• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.3
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• pp.231-238
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• 2022

Purpose. This study aimed to investigate the effect of different veneering methods on the tensile bond strength between polyetherketoneketone (PEKK) and denture base resins. Materials and methods. A total of 80 PEKK T-shaped specimens were fabricated and the primer (Visio.link) was applied after airborne-particle abrasion with 110 ㎛ alumina oxide powder. According to the veneering method, the specimens were divided into four groups (n = 20) to be veneered with the gingival colored packable photopolymerized composite resin (SR Adoro); flowable photopolymerized composite resin, (Crea.lign); heat-polymerized resin (Vertex); and self-polymerized resin (ProBase Cold). Each group was divided into two subgroups (n = 10) according to the artificial thermal aging. After the tensile bond strength measurement via universal testing machine, the fracture sections of all specimens were observed. Two-way ANOVA and Tukey's HSD post hoc test were used for the statistical analysis (α = .05). Results. The results of the two-way ANOVA showed statistically significant differences in the tensile bond strength according to the veneering method and artificial thermal aging of denture base resins (P<.001). The highest tensile bond strength showed in the packable photopolymerized resin group before and after the artificial thermal aging. The lowest tensile bond strength showed in the heat-polymerized resin group. The mixed and adhesive fracture showed in all groups. Conclusion. The veneering method and artificial thermal aging can influence in the tensile bond strength between the resin and PEKK. The artificial thermal aging can reduce the tensile bond strength.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.567-582
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• 2022

Chainsaw use for motor-manual timber harvesting in South Korea is associated with worker safety issues. However, forestry operations such as timber harvesting have already been mechanized to reduce hazards to workers and increase productivity. This study analyzed the productivities and costs of felling and processing, felling and processing using an excavator-based stroke harvester for Pinus rigida and Quercus mongolica stands. To efficiently operate the stroke harvester, we developed a regression equation to estimate the productivities of felling and processing, felling, and processing operations,and we conducted sensitivity analysis of the operation costs using DBH and machine utilization. The felling and processing productivity was 6.53 and 4.02 m³/SMH for P. rigida a nd Q. mongolica, respectively, and the cost was 17,983 and 29,210 won/m³, respectively. The felling productivity for P. rigida a nd Q. mongolica wa s 40.9 and 23.0 m³/SMH, respectively, and the cost was 2,667 and 4,743 won/m³, respectively. The processing productivity for P. rigida and Q. mongolica was 8.25 and 7.75 m³/SMH, respectively, and the cost was 15,296 and 16,283 won/m³, respectively. In the developed regression equation, the DBH, traveling distance, and number of cuttings were found to be important factors (p<0.05). Therefore, it is necessary to construct a DB considering the various conditions and species associated with harvester operations, and further research is needed to increase the accuracy of predicting operation productivity and costs.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.423-431
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• 2022

Due to increase of interest in 'carbon neutrality', attempts at agricultural use of CO₂ are increasing. In this study, we used the dry-ice made by CO₂ as by-product in thermoelectric power plant on CO₂ fertilization for production of leafy vegetable in greenhouses. The dry-ice was supplied on three leafy vegetable farms (Allium tuberosum Rottl. ex Spreng, Aster scaber, and Oenanthe stolonifera DC.) located in Hadong, Gyeongsangnamdo. Two greenhouses were used in each leaf vegetable crops, one greenhouse used as the control (non-treatment), other greenhouse used as supplied CO₂. For CO₂ fertilization, a gas sublimated from dry ice was supplied to the greenhouse using a specially designed prototype supply machine. A. tuberosum greenhouse has no difference of CO₂ concentration between the control, and CO₂ fertilization and shown high CO₂ concentration both greenhouses. However, the CO₂ concentrations in A. scaber and O. stolonifera greenhouses were increased in CO₂ fertilization treatment. The growth of A. scaber and O. stolonifera were increased in CO₂ fertilization, and the yield also increased to 36% and 25% than the control, respectively. As a result of economic analysis, the A. scaber has increase of income rate, however A. tuberosum and O. stolonifera has decreased income rate. Thus, the use of the dry-ice made by CO₂ as by-product in thermoelectric power plant has possibility to increase productivity of the leafy vegetable in greenhouse and have agricultural use value.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.25 no.1
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• pp.77-86
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• 1980

Asking to change the cropping patterns to save the labor and capitals in paddy rice cultivation, the study was intended to know the weed problems under the various possible cultural systems; namely, direct seeding (in broadcast and row), machine transplanting and hand transplanting. Under the conditions as weedy check plots, paddy yields were significantly variated among cropping systems, and the functions of panicle No. and spikelet No. to the yield were neglected, among others. However, the yield and yield components were narrowed among cropping systems, and the function of spikelets number per area was comparatively improved to the others.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1216-1221
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• 2022

Ships tend to be larger to increase the efficiency of cargo transportation. Larger ships lead to increased travel time for ship workers, increased work intensity, and reduced work efficiency. Problems such as increased work intensity are reducing the influx of young people into labor, along with the phenomenon of avoidance of high intensity labor by the younger generation. In addition, the rapid aging of the population and decrease in the young labor force aggravate the labor shortage problem in the maritime industry. To overcome this, the maritime industry has recently introduced technologies such as an intelligent production design platform and a smart production operation management system, and a smart autonomous logistics system in one of these technologies. The smart autonomous logistics system is a technology that delivers various goods using intelligent mobile robots, and enables the robot to drive itself by using sensors such as lidar and camera. Therefore, in this paper, it was checked whether the mobile robot could autonomously drive to the stop sign by detecting the passage way of the ship deck. The autonomous driving was performed by detecting the passage way of the ship deck through the camera mounted on the mobile robot based on the data learned through Nvidia's End-to-end learning. The mobile robot was stopped by checking the stop sign using SSD MobileNetV2. The experiment was repeated five times in which the mobile robot autonomously drives to the stop sign without deviation from the ship deck passage way at a distance of about 70m. As a result of the experiment, it was confirmed that the mobile robot was driven without deviation from passage way. If the smart autonomous logistics system to which this result is applied is used in the marine industry, it is thought that the stability, reduction of labor force, and work efficiency will be improved when workers work.