• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• The Journal of Bigdata
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• v.8 no.1
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• pp.157-165
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• 2023

Seafood is a major source of protein in many countries and its consumption is increasing. In Korea, consumption of seafood is increasing, but self-sufficiency rate is decreasing, and the importance of safety management is increasing as the amount of imported seafood increases. There are hundreds of species of aquatic products imported into Korea from over 110 countries, and there is a limit to relying only on the experience of inspectors for safety management of imported aquatic products. Based on the data, a model that can predict the customs inspection results of imported aquatic products is developed, and a machine learning classification model that determines the non-conformity of aquatic products when an import declaration is submitted is created. As a result of customs inspection of imported marine products, the nonconformity rate is less than 1%, which is very low imbalanced data. Therefore, a sampling method that can complement these characteristics was comparatively studied, and a preprocessing method that can interpret the classification result was applied. Among various machine learning-based classification models, Random Forest and XGBoost showed good performance. The model that predicts both compliance and non-conformance well as a result of the clearance inspection is the basic random forest model to which ADASYN and one-hot encoding are applied, and has an accuracy of 99.88%, precision of 99.87%, recall of 99.89%, and AUC of 99.88%. XGBoost is the most stable model with all indicators exceeding 90% regardless of oversampling and encoding type.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• 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.

• Journal of Astronomy and Space Sciences
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• v.21 no.2
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• pp.141-152
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• 2004

Optics fabrication process for precision space optical parts includes bound abrasive grinding, loose abrasive lapping and polishing. The traditional bound abrasive grinding with bronze bond cupped diamond wheel leaves the machine marks of about $20{mu}m$ rms in height and the subsurface damage of about 1 ${mu}m$ rms in height to be removed by subsequent loose abrasive lapping. We explored an efficient quantitative control of precision CNC grinding. The machining parameters such as grain size, work-piece rotation speed and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis. The effectiveness of such grinding prediction models was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment achieved the predictability down to ${pm}20$ nm in height and the surface roughness down to 36 nm in height. This study contributed to improvement of the process efficiency reaching directly the polishing and figuring process without the need for the loose abrasive lapping stage.

• Journal of the Korean Wood Science and Technology
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• v.40 no.1
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• pp.26-37
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• 2012

Recently, the demand and supply on the Hanok have been increased. However, Hanok should be requested larger section of structural members because of excessive roof weight. So, structural skin-timber was manufactured to get a lightweight structural member. The structural skin-timber has exterior shape with larger section but a great volume of wood be removed. The reduced strength of structural skin-timber can be supplemented by hybridizaion of structural member. Japanese larch and Domestic pine were used to manufacture the structural skin-timber. Structural skin-timbers of rectangular shape and cylinder shape were manufactured and tested to evaluate the bending properties. The intended strength property could not be obtained because member had been suffered severe damage by precision deficiency of manufacturing machine. However, if precision of manufacturing machine would be improved and additional hybridizaion of structural skin-timber would be done, lightweight structural member will be able to be manufactured. Structural skin-timber did not showed statistical significancy between two species, so it is possible to use pine mixed with larch. Only MOR of larch showed statistical significancy between rectangular shape and cylinder shape, so it is necessary to use of those as separate things. However, the rest of skin-timber can be judged mixed using because of non statistical significancy. The objective of this study was the development of lightweight larger structural member with relatively strength. If hybrid member of skin-timber could be developed with wood-ceramics, lightweight steel and more, it can be possible to be used as a building material of Hanok, interior material, post & beam construction material and more.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.638-643
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• 2004

소형화된 기계가공시스템은 사용재료의 다양화와 에너지 및 공간의 감소와 같은 장점을 가지고 작고 정밀한 부품을 가공할 수 있는 시스템으로 주목받고 있다. 이러한 시스템이 비록 그 크기가 일반적인 가공시스템에 비해 작지만 정렬 및 조립공정, 기계요소의 불완정성에 의한 기하학적 오차는 여전히 존재한다. 기하학적 오차 평가는 기계시스템의 정밀도를 효과적으로 적은 비용으로 향상시킬 수 있는 오차보정기술을 적용할 수 있는 토대가 된다. 일반적으로, 3 축의 직선축으로 이루어진 공작기계는 21 개의 오차요소를 가진다. 레이져간섭계는 이러한 오차요소를 평가하는데 널리 사용되고 있지만 광학계를 정렬하고 설치하는 데 많은 어려움이 있으며 한번의 설치로 한 개의 오차요소만이 측정 가능하다. 또한, 소형공작기계의 경우, 그 크기로 인해 기존의 레이져 간섭계를 직접적으로 적용할 수 없다. 따라서, 본 연구에서는 소형공작기계를 포함한 소형가공시스템의 기하학적 오차 평가를 위한 새로운 다자유도 측정시스템을 제안하였다. 5 개의 정전용량변위센서를 사용하는 이 시스템을 통해 한 축의 움직임에 따른 5 개의 오차요소를 동시에 측정 가능하다. 균질 변환행렬을 이용한 측정알고리듬을 구성하고 이를 모의시험을 통해 평가하였다. 수학적 모델링을 통해 각 센서의 출력값을 유도하고 이를 이용하여 각 오차요소를 계산하기 위한 식을 유도하였다. 여기서, 단순화된 식을 적용한 경우, 임의의 오차에 대한 측정 알고리듬의 정확도를 평가하였다. 또한, 측정 시스템의 설치시 발생하는 셋업오차에 대한 측정 알고리듬의 민감도 분석을 행하였다. 제안하는 측정 시스템은 구조가 간단하고 고가의 부가장비가 필요치 않다. 또한, 적은 비용으로 구성할 수 있으며 높은 측정 정밀도를 가지고 소형가공시스템에 필요한 오차 평가를 행할 수 있다.가 함유된 계란을 생산하고 섭취하였을 때 특정항체들의 결합을 통해 병원성 미생물의 성장이나 군체를 형성하는 것을 무력화시켜 결과적으로 병원균을 감소시키거나 억제시킨다는 점이다. 오늘날 약물에 내성을 지닌 박테리아의 출현으로 질병감염을 막는데 항생제의 사용효과가 점차 감소하고 있기 때문에 이러한 항생제를 대체할 수 있는 방안으로 계란항체를 이용할 수 있다.한 중공 플랜지 형상의 단조 방법 중 보다 적절한 단조방법인 압조 단조에 있어서 일반적으로 사용되고 있는 SM10C에 대한 유한요소 해석을 수행하였으며, 제품의 형상비에 따라 폴딩 결함의 발생 유무를 검토하고, 폴딩 결함 없이 단조하기 위한 중공 플랜지의 형상한계 비를 제시하였다.도 경미하게 나타났으나, 경련이 나타난 쥐에서는 KA만을 투여한 흰쥐와 구별되지 않았다. 이상의 APT의 항산화 효과는 KA로 인한 뇌세포 변성 개선에 중요한 인자로 작용할 것으로 사료되나, 보다 명확한 APT의 기전을 검색하고 직접 임상에 응응하기 위하여는 보다 다양한 실험 조건이 보완되어야 찰 것으로 생각된다. 항우울약들의 항혈소판작용은 PKC-기질인 41-43 kD와 20 kD의 인산화를 억제함에 기인되는 것으로 사료된다.다. 것으로 사료된다.다.바와 같이 MCl에서 작은 Dv 값을 갖는데, 이것은 CdCl$_{4}$$^{2-}$ 착이온을 형성하거나 ZnCl$_{4}$$^{2-}$ , ZnCl$_{3}$$^{-}$같은 이온과 MgCl$^{+}$, MgCl$_{2}$같은 이온종을 형성하기 때문인것 같다. 한편 어떠한 용리액에서던지 NH$_{4}$$^{+}$의 경

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.2
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• pp.38-43
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• 2015

Tungsten inert gas (TIG) welding is commonly used in industries that require airtightness, watertightness, oiltightness, and precision. It is a non-consumable welding method that is commonly used for the welding of non-ferrous metals, but it can be used to weld most metals. The methods of TIG welding can be divided into three types. The first, manual welding is done directly on the metal by a welder with a torch. The second, semi-automatic welding, gets help from a material supplying machine, but it is conducted by a welder. Lastly, automated welding is conducted fully by a machine during its process and operation. Depending on the selection of electrode, the amount of heat that is applied to the base material and the electrode rod changes and makes the shape of welded parts different. A direct-current positive electrode was used for this study. Through the change of shielding gas type on a structural steel (SS-400) that is commonly used in industry, the composition and shape changes in welded parts were detected after welding. The heat-affected area, hardness value, and tensile strength were also identified through hardness testing and tensile testing. In this study, it was found that the higher hardness value of the heat-affected is, the weaker the tensile strength becomes.

• Explosives and Blasting
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• v.31 no.2
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• pp.14-24
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• 2013

The most effective way of the rock removing works in the downtown area is to removing rocks by splitting the rock by blasting with small amount of explosives in the hole. However environmental factors not only limit the applications but also increase the forbidden area. As this is a distributed multi-stage blasting method and to reduce vibration by applying the optimized precisioncontrol-blasting method, it is applicable in all situations. The process is to fix the stemming-help plastic sheet to the hole entrance when stemming explosives and insert detonator and explosive primer with same delay time, two or three sets. This method is more efficient in the downtown area where claims and dispute from vibration are expected. This method is easily usable by designing blast pattern even in the area where delay time blasting is difficult after multi-stage explosive stemming due to short length of blast hole (1.2~3.0m) and there is no detonator wire shortage or dead-pressure.