• Korean Journal of Health Education and Promotion
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• v.26 no.5
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• pp.15-26
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• 2009

Objectives: This paper is intended to illustrate and to discuss the organization and functioning of community resident groups (CRGs) in a community-based participatory health promotion program for healthy aging. Methods: CRGs were convened in 12 government-subsidized apartment communities for low-income seniors in Pennsylvania, U.S.A., to promote healthy aging. Researchers facilitated CRG meetings following a 6-step process of community empowerment and utilizing a social ecological model for assessment and planning. Almost 200 project-related documents were qualitatively analyzed using matrix analysis principles such as cross-classification of multiple dimensions to identify patterns in the data and matrix building for displaying such patterns. Results: CRGs were venues at which apartment building residents could interact, discuss health priorities, and become change agents in their building. CRG members' community health priorities were about their daily living, including building conditions, poor access to fresh food, and unhealthy resident relations. Specific patterns arose in analysis indicating that leadership withing the CRGs, consistency of meetings and participants' attendance, and ability to link health concerns to daily experience impacted the CRGs' capability to identify and accomplish their goals. Conclusion: Community health issues and solutions to those issues identified by CRGs were unique to community contexts and interests. Consistent participation by community members, a consistent pattern of group activities such as monthly meetings, and having established leadership to manage CRG activities were prominent characteristics of community group functioning.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.15-28
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• 2015

As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.1-7
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• 2000

Traditionally, three dimension model s have been used for building virtual worlds, and a data structure called the "scene graph" is often employed to organize these 3D objects in the virtual space. On the other hand, image-based rendering has recently been suggested as a probable alternative VR platform for its photo-realism, however, due to limited interactivity. it has only been used for simple navigation systems. To combine the merits of these two approaches to object/scene representations, this paper proposes for a scene graph structure in which both 3D models and various image-based scenes/objects can be defined. traversed, and rendered together. In fact, as suggested by Shade et al. [1]. these different representations can be used as different LOD's for a given object. For in stance, an object might be rendered using a 3D model at close range, a billboard at an intermediate range. and as part of an environment map at far range. The ultimate objective of this mixed platform is to breath more interactivity into the image based rendered VE's by employing 3D models as well. There are several technical challenges in devising such a platform : designing scene graph nodes for various types of image based techniques, establishing criteria for LOD/representation selection. handling their transition s. implementing appropriate interaction schemes. and correctly rendering the overall scene. Currently, we have extended the scene graph structure of the Sense8's WorldToolKit. to accommodate new node types for environment maps. billboards, moving textures and sprites, "Tour-into-the-Picture" structure, and view interpolated objects. As for choosing the right LOD level, the usual viewing distance and image space criteria are used, however, the switching between the image and 3D model occurs at a distance from the user where the user starts to perceive the object's internal depth. Also. during interaction, regardless of the viewing distance. a 3D representation would be used, if it exists. Finally. we carried out experiments to verify the theoretical derivation of the switching rule and obtained positive results.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of Agricultural Extension & Community Development
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• v.19 no.4
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• pp.961-987
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• 2012

The present study is to investigate the characteristics and determinants of spatial distribution of strong small farm by defining the term, strong small farm (SSF) extracting the SSF households data dealing with apples, from 2010 Census of Agriculture, Forestry and Fisheries, Korea. Spatial distribution and concentration of SSF are analyzed based on spatial clustering techniques. We construct discrete dependent variables on strong and non-strong small farms and then analyze the determinants of the SSFs using probit model, with independent variables including population and economic characteristics and management characteristics. As of 2010, the apple SSFs, 1,529 households in total, are geographically concentrated in Gyeonsangbuk-do according to the analysis results. The determinants of SSF are similar to those of farms' earnings. When located in the apple producing area, and participating in producers organization while selling products directly, the farm is highly likely an SSF. The findings and results of the present study are expected to provide fundamental information helpful for preparing and implementing policies for SSFs in that the present study investigates the characteristics of SSF, which is a prerequisite step for SSF-related policies.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.200-210
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• 2023

Background: Cambaroides similis is an endangered candidate species living in the stream of South Korea. Freshwater crayfish is known to decline rapidly not only domestically, but also internationally. Its decline is projected to be further exacerbated due to climate change. Understanding physical characteristics of the habitat is crucial for the conservation of an organism. However, comprehensive data regarding the distribution and physical habitat characteristics of C. similis are currently unavailable in South Korea. Thus, the objective of this study was to ascertain preferred ranges for water depth, current velocity, and streambed substrate of C. similis using Weibull model. Results: In this study, C. similis was found at 59 sites across 12 regions in South Korea. Its optimal water depth preferences ranged from 11.9 cm to 30.1 cm. Its current velocity preferences ranged from 9.8 cm s^-1 to 29.1 cm s^-1. Its substrate preferences ranged from -5.1 𝜱_m to -2.5 𝜱_m. Median values of central tendency were determined as follows: water depth of 21.4 cm, current velocity of 21.2 cm s^-1, and substrate of -4.1 𝜱_m. Mean values of central tendency were determined as follows: water depth of 21.8 cm, current velocity of 22.0 cm s^-1, and substrate of -4.4 𝜱_m. Mode values of central tendency were determined as follows: water depth of 21.7 cm, current velocity of 20.1 cm s^-1, and substrate of -3.7 𝜱_m. Conclusions: Based on habitat suitability analysis, physical microhabitat characteristics of C. similis within a stream were identified as Run section with coarse particle substrate, low water depth, and slow current velocity. Due to high sensitivity of these habitats to environmental changes, prioritized selection and assessment of threats should be carried out as a primary step.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.3-10
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• 2008

Cadastral map is a public book that has been composed by continuous parcel having location, number, classification, boundary and an area based on Cadastral Law. A few years ago, cadastral map had been managed by form drawn on 2 dimension plane paper with 7 regular scales. Recently as computer systems are upgrading, cadastral map was able to have a chance to develope one step. Its type has been remade from raster to vector. In result, the cadastral map of vector type becomes to apply variously. Therefore, digital cadastral map has been ready a system to be use with multi-propose by KLIS(Korean Land Information System). In this research, it concretely want presentation of status using land more than original parcel on basic coordination cadastral map and KLIS(Korean Land Information System). The cadastral map is composed as parcel unit was applied by new presentation technique to "Model Research on One Parcel Presentation Technique for Land Status of Cadastral Map". The function of cadastral map on One Parcel Presentation Technique which is not only location relation of possession right and expression of states using land in 28 classifications demonstrated on the cadastral law but also used as foundation data of GIS construct business is developed by lines and classification of parcel to center around public sites of roads, rails, drains and rivers. especially, this research is composed of technique elevation and development of One Parcel Projection Technique of cadastral map in using object of roads among public sites.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.197-205
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• 2002

The objectives of this Paper is to implement a diagnostic classifier of differential laryngeal diseases from acoustic signals acquired in a noisy room. For this Purpose, the voice signals of the vowel /a/ were collected from Patients in a soundproof chamber and got mixed with noise. Then, the acoustic Parameters were analyzed, and hierarchical neural networks were applied to the data classification. The classifier had a structure of five-step hierarchical neural networks. The first neural network classified the group into normal and benign or malign laryngeal disease cases. The second network classified the group into normal or benign laryngeal disease cases The following network distinguished polyp. nodule. Palsy from the benign laryngeal cases. Glottic cancer cases were discriminated into T1, T2. T3, T4 by the fourth and fifth networks All the neural networks were based on multilayer perceptron model which classified non-linear Patterns effectively and learned by an error back-propagation algorithm. We chose some acoustic Parameters for classification by investigating the distribution of laryngeal diseases and Pilot classification results of those Parameters derived from MDVP. The classifier was tested by using the chosen parameters to find the optimum ones. Then the networks were improved by including such Pre-Processing steps as linear and z-score transformation. Results showed that 90% of T1, 100% of T2-4 were correctly distinguished. On the other hand. 88.23% of vocal Polyps, 100% of normal cases. vocal nodules. and vocal cord Paralysis were classified from the data collected in a noisy room.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.246-259
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• 2008

This study was to minimize the trial and error in the primary step of the C4I system(ATCIS) of the each army corps on the front line, and test the economy and efficiency was tested by reviewing related papers and the system characteristics of other countries. The relationship was researched by analyzing the collected survey data and survey data related to the user's requirement level such as the army standards, that is, commonality, timeliness, simplification, automaticity, field availability and viability, multi-stage security and interoperability, unification. The result showed that the C4I system was efficiently operated through the system reliability for the specification of the system and operation manual, maneuverability and security, adaptability of the war field and system support and management, and good education and training about system operation, and less system maintenance and supplementary element. As a result, the development plan confirmed that the continuous operator education and the construction of the maintenance, and the upgrade digitalization(C4ISR+D) with the korean characteristics based on IT of network systems, and system development of the measurement model of the operator performance must be continuously supplemented in the near future.