• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.688-693
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• 2022

In this paper, channel propagation path loss was measured in building corridors for frequency bands of 3.7 GHz and 28 GHz, which are used in 5G mobile communication, and compared and analyzed with CI (Close-In) and FI (Floating-Intercept) channel models. To measure the propagation path loss, the measurement was performed while moving the receiver (Rx) from the transmitter (Tx) by 10 m. As a result of the measurement, the PLE (Path Loss Exponent) values of the CI model at 3.7 GHz and 28 GHz were 1.5293 and 1.7795, respectively, and the standard deviations were analyzed as 9.1606 and 8.5803, respectively. In the FI model, 𝛼 values were 79.5269 and 70.2012, 𝛽 values were -0.6082 and 1.2517, respectively, and the standard deviations were 5.8113 and 4.4810, respectively. In the analysis results through the CI model and the FI model, the standard deviation of the FI model is smaller than that of the CI model, so it can be seen that the FI model is similar to the actual measurement result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.458-459
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• 2021

Recently, researches using deep learning technology based on Wi-Fi fingerprints have been conducted for accurate services in indoor location-based services. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. At this time, continuous sequential data is required as training data. However, since Wi-Fi fingerprint data is generally managed only with signals for a specific location, it is inappropriate to use it as training data for an RNN model. This paper proposes a path generation method through prediction of a moving path based on Wi-Fi fingerprint data extended to region data through clustering to generate sequential input data of the RNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.456-457
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• 2021

Recently, in order to more accurately provide indoor location-based services, technologies using Wi-Fi fingerprints and deep learning are being studied. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. When using an RNN model for indoor positioning, the collected training data must be continuous sequential data. However, the Wi-Fi fingerprint data collected to determine specific location information cannot be used as training data for an RNN model because only RSSI for a specific location is recorded. This paper proposes a region clustering technique for sequential input data generation of RNN models based on Wi-Fi fingerprint data.

• Journal of the Korean Geotechnical Society
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• v.20 no.9
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• pp.47-56
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• 2004

A new fragmentation model, called the GRS (the generation model of weathered residual soils) model, was proposed in this study, This model could identify the formation of a residual soil. This model is based on the phenomena that as the soil was weathered more highly, soil particles were smaller and pores were more expanded simultaneously. The possibility of fragmentation, $P_F,$ which was based on the fractal theory, was introduced in this model. There were some fundamental notions in the GRS model that soil particles were generated as the rock is fragmented, and the fragmentation of the rock was performed step by step. The $P_F,$ of the rock was not constant at each fragmentation steps. As a result of application on the GRS model, there were more residue where $P_{Fi}s$ were small at any particle size. There was a S-shape of PSD curve at the concave shape of $P_{Fi},$ and the PSD curve goes to a gaped graded curve at the convex shape of $P_{Fi}.$ The shape of PSD curve was concave in the case of small $P_{Fi}s.$ The value of $P_{Fi}$ increased with the coefficient of uniformity $(C_u)$ and the fragmentation fractal dimension $(D_r),$ but had no relation with the coefficient of gradation $(C_C)$.

• ETRI Journal
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• v.37 no.5
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• pp.845-855
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• 2015

To enable the coexistence of Licensed Assisted Access (LAA) and Wi-Fi in 5 GHz unlicensed bands, a new channel access mechanism is proposed. Accounting for the fairness between LAA and Wi-Fi, the proposed mechanism finds the optimal transmission time ratio by adaptively adjusting the transmission durations for LAA and Wi-Fi. In addition, we propose a new analytical model for the distributed coordination function of IEEE 802.11 through some modifications of conventional analytical models for saturation and non-saturation loads. By computing the activity ratio of Wi-Fi, the proposed analytical model is able to control the time ratio between LAA and Wi-Fi, which is required for practical implementation of the proposed access mechanism. Through numerical simulations, the proposed channel access mechanism is compared with conventional methods in terms of throughput and utility.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.62-68
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• 2016

The fingerprint, which is one of the methods of indoor localization using WiFi, has been frequently studied because of its ability to be implemented via wireless access points. This method has low positioning resolution and high computational complexity compared to other methods, caused by its dependence of reference points in the radio map. In order to compensate for these problems, this paper presents a radio map designed algorithm based on the log-distance path loss model fusing a WiFi and BLE fingerprint. The proposed algorithm designs a radio map with variable values using the log-distance path loss model and reduces distance errors using a median filter. The experimental results of the proposed algorithm, compared with existing fingerprinting methods, show that the accuracy of positioning improved by from 2.747 m to 2.112 m, and the computational complexity reduced by a minimum of 33% according to the access points.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.54-62
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• 2012

A Wi-Fi mesh network providing multi-hop wireless connections based on IEEE 802.11 PHY/MAC technology has recently received a significant attention as a network infrastructure that interconnects RFID systems and wireless sensor networks (WSNs). However, the current IEEE 802.11 contention-based MAC protocol cannot fully utilize the network capacity due to eithor frame collisions or unused network resources. In this paper, we propose a novel multi-channel scheduling MAC (MCS-MAC) protocol for Wi-Fi mesh networks. Under the secondary interference model of Wi-Fi mesh networks, the MCS-MAC protocol can maximize the network throughput via activation of collision-free links that has a maximal link weight. Through the simulations, we show that the throughput of the MCS-MAC protocol is at least three times higher than that of existing MAC protocols in Wi-Fi mesh networks.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.177-182
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• 2012

We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian's motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.

• Journal of the military operations research society of Korea
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• v.9 no.1
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• pp.15-28
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• 1983

In sequential capital allocation processes, the information on the future availability (flexibility) of invested funds provides a decision maker with additional insight into the characteristics of alternatives. The investment decision with consideration of flexibility and profitability results in more wealth accumulation than the decision without considering flexibility does in sequential investment processes. To utilize the information on the flexibility under certainty, the PV/FI decision model is developed.