As the acceptance criteria for low-intermediate-level radioactive waste cave disposal facilities of Korea Radioactive Waste Agency (KORAD) were revised, the requirements for characterization of whether radioactive waste contains hazardous substances have been strengthened. In addition, As the recent the Nuclear Safety and Security Commission Notice (Regulations on Delivery of Low- Medium-Level Radioactive Waste) scheduled to be revised, the management targets and standards for hazardous substances are scheduled to be specified and detailed. Accordingly, the Korea Atomic Energy Research Institute (KAERI) needs to prepare management methods and procedures for hazardous substances. In particular, in order to characterize the chemical requirements (explosiveness, ignitability, flammability, corrosiveness, and toxicity) contained in radioactive waste, it must be proven through documents or data that each item does not contain hazardous substances, and quality assurance for the overall process must be provided. In order to identify the characteristics of radioactive waste that will continue to be generated in the future, KAERI needs to introduce a management system for hazardous substances in radioactive waste and establish a quality assurance system. Currently, KAERI is thoroughly managing chelates (EDTA, NTA, etc.), but the detailed management procedures for hazardous substances related to chemical requirements in radioactive waste in the radiation management area specified above are insufficient. The KAERI’s Laboratory Safety Information Network has a total periodic regulatory review system in place for the purchase, movement, and disposal of chemical substances for each facility. However, there is no documents or data to prove that the hazardous substances held in the facility are not included in the radioactive waste, and there are no procedures for managing hazardous substances. Therefore, it is necessary to establish procedures for the management of hazardous substances, and we plan to prepare management procedures for hazardous substances so that chemical substances can be managed according to the procedures at each facility during preliminary inspection before receiving radioactive waste. The procedure provides definitions of terms and types of management targets for each characteristic of the chemical requirements specified above (explosiveness, ignition, flammability, corrosiveness, and toxicity). In addition, procedure also contains treatment methods of radioactive waste generated by using hazardous substances and management methods of in/out, quantity, history of that substances, etc. As the law is revised in the future, management will be carried out according to the relevant procedures. In this study, we aim to present the hazardous substance management procedures being established to determine whether radioactive waste contains hazardous substances in accordance with the revised the notice and strengthened acceptance criteria. Through this, we hope to contribute to improving reliability so that radioactive waste could be disposed of thoroughly and safely.

Molybdenum-tungsten (Mo-W) alloy sputtering targets are widely utilized in fields like electronics, nanotechnology, sensors, and as gate electrodes for TFT-LCDs, owing to their superior properties such as hightemperature stability, low thermal expansion coefficient, electrical conductivity, and corrosion resistance. To achieve optimal performance in application, these targets’ purity, relative density, and grain size of these targets must be carefully controlled. We utilized nanopowders, prepared via the Pechini method, to obtain uniform and fine powders, then carried out spark plasma sintering (SPS) to densify these powders. Our studies revealed that the sintered compacts made from these nanopowders exhibited outstanding features, such as a high relative density of more than 99%, consistent grain size of 3.43 μm, and shape, absence of preferred orientation.

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

사출성형공정은 열가소성 수지를 가열하여 유동상태로 만들어 금형의 공동부에 가압 주입한 후에 금형 내에서 냉각시키는 공정으로, 금형의 공동모양과 동일한 제품을 만드는 방법이다. 대량생산이 가능하고, 복잡한 모양이 가능한 공정으로, 수지온도, 금형온도, 사출속도, 압력 등 다양한 요소들이 제품의 품질에 영향을 미친다. 제조현장에서 수집되는 데이터는 양품과 관련된 데이터는 많은 반면, 불량품과 관련된 데이터는 적어서 데이터불균형이 심각하다. 이러한 데이터불균형을 효율적으로 해결하기 위하여 언더샘플링, 오버샘플링, 복합샘플링 등이 적용되고 있다. 본 연구에서는 랜덤오버샘플링(ROS), 소수 클래스 오버 샘플링(SMOTE), ADASTN 등의 소수클래스의 데이터를 다수클래스만큼 증폭시키는 오버샘플링 기법을 활용하고, 데이터마이닝 기법을 활용하여 품질예측을 하고자 한다.

Environmental issues such as global warming due to fossil fuel use are now major worldwide concerns, and interest in renewable and clean energy is growing. Of the various types of renewable energy, green hydrogen energy has recently attracted attention because of its eco-friendly and high-energy density. Electrochemical water splitting is considered a pollution-free means of producing clean hydrogen and oxygen and in large quantities. The development of non-noble electrocatalysts with low cost and high performance in water splitting has also attracted considerable attention. In this study, we successfully synthesized a NiCo2O4/NF electrode for an oxygen evolution reaction in alkaline water splitting using a hydrothermal method, which was followed by post-heat treatment. The effects of heat treatment on the electrochemical performance of the electrodes were evaluated under different heat-treatment conditions. The optimized NCO/NF-300 electrode showed an overpotential of 416 mV at a high current density of 50 mA/cm2 and a low Tafel slope (49.06 mV dec-1). It also showed excellent stability (due to the large surface area) and the lowest charge transfer resistance (12.59 Ω). The results suggested that our noble-metal free electrodes have great potential for use in developing alkaline electrolysis systems.

Tungsten disulfide (WS2) nanosheets have attracted considerable attention because of their unique optical and electrical properties. Several methods for fabrication of WS2 nanosheets have been developed. However, methods for mass production of high-quality WS2 nanosheets remain challenging. In this study, WS2 nanosheets were fabricated using mechano-chemical ball milling based on the synergetic effects of chemical intercalation and mechanical exfoliation. The ball-milling time was set as a variable for the optimized fabricating process of WS2 nanosheets. Under the optimized conditions, the WS2 nanosheets had lateral sizes of 500–600 nm with either a monolayer or bilayer. They also exhibited high crystallinity in the 2H semiconducting phase. Thus, the proposed method can be applied to the exfoliation of other transition metal dichalcogenides using suitable chemical intercalants. It can also be used with highperformance WS2-based photodiodes and transistors used in practical semiconductor applications.

The demand for chiller equipment that keeps each machine at a constant temperature to maintain the best possible condition is growing rapidly. PID (Proportional Integral Derivation) control is a popular temperature control method. The error, which is the difference between the desired target value and the current system output value, is calculated and used as an input to the system using a proportional, integrator, and differentiator. Through such a closed-loop configuration, a desired final output value of the system can be reached using only the target value and the feedback signal. Furthermore, various temperature control methods have been devised as the control performance of various high-performance equipment becomes important. Therefore, it is necessary to design for accurate data-driven temperature control for chiller equipment. In this research, support vector regression is applied to the classic PID control for accurate temperature control. Simulated annealing is applied to find optimal PID parameters. The results of the proposed control method show fast and effective control performance for chiller equipment.

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

Cobalt (Co) is mainly used to prepare cathode materials for lithium-ion batteries (LIBs) and binder metals for WC-Co hard metals. Developing an effective method for recovering Co from WC-Co waste sludge is of immense significance. In this study, Co is extracted from waste cemented carbide soft scrap via mechanochemical milling. The leaching ratio of Co reaches approximately 93%, and the leached solution, from which impurities except nickel are removed by pH titration, exhibits a purity of approximately 97%. The titrated aqueous Co salts are precipitated using oxalic acid and hydroxide precipitation, and the effects of the precipitating agent (oxalic acid and hydroxide) on the cobalt microstructure are investigated. It is confirmed that the type of Co compound and the crystal growth direction change according to the precipitation method, both of which affect the microstructure of the cobalt powders. This novel mechanochemical process is of significant importance for the recovery of Co from waste WC-Co hard metal. The recycled Co can be applied as a cemented carbide binder or a cathode material for lithium secondary batteries.

Energy storage systems should address issues such as power fluctuations and rapid charge-discharge; to meet this requirement, CoFe2O4 (CFO) spinel nanoparticles with a suitable electrical conductivity and various redox states are synthesized and used as electrode materials for supercapacitors. In particular, CFO electrodes combined with carbon nanofibers (CNFs) can provide long-term cycling stability by fabricating binder-free three-dimensional electrodes. In this study, CFO-decorated CNFs are prepared by electrospinning and a low-cost hydrothermal method. The effects of heat treatment, such as the activation of CNFs (ACNFs) and calcination of CFO-decorated CNFs (C-CFO/ACNFs), are investigated. The C-CFO/ACNF electrode exhibits a high specific capacitance of 142.9 F/g at a scan rate of 5 mV/s and superior rate capability of 77.6% capacitance retention at a high scan rate of 500 mV/s. This electrode also achieves the lowest charge transfer resistance of 0.0063 Ω and excellent cycling stability (93.5% retention after 5,000 cycles) because of the improved ion conductivity by pathway formation and structural stability. The results of our work are expected to open a new route for manufacturing hybrid capacitor electrodes containing the C-CFO/ACNF electrode that can be easily prepared with a low-cost and simple process with enhanced electrochemical performance.

The purpose of this study is to compare the effects of input- and output-based planning (reading a sample passage vs. writing a draft) on the oral performance of L2 learners with low-proficiency. In this study, 16 Korean female junior college students of low English proficiency were divided into two different planning groups. The reading group was required to read a sample passage of the given topic, designed to encourage “noticing” and “focus on form” using input enhancement, while the writing group was asked to write a draft of their speech, using only their own L2 knowledge. After such planning activities, both groups recorded their assigned speaking tasks using Kakao Talk. Eight planning activities and oral performances were completed over the period of the semester. In order to compare the effects of input- and output-based planning on the improvement of overall proficiency, pre- and post-tests, in which the students produced the same narratives, were analyzed using Mann-Whitney U and Wilcoxon signed-rank tests. Furthermore, this study explored any difference in speaking performance after each type of planning and what the learners were actually doing during planning time. The results showed that output-based planning had positive effects on speaking performance and its repeated practice led to the improvement of overall proficiency.

Rice is widely dominated by two strain, Indica (Oryza sativa indica) and Japonica (Oryza sativa japonica), and there is a great amount of the genetic diversity between two strain. Understanding the genetic difference and variants between two strain requires high-quality reference genome information. While Nipponbare, a japonica reference sequence, has been mainly used as the rice reference genome, the Indica reference sequences, R93-11 and R498, were recently published. Based on the two reference sequences, in this study, we investigated the effect of the reference genome in the rice genetic study. To this end, we utilized relevant bioinformatic tools and statistical methods to analyze all possible combinations of the reference genome and the variety. In this way, we suggested possible combinations of the reference sequence and the variety suitable for the genetic study of rice.

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, v-ASVR (v-Asymmetric Support Vector Regression), which considers the asymmetry of є-tube and the asymmetry of penalties for data out of є-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that v-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of є-tube and the asymmetry of penalties for data out of є-tube, the ratio of the number of data belonging to є-tube can be adjusted with v. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the є-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within є. In most studies, the є-insensitive loss function is used symmetrically, and it is of interest to determine the value of є. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of є and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of є. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of є and the slope of the penalty using the parameters p1 and p2 , respectively. Moreover, the figures show that the asymmetry of the width of є and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.