Size-controlled synthesis of monodisperse gold nanooctahedrons and their surface-enhanced Raman scattering properties

Kim, Dongheun,Heo, Jinhwa,Kim, Minjung,Lee, Young Wook,Han, Sang Woo Elsevier 2009 Chemical physics letters Vol.468 No.4

<P><B>Graphical abstract</B></P><P>Monodisperse octahedral gold nanocrystals with controllable size were prepared in aqueous environment.</P><ce:figure></ce:figure> <P><B>Abstract</B></P><P>Nanocrystalline gold octahedrons were synthesized in an aqueous environment via the reduction of HAuCl<SUB>4</SUB> by ascorbic acid with the addition of NaOH. Highly monodisperse octahedral particles with controlled sizes ranging from 20 to 40nm were prepared in high-yield by varying the reaction temperature. The structural and optical properties of the synthesized gold nanoparticles were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and UV–vis spectroscopy. The prepared nanoparticles exhibited efficient surface-enhanced Raman scattering (SERS) properties, and their SERS activities were highly size-dependent.</P>

Real-space mapping of the strongly coupled plasmons of nanoparticle dimers.

Kim, Deok-Soo,Heo, Jinhwa,Ahn, Sung-Hyun,Han, Sang Woo,Yun, Wan Soo,Kim, Zee Hwan American Chemical Society 2009 NANO LETTERS Vol.9 No.10

<P>We carried out the near-field optical imaging of isolated and dimerized gold nanocubes to directly investigate the strong coupling between two adjacent nanoparticles. The high-resolution (approximately 10 nm) local field maps (intensities and phases) of self-assembled nanocube dimers reveal antisymmetric plasmon modes that are starkly different from a simple superposition of two monomeric dipole plasmons, which is fully reproduced by the electrodynamics simulations. The result decisively proves that, for the closely spaced pair of nanoparticles (interparticle distance/particle size approximately 0.04), the strong Coulombic attraction between the charges at the interparticle gap dominates over the intraparticle charge oscillations, resulting in a hybridized dimer plasmon mode that is qualitatively different from those expected from a simple dipole-dipole coupling model.</P>

A Knowledge Integration Model for Corporate Dividend Prediction

Jinhwa Kim,Chaehwan Won,Jae Kwon Bae 한국경영정보학회 2008 한국경영정보학회 학술대회논문집 Vol.2008 No.-

Dividend is one of essential factors determining the value of a firm. According to the valuation theory in finance, discounted cash flow (DCF) is the most popular and widely used method for the valuation of any asset. Since dividends play a key role in the pricing of a firm value by DCF, it is natural that the accurate prediction of future dividends should be most important work in the valuation. Although the dividend forecasting is of importance in the real world for the purpose of investment and financing decision, it is not easy for us to find good theoretical models which can predict future dividends accurately except Marsh and Merton (1987) model. Thus, if we can develop a better method than Marsh and Merton in the prediction of future dividends, it can contribute significantly to the enhancement of a firm value. Therefore, the most important goal of this study is to develop a better method than Marsh and Merton model by applying artificial intelligence techniques.

Size-controlled synthesis of octahedral gold nanoparticles and their application to SERS

Jinhwa Heo,Hyung Ju Park,Kihyun Kwon,Young Wook Lee,Duk-Soo Kim,Zee Hwan Kim,Sang Woo Han,Wan Soo Yun 한국진공학회 2007 한국진공학회 학술발표회초록집 Vol.16 No.2

Product Development with Data Mining Techniques

Jinhwa Kim(김진화),Jae Kwon Bae(배재권),Lu Pei Yong(페이용) 한국경영과학회 2009 한국경영과학회 학술대회논문집 Vol.2009 No.10

Many enterprises have been devoting a significant portion of their budget to product development in order to distinguish their products from those of their competitors and to make them better fit the needs and wants of customers. Hence, businesses should develop product designing that could satisfy the customers’ requirements since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C4.5 algorithms are methodology of association rule and decision tree for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing.

An Empirical Data Driven Optimization Approach By Simulating Human Learning Processes

Kim, Jinhwa 한국경영과학회 2004 한국경영과학회지 Vol.29 No.4

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate, "Undecidable" problems are considered as best possible application areas for this suggested approach. The concept of an "undecidable" problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach "SLO : simulated learning for optimization." Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

Concise Synthesis of Dimemorfan (DF) Starting from 3-Hydroxymorphinan (3-HM)

Kim, Jong Yup,Kim, Hyoung-Chun,Kim, Jeongmin,Lee, Jinhwa The Pharmaceutical Society of Japan 2008 Chemical & pharmaceutical bulletin Vol.56 No.7

<P>Dimemorfan (DF) has been known to possess neuroprotective properties. While this promising compound deserves further biological evaluation, synthetic methods have not improved since Murakami group unveiled the synthetic efforts in 1972. Herein a succinct synthesis toward DF from commercially available 3-hydroxymorphinan (3-HM) is disclosed. Other morphinan analogs have been effectively prepared by adopting the similar methodology.</P>

Dynamic Web Pages Personalization using Intimacy Theory

Kim Jinhwa 한국전자거래학회 2004 한국전자거래학회 학술대회 발표집 Vol.2004 No.-

지식 누적을 이용한 실시간 주식시장 예측

김진화(Jinhwa Kim),홍광헌(Kwang Hun Hong),민진영(Jin Young Min) 한국지능정보시스템학회 2011 지능정보연구 Vol.17 No.4

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study.

인공지능을 이용한 개인의 소득 예측 사례

김진화(Jinhwa kim),Arun Doddapaneni 한국지능정보시스템학회 2011 한국지능정보시스템학회 학술대회논문집 Vol.2011 No.5

In this study customer income is predicted using U.S. census data employing data mining techniques. A comparative performance analysis of decision tree c5.0, back propagation neural networks, and linear regression models is conducted. This study also suggests the strategic implications of the results from the customer relations perspective for marketing strategy in practice and these results have various practical implications to retail banks with regards to decision making and strategic planning. This can also be applied for fraud detection of income gaps between predicted and actual incomes. The results show that decision tree c5.0 outperforms other models in predictability hence focusing more on decision tree and its advantages in lieu of prediction accuracy. In the mean time a set of decision rules are also extracted from the trained decision tree c5.0 in order to improve the clarity and explicability of the income prediction model.