• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.69-77
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• 2016

Recently, the importance of visual analytics has been recognized in the field of business intelligence. From the view of business intelligence, visual analytics aims for acquiring valuable insights for decision making by interactively visualizing a variety of business information. In this paper, we propose a treemap-based method for visualizing work-sharing relationships among business process performers. A work-sharing relationship is established between two performers who jointly participate in a specific activity of a business process and is an important factor for understanding organizational structures and behaviors in a process-centric organization. To this end, we design and implement a treemap-based visualization tool for representing work-sharing relationships as well as basic hierarchical information in business processes. Finally, we evaluate usefulness of the proposed visualization tool through an operational example using XPDL (XML Process Definition Language) process models.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.1-15
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• 2001

Expressing complex query conditions in a concise and intuitive way has been a challenge in the design of visual object-oriented query languages. We propose a visual query language called VOQL (Visual Object oriented Query Language) for object oriented databases. By employing the visual notation of graph and Venn diagram, the database schema and the advanced features of object oriented queries such as multi-valued path expressions and quantifiers can be represented in a simple way. VOQL has such good features as simple and intuitive syntax, well-defined semantics, and excellent expressive power of object-oriented queries compared with previous visual object-oriented query languages.

• Sleep Medicine and Psychophysiology
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• v.5 no.2
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• pp.194-201
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• 1998

Objectives : This study was aimed to investigate the differences in intelligence and neuropsychological test findings between patients with obsessive-compulsive disorder(OCD) and normal controls, and to find out brain functions. Methods : To examine the brain functions, Halsted Reitan neuropsychological test, computerized neuropsychological test, Wechsler Memory scale and K-WAIS were applied. Subjects of this study consisted of 12 patients with OCD and 17 normal controls who were matched for age, handedness and education year. Results : The verbal intelligence of OCD was significantly higher than that of normal controls. But there was no significant difference in total and performance intelligence between groups. The total time of tactual performance test in OCD was significantly delayed than that in normal controls. Also the visual recall of Wechsler memory scale in OCD was more impaired than that in normal controls. Conclusion : These findings support that visual-spatial memory, which is related to basal ganglia, is impaired in OCD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1142-1166
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• 2020

Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.102-118
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• 2022

With the development of digital content software production technology and the technological progress of related hardware, the social status quo in the post-epidemic era, the popularization and application of 5G networks, the market and consumers' increasing demand for digital content products, artificial intelligence, virtual digital human, virtual Idols, virtual live, self-media content and metaverse-related content industries are all developing rapidly. Virtual idols, virtual digital human, etc. are not only accelerating innovation in production technology. The economic cost, technical difficulty and time requirements of production are also greatly reduced. With the arrival and development of the Metaverse, the author believes that the content industry with virtual digital humans as the core will continue to develop in the direction of refinement, specialization, facilitation and customization. In this article, we will analyze and study the production of virtual digital human based on Photoscan technology and Unreal Engine 5 Metahuman software, and discuss the application status and future development of related content.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.79-90
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• 2007

The image quality measure is an important issue in the image processing. Several methods which measure the image quality have been proposed and these are based on the mathematical point of view. However, there is difference between the mathematicalmeasure and the measure based on the human visual system and a new measure has to be proposed because the final target of the image is a human visual system In this paper, a statistical image quality measure which is considered the human visual feature was suggested. The human visual system is using the global quality of the image and the local quality of the image and the local quality is more important to human visual system. In this paper, the image divided into several segments and the image qualities were calculated respectively. After then, the statistical method using scoring was applied to the image qualities. The result of the image quality measure was similar to the result of measure based on the human visual system.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.174-176
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• 2022

In this paper, we present a mobile application for 'personalized customized learning' for children with articulation disorders using an artificial intelligence (AI) algorithm. A dataset (Data Set) to analyze, judge, and predict the learner's articulation situation and degree. In particular, we designed a prototype model by looking at how AI can be improved and advanced compared to existing applications from the UX/UI (GUI) aspect. So far, the focus has been on visual experience, but now it is an important time to process data and provide a UX/UI (GUI) experience to users. The UX/UI (GUI) of the proposed mobile application was to be provided according to the learner's articulation level and situation by using CRNN (Convolution Recurrent Neural Network) of DeepLearning and Auto Encoder GPT-3 (Generative Pretrained Transformer). The use of artificial intelligence algorithms will provide a learning environment with a high degree of perfection to children with articulation disorders, thereby enhancing the learning effect. I hope that you do not have any fear or discomfort in conversation by improving the perfection of articulation with 'personalized and customized learning'.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.314-321
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• 2005

In this paper an intelligence embedding quadruped pet robot is described. It has 15 degrees of freedom and consists of various sensors such as CMOS image, voice recognition and sound localization, inclinometer, thermistor, real-time clock, tactile touch, PIR and IR to allows owners to interact with pet robot according to human's intention as well as the original features of pet animals. The architecture is flexible and adopts various embedded processors for handling sensors to provide modular structure. The pet robot is also used for additional purpose such like security, gaming visual tracking, and research platform. It is possible to generate various actions and behaviors and to download voice or music files to maintain a close relation of users. With cost-effective sensor, the pet robot is able to find its recharge station and recharge itself when its battery runs low. To facilitate programming of the robot, we support several development environments. Therefore, the developed system is a low-cost programmable entertainment robot platform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5112-5128
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• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.