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.

This study investigates the effect of the DDL (Data-Driven Learning) approach on the English sentence writing ability of 6th graders in elementary school. To this end, a total of seven English textbooks were used to build a corpus. Five teachers were then asked to conduct five lessons using a weak version of DDL in their 6-grade EFL classrooms. Students were asked to complete a pre- and post-test and a pre- and post-survey, and a selected number of students and four of the five teachers had in-depth interviews with the researcher. The results are as follows: First, DDL using the textbook corpus was found to be adequate for helping elementary students improve their sentence-writing ability. Second, DDL had a significant effect on upper, middle, and lower level groups of students. Third, the students felt that DDL was neither unfamiliar nor difficult. Fourth, teachers with little teaching experience found it easy to conduct their classes using the DDL approach. This study implies that DDL is an effective approach to teaching communicative functions and language forms in the elementary English classroom and can be useful for all levels of elementary students.

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.

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.

철근콘크리트 구조물은 반영구적인 구조물로 여겨지고 있으나 해상 구조물 및 해상에 인접한 구조물은 부식에 대한 위험에 노출이 되어 있다. 내륙 구조물의 경우에도 제설제 사용으로 인한 콘크리트의 중성화, 콘크리트 균열 등의 다양한 부식 요인이 발생한다. 또한 건설공사 시 철근은 외부환경에 장시간 노출된 상태로 보관이 되어 부식되기 쉽다. 따라서 본 연구에서 는 철근콘크리트 보의 인장 주철근을 3%와 10%의 부식률로 부식을 시켜 휨 실험을 통해 부식률에 따른 철근콘크리트 보의 휨 거동을 나타내었다. 철근콘크리트 보와 콘크리트 공시체를 동시에 제작하여 재료실험을 수행하였으며, 전위차 부식촉진법을 활용하여 철근콘크리트 보의 부식을 촉진시켰다. 실험결과 부식으로 인해 철근콘크리트 보의 초기 강성이 증가하였으며, 10%의 부식률에서는 철근콘크리트 보의 전단파괴의 발생 및 누적에너지소산능력이 취약하게 나타났다.