• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.195-202
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• 2016

The government has run and managed various forms of direct payment systems, such as the paddy and field direct payment, to ease the instability of farm incomes with respect to market opening, and preserve farm income. Direct payments to the agricultural sector is a center in the key policy instrument that plays an important role in income stabilization. Despite the large amount of spending in the farm unit, the status of direct payment, and policy effects the analysis of direct payments, such as stability of income contribution, are insufficient. This paper, using the farm unit DB in 2014 and 2015, performed farm level analysis of direct payment, and derived the implications of the performance evaluation system. As a result, the distribution of direct payment showed considerable bias to the left side compared to the normal distribution curve. Approximately half of the farms (49.3%) in 2014 DB should receive below 100,000 won per year by a direct payment. A larger-scale farm showed a significantly increased income effect and income stabilizing effect because direct payments make higher contributions to farm income in proportional to the area. In the more elderly farmers, a high contribution by direct payment to farm income was found to be an advantage; however, in small-scale farms of less than 0.5ha, direct payment contribution on farm household income was only 3%. In large-scale farms, 10ha or more, the contribution to farm income were found to be 29.4%. The income of large farms was 10 times larger than small farmers, and the direct payment entitlements that were received were 110 times larger. Through this policy, direct payments are required for future improvements and modifications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6099-6111
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• 2015

This study was a systematic review and meta-analysis comparing the effects of aquatic exercise and land-based exercise in the treatment of knee osteoarthritis. 7 studies (n=449) met selection and exclusion criteria out of 287 potential studies obtained from the literature search via Ovid-Medline, Cochrane Library CENTRAL, CINAHL, RISS and KISS. The overall risk of bias of selected studies using SIGN (Scottish Intercollegiate Guidelines Network) checklist for randomized controlled trials (RCT) was regarded as low. As a result of meta analysis, Standardized Mean Difference (SMD) for pain was -0.26(95% CI -0.49, -0.03, p=0.03, $I^2=14%$), which implies that aquatic exercise groups had significant less pain than land-based exercise groups. On the other hand, there was no significant difference between aquatic exercise groups and land based exercise groups for flexion Range of Motion (ROM) (-0.12, 95% CI -0.51, 0.27, p=0.53, $I^2=0%$), extension ROM (-0.04, 95% CI -0.55, 0.48, p=0.89, $I^2=43%$), physical function (-0.12, 95% CI -0.44, 0.19, p=0.44, $I^2=0%$), Quality of Life (QOL) (-0.15, 95% CI -0.54, 0.24, p=0.46, $I^2=0%$). This study has some limitations due to few RCTs comparing aquatic exercise groups and land-based exercise groups in the treatment of knee osteoarthritis. Therefore, further RCTs should be conducted along with long-term outcomes.

• Journal of radiological science and technology
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• v.26 no.4
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• pp.39-46
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• 2003

Digital radiographic systems based on solid-state detectors, commonly referred to as flat-panel detectors, are gaining popularity in clinical practice. Large area, flat panel solid state detectors are being investigated for digital radiography. The purpose of this work was to evaluate the active matrix flat panel digital x-ray detectors in terms of their modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE). In this paper, development and evaluation of a selenium-based flat-panel digital x-ray detector are described. The prototype detector has a pixel pitch of $139\;{\mu}m$ and a total active imaging area of $14{\times}8.5\;inch^2$, giving a total 3.9 million pixels. This detector include a x-ray imaging layer of amorphous selenium as a photoconductor which is evaporated in vacuum state on a TFT flat panel, to make signals in proportion to incident x-ray. The film thickness was about $500\;{\mu}m$. To evaluate the imaging performance of the digital radiography(DR) system developed in our group, sensitivity, linearity, the modulation transfer function(MTF), noise power spectrum (NPS) and detective quantum efficiency(DQE) of detector was measured. The measured sensitivity was $4.16{\times}10^6\;ehp/pixel{\cdot}mR$ at the bias field of $10\;V/{\mu}m$ : The beam condition was 41.9\;KeV. Measured MTF at 2.5\;lp/mm was 52%, and the DQE at 1.5\;lp/mm was 75%. And the excellent linearity was showed where the coefficient of determination ($r^2$) is 0.9693.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.549-560
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• 2015

With the global scale impact of atmospheric $CO_2$ in global warming and climate system, it is necessary to monitor the $CO_2$ concentration continuously on a global scale, where satellite remote sensing has played a significant role recently. In this study, global monthly $CO_2$ concentrations obtained by satellite remote sensing were compared with ground-based measurements at Anmyeon-do and Gosan Korean Global Atmosphere Watch Center. Atmospheric $CO_2$ concentration has increased from 371.87 ppm in January 1999 to 405.50 ppm in December 2013 at Anmyeon-do station (KMA, 2013). Comparison of the continuous measurements by flask air sampling at Anmyeon-do shows the same trend and seasonal variations with those of global monthly mean dataset. Nevertheless, the trends of $CO_2$ over Northeast Asia showed the higher than those of global and the trends also changes with different slope. $CO_2$ products derived from Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS) were compared with ground-based measurement at Anmyeon-do. The monthly mean values of GOSAT and AIRS data are systemically lower than those obtained at Anmyeon-do, however, the seasonal cycle of satellite products present the similar trend with values of global and Anmyeon-do. The accuracy of $CO_2$ products from GOSAT and AIRS were evaluated statistically for two years from January 2011 to December 2012. GOSAT showed good correlation with the correlation coefficient, RMSD and bias of 0.947, 5.610 and -5.280 to ground-based measurements respectively, while AIRS showed reasonable comparison with 0.737, 8.574 and -7.316 at Anmyeon-do station, respectively.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.586-603
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• 2004

One of the subtle problems that make noise control difficult for engineers is “the invisibility of noise or sound.” The visual image of noise often helps to determine an appropriate means for noise control. There have been many attempts to fulfill this rather challenging objective. Theoretical or numerical means to visualize the sound field have been attempted and as a result, a great deal of progress has been accomplished, for example in the field of visualization of turbulent noise. However, most of the numerical methods are not quite ready to be applied practically to noise control issues. In the meantime, fast progress has made it possible instrumentally by using multiple microphones and fast signal processing systems, although these systems are not perfect but are useful. The state of the art system is recently available but still has many problematic issues : for example, how we can implement the visualized noise field. The constructed noise or sound picture always consists of bias and random errors, and consequently it is often difficult to determine the origin of the noise and the spatial shape of noise, as highlighted in the title. The first part of this paper introduces a brief history, which is associated with “sound visualization,” from Leonardo da Vinci's famous drawing on vortex street (Fig. 1) to modern acoustic holography and what has been accomplished by a line or surface array. The second part introduces the difficulties and the recent studies. These include de-Dopplerization and do-reverberation methods. The former is essential for visualizing a moving noise source, such as cars or trains. The latter relates to what produces noise in a room or closed space. Another mar issue associated this sound/noise visualization is whether or not Ivecan distinguish mutual dependence of noise in space : for example, we are asked to answer the question, “Can we see two birds singing or one bird with two beaks?"

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.46-55
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• 2016

This work proposes a 13b 100MS/s 45nm CMOS ADC with a high dynamic performance for IF-domain high-speed signal processing systems based on a four-step pipeline architecture to optimize operating specifications. The SHA employs a wideband high-speed sampling network properly to process high-frequency input signals exceeding a sampling frequency. The SHA and MDACs adopt a two-stage amplifier with a gain-boosting technique to obtain the required high DC gain and the wide signal-swing range, while the amplifier and bias circuits use the same unit-size devices repeatedly to minimize device mismatch. Furthermore, a separate analog power supply voltage for on-chip current and voltage references minimizes performance degradation caused by the undesired noise and interference from adjacent functional blocks during high-speed operation. The proposed ADC occupies an active die area of $0.70mm^2$, based on various process-insensitive layout techniques to minimize the physical process imperfection effects. The prototype ADC in a 45nm CMOS demonstrates a measured DNL and INL within 0.77LSB and 1.57LSB, with a maximum SNDR and SFDR of 64.2dB and 78.4dB at 100MS/s, respectively. The ADC is implemented with long-channel devices rather than minimum channel-length devices available in this CMOS technology to process a wide input range of $2.0V_{PP}$ for the required system and to obtain a high dynamic performance at IF-domain input signal bands. The ADC consumes 425.0mW with a single analog voltage of 2.5V and two digital voltages of 2.5V and 1.1V.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.65-73
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• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.79-92
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• 2019

When asked about the success factors of successful entrepreneurs and celebrities, he says he was lucky. The remarkable fact is that the attitude about luck is different. However, despite the fact that the belief that we believe is lucky is actually a dominant concept, there has not been much scientific verification of luck. In this study, we saw good luck not being determined randomly by the external environment, but by being able to control luck through the internal attributes of individuals. This study is significant that we have empirically elucidated what kind of efforts have gained good luck, whereas previous research has largely ended in vague logic where luck ends up with an internal locus of control among internal entrepreneurial qualities and efforts can make a successful entrepreneur. We introduced the concept of good luck belief to avoid confirmation bias, which is, to interpret my experience in a direction that matches what I want to believe, and used a good luck belief questionnaire in previous studies and tried to verify that those who have a good belief can increase entrepreneurial opportunity capability through planned happenstance skills. The reason for choosing the entrepreneurial opportunity capacity as a dependent variable was based on the conventional research, that is, the process of recognizing and exploiting the entrepreneurial opportunity is an important part of the entrepreneurship research For empirical research, we conducted a questionnaire survey of a total of 332 people, and the results of the analysis turned out that the belief of good luck has all the positive impacts of planned happenstance skills' sub-factors: curiosity, patience, flexibility, optimism and risk tolerance. Second, we have shown that only the perseverance, optimism, and risk tolerance of planned happenstance skills' sub-factors have a positive impact on this opportunity capability. Thirdly, it was possible to judge that the sub-factors of planned happenstance skills, patience, optimism, and risk tolerance, had a meditating effect between belief in luck and entrepreneurial opportunity capability. This study is highly significant in logically elucidating that people in charge of business incubation and education can get the specific direction when planning a training program for successful entrepreneur to further enhance the entrepreneurial opportunity ability, which is an important ability for the entrepreneur's success.