• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.69-72
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• 2000

In this paper, a novel algorithm for digital watermarking is proposed. We use two pattern keys from BCH (15, 7) code and one randomizing key. In the embedding process, optimal watermark coefficients are determined by statistical analysis of the DCT coefficients from the standpoint of HVS. In the detection, watermark coefficients are restored by correlation matching of the possible pattern keys and minimizing the estimation errors. Attacks tested in the experiments ate image enhancement and image compression (JPEG). Performance is evaluated by BER of the logo images and SNR/PSNR of the restored images. Our method has higher performance against JPEG attacks. Analysis for the performance is included.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.113-121
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• 2000

This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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• pp.362-367
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• 2012

Though a micro pump is a crucial element in miniaturized bio-fluidic systems or drug delivery systems, most of the conventional micro pumps still have some limitations to miniaturize their controller system and to obtain the sufficient back pressure which can rise over the inner pressure of human body or experimental animals. In this paper, to overcome these limitation, a new PZT disc and its controller were designed and fabricated to get the sufficient flowrate and the back pressure with guaranteeing embeddability of the controller into pumping body. The amplitudes of the disc deflections were as large as 40 ${\mu}m$ at 200 V - 100 Hz condition. As results of experiments, the flow rate and the back pressure increase when the frequency increases. The obtainable maximum flow rate and back pressure are 5.2 ml/min at 95 Hz and 13.14 kPa at 90 Hz respectively.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.217-223
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• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.

• Structural Engineering and Mechanics
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• v.25 no.2
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• pp.239-268
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• 2007

This paper deals with optimal stacking sequence design of laminate composite structures. The stacking sequence optimisation of laminate composites is formulated as a combinatorial problem and is solved using Simulated Annealing (SA), an algorithm devised based on inspiration of physical process of annealing of solids. The combinatorial constraints are handled using a correction strategy. The SA algorithm is strengthened by embedding Tabu search in order to prevent recycling of recently visited solutions and the resulting algorithm is referred to as tabu embedded simulated Annealing (TSA) algorithm. Computational performance of the proposed TSA algorithm is enhanced through cache-fetch implementation. Numerical experiments have been conducted by considering rectangular composite panels and composite cylindrical shell with different ply numbers and orientations. Numerical studies indicate that the TSA algorithm is quite effective in providing practical designs for lay-up sequence optimisation of laminate composites. The effect of various neighbourhood search algorithms on the convergence characteristics of TSA algorithm is investigated. The sensitiveness of the proposed optimisation algorithm for various parameter settings in simulated annealing is explored through parametric studies. Later, the TSA algorithm is employed for multi-criteria optimisation of hybrid composite cylinders for simultaneously optimising cost as well as weight with constraint on buckling load. The two objectives are initially considered individually and later collectively to solve as a multi-criteria optimisation problem. Finally, the computational efficiency of the TSA based stacking sequence optimisation algorithm has been compared with the genetic algorithm and found to be superior in performance.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.715-726
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• 2006

Web service technologies provide a standard means for interoperation and integration of heterogeneous applications distributed over the Internet. For efficient execution of hierarchically interacting composite web services, this paper proposes an approach to distribute web service invocations over peer systems effectively, exploiting intensional XML data embedding external service calls as a result of well services. A cost-based optimization problem on the execution of web services using intensional results was formalized, and a heuristic search method to find an optimal solution and a greedy algorithm to generate an efficient invocation plan quickly were suggested in this paper. Experimental evaluation shows that the proposed greedy algorithm provides near-optimal solutions in an acceptable time even for a large number of Web services.

• Computational Structural Engineering
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• v.9 no.3
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• pp.179-186
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• 1996

A simple nonlinear programming(NLP) formulation for the optimal topology problem of structures is developed and examined. The NLP formulation is general, and can handle arbitrary objective functions and arbitrary stress, displacement constraints under multiple loading conditions. The formulation is based on simultaneous analysis and design approach to avoid stiffness matrix singularity resulting from zero sizing variables. By embedding the equilibrium equations as equality constraints in the nonlinear programming problem, we avoid constructing and factoring a system stiffness matrix, and hence avoid its singularity. The examples demonstrate that the formulation is effective for finding an optimal solution, and shown to be robust under a variety of constraints.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.193-199
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• 2023

In this study, an analysis was conducted to analyze the structural stability of the piezoelectric power generation module and to determine the optimal burying hole interval for concrete, the installation site of the power generation module. A piezoelectric element refers to a functional ceramic having a piezoelectric direct effect that converts mechanical energy into electrical energy and a piezoelectric reverse effect. In the analysis of the piezoelectric power generation module, the load condition was applied with about 16 tons and a total of 10 wheels in consideration of the container trailer. The purpose was to evaluate the stability of major components of the piezoelectric power generation module through finite element analysis. In order to determine the optimal burying location of the concrete ground for burying the piezoelectric power generation module, the stability of the ground structure according to the distance of the holes was determined. As a result of the analysis, the maximum stress of the piezoelectric power generation module was generated in the support spring, showing a stress of about 276.7 MPa. It was found that the spacing of holes for embedding the piezoelectric power generation module should be set to a minimum of 100 mm or more.