• Journal of the Korean Geotechnical Society
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• v.25 no.11
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• pp.53-60
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• 2009

Construction of a tunnel induces rock masses damage around the tunnel. The degree of damage produced on rock masses will affect on the mechanical and hydraulic behaviors of the rock masses. In this paper, P wave velocity measured by cross-hole test was used to assess rock masses damage around the test tunnel. Initiation of source signal was carried out using mechanical impact at the source installed borehole. In consequence, the generated P wave signal was low noise and apparent wave form, which allows accurate pick-up of first arrival time. From the test, the region where rock damage is expected shows relatively low P wave velocity. In addition, with multiple points of P wave velocity measurement along each cross-hole, two dimensional P wave tomography was obtained. The tomography provides apparent view of the rock damage behind the tunnel. The measured P wave velocity was correlated with features of rock masses, porosity and Q value.

• Design Convergence Study
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• v.15 no.2
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• pp.333-345
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• 2016

Recently, an intelligent product in which information and robotic technologies are applied to an existing common product, called a mother product has been developed. In order to develop intelligent products which could be accepted by users, various intelligent product design methods have been introduced considering various interaction aspects or intelligent parts to be made. However, as an intelligent product is originated in a mother product, intelligent product design methods based on product attributes need to be explored. In this study, the impact of intelligent product types by product attributes on users' acceptance was investigated by comparing hedonic intelligent products and utilitarian intelligent products. An experiment was executed with child slippers as a case. As a result, participants evaluated utilitarian intelligent products more positively than hedonic intelligent products. They showed higher purchase intention and willingness to pay toward utilitarian intelligent products than hedonic intelligent products. In the case of child slippers, even though the hedonic attributes could be expected as they are child products, utilitarian attributes were perceived as much more important than hedonic attributes as the child slippers are related to the floor noise which is a severe social problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Journal of Environmental Science International
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• v.33 no.4
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• pp.269-277
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• 2024

Scientific forensic techniques are used to verify environmental impact of groundwater pollution, surface water pollution, air pollution, noise, and vibration according to residents' complaints in connection with construction and civil engineering works. In this study, we investigated the contamination of groundwater and the lowering of the groundwater level in an area surrounding a tunnel excavation site for the Andong-Yeongdeok national road, using a forensic hydrogeological technique. We reviewed the groundwater level and water quality of well GW1 in the area surrounding the tunnel excavation site as well as tunnel construction information and then we analyzed the correlations among the obtained data. Before tunnel excavation, the water level of well GW1 was lower than the tunnel elevation. Considering the relationship between the precipitation, tunnel discharge, tunnel depth, and groundwater level of well GW1, the groundwater flowed from the tunnel to well GW1. Moreover, the tunnel discharge and groundwater levels were not related to each other. The pH of well GW1 was 8.4 before tunnel excavation. During excavation, the pH declined to 8.1-8.2 at the beginning, and increased to 8.8 at the end of the excavation. The fluorine concentration in well GW1 was 2.49 mg/L, 1.91-3.22 mg/L, and 1.7-2.67 mg/L, respectively, before, during, and after the excavation. The sulfate ion concentration was very high, over 2,000 mg/L, before and during the excavation; after the excavation, it was between 200 and 323 mg/L. Turbidity was 1.47, 10.5, and 4.51 NTU before, during, and after tunnel excavation, respectively. Therefore, the excavation of this tunnel is not related to the groundwater quality of well GW1.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.69-75
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• 2010

In this paper, the pitch is detected after the elimination of formant ingredients by flattening the spectrum in frequency domain. In order to remove impact of formant and transition frequency in the signal spectrum, formant envelop is made by linear interpolation with any points each sub-band and the spectrum of speech signal is compensated by the reverse of the envelop interpolated linearly after we divide frequency band into several segment based on LSP and detect the points. The experimental result showed the proposed method appeared an outstanding performance in compared with LPC, Cepstrum, Lifter methods. The method reduced the gross error rate 1.30% than the LPC method which appeared a good performance except the proposed method. Also, the proposed method showed low error rate in noise environment.

• Journal of Environmental Science International
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• v.33 no.3
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• pp.217-226
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• 2024

Manufacturing workers face increased fatigue and stress due to environmental factors in workplace such as noise and vibration. Addressing this issue requires creating conducive rest spaces; however, the existing conditions of rest spaces in manufacturing workplace are subpar and lack sufficient scholarly evidence. This study investigated the effect of nature-based rest spaces on the physical and emotional recovery from fatigue on manufacturing workers. Three manufacturing complexes with nature-friendly rest spaces were selected, and 63 manufacturing workers participated in the study. The measurement tools included the Multidimensional Fatigue Scale (MFS) for fatigue levels, physiological indicators (blood pressure and heart rate), and emotional indicators (Zuckerman Inventory of Personal Reaction Scale; ZIPERS, Perceived Restorativeness Scale; PRS, Profile of Mood States; POMS and State-Trait Anxiety Inventory; STAI). The study compared recovery levels during a 7-minute rest between a space without plants and a space with natural elements. The results indicated a significant reduction in systolic and diastolic blood pressure of participants in green rest spaces compared with those in conventional rest spaces. Regarding fatigue levels, green rest spaces showed a decrease in systolic blood pressure in the middle-fatigue and high-fatigue groups. Positive feelings increased in green spaces, whereas negative emotions decreased, suggesting that short breaks in nature-friendly environments effectively promote workers' physical and emotional recovery. Furthermore, this study emphasizes the importance of green space in various work environments to promote well-being in workers.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Journal of Korea Foresty Energy
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• v.17 no.1
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• pp.47-55
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• 1998

In the search for broadband damping composites, it is desirable to have polymers with a broad and high loss region, covering the entire temperature and frequency range of interest. Interpenetrating polymer networks, IPN's, are materials composed of two or more crosslinked polymers intimately and irrevocably interwinded. The resulting distribution of microenviron-ments can result in a materials with a high mechanical loss broad end over that of either polymer component alone. In this study, several series of copolymer, crosslinked copolymer and copolymer/copolymer IPN's were synthesized for possible use as broadband damping materials. Then their dynamic tensile properties were measured and compared with the damping properties of sandwich composites. Dynamic mechanical analysis showed that the temperature of loss peak may be varied over a wide temperature range with formulation. The compatibility of IPN`s was depended on the compatibility of A and B polymers as well as crosslink density. The damping factor(tan ${\delta}_c$) of composites became greater when a polymer of approximate storage module(E`) range of 5X10$^7$ to 10$^9$ dyne/cm$^2$ and large tan ${\delta}$ at the same time was used. The damping properities of poly (2-EHA80-co-St20)/poly(2-EHA20-co-St80) IPN`s crosslinked with 3%-DEGDM were relatively better over a broad temperature range.