• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4043-4064
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• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4022-4032
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• 2021

In this study, machine learning (ML) techniques were used to model surveillance test data of nuclear power plants from an international database of the ASTM E10.02 committee. Regression modeling was conducted using various techniques, including Cubist, XGBoost, and a support vector machine. The root mean square deviation of each ML model for the baseline dataset was less than that of the ASTM E900-15 nonlinear regression model. With respect to the interpolation, the ML methods provided excellent predictions with relatively few computations when applied to the given data range. The effect of the explanatory variables on the transition temperature shift (TTS) for the ML methods was analyzed, and the trends were slightly different from those for the ASTM E900-15 model. ML methods showed some weakness in the extrapolation of the fluence in comparison to the ASTM E900-15, while the Cubist method achieved an extrapolation to a certain extent. To achieve a more reliable prediction of the TTS, it was confirmed that advanced techniques should be considered for extrapolation when applying ML modeling.

• Wind and Structures
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• v.36 no.6
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.203-212
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• 2024

Simultaneous motion is essential in the activities of daily living (ADL). For motion intention recognition, surface electromyogram (sEMG) and corresponding motion label is necessary. However, this process is time-consuming and it may increase the burden of the user. Therefore, we propose a simultaneous motion recognition framework using data augmentation based on muscle activation model. The model consists of multiple point sources to be optimized while the number of point sources and their initial parameters are automatically determined. From the experimental results, it is shown that the framework has generated the data which are similar to the real one. This aspect is quantified with the following two metrics: structural similarity index measure (SSIM) and mean squared error (MSE). Furthermore, with k-nearest neighbor (k-NN) or support vector machine (SVM), the classification accuracy is also enhanced with the proposed framework. From these results, it can be concluded that the generalization property of the training data is enhanced and the classification accuracy is increased accordingly. We expect that this framework reduces the burden of the user from the excessive and time-consuming data acquisition.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Korean Journal of Social Welfare
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• v.57 no.4
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• pp.147-175
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• 2005

Efforts to identify the public's perception of social welfare as an academic discipline have never been conducted in Korea since the establishment of social welfare department in 1947 at Ewha Womans University. Such efforts are very meaningful in identifying directions and tasks to strengthen Korean social welfare as well as in clarifying and promoting our understanding concerning status of the academic discipline. This study attempts to explore and describe the degree of the public's perception in Korea with analyzing data surveyed in 2004 by our interdisciplinary research team. This study develops and uses a questionnaire having a Likert scale format that is composed of 8 points and measures the public's perception in the following dimensions: (1) personal interests on academic discipline; (2) contribution of academic discipline; (3) prospect of academic discipline; (4) importance of academic discipline; (5) expertise of academic discipline; and (6) personal knowledge on academic discipline. To avoid social desirability and promote objectivity with comparative measurement, this study selects ten representative academic disciplines as follows: medicine; physics; biology; social welfare; economics; psychology; sociology; political science; library science; and communication & journalism. This study attempts to identify (1) the degree of the public's perception on ten academic disciplines; (2) the position of social welfare by comparing it with each academic discipline and by comparing mean of social welfare with overall mean of six social science disciplines in the six dimensions; (3) the differences in the public's perceptions of social welfare on six dimensions by the respondents' status factor(high school students, college and graduate students, and citizens) and gender factor by using MANCOVA, and (4) the differences in the public's perceptions of social welfare on six dimensions by major factor(social welfare, social science majors, and natural science majors) and gender factor of college and graduate school students by using MANCOVA. The results of data analysis are as follows: (1) while the 3,319 respondents gave relatively high rating on natural sciences in the dimensions of contribution and expertise, they did the same on social sciences in the dimensions of personal interests and personal knowledge; (2) in overall comparisons, while the 3,319 respondents gave relatively high rating on social welfare in the dimensions of contribution, prospect and importance, they gave the lowest rating on the expertise of social welfare; (3) in the comparisons with social science disciplines, while the 3,319 respondents gave relatively high rating on social welfare in the dimensions of contribution, prospect and importance, they gave the lowest rating on the expertise of social welfare; (4) when analyzing all the respondents, there were differences in the vector of personal interests, contribution, prospect, importance, expertise, and personal knowledge by status factor, gender factor, and interaction effect factor; and (5) when analyzing only the respondents in college and graduate schools, there were differences in the vector of personal interests, contribution, prospect, importance, expertise, and personal knowledge by only major factor and gender factor. The results provide empirical backgrounds for discussing current image, status and major characteristics of social welfare as a discipline in Korea. Indeed, this study provides new meaningful and thoughtful guide for further investigation on the topic. In addition, contributing to clarifying and broadening our understandings about the public's perception on social welfare in Korea, this study discusses the tasks for dealing with expertise issue that is the most vulnerable issue of Korean social welfare discipline and research directions to strengthen and promote social welfare discipline in Korea.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.187-196
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• 2011

Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.45-54
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• 2012

Location-based services with GPS positioning technology as a key technology, but recognizing the current location through satellite communication is not possible in an indoor location-aware technology, low-power short-range communication is primarily made of the study. Especially, as Chirp Spread Spectrum(CSS) based location-aware approach for low-power physical layer IEEE802.15.4a is selected as a standard, Ranging distance estimation techniques and data transfer speed enhancements have been more developed. It is known that the distance measured by CSS ranging has quite a lot of noise as well as its bias. However, the noise problem can be adjusted by modeling the non-zero mean noise value by a scaling factor which corresponds to the change of magnitude of a measured distance vector. In this paper, we propose a localization system using the CSS signal to measure distance for a mobile node taken a measurement of the exact coordinates. By applying the extended kalman filter and least mean squares method, the localization system is faster, more stable. Finally, we evaluate the reliability and accuracy of the proposed algorithm's performance by the experiment for the realization of localization system.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.3 no.2
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• pp.59-70
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• 1998

Two-dimensional trend-vector model of sediment transport is first tested in the tidal flat of Garolim Bay, mid-western coast of the Korean Peninsula. Three major parameters of surface sediment, i.e., mean grain size, sorting and skewness, are used for defining the best-fitting transport trend-vector on the sand ridge and muddy sand flat. These trend vectors are compared with the real transport directions determined from morphology, field observation and bedforms. The 15 possible cases of trend vectors are calculated from total sediments. In order to find the role of coarse sediments, trend vectors from sediments coarser than < 4.5 ${\phi}$, (sand size) are separately calculated from those of total sediments. As compared with the real directions, the best-fitting transport-vector model is the "case M" of coarse sediments which is the combined trend vectors of two cases: (1) finer, better sorted and more negatively skewed and (2) coarser, better sorted and more positively skewed. This indicates sand-size grains are formed by simpler hydrodynamic processes than total sediments. Transported sediment grains are better sorted than the source sediment grains. This indicates that consistent hydrodynamic energy can make sediment grains better sorted, regardless of complicated mechanisms of sediment transport. Consequently, both transported vector model and real transported direction show that the source of sediments are located outside of bay (offshore Yellow Sea) and in the baymouth. These source sediments are transported through the East Main Tidal Channel adjacent the baymouth. Some are transported from the subtidal zone to the upper tidal flat, but others are transported farther to the south, reaching the south tidal channel in the study area. Also, coarse sediment grains on the sand ridge are originally from the baymouth, and transported through the subtidal zone to the south tidal channel. These coarse sediments are moved to the northeast, but could not pass the small north tidal channel. It is interpreted that the great amount of coarse sediments is returned back to the outside of the bay (Yellow Sea) again through the baymouth during the ebb tide. The distribution of muddy sand in the northeastern part of study area may result from the mixing of two sediment transport mechanisms, i.e., suspension and bedload processes. The landward movement of sand ridge and the formation of the north tidal channel are formed either by the supply of coarse sediments originating from the baymouth and outside of the bay (subaqueous sand ridges including Jang-An-Tae) or by the recent relative sea-level rise.