There is a widely distributed dialect classifier “蔸” in the area south of the Yangtze River. This classifier is grammaticalized from the noun “蔸” which means “roots (or stem close to the root) of plants” through a metonymy mechanism. The etymology of “蔸” is probably “株”. The classifier “蔸” was a plant classifier when it was first formed, collocating with nouns related to the plant category. Later, its usage expanded. And the word went through a process of categorization. In some areas of Guangxi and Guangdong, it developed the usage of appearance classifiers, which can be collocated with one-dimensional objects in non-plant categories and even abstract nouns. In more limited areas, such as Guangzhou, the classifier “蔸” can even be paired with a noun referring to a person, but in this case its counting function is weak and the whole expression’s subjectivity is strong.

In electric vehicles, the core is a secondary cell battery. Raw material is pulverized by the grinding disc in the Classifier Separator Mill (CSM) and rises through the Classifier Wheel. Both require characteristics to withstand high-speed rotation, including abrasion, corrosion, and shock. Our study analyzes the impact of RPM and heat source on temperature, convergence, and durability. In conclusion, high heat increases flow, while high RPM reduces the maximum temperature but may harm durability. Proper RPM settings enhance durability.

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

In order to reduce environmental pollution, it is necessary to increase the recycling rate of waste. For this, the separation of recyclables is of utmost importance. The paper conducted a study to automatically discriminate containers by material for beverage containers among recyclables. We developed an algorithm that automatically recognizes containers by four materials: metal, glass, plastic, and paper by measuring the vibration signal generated when the beverage container collides with the bottom plate of the collection box. The amplitude distribution, time series information, and frequency information of the vibration signal were used to extract the characteristics indicating the characteristic difference of the vibration signal for each material, and a classifier was developed through machine learning using these characteristics.

There are universal misreads on the generation and parent-word of the classifier “Ke(顆)” in Chinese. We hold that the Classifier “Ke(顆)” born in the period between Qin and Han dynasties, its parent-word should be the word Guo(果)。The word Guo(果) figured fruit, expands to a measuring unit of small round thing. People start borrowing the Chinese character “Ke(顆)” to record the Classifier “Ke(顆).” Under the influence of Chu dialect, people borrowed the Chinese character “Ke(顆 )” to share responsibility for the Chinese character “Kuai(塊)” in the Han &Wei six dynasties.

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

한국어의 분류사는 그 목록을 범주화시키기 어려울 정도로 다양하고 복잡해서 한국어 학습자들이 학습하는데 상당한 어려움을 겪는다. 그러나 지금까지 한국어 교육에서의 분류사 연구는 한국어 분류사의 특성이나 다른 언어와의 분류사를 비교․대조하는 데 그치고 있어 실제 수업에 적용할 수 있는 분류사 목록이 체계적으로 제시된 연구는 없다. 또한 기존 논의에서 제시된 분류사 목록 중에는 실제 생활과는 동떨어진 것들이 많아 한국어 수업에 적용하기 어렵다. 따라서 이 연구는 효율적인 분류사 교수를 위한 기초 작업으로 학습 단계별 분류사 목록을 제시하는 데 목적이 있다. 이를 위해 본고는 분류사의 개념과 특성을 파악하고, 다양한 텍스트들을 기초로 90개의 분류사를 추출하여 난이도에 따라 각각의 학습 단계를 선정하였다.

매년 많은 양의 플라스틱 폐기물이 발생되면서 폐플라스틱을 순환 자원화하기 위해 여러 공공기관, 연구소에서는 폐플라스틱 자동선별 시스템을 구축하기 위한 노력을 하고 있다. 이미 국내 지자체 재활용 선별장 등에서는 근적외선 분광법(NIR)을 활용한 자동선별 시스템을 구축 및 활용하고 있지만 검정색 플라스틱 제품군의 물리적 성상인 근적외선 파장의 과도한 흡수로 인한 스펙트럼 분석이 어려워 자동분류가 힘든 실정이다. 이러한 문제들을 해결하기 위해 NIR 분광장비가 아닌 LIBS 분광기를 사용하여 데이터를 구축 및 분석하고 지능형 알고리즘을 이용하여 자동 선별이 가능한 흑색 플라스틱의 재질별 선별 분류기 구축하고자 한다. LIBS분광장비는 시료가 기체, 액체 및 고체 상태와 관계없이 주기율표 상의 거의 모든 원소에 대하여 정성 정량 분석이 가능한 장비로 시료의 전처리 과정이 필요 없으며, 분석 시간이 매우 짧기 때문에 실시간 분석이 가능하다는 장점을 가진다. 이러한 LIBS 분광장비를 이용하여 데이터를 추출하고 이를 분석하여 인공지능 알고리즘을 이용한 분류기를 설계하고자 한다. 검은색 플라스틱을 인공지능 알고리즘을 통하여 재질별 자동 선별하도록 설계하여 산업적･경제적인 효율의 향상을 기대할 수 있다.

Korean is classified into classifier languages, which require a classifier regardless of whether nouns are count or mass, while English is a typical non-classifier language, where only mass nouns require classifiers when counting. While most of the previous studies have focused on the acquisition order of classifiers based on their semantic features (e,g, human, shape, function, etc.), it should be noted that the correct use of classifiers requires syntactic knowledge as well as semantic knowledge (K. Lee and S. Lee, 2009). However, little is known about the L2 acquisition of syntactic knowledge related to classifier construction such as EPP feature (Byun and Ha, 2015). Against this background, this study aims to articulate what must be acquired by English-speaking learners for the correct use of Korean numeral-classifier constructions, and to investigate whether the syntactic properties are acquirable for them. 85 adult English-speaking learners at four proficiency levels (low intermediate; high intermediate; low advanced; advanced) and 31 native Korean speakers performed an elicitation task. The findings show that the advanced group produced the correct classifier constructions in the majority of cases in a similar way to the native speaker (NS) control group, while the lower proficiency groups supplied classifiers significantly less frequently than the NSs. The overall group results suggest that highly proficient learners have successfully acquired the required syntactic knowledge, even though its complete acquisition is delayed until advanced stages of L2 development.