|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Co含量对磁性非晶合金Fe85-xCoxTi7Hf6B2电学特性的影响显示文摘研究了由单铜辊真空熔融纺丝技术制备的扁平带状铁基磁性非晶态合金Fe(85-x)CoxTi7Hf6B2(x=20, 30, 35,原子分数)的电学特性。这种软铁磁性非晶合金与其他含FeCo类合金一样,当Co含量达到30%附近时其饱和磁化强度会呈现最大值。尽管如此,体系中Co含量的改变并没有对这种铁基非晶合金的磁性能产生较大的影响。值得注意的是,在这种铁基非晶合金体系中Co含量的改变可导致其电学特性发生显著的转变。比如:当Co含量为20%时,其电阻温度系数为负值,表现出类似于半导体的电阻温度特性和P型半导体的电学特性;然而,当Co含量增加到30%和35%时,其电阻温度系数则转变为正值,呈现出类似一般金属导体的电阻温度特性。不仅如此,根据该非晶合金中各组成元素的电负性和电离势揭示了Co含量可调控载流子(空穴)浓度的物理机制,为新型铁基非晶合金材料的设计提供新方法。 | 陈澄 黄琳 Kim Sumin 朴红光 Haein Choi-Yim Kim Dong-Hyun | 2019 | 稀有金属2019，43，7： | 2 |
| 2 | Structure and mechanical properties of bulk glass - forming Ni - Nb - Sn alloys 显示文摘 | Haein Choi - Yim Donghua Xu Mary Laura Lind et al | 2006 | Scripta Materialia2006，54，2： | 1 |
| 3 | Quasistatic and dynamic deformation of tungsten reinforced Zr 57 Nb 5 Al 10 Cu 15.4 Ni 12.6 bulk metallic glass matrix composites显示文摘 | Haein Choi-Yim Robert D Conner Frigyes Szuecs William L Johnson | 2001 | Scripta Materialia2001，，9： | 1 |
| 4 | Ni-based bulk metallic glass formation in the Ni-Nb-Sn and Ni-Nb-Sn-X(X = B,Fe,Cu) alloy systems显示文摘 | Haein Choi-Yim Donghua Xu | 2003 | Appl Phys Lett2003，82，7： | 1 |
| 5 | Scripta Materialia显示文摘 | Haein Choi-Yim | 2001 | 45:10392001，45，： | 1 |
| 6 | Scfipta Materialia显示文摘 | Choi-Yim Haein Conner Robert D Szuecs Frigyes | 2001 | 45:10392001，45，： | 1 |
| 7 | 显示文摘 | Haein Choi-Yim | 2001 | Scripta Materialia2001，45，9： | 1 |
| 8 | Compositional dependence of thermal and elastic properties of Cu-Ti-Zr-Ni bulk metallic glasses显示文摘Starting from the quaternary Cu47Ti34Zr11Ni8 alloy,the compositional dependence of thermal and elastic properties of Cu-Ti-Zr-Ni alloys was systematically investigated.Quaternary Cu-Ti-Zr-Ni alloys can be cast directly from the melt into copper molds to form fully amorphous strips or rods with the thickness of 3-6 mm.The evidence of the amorphous nature of the cast rods was provided by X-ray spectra.The measured glass transition temperature(Tg) and crystallization temperature(Tx) were obtained for the alloys using differential scanning calorimetry(DSC) at the heating rate of 20 K/s.In the results,the differences between the glass temperature and the crystallization temperature(ΔTx=Tx-Tg) are measured with values ranging up to 33-55 K.The reduced glass transition temperature(Trg),which is the ratio of the glass temperature to the liquidus temperature(Tl),is often used as an indication of the glass-forming ability of metallic alloys.For the present Cu-Ti-Zr-Ni alloys,this ratio is typically in the range of 0.5838-0.5959,characteristic of metallic alloys with good glass-forming ability.The elastic constants for several selected alloys were measured using ultrasonic methods.The values of the elastic shear modulus,bulk modulus,and Poisson's ratio were also given. | Jihye An Hyunjune Yim Choi-Yim Haein | 2010 | International Journal of Minerals,Metallurgy and Materials2010，17，3： | 0 |
| 9 | Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators显示文摘Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments. | Hae Sun Jung Seon Hong Lee Haein Lee Jang Hyun Kim | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 10 | Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning显示文摘Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application services.Particularly,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere.To provide an improved service,it is important to analyze online reviews that contain user satisfaction.Several previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey data.These methods may not be appropriate for analyzing the users of mobile applications.To overcome this limitation,several researchers perform user experience analysis through online reviews and star ratings.However,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text.This variation disturbs the performance of machine learning.To alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is introduced.The current study aims to build a more accurate sentiment classifier based on machine learning with VADER.In this study,five sentiment classifiers are used,such as Naïve Bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models. | Seon Hong Lee Haein Lee Jang Hyun Kim | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 11 | ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers显示文摘Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value.Recently,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is essential.Several previous studies limited their focus to specific countries or have not used big data.Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG.To address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific papers.These big data were analyzed by topic modeling.Topic modeling can derive hidden semantic structures within the text.Through this process,it is possible to collect information on public and academic sentiment.The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling technique.In addition,changes in subject patterns over time were considered using dynamic topic modeling.As a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas. | Haein Lee Seon Hong Lee Kyeo Re Lee Jang Hyun Kim | 2023 | Computers, Materials & Continua2023，，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员