• Journal of the Korean Geosynthetics Society
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• v.18 no.3
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• pp.55-67
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• 2019

Deep soft ground in mouth of Nackdong river requires to be analysed with prediction method concerning characteristics of secondary consolidation from the beginning because it causes excessive settlement due to time-dependant secondary consolidation characteristics. This study investigated characteristics of extended settlement by conducting one-dimensional theory, elasto-plastic model and visco-elasto-plastic model as well as analyzing long-term measuring data observed over 2,000 days. According to one-dimensional theory and elasto-plastic model, there is not definite correlation between height of embankment and depth of soft ground while visco-elasto-plastic model showed similar result of settlement to that of long-term measuring data. Consequently it is suggested that applying visco-elasto-plastic model to developing deep underground place as studied area on predicting extended settlement before construction prevents economic loss and delay during process by preparing secondary consolidation characteristics.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.21-29
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• 2021

The emerging smart world based on IoT requires deployment of a large number of diverse sensors to generate data pertaining to different applications. Recent years have witnessed a plethora of IoT solutions beneficial to various application domains, IoT techniques also help boost agricultural productivity by increasing crop yields and reducing losses. This paper presents a predictive IoT smart farm platform for forcast services. We built an online agricultural forecasting service that collects microclimate data from weather stations in real-time. To demonstrate effectiveness of our proposed system, we designed a frost and pest forecasting modes on the microclimate data collected from weather stations, notifies the possibilities of frost, and sends pest forecast messages to farmers using push services so that they can protect crops against damages. It is expected to provide effectively that more precise climate forecasts thus could potentially precision agricultural services to reduce crop damages and unnecessary costs, such as the use of non-essential pesticides.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.135-141
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• 2021

Interest is growing in the design and development of square wave driven BLDC permanent magnet motors suitable for industrial automation, and the development of position detection circuits and drivers. However, this motor is somewhat limited in its application despite the price and functional advantages due to the decrease in efficiency due to switching loss and vibration and noise. In the process of designing and assembling a BLDC motor, the magnetic pole angle is not uniform or the magnetic flux distribution is distorted due to problems in magnetic circuit design or product non-uniformity in the assembly process. Therefore, these things cause position detection deviation and deteriorate the motor characteristics. In addition, the sine wave driven BLDC system can operate stably only when the signal generated from the position sensor is accurately fed back to the driver. However, since the generated signal cannot perform stable position detection due to the occurrence of DC offset component due to magnetic flux density deviation or magnetization technology, which is an external influence, this study intends to study the proposed circuit that can remove the DC offset component.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.121-130
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• 2007

In this paper, we propose a reversible watermarking scheme in the JPEG compression domain. The reversible watermarking is useful to authenticate the content without the quality loss because it preserves the original content when embed the watermark information. In the internet, for the purpose to save the storage space and improve the efficiency of communication, digital image is usually compressed by JPEG or GIF. Therefore, it is necessary to develop a reversible watermarking in the JPEG compression domain. When the watermark is embedded, the lossless compression was used and the original image is recovered during the watermark extracting process. The test results show that PSNRs are distributed from 38dB to 42dB and the payload is from 2.5Kbits to 3.4Kbits where the QF is 75. Where the QF of the Lena image is varied from 10 to 99, the PSNR is directly proportional to the QF and the payload is around $1.6{\sim}2.8Kbits$.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1225-1236
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• 2020

Fuzzing is an automated software testing methodology that dynamically tests the security of software by inputting randomly generated input values outside of the expected range. KISA is releasing open source for standard cryptographic algorithms, and many crypto module developers are developing crypto modules using this source code. If there is a vulnerability in the open source code, the cryptographic library referring to it has a potential vulnerability, which may lead to a security accident that causes enormous losses in the future. Therefore, in this study, an appropriate security policy was established to verify the safety of block cipher source codes such as SEED, HIGHT, and ARIA, and the safety was verified using differential fuzzing. Finally, a total of 45 vulnerabilities were found in the memory bug items and error handling items, and a vulnerability improvement plan to solve them is proposed.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.333-338
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• 2020

This paper proposes an augmented reality-based electronic circuit education system as a way for electronic circuit education, which is the basis of ICT convergence technology field. It consists of a hardware module that can identify the actual circuit and a mobile educational content that can check the current flow, input, output, and measured value by applying augmented reality technology. An experiment was conducted on image recognition, which is the main performance, for the purpose of stable operation of the system, and as the experimental method the recognition rate was measured by changing the distance between the hardware module and the mobile device to a certain interval. As a result of the experiment, the recognition rate was 100 percent at a distance of 25[Cm] or higher, and it was confirmed that the recognition rate decreased by 12% at a distance below 25[Cm], which can be said to be the effect of an error that results in image loss taken due to close distance. In the future, we plan to apply the education system presented in this paper to classes, which increases the efficiency of classes and improve students' interest and understanding of the subject.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.89-94
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• 2021

Recently, the boundaries between people who produce and consume digital contents disappears due to the massive developments in information and communications technology (ICT) and the rapidly increasing spread of smartphones unlike in the traditional mass media (e.g., newspapers, radios, and TVs). Through the open service platform, the problem perception for each individual remains the same, but the problem solving methods varies as the service types have been diversified. The creation of added value through the growth of the new media platform industry is expected to enrich our lives, but it can also cause severe social side effects. For example, communication problems between social classes due to the information gap have led to generational conflict, and if such problems persist, it can cause national and social losses. Therefore, this paper analyzes the policy efforts to resolve the information gap and the necessity of the legalization of multimedia literary education to maximize the synergy effect through psychological model.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.535-540
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• 2021

Multi-document refers to a document consisting of various topics, not a single topic, and a typical example is online reviews. There have been several attempts to summarize online reviews because of their vast amounts of information. However, collective summarization of reviews through existing summary models creates a problem of losing the various topics that make up the reviews. Therefore, in this paper, we present method to summarize the review with minimal loss of the topic. The proposed method classify reviews through processes such as preprocessing, importance evaluation, embedding substitution using BERT, and embedding clustering. Furthermore, the classified sentences generate the final summary using the trained Transformer summary model. The performance evaluation of the proposed model was compared by evaluating the existing summary model, seq2seq model, and the cosine similarity with the ROUGE score, and performed a high performance summary compared to the existing summary model.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.223-228
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• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.