• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.140-146
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• 2020

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.89-97
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• 2013

RADCONS Ver. 1.0 (RADiological CONSequence Assessment Program) was developed for radiological risk assessment in this study. A Gaussian plume model was used to analyze the fate and transport of radionuclides released into the air in case of accidents. Both single meterological data and time series meterological data can be used in RADCONS. To assess the radiological risk of the early phase after an accident, ED (Effective Dose) estimated by both deterministic and probabilistic approaches are presented. These EDs by deterministic and probabilistic will be helpful to efficient decision making for decision makers. External doses from deposited materials by time are presented for quantifying the effects of mid and late phases of an accident. A radiological risk assessment was conducted using RADCONS for an accident scenario of 1 Ci of Cs-137. The maximum of ED for radii of 1,000 meters from the accident point was 8.51E-4 mSv. After Monte-Carlo simulation, considering the uncertainty of the breathing rate and dispersion parameters, the average ED was 8.49E-4, and the 95 percentile was 1.10E-3. A data base of the dose coefficients and a sampling module of the meteorological data will be modified to improve the user's convenience in the next version.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.9-15
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• 2020

Wireless sensor nodes adopting the advanced preamble detection function, Message-In-Mesage (MIM), maximize the concurrent transmission opportunities due to the capture effect, result in improving the system performance significantly compared to the legacy IEEE 802.15.4 based sensor devices. In this paper, we propose an MIM capture probability model to analyze the performance gains by applying the MIM function to the wireless sensor nodes. We implemented the IEEE 802.15.4 and MIM by Python and performed extensive simulations to verify the performance gains through MIM capture effects. The evaluation results show that the MIM sensors achieve 34% system throughput gains and 31% transmission delay gains over the legacy IEEE 802.15.4-based sensors, which confirm that it was consistent with the analysis result of the proposed MIM capture probability model.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.40-46
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• 2020

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.35-39
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• 2020

DNN error is small compared to the conventional speech recognition system, DNN is difficult to parallel training, often the amount of calculations, and requires a large amount of data obtained. In this paper, we generate a phoneme unit to estimate the GMM parameters with each phoneme model parameters from the GMM to solve the problem efficiently. And it suggests ways to improve performance through clustering for a specific vocabulary to effectively apply them. To this end, using three types of word speech database was to have a DB build vocabulary model, the noise processing to extract feature with Warner filters were used in the speech recognition experiments. Results using the proposed method showed a 97.9% recognition rate in speech recognition. In this paper, additional studies are needed to improve the problems of improved over fitting.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.1-7
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• 2008

The general bus architecture consists of masters, slaves, arbiter, decoder and so on in shared bus. Specially, as several masters do not concurrently receive the right of bus usage, the arbiter plays an important role in arbitrating between shared bus and masters. Fixed priority, round-robin, TDMA and Lottery methods are developed in general arbitration policies, which lead the efficiency of bus usage in shared bus. On the other hand, the bus architecture can be modified to maximize the system performance. In the paper, we propose the flying master bus architecture that supports the parallel bus communication and analyze its merits and demerits following various arbitration policies that are mentioned above, compared with normal shared bus. From the results of performance verification using TLM(Transaction Level Model), we find that more than 40% of the data communication performance improves, regardless of arbitration policies. As the flying master bus architecture advances its studies and applies various SoCs, it becomes the leading candidate of the high performance bus architecture.

• Journal of Convergence for Information Technology
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• v.7 no.4
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• pp.53-60
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• 2017

The purpose of this study was to identify the factors affecting intention and attitude of artificial intelligence technology(AI) of university students in healthcare using UTAUT model. Participants were 278 college students and the data were collected through self-reported questionnaire from May 15 to June 14, 2016. The collected data were analyzed using PASW Statistics/AMOS 22.0. The results were as follows. The effect of expectation factor, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology Intention. Factor of expectation effect, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology. As a result of verifying the significance of the indirect effect, it can be seen that the direct effect of the anxiety factor on the attitude factor is partially mediated by the use intention factor and the intention to use was partially mediated in the direct effect of the usefulness factor of the task on the attitude factor. This result means that it is important to increase the expectation factors, social effects, and perceived usefulness through accurate information based on facts and to reduce vague anxiety in order to increase the positive intention and attitude of university students' use of AI technology.

• Journal of Convergence for Information Technology
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• v.10 no.3
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• pp.1-6
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• 2020

Information System is influenced by the innovation of new IT. Therefore, IS should response to external environment's changes quickly. Particularly, resilience should be considered in barriers of IS. This study suggests a new information system evaluation model in which resilience is added to the existing factors of Delone and Mclean. Then the effect of resilience is evaluated through the DEA(Data Envelopment Analysis) based on a survey targeting 115 users of a mid-sized manufacturing company. The results show that the effect of resilience is stronger than any other factors in the previous researches. We, thus, suggest that the resilience should be included as an evaluation factor of the ISO27001 information security standard in order to enhance the absorptive capacity of the information system.

• Journal of Convergence for Information Technology
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• v.10 no.3
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• pp.67-75
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• 2020

This paper investigated the information spillover effect between stock market and bond market with the KOSPI daily index and MMF yield data. The overall analysis period is from May 2, 1997 to August 30, 2019. The empirical analysis was conducted by dividing the period from May 2, 1997 to December 30, 2008 before the global financial crisis, and from December 30, 2008 to August 30, 2019 after the global financial crisis, and the overall analysis period. The analysis shows that the EGARCH model considering asymmetric variability is suitable. The price spillover effect and volatility spillover effect existed in both directions between the stock market and the bond market, and the price transfer effect was greater in the period before the global financial crisis than in the period after the global financial crisis. Asymmetric volatility in information between stock and bond markets appears to exist in both markets.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.47-53
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• 2019

The purpose of the study was to utilize VECM(Vector Error Correction Model) and detect causal relationships among shipping freight rates. Shipping freight rates used in this study were BDI(Baltic Dry Index), HRCI(Howe Robinson Containership Index), WS(World Scale rate) and SCFI(Shanghai Containerized Freight Index). Using weekly data published since August 2nd, 2013 to September 6th, 2019, it was discovered that BDI and WS were heavily influenced by past week's BDI and WS respectively. VECM also found that one percent increase in WS resulted in 0.022% increase in following week's HRCI data. One percent increase in HRCI affects SCFI by 0.77% on the following week. This study believes that finding may help each shipping market of shipping freight rates estimates, thereby encouraging decision markers to exercise discretion and establish best interest decision.