• Tunnel and Underground Space
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• v.32 no.5
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• pp.298-311
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• 2022

Some problems, such as aging workers, a decreased population due to a low birth rate, and shortage of skilled workers, are rising in construction sites. Therefore research for smart construction technology that can be improved for productivity, safety, and quality has been recently developed with government support by replacing traditional construction technology with advanced digital technology. In particular, the motor grader that mainly performs road surface flattening is a construction machine that requires the application of automation technology for repetitive construction. It is predicted that the construction period will be shortened if the construction automation technology such as trajectory tracking, automation work, and remote control technology is applied. In this study, we introduce the hardware and software architecture of the smart motor grader to apply unmanned and automation technology and then analyze the traditional earthwork method of the motor grader. We suggested the application plans for the path pattern and blade control method of the smart motor grader based on this. In addition, we verified the performance of waypoint-based path-following depending on scenarios and the blade control's performance through tests.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.321-330
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• 2022

Busan New Port manages the largest volume of traffic among Korean ports, and accounts for 68.5% of the total volume of the Busan port. Due to this increase in volume, ultra large container ships call at Busan New Port. When the additional south container terminal as well as ongoing construction project of the west container terminal are completed, various encounters may occur at the Busan New Port entrance, which may cause collision risk.s Thus, the purpose of this study was to provide a plan to improve the safety of vessel traffic, in the in/out bound fairway of Busan New Port. For this purpose, the status of arrivals and departures of vessels in Busan New Port, was examined through maritime traffic flow analysis. Additionally, risk factors and safety measures were identified, by AHP analysis with ship operators of the study area. Also, based on the derived safety measures, scenarios were set using the Environmental Stress model (ES model), and the traffic risk level of each safety measure was identified through simulation. As a result, it is expected that setting the no entry area for one-way traffic would have a significant effect on mitigating risks at the Busan New Port entrance. This study can serve as a basis for preparing safety measures, to improve the navigation of vessels using Busan New Port. If safety measures are prepared in the future, it is necessary to verify the safety by using the traffic volume and flow changes according to the newly-opened berths.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.393-402
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• 2023

Drought, which has been increasing in frequency and magnitude due to recent abnormal weather events, poses severe challenges in various sectors. To address this issue, it is important to develop technologies for drought monitoring, forecasting, and response in order to implement effective measures and safeguard the ecological health of aquatic systems during water scarcity caused by drought. This study aimed to predict water quality fluctuations during drought periods by integrating the watershed model HSPF and the water quality model QUAL-MEV. The researchers examined the SPI and RCP 4.5 scenarios, and analyzed water quality changes based on flow rates by simulating them using the HSPF and QUAL-MEV models. The study found a strong correlation between water flow and water quality during the low flow. However, the relationship between precipitation and water quality was deemed insignificant. Moreover, the flow rate and SPI6 exhibited different trends. It was observed that the relationship with the mid- to long-term drought index was not significant when predicting changes in water quality influenced by drought. Therefore, to accurately assess the impact of drought on water quality, it is necessary to employ a short-term drought index and develop an evaluation method that considers fluctuations in flow.

• Korea Trade Review
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• v.43 no.5
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• pp.47-66
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• 2018

This study analyzes the potential economic effects of a trade war between the U.S. and China. The CGE model is used to estimate the macroeconomic variables of each country and the change in imports/exports by industry by considering three different scenarios: (i) the US imposes a 25% of tariff on China; (ii) the US and China impose a 25% tariff bilaterally; (iii) the United States expands protection in vehicles and metals to Korea, Japan, and the EU. According to the results of the study, when the US and China initiate a trade war, GDP and welfare of both countries decline. China's decline in GDP and welfare are larger than those of the United States, which implies a trade war is more favorable to the U.S. than to China. In the long run, China's GDP and welfare decline widens further. While the trade volumes of the US and China are greatly reduced, the trade volumes of other countries does not significantly fluctuate. Finally, if the US extends protection policy to Korea, Japan and the EU, it creates undesirable effects on the US. In particular, damage to the US jeopardizes its advantageous position in a trade war with China. In order to emphasize the unfairness of protectionist policy and the damage to Korean industry, Korea needs to establish a strategy to counter US protectionist policy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.443-451
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• 2010

This paper proposes the methodology about advisory safety speed based on real-time vehicular data collected from highway. The proposed model is useful information to drivers by appling seamless wireless communication and being collected from ECU(Engine Control Unit) equipment in every vehicle. Furthermore, this model also permits the use of realtime sensing data like as adverse weather and road-surface data. Here, the advisory safety speed is defined "the safety speed for drivers considering the time-dependent traffic condition and road-surface state parameter at uniform section", and the advisory safety speed model is developed by considering the parameters: inter-vehicles safe stopping distance, statistical vehicle speed, and real-time road-surface data. This model is evaluated by using the simulation technique for exploring the relationships between advisory safety speed and the dependent parameters like as traffic parameters(smooth condition and traffic jam), incident parameters(no-accident and accident) and road-surface parameters(dry, wet, snow). A simulation's results based on 12 scenarios show significant relationships and trends between 3 parameters and advisory safety speed. This model suggests that the advisory safety speed has more higher than average travel speed and is changeable by changing real-time incident states and road-surface states. The purpose of the research is to prove the new safety related services which are applicable in SMART Highway as traffic and IT convergence technology.

• Journal of Environmental Impact Assessment
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• v.32 no.5
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• pp.318-337
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• 2023

This study utilizes system dynamics to examine the effects of fine dust reduction policies on PM10 concentration and health. System dynamics has the advantage of modeling the dynamic and circular relationship between PM₁₀ emission sources, reduction policies, PM₁₀ concentration, and health effect. The study created policy scenarios for Korea's representative fine dust reduction policies - industrial PM₁₀ emission control, diesel vehicle regulation, expansion of electric vehicles, and expansion of parks and green areas - and compared the results with the 2030 baseline if the current trend is maintained. The analysis showed that the policy of supporting electric vehicles reduced PM₁₀ concentration by 0.21 ㎍/m³ and reduced the number of people with circulatory diseases by 494, and the effect was evenly distributed across the country. The industrial emissions regulation scenario resulted in the highest PM₁₀ concentration reduction of 0.22 ㎍/m³, but had a lower reduction in the number of people affected (358) than the EV support strategy, which could be attributed to the fact that this policy had a particularly high PM₁₀ reduction effect in industrial areas such as Danyang-gun, Chungcheongbuk-do, and Sahagu, Busan. As a policy implication, this study suggests that it is necessary to apply fine dust policies tailored to the characteristics of local emission sources.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.849-858
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• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1451-1466
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• 2023

In the context of maritime emergencies, the utilization of drones has rapidly increased, with a particular focus on their application in search and rescue operations. Deep learning models utilizing drone images for the rapid detection of distressed vessels and other maritime drift objects are gaining attention. However, effective training of such models necessitates a substantial amount of diverse training data that considers various weather conditions and vessel states. The lack of such data can lead to a degradation in the performance of trained models. This study aims to enhance the performance of deep learning models for distress ship detection by developing a maritime environment simulator to augment the dataset. The simulator allows for the configuration of various weather conditions, vessel states such as sinking or capsizing, and specifications and characteristics of drones and sensors. Training the deep learning model with the dataset generated through simulation resulted in improved detection performance, including accuracy and recall, when compared to models trained solely on actual drone image datasets. In particular, the accuracy of distress ship detection in adverse weather conditions, such as rain or fog, increased by approximately 2-5%, with a significant reduction in the rate of undetected instances. These results demonstrate the practical and effective contribution of the developed simulator in simulating diverse scenarios for model training. Furthermore, the distress ship detection deep learning model based on this approach is expected to be efficiently applied in maritime search and rescue operations.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.