• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.20 no.3
• /
• pp.119-130
• /
• 2015

Impacts of Argo temperature assimilation on the analysis fields in the East Sea is investigated by using DAESROM, the East Sea Regional Ocean Model with a 3-dimensional variational assimilation module (Kim et al., 2009). Namely, we produced analysis fields in 2009, in which temperature profiles, sea surface temperature (SST) and sea surface height (SSH) anomaly were assimilated (Exp. AllDa) and carried out additional experiment by withdrawing Argo temperature data (Exp. NoArgo). When comparing both experimental results using assimilated temperature profiles, Root Mean Square Error (RMSE) of the Exp. AllDa is generally lower than the Exp. NoArgo. In particular, the Argo impacts are large in the subsurface layer, showing the RMSE difference of about $0.5^{\circ}C$. Based on the observations of 14 surface drifters, Argo impacts on the current and temperature fields in the surface layer are investigated. In general, surface currents along the drifter positions are improved in the Exp. AllDa, and large RMSE differences (about 2.0~6.0 cm/s) between both experiments are found in drifters which observed longer period in the southern region where Argo density was high. On the other hand, Argo impacts on the SST fields are negligible, and it is considered that SST assimilation with 1-day interval has dominant effects. Similar to the difference of surface current fields between both experiments, SSH fields also reveal significant difference in the southern East Sea, for example the southwestern Yamato Basin where anticyclonic circulation develops. The comparison of SSH fields implies that SSH assimilation does not correct the SSH difference caused by withdrawing Argo data. Thus Argo assimilation has an important role to reproduce meso-scale circulation features in the East Sea.

• The KIPS Transactions:PartA
• /
• v.11A no.7 s.91
• /
• pp.555-562
• /
• 2004

Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute $L_2$ Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.39 no.3
• /
• pp.197-210
• /
• 2003

The non-float midwater pair trawl was effective in the mouth opening and control of the working depth in midwater and bottom. In contrast, we confirmed that it was difficult to keep the net at surface above 30 m of the depth by means of the full scale experiment in the field and the model test in the circulation water channel. To solve this problem, the kites were attached to the head rope of the non-float midwater pair trawl. In this study, four kinds of the model experiments were carried out with the purpose of applying the kite to the korean midwater pair trawl. The results obtained can be summarized as follows: 1. The working depth of the non-float midwater pair trawl with the kite was shallower than that of the proto type and non-float type. The working depth of the kite type was approximately 20m with 2 kites and about 5m with 4 kites under 4.0 knot. The working depth was almost constant but the depth of the head rope sank approximately 15m and 10m according to the increase in the front weight and the wing-end weight, respectively. The changing aspect of the working depth was constant, but the depth of the head rope sank approximately 22m according to the increase in the lower warp length (dL). 2. The hydrodynamic resistance of the kite type was almost increased in a linear form in accordance with the flow speed increase from 2.0 to 5.0 knot. The increasing grate of the hydrodynamic resistance tended to increase in accordance with the increase in flow speed. The hydrodynamic resistance of the kite type was larger approximately 5～10 ton larger than that of the non-float type and the proto type. The hydrodynamic resistance of the kite type increased approximately 3ton with the changing of the front weight from 1.40 to 3.50 ton and approximately 4 ton with the changing of the wing-end weight from 0 to 1.11 ton and approximately 5.5 ton with the changing lower warp length (dL) from 0 to 40 m, respectively. 3. The net height of the kite type was increased approximately 10 m with the change in the kite area from $2,270mm^2$ to 4,540 $\textrm{mm}^2$. The net height of the kite type was aproximately 50 m and 30 m larger than that of the proto type and the non-float type, respectively. The changed aspect of the net width was approximately 5m with the variation of the flow speed from 2.0 to 5.0 knot. 4. The filtering volume of the kite type was larger than that of the proto type and the non-float type by 28%, 34% at 2.0 knot of the flow speed and 42%, 41% at 3.0 knot, and 62%, 45% at 4.0 knot, and 74%, 54% at 5.0knot, respectively. The optimal towing speed was approximately 3.0 knot for the proto type and was over 4.0 knot for the non-float type, and the optimal towing speed reached 5.0 knot for the kite type. 5. The opening efficiency of the kite type was approximately 50% and 25% larger than that of the proto type and the non-float type, respectively.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.23-45
• /
• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Economic and Environmental Geology
• /
• v.51 no.2
• /
• pp.87-97
• /
• 2018

The cesium (Cs) removal from the contaminated water system has been considered to be difficult because the cesium likes to exist as soluble phases such as ion and complexes than the solid in water system. Many researches have focused on developing the breakthrough adsorbent to increase the cesium removal efficiency in water. In this study, the laboratory scale experiments were performed to investigate the feasibility of the adsorption process using the bamboo charcoal for the Cs contaminated water system. The Cs removal efficiency of the bamboo charcoal were measured and the optimal adsorption conditions were determined by the adsorption batch experiments. Total 5 types of commercialized bamboo charcoals in Korea were used to identify their surface properties from SEM-EDS and XRD analyses and 3 types of bamboo charcoals having large specific surface areas were used for the adsorption batch experiment. The batch experiments to calculate the Cs removal efficiency were performed at conditions of various Cs concentration (0.01 - 10 mg/L), pH (3 - 11), temperature ($5-30^{\circ}C$), and adsorption time (10 - 120 min.). Experimental results were fitted to the Langmuir adsorption isotherm curve and their adsorption constants were determined to understand the adsorption properties of bamboo charcoal for Cs contaminated water system. From results of SEM-EDS analyses, the surfaces of bamboo charcoal particles were composed of typical fiber structures having various pores and dense lamella structures in supporting major adsorption spaces for Cs. From results of adsorption batch experiments, the Cs-133 removal efficiency of C type bamboo charcoal was the highest among those of 3 bamboo charcoal types and it was higher than 75 % (maximum of 82 %) even when the initial Cs concentration in water was lower than 1.0 mg/L, suggesting that the adsorption process using the bamboo charcoal has a great potential to remove Cs from the genuine Cs contaminated water, of which Cs concentration is low (< 1.0 mg/L) in general. The high Cs removal efficiency of bamboo charcoal was maintained in a relatively wide range of temperatures and pHs, supporting that the usage of the bamboo charcoal is feasible for various types of water. Experimental results were similar to the Langmuir adsorption model and the maximum amount of Cs adsorption (qm:mg/g) was 63.4 mg/g, which was higher than those of commercialized adsorbents used in previous studies. The surface coverage (${\theta}$) of bamboo charcoal was also maintained in low when the Cs concentration in water was < 1.0 mg/L, investigating that the Cs contaminated water can be remediated up with a small amount of bamboo charcoal.

• Journal of Global Scholars of Marketing Science
• /
• v.18 no.3
• /
• pp.59-88
• /
• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.