operators and consumers. Since femtocell is frequently turned on and off by a personal owner, not by a network operator, one of the key issues is that femtocell should be identified autonomously without system information to support handover from macrocell to femtocell. In this paper, we propose a dynamic reservation scheme of Physical Cell Identities (PCI) for 3GPP LTE femtocell systems. There are several reserving types, and each type reserves a different number of PCIs for femtocell. The transition among the types depends on the deployed number of femtocells, or the number of PCI confusion events. Accordingly, flexible use of PCIs can decrease PCI confusion. This reduces searching time for femtocell, and it is helpful for the quick handover from macrocell to femtocell. Simulation results show that our proposed scheme reduces average delay for identifying detected cells, and increases network capacity within equal delay constraints.