• The KIPS Transactions:PartC
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• v.8C no.1
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• pp.51-59
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• 2001

In order to support various QoS, IETF has proposed the Differentiated Services Model which provides discrimination service according to t the user’s requirements and payment intention intention for each traffic characteristic. This model is an excellent mechanism, which is not too c complicated in terms of the management for service and network model. Also, it has scalability that satisfies the requirement of Differentiated Services. In this paper, We define the Differentiated Services Model using feedback control, propose its control procedure, and analyze its p performance. In conventional model, non-adaptive traffic, such as UDP traffic, is more occupied the network resource than adaptive traffic, such a as TCP traffic. On the other hand, the Differentiated Services Model using feedback control fairly utlizes the network resources and even p prevents congestion occurrence due to its ability of congestion expectation.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2005.05a
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• pp.151-153
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• 2005

We investigated the brain substrate of repetition priming on the implicit memory taskusing low-resolution electromagnetic tomography (LORETA) with high-density 128 channel EEG and individual MRI as a realistic head model. Thirteen right-handed, healthy subjects performed a word/nonword discrimination task, in which the words and nonwords were presented visually,and some of the words appeared twice with a lag of one or five items. All of the subjects exhibited repetition priming with respect to the behavioral data, in which a faster reaction time was observed to the repeated word (old word) than to the first presentation of the word (new word). The old words elicited more positive-going potentials than the new words, beginning at 200 ms and lasting until 500 ms post-stimulus. We conducted source reconstruction using LORETA at a latency of 400 ms with the peak mean global field potentials and used statistical parametric mapping for the statistical analysis. We found that the source elicited by the old words exhibited a statistically significant current density reduction in the left inferior frontal gyrus. This is the first study to investigate the generators of repetition priming using voxel-by-voxel statistical mapping of the current density with individual MRI and high-density EEG.

• Journal of the Ergonomics Society of Korea
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• v.33 no.5
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• pp.377-386
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• 2014

Objective: In this study, we used resting-state fMRI data to map differences in functional connectivity between a comprehensive set of 8 distinct cortical and subcortical brain regions in healthy controls and Internet addicts. We also investigated the relationship between resting state connectivity strength and the level of psychopathology (ex. score of internet addiction scale and score of Barratt impulsiveness scale). Background: There is a lot of evidence of relationship between Internet addiction and impaired inhibitory control. Clinical evidence suggests that Internet addicts have a high level of impulsivity as measured by behavioral task of response inhibition and a self report questionnaire. Method: 15 Internet addicts and 15 demographically similar non-addicts participated in the current resting-state fMRI experiment. For the connectivity analysis, regions of interests (ROIs) were defined based on the previous studies of addictions. Functional connectivity assessment for each subject was obtained by correlating time-series across the ROIs, resulting in $8{\times}8$ matrixs for each subject. Within-group, functional connectivity patterns were observed by entering the z maps of the ROIs of each subject into second-level one sample t test. Two sample t test was also performed to examine between group differences. Results: Between group, the analysis revealed that the connectivity in between the orbito frontal cortex and inferior parietal cortex, between orbito frontal cortex and putamen, between the orbito frontal cortex and anterior cingulate cortex, between the insula and anterior cingulate cortex, and between amydgala and insula was significantly stronger in control group than in the Internet addicts, while the connectivity in between the orbito frontal cortex and insula showed stronger negative correlation in the Internet addicts relative to control group (p < 0.001, uncorrected). No significant relationship between functional connectivity strength and current degree of Internet addiction and degree of impulsitivy was seen. Conclusion: This study found that Internet addicts had declined connectivity strength in the orbitofrontal cortex (OFC) and other regions (e.g., ACC, IPC, and insula) during resting-state. It may reflect deficits in the OFC function to process information from different area in the corticostriatal reward network. Application: The results might help to develop theoretical modeling of Internet addiction for Internet addiction discrimination.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.15-25
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• 2024

For effective analysis of animal ecosystems, technology that can automatically identify the current status of animal habitats is crucial. Specifically, animal sound classification, which identifies species based on their sounds, is gaining great attention where video-based discrimination is impractical. Traditional studies have relied on a single deep learning model to classify animal sounds. However, sounds collected in outdoor settings often include substantial background noise, complicating the task for a single model. In addition, data imbalance among species may lead to biased model training. To address these challenges, in this paper, we propose an animal sound classification scheme that combines predictions from multiple models using Focal Loss, which adjusts penalties based on class data volume. Experiments on public datasets have demonstrated that our scheme can improve recall by up to 22.6% compared to an average of single models.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.103-111
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• 2020

Performance indicators have often investigated and developed in order to identify foundational elements and factors for an enhancement of performance in sports. In order to identify the valid performance indicators it is important that the indicators used within a performance analysis system discriminate between the winning and losing performances within a match (Hughes and Bartlett, 2002). However, the performance indicators proposed in research studies on basketball performance have not been used for real-time analysis and feedback within a coaching context. Such real-time support for the coach and players has been described within research on other sports (Choi et al., 2004; O'Donoghue, 2001; Palmer et al., 1997). Within the process of real-time feedback, the identification of relevant performance indicators that distinguish winning and losing performances should be the first stage of the development of a real-time analysis system. Therefore, this study investigated the differences between winning and losing teams in terms of a set of performance indicators gathered during the analysis of 10 English National Basketball League matches. Winning and losing teams were compared using whole match data (N=10) as well as individual quarters (N=40). A series of Wilcoxon Signed Ranks tests was used to identify the relevant performance indicators that discriminate between winning and losing performers within whole matches and individual quarters. The tests found that 3 point shots made (p<0.05) and Assists (p<0.05) were significantly different between winning and losing teams within matches. However, 2 point shots made (p<0.05), 2 point shots attempted (P<0.05), percentages of 2 point shots scored (p<0.05), 3 point shots made (p<0.05), Defensive Rebounds (p<0.05) and Assists (p<0.05) were significantly different between winning and losing performance within quarters. The analysis task should be based on relevant performance indicators which explain the current performances to performance analysts and coaches. Within a real-time analysis and feedback scenario, this will have the additional benefit of supporting a decision based on immediate performance within the most recent quarter. Consequently, the real-time analysis system would use performance indicators which have the property of construct validity to support the decisions of the coach.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.225-236
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• 2013

3D contents could cause unique 3D visual fatigue. Screen disparity, image blurring, and crosstalk are known to be the three major factors responsible for the fatigue. Among these, screen disparity and image blurring are content factors, that is, one can directly manipulate contents themselves to handle visual fatigue caused by these two factors. On the other hand, because crosstalk is closely tied to physical characteristics of 3D display, it is difficult or even impossible to reduce crosstalk-driven visual fatigue unless one replaces 3D display itself (for example, from active to passive display). However, the effects of crosstalk on 3D visual fatigue depends on visual stimulus features (that is, contents), and thus it is possible to manipulate stimulus features in order to handle visual fatigue caused by crosstalk. Hence, this research tested the effects of visual stimulus features on crosstalk (which then causes 3D visual fatigue). Using relative depth discrimination task, we tested the effects of stimulus-background contrast, background texture density, and screen disparity on the degree of perceived crosstalk. The results showed that crosstalk decreases with presence of background texture and with less degree of screen disparity.

• Journal of radiological science and technology
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• v.35 no.4
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• pp.309-314
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• 2012

Nowadays, digital Radiography (DR) systems are widely used in clinical sites and substitute the analog-film x-ray imaging systems. The resolution of DR images depends on several factors such as characteristic contrast and motion of the object, the focal spot size and the quality of x-ray beam, x-ray scattering, the performance of the DR detector (x-ray conversion efficiency, the intrinsic resolution). The DR detector is composed of an x-ray capturing element, a coupling element and a collecting element, which systematically affect the system resolution. Generally speaking, the resolution of a medical imaging system is the discrimination ability of anatomical structures. Modulation transfer function (MTF) is widely used for the quantification of the resolution performance for an imaging system. MTF is defined as the frequency response of the imaging system to the input of a point spread function and can be obtained by doing Fourier transform of a line spread function, which is extracted from a test image. In clinic, radiologic technologists, who are in charge of system maintenance and quality control, have to evaluate or make routine check on their imaging system. However, it is not an easy task for the radiologic technologists to measure MTF accurately due to lack of their engineering and mathematical backgrounds. The objective of this study is to develop and provide for radiologic technologists a medical system imaging evaluation tool, so that they can measure and quantify system performance easily.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.329-334
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• 2004

The electronic nose (E-nose) recently finds its applications in medical diagnosis, specifically on detection of diabetes, pulmonary or gastrointestinal problem, or infections by examining odors in the breath or tissues with its odor characterizing ability. The odor recognition performance of E-nose can be improved by manipulating the sensor array responses of vapors in time-profile forms. The different chemical interactions between the sensor materials and the volatile organic compounds (VOC's) leave unique marks in the signal profiles giving more information than collection of the conventional piecemal features, i.e., maximum sensitivity, signal slopes, rising time. In this study, to use them in vapor recognition task conveniently, a novel time-profile method was proposed, which is adopted from digital image pattern matching. The degrees of matching between 8 different vapors were evaluated by using the proposed method. The test vapors are measured by the silicon-based gas sensor array with 16 CB-polymer composites installed in membrane structure. The results by the proposed method showed clear discrimination of vapor species than by the conventional method.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.1
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• pp.111-125
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• 2010

For a shift to a new paradigm that allows restoring solidarity among class, gender, and race, it is necessary to closely investigate the differences between Marxist view and poststructuralist view which provide theoretical basis for labor movement and for feminist movement, respectively. However, little effort has been devoted to this task. This paper critically compares two best wellknown geographers; Harvey's class-centered theory and Gibson-Graham's post-structuralist feminist approach by focusing on their understandings of "difference". David Harvey argues that racial/gender discrimination is another form of class-exploitation and puts priority on the solidarity based on the commonality of labor. On the contrary Gibson-Graham argues that the privileging of class above all else marginalizes other political dimension, and proposes the deconstruction of hegemonic discourse of capitalism and the construction of "community economies", Based on the critical survey of both theories, I propose that understanding the role that spatiality plays in capital accumulation process is the key to compromise two different approaches.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.