• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.

• Journal of radiological science and technology
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• v.34 no.1
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• pp.17-25
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• 2011

In this study, we examined the impact caused by chronic exposure to Mn by investigating the degree of brain activation based on the data of recognition activities using fMRI (functional magnetic resonance imaging). A questionnaire survey, blood tests, and fMRI tests were carried out with respect to two groups. Group 1 was an exposure group consisting of 15 male workers who are 34 years old or older, and who worked for longer than 10 years in a shipbuilding factory as a welder. Group 2 was a control group consisting of 15 workers in manufacturing industries with the same gender and age. The results showed that blood Mn concentration of Group 1($1.3\;{\mu}g/dl$) was significantly higher than that of Group 2($0.8\;{\mu}g/dl$)(p < 0.001), and Pallidal Index (PI) of Group 1 was also significantly higher than that of Group 2 (p < 0.001). PI value of the group whose blood Mn concentration was $0.93\;{\mu}g/dl$ or higher was significantly higher than that of the group whose blood Mn concentration was less than $0.93 \;{\mu}g/dl$ (p < 0.001). As for brain activity area within the control group, the right and the left areas of occipital cortex showed significant activity and the left area of middle temporal cortex, the right area of superior inferior frontal cortex and inferior parietal cortex showed significant activity. Unlike the control group, the exposure group showed significant activity on the right area of superior inferior temporal cortex, the left of insula area. In the comparison of brain activity areas between the two groups, the exposure group showed significantly higher activation than the control group in such areas as the right inferior temporal cortex, the left area of superior parietal cortex and occipital cortex, and cerebellum including middle temporal cortex. However, in nowhere the control group showed more activated area than the exposure group. As the final outcome, chronic exposure to Mn increased brain activity during implementation of arithmetic task. In an identical task, activation increased in superior inferior temporal cortex, and insula area. And it was discovered that brain activity increase in temporal area and occipital area was more pronounced in the exposure group than in the control group. This result suggests that chronic exposure to Mn in the work environment affects brain activation neuro-network.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.283-296
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• 2012

The goal of this study is to determine the social emotion model as an emotion sharing relationship and information sharing relationship based on the user's relations at social networking services. 26 social emotions were extracted by verification of compliance among 92 different emotions collected from the literature survey. The survey on the 26 emotion words was verified to the similarity of social relation types to the Likert 7-points scale. The principal component analysis of the survey data determined 12 representative social emotions in the emotion sharing relation and 13 representative social emotions in the information sharing relation. Multidimensional scaling developed the two-dimensional social emotion model of emotion sharing relation and of information sharing relation based on online communication environment. Meanwhile, insignificant factors in the suggest social emotion models were removed by the structural equation modeling analysis, statistically. The test result of validity analysis demonstrated the fitness of social emotion models at emotion sharing relationships (CFI: .887, TLI: .885, RMSEA: .094), social emotion model of information sharing relationships (CFI: .917, TLI: .900, RMSEA : 0.050). In conclusion, this study presents two different social emotion models based on two different relation types. The findings of this study will provide not only a reference of evaluating social emotions in designing social networking services but also a direction of improving social emotions.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1701-1708
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• 2011

A typical day in the subway transportation is used by hundreds of thousands are also concerned about the safety of the various workrooms with high underground fire or other less than in the subway users could be damaging even to be raised and there. In 2010, in fact, room air through vents in the fire because smoke and toxic gas accident victims, and train service suspended until such cases are often reported. In response to these incidents in subway stations, even if the latest IT technology, wireless sensor network technology and intelligent video surveillance technology by integrating fire and structural integrity, such as a comprehensive integrated surveillance system to monitor the development of intelligent urban transit system and are under study. In this study, prior to the application of the monitoring system into the field stations, authors carried out the ZigBee-based wireless sensor networks performance analyzation in the Chungmuro station. The test results at a communications room and ventilation room of the station are summarized and analyzed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.31-38
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• 2005

The architecture and the implementation details of a UTMI(USB2.0 Transceiver Macrocell Interface) compatible USB2.0 transceiver chip were presented. To confirm the validation of the incoming data in noisy channel environment, a squelch state detector and a current mode Schmitt-trigger circuit were proposed. A current mode output driver to transmit 480Mbps data on the USB cable was designed and an on-die termination(ODT) which is controlled by a replica bias circuit was presented. In the USB system using plesiochronous clocking, to compensate for the frequency difference between a transmitter and a receiver, a synchronizer using clock data recovery circuit and FIFO was designed. The USB cable was modeled as the lossy transmission line model(W model) for circuit simulation by using a network analyzer measurements. The USB2.0 PHY chip was implemented by using 0.25um CMOS process and test results were presented. The core area excluding the IO pads was $0.91{\times}1.82mm^2$. The power consumptions at the supply voltage of 2.5V were 245mW and 150mW for high-speed and full-speed operations, respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.99-107
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• 2010

In a single frequency network (SFN) for Advanced Television Systems Committee (ATSC) terrestrial digital television (DTV) system, the interferences induced by the multiple transmitters and/or repeaters using the same frequency are inevitable. Since the presence of interferences results in performance degradation of the SFN, it is crucial to manipulate the interferences by adjusting the transmit power and timing of each transmitter and repeater. In the ATSC terrestrial DTV system, in order to facilitate the interference manipulation process, a transmitter identification (TxID) signal which is uniquely embedded in the signal to be transmitted from each transmitter and repeater is recommended. Even though the injection level of the TxID signal is much lower than the DTV signal, the TxID signal injection infects the DTV signal. Hence, the effect of the TxID signal on the DTV signal must be investigated before deployment. In this paper, the effect of the TxID signal on the performance of legacy DTV receivers under additive white Gaussian noise and multipath channel environments is investigated not only with computer simulation but also with laboratory and field tests. The test results show that the average threshold of visibility degradation of the legacy DTV receivers due to the TxID signal injection is less than 0.2 dB at the TxID injection level of -30 dB.