• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.285-288
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• 2010

In this paper, we present a very useful method for updating digital satellite radio broadcast channel information. When a devices equipped with function to receive Digital Satellite Radio such as Home Theater, MP3 player, mobile phones, car audio system and various other types of Digital Devices, receives new Digital satellite radio (will be mentioned as XM radio onwards) broadcast channel information, only the current received XM radio broadcast channel and N number of pre/post nearby broadcast channels are scanned randomly in zigzag manner. Then the previous XM radio broadcast channel information updated with the newly received XM radio broadcast channel information. Since this method can prevent batch update for all XM radio channel, including some channels which less likely did not select by user, update process for real time frequently changed XM radio broadcast channel information can be performed efficiently with minimal or without delay.

• International Journal of High-Rise Buildings
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• v.3 no.4
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• pp.255-271
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• 2014

A method of smart system identification of super high-rise buildings is proposed in which super high-rise buildings are modeled by a shear-bending system. The method is aimed at finding the story shear and bending stiffnesses of a specific story only from the horizontal floor accelerations. The proposed method uses a set of closed-form expressions for the story shear and bending stiffnesses in terms of the limited floor accelerations and utilizes a reduced shear-bending system with the same number of elements as the observation points. A difficulty of prediction of an unstable specific function in a low frequency range can be overcome by introducing an ARX model and discussing its relation with the Taylor series expansion coefficients of a transfer function. It is demonstrated that the shear-bending system can simulate the vibration records with a reasonable accuracy. It is also shown that the vibration records at two super high-rise buildings during the 2011 Tohoku (Japan) earthquake can be simulated with the proposed method including a technique of inserting degrees of freedom between the vibration recording points. Finally it is discussed further that the time-varying identification of fundamental natural period and stiffnesses can be conducted by setting an appropriate duration of evaluation in the batch least-squares method.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.133-138
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• 2020

This paper introduces a method for improving accuracy using the first convolution layer, which is not used in most modified CNN(: Convolution Neural Networks). In CNN, such as GoogLeNet and DenseNet, the first convolution layer uses only the traditional methods(3×3 convolutional computation, batch normalization, and activation functions), replacing this with RGB-csb. In addition to the results of preceding studies that can improve accuracy by applying RGB values to feature maps, the accuracy is compared with existing CNN using a limited number of images. The method proposed in this paper shows that the smaller the number of images, the greater the learning accuracy deviation, the more unstable, but the higher the accuracy on average compared to the existing CNN. As the number of images increases, the difference in accuracy between the existing CNN and the proposed method decreases, and the proposed method does not seem to have a significant effect.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.115-125
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• 2012

Application of the Object-oriented Programming (OOP) method to the Finite Element Model (FEM) program has various strengths including the features of encapsulation, polymorphism and inheritance. However, this technique should be based upon a premise that the independency of the object method and data to be used is guaranteed. By attempting to apply the OOP to the FEM, existing researches go against the independency of the OOP which is an essential feature of the method. The reason is this: existing researches apply the OOP to modules in accordance with analysis procedures, although the data to be used is classified as an element unit in the FEM. Therefore, the required independency cannot be maintained as whole stiffness matrices and boundary conditions are combined together. Also, solutions are sought from analysis module after data is regrouped at the pre-processor, and their results are analyzed during the post-processor. As this is similar to a batch processing, it cannot use data at analysis, and recalculation should be done from the beginning if any condition is changed after the analysis is complete, which are limitations of the existing researches. This research implemented the Object-orientation of elements so that the three features of the OOP (i.e. encapsulation, polymorphism and inheritance) can be guaranteed and their independency maintained as a result. For this purpose, a model called 'Object-oriented Finite element Model ensuring the Independency of Elements (OFMIE)', which enables the analysis of targets through mutual data exchanges within instance, was developed. In conclusion, the required independency was achieved in the instance of the objected elements and the analysis results of previous conditions could be used for the analysis after changes. The number of repetitive calculations was reduced by 75 per cent through this gradual analysis processes.

• Geotechnical Engineering
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• v.14 no.5
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• pp.191-204
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• 1998

The feasibility of fly ash was evaluated as an alternative liner material to the conventional clay liner of landfills through modeling and laboratory experiments. In order to consider the effect of unsaturation on water flow through the liner, analyses were made to compare flow characteristics in saturated liner with that of unsaturated one. Contaminant migration characteristics in liners were investigated by batch experiment and modeling, in which phenol was employed as a model was solved by numerical techniques of finite difference method and predictor-corrector method to deal with high non-linearity. Sequential method was used to handle the system of differential equations. Results show that the alternative liner material is more capable of cutting off water flow in unsaturated condition and in preventing phenol from passing through it. It can be seen that, under the flow conditions considered in this study, the conventional saturation approach underestimates the amount of water passing through the liner and doers the cut-off capability against phenol significantly.

• Journal of the Korean Magnetics Society
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• v.8 no.2
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• pp.86-92
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• 1998

Homegeneous feedstock is necessary to make plastic magents with uniform magnetic properties, therefore the optrimized mixing route and the homogeneity evaluation method are demanded. In this paper, method of homogeneity evaluation and effect of homogeneity on the magnetic prperites were investigated using Sr-ferrite /EVA plastic magnets. The feedstocks with different homogeneity were prepared using batch mixer and single screw extruder. The homogeneities of feedstocks were tested by torgue sensor, capilary rheometer, and measurement of magnetic properties. Mixing torque measurement using torque sensor was an effective method to determine the critical powder loading, but it was nor suitable to suitable to determine the feedstock mixing quality. Particle alignment measurement of a plastic magent was very accurate to evaluate the homogeneity, but expensive equipments were required to make and measure the samples. Pressure measurement using capillary rheometer was a very effective and easy method with high accuracy. Homogeneous feedstock increased the particle alignment of plastic magnet. Remanet flux density and maximum energy product increased linearly and quadratically with increasing particle alignment, respectively.

• Journal of Microbiology
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• v.44 no.2
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• pp.155-161
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• 2006

Comparative analysis of microbial communities in a sequencing batch reactor which performed enhanced biological phosphorus removal (EBPR) was carried out using a cultivation-based technique and 16S rRNA gene clone libraries. A standard PCR protocol and a modified PCR protocol with low PCR cycle was applied to the two clone libraries of the 16S rRNA gene sequences obtained from EBPR sludge, respectively, and the resulting 424 clones were analyzed using restriction fragment length polymorphisms (RFLPs) on 16S rRNA gene inserts. Comparison of two clone libraries showed that the modified PCR protocol decreased the incidence of distinct fragment patterns from about 63 % (137 of 217) in the standard PCR method to about 34 % (70 of 207) under the modified protocol, suggesting that just a low level of PCR cycling (5 cycles after 15 cycles) can significantly reduce the formation of chimeric DNA in the final PCR products. Phylogenetic analysis of 81 groups with distinct RFLP patterns that were obtained using the modified PCR method revealed that the clones were affiliated with at least 11 phyla or classes of the domain Bacteria. However, the analyses of 327 colonies, which were grouped into just 41 distinct types by RFLP analysis, showed that they could be classified into five major bacterial lineages: ${\alpha},\;{\beta},\;{\gamma}-$ Proteobacteria, Actinobacteria, and the phylum Bacteroidetes, which indicated that the microbial community yielded from the cultivation-based method was still much simpler than that yielded from the PCR-based molecular method. In this study, the discrepancy observed between the communities obtained from PCR-based and cultivation-based methods seems to result from low culturabilities of bacteria or PCR bias even though modified culture and PCR methods were used. Therefore, continuous development of PCR protocol and cultivation techniques is needed to reduce this discrepancy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.647-654
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• 2022

Although the fields in which is utilized children's speech recognition is on the rise, the lack of quality data is an obstacle to improving children's speech recognition performance. This paper proposes a new method for improving children's speech recognition performance by additionally using adult speech data. The proposed method is a transformer based domain adversarial training using dynamically weighted loss to effectively address the data imbalance gap between age that grows as the amount of adult training data increases. Specifically, the degree of class imbalance in the mini-batch during training was quantified, and the loss function was defined and used so that the smaller the data, the greater the weight. Experiments validate the utility of proposed domain adversarial training following asymmetry between adults and children training data. Experiments show that the proposed method has higher children's speech recognition performance than traditional domain adversarial training method under all conditions in which asymmetry between age occurs in the training data.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.