• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.583-590
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• 2021

This paper is a study on the digitization of an analog 3D pen. The term digital implies features such as homeostasis, transformability, combinability, reproducibility, and convenience of storage. One device that produces a combination of these digital characteristics is a 3D printer, but its industrial use is limited due to low productivity and limitations with materials and physical characteristics. In particular, improvements are required to use 3D printers, such as better user accessibility owing to expertise and skills in modeling software and printers. Complementing this fact is the 3D pen, which is excellent in portability and ease of use, but has a limitation in that it cannot be digitized. Therefore, in order to secure a digitalization capability and ease of use, and to secure the safety of printing materials that pose controversial hazards during the printing process, research problems and alternatives have been derived by combining food, and digitization was demonstrated with a newly developed 3D pen. In order to digitize the 3D pen, a sensor in a structured device detects the motion of an analog 3D pen, and this motion is converted into 3D data (X-Y-Z coordinate values) through a spatial analysis algorithm. To prove this method, the similarity was confirmed by visualization using MeshLab version 1.3.4. It is expected that this food pen can be used in youth education and senior healthcare programs in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.3
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• pp.23-32
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• 2001

Since the LILI-128 cipher is a clock-controlled keystream generator, the speed of the keystream data is degraded in a clock-synchronized hardware logic design. Basically, the clock-controlled $LFSR_d$ in the LILI-128 cipher requires a system clock that is 1 ~4 times higher. Therefore, if the same clock is selected, the system throughput of the data rate will be lowered. Accordingly, this paper proposes a 4-bit parallel $LFSR_d$, where each register bit includes four variable data routines for feed feedback of shifting within the $LFSR_d$ . Furthermore, the timing of the propose design is simulated using a $Max^+$plus II from the ALTERA Co., the logic circuit is implemented for an FPGA device (EPF10K20RC240-3), and the throughput stability is analyzed up to a late of 50 Mbps with a 50MHz system clock. (That is higher than the 73 late at 45 Mbps, plus the maximum delay routine in the proposed design was below 20ns.) Finally, we translate/simulate our FPGA/VHDL design to the Lucent ASIC device( LV160C, 0.13 $\mu\textrm{m}$ CMOS & 1.5v technology), and it could achieve a throughput of about 500 Mbps with a 0.13$\mu\textrm{m}$ semiconductor for the maximum path delay below 1.8ns.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• Journal of Environmental Impact Assessment
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• v.30 no.2
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• pp.75-88
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• 2021

Six locations in the automated monitoring network at the Geum River Basin were selected forthis study. The water quality characteristics at two of the locations in the water quality monitoring network that were identical, or nearby, were examined, and their correlations were evaluated through statistical analysis. The results of the water quality analysis were converted to the water quality index and expressed in grades for comparison. For the data necessary for the study, public data from four years, from 2016-2019 were used and the evaluation parameters were water temperature, pH, EC, DO, TOC, TN, and TP. Results of the analysis showed that the water quality concentrations measured in the automated monitoring network and the water quality monitoring network differed in some measured values, but they tended to register variation in a specified ratio in most of the locations in the network. The analysis of the correlations of the parameters between the two monitoring networks found that water temperature, EC, and DO showed high correlations between the two monitoring networks. The TOC, TN, and TP showed high correlations, with a 0.7 or higher (correlation coefficient r), with the exception of some of the monitoring networks, although their correlations were lower than those of the basic parameters. The water quality index analysis showed that the water quality index values of the automated monitoring network and the water quality monitoring network were similar. The water quality index decreased and the pollution degree increased in the downstream direction, in both networks.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.291-298
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• 2022

In this paper, we present a method for dependency parsing with chunking in Korean. Dependency parsing is a task of determining a governor of every word in a sentence. In general, we used to determine the syntactic governor in Korean and should transform the syntactic structure into semantic structure for further processing like semantic analysis in natural language processing. There is a notorious problem to determine whether syntactic or semantic governor. For example, the syntactic governor of the word "먹고 (eat)" in the sentence "밥을 먹고 싶다 (would like to eat)" is "싶다 (would like to)", which is an auxiliary verb and therefore can not be a semantic governor. In order to mitigate this somewhat, we propose a Korean dependency parsing after chunking, which is a process of segmenting a sentence into constituents. A constituent is a word or a group of words that function as a single unit within a dependency structure and is called a chunk in this paper. Compared to traditional dependency parsing, there are some advantage of the proposed method: (1) The number of input units in parsing can be reduced and then the parsing speed could be faster. (2) The effectiveness of parsing can be improved by considering the relation between two head words in chunks. Through experiments for Sejong dependency corpus, we have shown that the USA and LAS of the proposed method are 86.48% and 84.56%, respectively and the number of input units is reduced by about 22%p.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.99-107
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• 2020

An underground integrated map is being built for underground safety management and is being updated periodically. The map update proceeds with the procedure of deleting all previously stored objects and saving newly entered objects. However, even unchanged objects are repeatedly stored, deleted, and stored. That causes the delay of the update time. In this study, in order to shorten the update time of the integrated map, an updated object and an unupdated object are separated, and only updated objects are reflected in the underground integrated map, and a system implementing this technology is described. For the updated object, an object comparison method using the center point of the object is used, and a quad tree is used to improve the search speed. The types of updated objects are classified into addition and deletion using the shape of the object, and change using its attributes. The proposed system consists of update object detection, extraction, conversion, storage, and history management modules. This system has the advantage of being able to update the integrated map about four times faster than the existing method based on the data used in the experiment, and has the advantage that it can be applied to both ground and underground facilities.