• The Korean Journal of Air & Space Law and Policy
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• v.34 no.1
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• pp.159-201
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• 2019

Along with Annex 14 Volume I establishment in 1951 and the set-up of restriction surface around the runway, aeronautical technique and navigation performance achieved dazzling growth, and the safety and precision of navigation greatly improved. However, restrictions on surrounding obstacles are still valid for safe operation of an aircraft. Standards and criteria for securing safety of aircraft operating around and on airport is stated in Annex 11 Air Traffic Services and Annex 14 Aerodrome etc. In particular, Annex 14 Volume I presents the criteria for limiting obstacles around an airport, such as natural obstacles such as trees, mountains and hills to prevent collisions between aircraft and ground obstacles, and artificial obstacles such as buildings and structures. On the other hand, Annex 14 Volume I, in the application of the obstacles limitation surfaces, apply the exception criteria, as it may not be possible to remove obstacles that violate the criteria if the aeronautical study determines that they do not impair the safety and regularity of aircraft operation. Aeronautical study has been applied and implemented in various countries including United States, Canada and Europe etc. accordingly, Korea established and amended some provisions of the Enforcement rules of the Aviation Act and established the Aeronautical study guidelines to approve exceptions. However, because ICAO does not provide specific guidelines on procedures and methods of Aeronautical study, countries conducting aeronautical study have established and applied their own procedures and methods. Reflecting this realistic situation, at the 12th World Navigation Conference and at the 38th General Assembly, the contracting States demanded a reexamination of the criteria for current obstacle limitation surfaces and methods of aeronautical study, and the ICAO dedicated a team of experts to prepare new standard. This study, in line with the movement of international change in obstacle limitation surface and aeronautical study, aims to compare and analyze current domestic and external standards on obstacle limitation and height limits, while looking at methods, procedure and systems for aeronautical study. In addition, expecting that aeronautical study will be used realistically and universally in assessing the impact of obstacles, we would recommend the institutional improvement of the aeronautical study along with the development of quantitative analysis methods using the navigation data in the current aeronautical study.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.140-146
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• 2019

In order to manage non-point sources, the paradigm of the system should be changed so that the management of non-point sources will be systematized from the beginning of the use and development of the land. It is necessary to change the method of national subsidy support and poeration plan for the non-point source management area. In order to increase the effectiveness of the non-point source reduction project, it is necessary to provide a minimum support ratio and to provide additional support according to the performance of the local government. A new system should be established to evaluate the performance of non-point source reduction projects and to monitor the operational effectiveness. It is necessary to establish the related rules that can lead the local government to take responsible administration so that the local governments faithfully carry out the non-point source reduction project and achieve the planned achievement and become the sustainable maintenance. Alternative solutions are needed, such as problems with the use of $100{\mu}m$ filter in automatic sampling and analysis, timely acquisition of water sampling and analysis during rainfall, and effective management of non-point sources network operation management. As an alternative, it is necessary to consider improving the performance of sampling and analysis equipment, and operate the base station. In addition, countermeasures are needed if the amount of pollutant reduction according to the non-point source reduction facility promoted by the national subsidy is required to be used as the development load of the TMDLs. As an alternative, it is possible to consider supporting incentive type of part of the maintenance cost of the non-point source reduction facility depending on the amount of pollutants reduction.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.599-609
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• 2021

A novel grounding system, which is presented in IEC 60364, has been adopted since 2021. A safety evaluation for the human body on the grounding system is required due to the various characteristics of the touch voltage and current passing when the human body experiences an electric shock. The Korea Electrical Safety Corporation (KESCO) and Korea Electric Association (KEA) have been conducting a safety technical education on the grounding system. On the other hand, it is difficult to instruct the electrical safety manager because of a lack of safety evaluations for the test equipment on the grounding system. Therefore, this paper modeled and implemented a test device for a safety evaluation depending on the grounding system of IEC 60364. Namely, this paper presents the modeling of the test device for a safety evaluation using PSCAD/EMTDC S/W, which is composed of an AC grid section, s test device section on the grounding system, and a sub-device section. This paper implemented a test device for safety evaluation, which consisted of an AC grid section, TT grounding system section, TN-S grounding system section, and monitoring section. From the simulation and test results with the safety characteristics of the human body in the TT and TN-S grounding system, when the fault impedances are 0[Ω], 10[Ω], and 100[Ω], the currents passing through the human body in the TT grounding system are 104[mA], 87.4[mA], and 35.5[mA], respectively. The corresponding currents in the TN-S grounding system are 54.9[mA], 4.1[mA], and 0.4[mA], respectively. Based on the results, the protection performance for an electric shock to the human body in the TN-S system is better than the TT system. This can be improved when the existing grounding system is changed from the TT system to the TN-S system.

• Journal of the Korean Geosynthetics Society
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• v.20 no.2
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• pp.23-33
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• 2021

In Korea, many reservoirs have been built for the purpose of solving the food shortage problem and supplying agricultural water. However, the current 75.6% of the reservoirs are in serious aged as more than 50 years have passed since the year of construction. In the case of such an aging reservoir, the stability due to scour and erosion inside the reservoir is very reduced, and if concentrated rainfall due to recent abnormal weather occurs, the aging reservoir may collapse, leading to a lot of damage to property and human life. Accordingly, each agency that manages aging reservoirs uses Ordinary Portland Cement (OPC) as an injection material and applies the grouting method. However, in the case of OPC, it may deteriorate over time and water leakage may occur again. And there are environmental problems such as consumption of natural resources and generation of greenhouse gases. So, there is a need to develop new materials and methods that can replace the OPC. In this study, an laboratory test and analysis were performed on the grout material developed to induce a curing reaction similar to that of OPC by recycling power plant byproduct. In addition, test in the field such as electric resistivity survey, Standard Penetration Test (SPT), and field permeability test were performed to analyzed to reinforcement effect and determine the possibility of using instead of OPC. As a results of the test, in the case of recycled power plant byproduct, the compressive strength was 2.9 to 3.2 times and the deformation modulus was 2.3 to 3.3 times higher, indicating that it is excellent in strength and can be used instead of OPC. And it was analyzed that the N value of the reservoir was increased by 1~2, and the coefficient of permeability (k) decreased to the level of 8.9~42.5%. showing sufficient reinforcing effect in terms of order.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.268-278
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• 2020

Flowering time has been put forward due to the recent abnormally warm winter, which often caused damages of flower buds by late frosts persistently. In the present study, cumulative chill unit and cumulative heat unit of Niitaka pear, which are required for releasing the endogenous dormancy and for flowering after breaking dormancy, respectively, were compared between flowering time prediction models used in South K orea. Observation weather data were collected at eight locations for the recent three years from 2018-2020. The dates of full bloom were also collected to determine the confidence level of models including DVR, mDVR and CD models. It was found that mDVR model tended to have smaller values (8.4%) of the coefficient of variation (cv) of chill units than any other models. The CD model tended to have a low value of cv (17.5%) for calculation of heat unit required to reach flowering after breaking dormancy. The mDVR model had the most accurate prediction of full bloom during the study period compared with the other models. The DVR model usually had poor skills in prediction of full bloom dates. In particular, the error of the DVR model was large especially in southern coastal areas (e.g., Ulju and Sacheon) where the temperature was warm. Our results indicated that the mDVR model had relatively consistent accuracy in prediction of full bloom dates over region and years of interest. When observation data for full bloom date are compiled for an extended period, the full bloom date can be predicted with greater accuracy improving the mDVR model further.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.689-698
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• 2021

Since the mine reclamation scheme was implemented from 2007 in Korea, various remediation programs have been decontaminated the pollution associated with mining and 254 mines were managed to reclamation from 2011 to 2015. However, as the total amount of contaminated mine drainage has been increased due to the discovery of potential hazards and contaminated zone, more efficient and economical treatment technology is required. Therefore, in this study, the adsorption properties of arsenic was evaluated according to the adsorbents which were derived from water treatment sludge(Alum based adsorbent, ABA-500) and granular ferric hydroxide(GFH), already commercialized. The alum sludge and GFH adsorbents consisted of aluminum, silica materials and amorphous iron hydroxide, respectively. The point of zero charge of ABA-500 and GFH were 5.27 and 6.72, respectively. The result of the analysis of BET revealed that the specific surface area of GFH(257 m²·g^-1) was larger than ABA-500(126~136 m²·g^-1) and all the adsorbents were mesoporous materials inferred from N₂ adsorption-desorption isotherm. The adsorption capacity of adsorbents was compared with the batch experiments that were performed at different reaction times, pH, temperature and initial concentrations of arsenic. As a result of kinetic study, it was confirmed that arsenic was adsorbed rapidly in the order of GFH, ABA-500(granule) and ABA-500(3mm). The adsorption kinetics were fitted to the pseudo-second-order kinetic model for all three adsorbents. The amount of adsorbed arsenic was increased with low pH and high temperature regardless of adsorbents. When the adsorbents reacted at different initial concentrations of arsenic in an hour, ABA-500(granule) and GFH could remove the arsenic below the standard of drinking water if the concentration was below 0.2 mg·g^-1 and 1 mg·g^-1, respectively. The results suggested that the ABA-500(granule), a low-cost adsorbent, had the potential to field application at low contaminated mine drainage.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.