• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.81-94
• /
• 2022

Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

• Journal of Advanced Navigation Technology
• /
• v.20 no.4
• /
• pp.367-373
• /
• 2016

We propose an improved direction of arrival (DoA) estimation algorithm based on co-prime array and propagator method. The propagator method with co-prime array does not require singular value decomposition (SVD) requiring much less computational complexity but exhibiting somewhat worse performance in comparison with MUSIC based on co-prime array. We notice that one cause of the performance degradation was in the avoidance of the usage of the diagonal elements of the signal autocorrelation matrix that contains the noise power spectral density. So we propose an algorithm with the diagonal elements of the signal autocorrelation matrix based on the fact that the noise power spectral density can be estimated using noise observation over a long period of time. We observe, through simulations, that the proposed scheme in this paper improves the performance, with 4 times more computational requirement, by signal-to-noise ratio of 1.5dB and by DoA resolution of $0.7^{\circ}$ at the detection probability of 95% compared with the previously introduced co-prime array propagator scheme, resulting in performance much closer to that of co-prime array-based MUSIC scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.89-93
• /
• 2013

This paper is estimating a target direction of arrival with incident to receiver in spatial. This paper presented covariance using constraint matrix to correlation interference signal cancellation in multi input multi output array antennas system. we proposed a target direction of arrival estimation algorithm using cost function and minimum variance method. Through simulation, we were analysis a performance to compare general SPT-LCMV algorithm and proposal algorithm. We showed that proposal algorithm improve more target estimation than general SPT-LCMV algorithm in direction of arrival.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.12
• /
• pp.6038-6053
• /
• 2017

In this study, we propose an automatic melody extraction algorithm using deep learning. In this algorithm, feature images, generated using the energy of frequency band, are extracted from polyphonic audio files and a deep learning technique, a convolutional neural network (CNN), is applied on the feature images. In the training data, a short frame of polyphonic music is labeled as a musical note and a classifier based on CNN is learned in order to determine a pitch value of a short frame of audio signal. We want to build a novel structure of melody extraction, thus the proposed algorithm has a simple structure and instead of using various signal processing techniques for melody extraction, we use only a CNN to find a melody from a polyphonic audio. Despite of simple structure, the promising results are obtained in the experiments. Compared with state-of-the-art algorithms, the proposed algorithm did not give the best result, but comparable results were obtained and we believe they could be improved with the appropriate training data. In this paper, melody extraction and the proposed algorithm are introduced first, and the proposed algorithm is then further explained in detail. Finally, we present our experiment and the comparison of results follows.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.4
• /
• pp.167-172
• /
• 2014

In this paper we propose a waveform design algorithm that localizes the maximum output power in the target direction. We extend existing monostatic radar optimal waveform design schemes to bistatic multiple-input multiple-output (MIMO) radar systems. The algorithm simultaneously calculates the direction of departure (DoD) and the direction of arrival (DoA) using a two-dimensional multiple signal classification (MUSIC) method, and successfully localizes the maximum transmitted power to the target locations by exploiting the calculated DoD. The simulation results confirm the performance of the proposed algorithm.

• Journal of Korea Water Resources Association
• /
• v.33 no.S1
• /
• pp.3-10
• /
• 2000

A newly developed heuristic algorithm, Harmony Search, is applied to the parameter calibration problem of the nonlinear Muskingum model. The Harmony Search could, mimicking the improvisation of music player, find better parameter values for in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in the aspect of SSQ(the sum of the square of the deviations between the observed and routed outflows) as well as in the aspects of SAD(the sum of the absolute value of the deviations), DPO(deviations of peak of routed and actual flows) and DPOT(deviatios of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of asuming the initial values of desing parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2000.05a
• /
• pp.3-10
• /
• 2000

A newly developed heuristic algorithm, Harmony Search, is applied to the parameter calibration problem of the nonlinear Muskingum model. The Harmony Search could, mimicking the improvisation of music players, find better parameter values for in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in the aspect of SSQ (the sum of the square of the deviations between the observed and routed outflows) as well as in the aspects of SAD (the sum of the absolute value of the deviations), DPO (deviations of peak of routed and actual flows) and DPOT (deviations of peak time of rented and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converse to a better solution.

• Journal of Advanced Navigation Technology
• /
• v.25 no.5
• /
• pp.403-408
• /
• 2021

Harmony Search(HS) algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and has been successfully applied to solve different optimization problems. In order to further improve the performance of HS, this paper proposes a new method which is called Fast Harmony Search(FSH) algorithm. For the purpose, this paper suggest a method to unify two independent improvisation processes by newly defining the boundary value of a object variable using HM. As the result, the process time of the algorithm is shorten and explicit decision of bandwidth is no more needed. Furthermore, exploitative power of random selection is improved. The numerical results reveal that the proposed algorithm can find better solutions and is faster when compared to the conventional HS.

• Journal of Positioning, Navigation, and Timing
• /
• v.11 no.3
• /
• pp.181-189
• /
• 2022

The public open signals from Global Navigation Satellite System (GNSS) including Global positioning system (GPS) are used widely by many peoples in the world except for the public regulated restriction signals which are encrypted. Nowadays there are growing concerns about GNSS signal spoofing which can deceive the GNSS receivers by abusing these open services. To counter these spoofing threats, many researches have been studied including array antenna techniques which can detect the direction of arrival by means of Multiple Signal Classification (MUSIC) algorithm. Originally the array antenna techniques were developed to countermeasure the jamming signal in electronic warfare by using the nulling or beamforming algorithm toward a certain direction. In this paper, we study the anti-spoofing techniques using array antenna to overcome the jamming and spoofing issues simultaneously. First, we will present the theoretical analysis results of spoofing signal response of Minimum Variance Distortionless Response (MVDR) algorithm in array antenna. Then the eigenvector algorithm of covariance matrix is suggested and verified to work with the existing anti-jamming method. The modeling and simulation are used to verify the effectiveness of the anti-spoofing algorithm. Also, the field test results show that the array antenna system with the proposed algorithms can perform the anti-spoofing function. This anti-spoofing method using array antenna is very effective in the view point of solving both the jamming and spoofing problems using the same array antenna hardware.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2574-2587
• /
• 2019

Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.