• Journal of Environmental Science International
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• v.4 no.2
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• pp.223-232
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• 1995

New two macrocyclic compounds using as carriers of liquid emulsion menbrame, have been synthesized. These reuslts provide evidance for the usefulness of the theory in designing the systems. The efficiency of selective transport for heavy metal ions have been discussed from the membrane systems that make use of $SCN^-$,<>,$I^-$,CN- and $Cl^-$ ion as co-anions in source phase and make use of $S_2O_3^{2-}$ and $P_2O_7^{4-}$ ion as receiving phase, respectively. The transport rate of M(II) was highest when a maximum amount of the M(II) in the source phase was present as$Cd(SCN)_2$$(P[SCN^-]= 0.40M)$, $Hg(SCN)_2([SCN^-]=0.40M)$ and Pd(CN)$([CN^-]= 0.40M)$. The Cd(II) and Pb(II) over each competitive cations were well transprted with 0.3M-S2032- and 0.3M-P2O74-, respectively in the receiving phase. Results of this study indicate that two criteria must be met in order to have effective macrocycle-mediated transport in these emulsion system. First one must effective extraction of the $M^{n+}$ into the toluene systems. The effectiveness of this extraction is the greatest if locK for $M^{n+}$macrocycle interaction is large and if the macrocycle is very insoluble in the aqueous phase. Second, the ratio of the locK values (or Mn+-receiving phase ($S_2O_3^{2-}$- or $P_2O_7^{4-}$) to $M^{n+}$-macrocycle (($L_1$이나 $L_2$) interaction must be large enough to ensure quantitative stripping of Mn+(($Cd^{2+}$,$Pb^{2+}$)at the toluene receiving Phase interface. $L_1$(3.5-benzo-10,13,18,21-tetraoxa-1,7,diazabicyclo(8,5,5) eicosan) forms a stable ($Cd^{2+}$ and >,$Pb^{2+}$ complexes and $L_1$ is very insoluble in water and its $Cd^{2+}$ and >,$Pb^{2+}$ complex is considerably less stable than $Cd^{2+}$-(S2O3)22- and $Pd^{2+}-P_2O_7^{4-}$ complexes. On the other hand, the stability of the $Hg^{2+}$)+-$L_1$( complex exceed that of the $Hg^{2+}$- (S2O3)22- and Hg2+-P2O74-, and the distribution coefficient of $L_2$(5,8,15,18,23,26-hexaoxa-1,12- diazabicyclo-(10,8,8) octacosane) is much smaller than that of $L_1$. Therefore, the partitioning of Lr is favored by the aqueous receiving Phase, and little heavy metal ions transport is seen despite the large logK for $Hg^{2+}$+-$L_1$ and $Mn^+$($Cd^{2+}$+, $Pb^{2+}$+ and $Hg^{2+}$)-$L_2$ interactions. Key Words : macrocycles, transport, heavy metal, co-anion, source phase, receiveing, complex separation, interaction, destribution coefficient.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.