• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.315-322
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• 2024

In this paper, we proposes real-time operating system based PCM encoder for telemetry system that must transmit frames within a set time. In the case of large aircraft, the complexity of the system is increasing because a lot of state information is measured from each sensor and peripheral device. In addition, as the amount measurement data increases, the role of PCM encoder to transmit frames within a set time is becoming important. Existing encoder is inflexible when changing specifications or implementing additional features. Therefore, a design is needed to supplement this. We propose a PCM encoder design applying uC/OS-II. In order to confirm the validity, a simulation was performed to measure the execution time of the task to confirm the performance.

• Journal of Science Education
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• v.37 no.1
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• pp.142-156
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• 2013

The purpose of this study was to analyze observation characteristics through eye movement according to cognitive style. For this, developed observation task that can be shown the difference between wholistic cognitive style group and analytic cognitive style group, measured eye movement of university students who has different cognitive style, as given observation task. It is confirmed the difference between two cognitive style groups by analysing gathered statistics and visualization data. The findings of this study were as follows; First, Compared observation sequence and pattern by cognitive style, analytic cognitive style group is concerned with spider first and moving on surrounding environment, whereas wholistic cognitive style group had not fixed pattern as observing spider itself and surrounding area of spider alternately or looking closely on particular part at first. When observing entire feature and partial feature, wholistic cognitive style group was moving on Fixation from outstanding factor without fixed pattern, analytic cognitive style had certain directivity and repetitive investigation. Second, compared the ratio of observation, analytic cognitive style group gave a large part to spider the very thing, wholistic cognitive style group gave weight to surrounding area of spider, and analytic group shown higher concentration on observing partial feature, wholistic cognitive style group shown higher concentration on observing wholistic feature. Wholistic cognitive style group gave importance to partial features in surrounding area, and wholistic feature of spider than analytic cognitive style group, analytic cognitive style group was focus on partial features of spider than wholistic cognitive style group. Through the result of this study, there are differences of observing time, frequency, object, area, sequence, pattern and ratio from cognitive styles. It is shown the reason why each student has varied outcome, from the difference of information following their cognitive style, and the result of this study help to figure out and give direction to what observation fulfillment is suitable for each student.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.331-367
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• 2008

This study aims to validate a web-based neuropsychological testing tool developed by Kwak(2007) and to suggest solutions to potential problems that can deteriorate its validity. When it targets a wider range of subjects, a web-based neuropsychological testing tool is challenged by high drop-out rates, lack of motivation, lack of interactivity with the experimenter, fear of computer, etc. As a possible solution to these threats, this study aims to redesign the user interface of a web-based attention testing tool through three phases of study. In Study 1, an extensive analysis of Kwak's(2007) attention testing tool was conducted to identify potential usability problems. The Heuristic Walkthrough(HW) method was used by three usability experts to review various design features. As a result, many problems were found throughout the tool. The findings concluded that the design of instructions, user information survey forms, task screen, results screen, etc. did not conform to the needs of users and their tasks. In Study 2, 11 guidelines for the design of web-based attention testing tools were established based on the findings from Study 1. The guidelines were used to optimize the design and organization of the tool so that it fits to the user and task needs. The resulting new design alternative was then implemented as a working prototype using the JAVA programming language. In Study 3, a comparative study was conducted to demonstrate the excellence of the new design of attention testing tool(named graphic style tool) over the existing design(named text style tool). A total of 60 subjects participated in user testing sessions where their error frequency, error patterns, and subjective satisfaction were measured through performance observation and questionnaires. Through the task performance measurement, a number of user errors in various types were observed in the existing text style tool. The questionnaire results were also in support of the new graphic style tool, users rated the new graphic style tool higher than the existing text style tool in terms of overall satisfaction, screen design, terms and system information, ease of learning, and system performance.

• Journal of Korean Society of Archives and Records Management
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• v.10 no.2
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• pp.75-99
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• 2010

Digital resources are widely used in our modern society. However, we are facing fundamental problems to maintain and preserve digital resources over time. Several standard methods for preserving digital resources have been developed and are in use. It is widely recognized that metadata is one of the most important components for digital archiving and preservation. There are many metadata standards for archiving and preservation of digital resources, where each standard has its own feature in accordance with its primary application. This means that each schema has to be appropriately selected and tailored in accordance with a particular application. And, in some cases, those schemas are combined in a larger frame work and container metadata such as the DCMI application framework and METS. There are many metadata standards for archives of digital resources. We used the following metadata standards in this study for the feature analysis me metadata standards - AGLS Metadata which is defined to improve search of both digital resources and non-digital resources, ISAD(G) which is a commonly used standard for archives, EAD which is well used for digital archives, OAIS which defines a metadata framework for preserving digital objects, and PREMIS which is designed primarily for preservation of digital resources. In addition, we extracted attributes from the decision tree defined for digital preservation process by Digital Preservation Coalition (DPC) and compared the set of attributes with these metadata standards. This paper shows the features of these metadata standards obtained through the feature analysis based on the records lifecycle model. The features are shown in a single frame work which makes it easy to relate the tasks in the lifecycle to metadata elements of these standards. As a result of the detailed analysis of the metadata elements, we clarified the features of the standards from the viewpoint of relationships between the elements and the lifecycle stages. Mapping between metadata schemas is often required in the long-term preservation process because different schemes are used in the records lifecycle. Therefore, it is crucial to build a unified framework to enhance interoperability of these schemes. This study presents a basis for the interoperability of different metadata schemas used in digital archiving and preservation.

• 재활복지
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• v.14 no.3
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• pp.225-256
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• 2010

This study aims to provide the basic data of the rehabilitation program for the schoolchild with intellectual disability by designing new framework of the features of postural control for the schoolchild with intellectual disability. For this, the study investigated what sensations the schoolchild are using to maintain posture by selectively or synthetically applying vision, vestibular sensation and somato-sensation, and how the coordinative sensory system of the schoolchild is responding to any sway referenced sensory stimulus. The study intended to prove the limitation of motor system in estimating the postural stability by providing the cognitive motor task, and provided the features of postural control of the schoolchild with intellectual disability by measuring the onset times and orders of muscle contraction of neuron-muscle when there is a postural control taking place due to the exterior disturbance. Furthermore, by comparatively analyzing the difference between the normal schoolchild and the intellectually disabled schoolchild, this study provided an optimal direction for treatment planning when the rehabilitation program is applied in the postural control ability training program for the schoolchild with intellectual disability. Taking gender and age into consideration, 52 schoolchild including 26 normal schoolchild and 26 intellectually disabled schoolchild were selected. To measure the features of postural control, CTSIB test, and postural control strategy test were conducted. The result of experiment is as followed. First, the schoolchild with intellectual disability showed different feature in using sensory system to control posture. The normal schoolchild tended to depend on somato-sensory or vision, and showed a stable postural control toward a sway referenced stimulus on somato-sensory system. The schoolchild with intellectual disability tended to use somato-sensory or vision, and showed a very instable postural control toward a sway referenced vision or a sway referenced stimulus on somato-sensory system. In sensory analysis, the schoolchild with intellectual disability showed lower level of proficiency in somato-sensation percentile, vision percentile and vestibular sensation percentile compare to the normal schoolchild. Second, as for the onset times and orders of muscle contraction for strategies of postural control when there is an exterior physical stimulus, the schoolchild with intellectual disability showed a relatively delayed onset time of muscle control, and it was specially greater when the perturbation is from backward. As for the onset orders of muscle contraction, it started from muscles near coax then moved to the muscles near ankle joint, and the numbers and kinds of muscles involved were greater than the normal schoolchild. The normal schoolchild showed a fast muscle contracting reaction from every direction after the perturbation stimulus, and the contraction started from the muscles near the ankle joint and expanded to the muscles near coax. From the results of the experiments, the special feature of the postural control of the schoolchild with intellectual disability is that they have a higher dependence on vision in sensory system, and there was no appropriate integration of swayed sensation observed in upper level of central nerve system. In the motor system, the onset time of muscle contraction for postural control was delayed, and it proceeded in reversed order of the normal schoolchild. Therefore, when use the clinical physical therapy to improve the postural control ability, various sensations should be provided and should train the schoolchild to efficiently use the provided sensations and use the sensory experience recorded in upper level of central nerve system to improve postural control ability. At the same time, a treatment program that can improve the processing ability of central nerve system through meaningful activities with organizing and planning adapting reaction should be provided. Also, a proprioceptive motor control training program that can induce faster muscle contraction reaction and more efficient onset orders from muscularskeletal system is need to be provided as well.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.61-78
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• 1991

This paper descibes the study of application of a modified Neocognitron model with backward path for the recognition of Hangul(Korean) syllabic characters. In this original report, Fukushima demonstrated that Neocognitron can recognize hand written numerical characters of $19{\times}19$ size. This version accepts $61{\times}61$ images of handwritten Hangul syllabic characters or a part thereof with a mouse or with a scanner. It consists of an input layer and 3 pairs of Uc layers. The last Uc layer of this version, recognition layer, consists of 24 planes of $5{\times}5$ cells which tell us the identity of a grapheme receiving attention at one time and its relative position in the input layer respectively. It has been trained 10 simple vowel graphemes and 14 simple consonant graphemes and their spatial features. Some patterns which are not easily trained have been trained more extrensively. The trained nerwork which can classify indivisual graphemes with possible deformation, noise, size variance, transformation or retation wre then used to recongnize Korean syllabic characters using its selective attention mechanism for image segmentation task within a syllabic characters. On initial sample tests on input characters our model could recognize correctly up to 79%of the various test patterns of handwritten Korean syllabic charactes. The results of this study indeed show Neocognitron as a powerful model to reconginze deformed handwritten charavters with big size characters set via segmenting its input images as recognizable parts. The same approach may be applied to the recogition of chinese characters, which are much complex both in its structures and its graphemes. But processing time appears to be the bottleneck before it can be implemented. Special hardware such as neural chip appear to be an essestial prerquisite for the practical use of the model. Further work is required before enabling the model to recognize Korean syllabic characters consisting of complex vowels and complex consonants. Correct recognition of the neighboring area between two simple graphemes would become more critical for this task.

• Journal of The Korean Association For Science Education
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• v.33 no.4
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• pp.778-793
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• 2013

The purpose of this study is to analyze observation characteristics through eye movement according to cognitive styles. To do this, we developed observation tasks that show the differences between wholistic cognitive style group and analytic cognitive style group, measured eye movement of university students with different cognitive styles after being given an observation task. The difference between two cognitive style groups is confirmed by analysing gathered statistics and visualization data. The findings of this study are as follows: First, to compare fixation time and frequency, we compared the average value of total time used in the observation task by the wholistic cognitive style group and analytic cognitive style group. The numbers of Fixation (total) and number of Fixations (30s), is based on the fact that the wholistic cognitive style group has more numbers of fixation (Total) and number of fixations (30s) means the wholistic cognitive style group can observe more points or overall features than the analytic cognitive style group, in contrast, the analytic cognitive style group tend to focus on a particular detail, and observe less numbers of points. Second, to compare observation object and area by cognitive style, the outcome of analysing visualization data shows that wholistic cognitive style group observes the surrounding environment of spider and web on a wider area, on the other hand, the analytic cognitive style group observes by focusing on the spider itself. Through the result of this study, there are differences in observation time, frequency, object, area, and ratio from the two cognitive styles. It also shows the reason why each student has varied outcome, from the difference of information following their cognitive styles, and the result of this study helps to figure out and give direction as to what observation fulfillment is more suitable for each student.

• Asia Marketing Journal
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• v.16 no.1
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• pp.71-100
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• 2014

While the role of emotional antecedents of effective selling behavior would be important, the issue has not been fully addressed in the sales literature. To fill this gap, we conceptualize and empirically examine the relationships among salesperson's emotional regulation processes such as emotional intelligence (EI) and emotional labor (EL), effective selling behavior, and sales performance on the basis of educational, occupational, social psychology literature and marketing literature (e.g., Henning-Thurau, Groth, Paul, and Gremler 2006; Kidwell et al. 2011; Liu et al. 2008; Mayer, Salovey, and Caruso 2008). First, salesperson's EI is defined as his or her capability that enables correct perceptions about emotional situations in sales interactions. The EI is expected to work as psychological resources for different types of EL (i.e., deep acting and surface acting) to be performed by salesperson as emotional expression strategies (e.g., Lie et al. 2008). It is, then, expected that the features of EL selected by the salesperson would lead to different levels of adaptive selling behavior (ASB) and thereby sales performance (Monaghan 2006). Further, given that salesperson's customer orientation (CO) is found to be an important correlate of ASB (Franke and Park 2006), it is expected that CO would moderate the relationship between EL and ASB (Rozell, Pettijohn, and Parker 2004). Hence, this research attempts to shed additional light on emotionally-driven (EL) as well as cognitively-driven (CO) antecedents of ASB (Frank and Park 2006). The findings of the survey research, done with 336 salespersons in insurance and financial companies, are summarized as follows. First, salespersons with a high level of EI are found to use both deep acting (regulating the emotions themselves) and surface acting (controlling only emotional expressions) in a versatile way, when implementing EL. Second, the more the salesperson performs deep acting, the more he or she shows ASB. It is, then, important for salespersons to use deep acting more frequently in the EL process in order to enhance the quality of interacting with customers through ASB. On the other hand, the salesperson's surface acting did not have a significant relationship with ASB. Moreover, CO was found to moderate the relationship between the salesperson's deep acting and ASB. That is, the context of high CO culture and individual salesperson's deep acting would synergistically make the selling efforts adaptive to customer preferences. Conceptualizing and empirically verifying the antecedent roles of important emotional constructs such as EI and EL in salesperson's effective selling behavior (ASB) and sales performance is a major theoretical contribution in the sales literature. Managerially, this research provides a deeper understanding on the nature of tasks performed by salespersons in service industries and a few guidelines for managing the sales force. First, sales organizations had better consciously assess EI capacity in the selection and nurturing processes of salespersons, given that EI can efficiently drive EL and the resulting effective selling behavior and performance. Further, the concept of EL could provide a framework to understand the salespersons' emotional experiences in depth. Especially, sales organizations may well think over how to develop deep acting capabilities of their sales representatives. In this direction, the training on deep acting strategies would be an essential task for improving effective selling behavior and performance of salespersons. This kind of training had better incorporate the perspectives of customers such that many customers can actually discern whether salespersons are doing either surface acting or deep acting. Finally, based on the synergistic effects of deep acting and CO culture, how to build and sustain CO is always an ever-important task in sales organizations. While the prior sales literature has emphasized the process and structure of highly customer-oriented sales organization, our research not only corroborates the important aspects of customer-oriented sales organization, but also adds the important dimension of competent sales representatives who can resonate with customers by deep acting for sales excellence.