• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.149-152
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• 2001

The goals of this papper are not only to maximize of performance but also to reduce the response time for the real-time printed alphabets recognition system using the backpropagation algorithm in the artificial neural network. The Genesis board and MIL(Matrox Image Library) package were used to real-time acquisition, processing and display of images. Through this experiment proved the possibility of real-time recognition processing by comparing response times of the system and proposing the method to reduce of order of the output vectors.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.79-87
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• 2018

The current PC user authentication uses character password based on user's knowledge. However, this can easily be exploited by password cracking or key-logging programs. In addition, the use of a difficult password and the periodic change of the password make it easy for the user to mistake exposing the password around the PC because it is difficult for the user to remember the password. In order to overcome this, we propose user gesture recognition and challenge-response authentication. We apply user's hand gesture instead of character password. In the challenge-response method, authentication is performed in the form of responding to a quiz, rather than using the same password every time. To apply the hand gesture to challenge-response authentication, the gesture is recognized and symbolized to be used in the quiz response. So we show that this method can be applied to PC user authentication.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.84-87
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• 2018

Handwritten letter recognition is important for numerous real-world applications and many topics like human-machine interaction, education, entertainment, and more. This paper describes the implementation of a real-time handwritten letters recognition system on a common embedded computer. Recognition is performed using a customized convolutional neural network, which was designed to work with low computational resources such as the Raspberry Pi platform. The experimental results show that the proposed real-time system achieves an outstanding performance in the accuracy rate and the response time for recognition of twenty-six handwritten letters.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.367-372
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• 2021

The purpose of this study is to suggest that the movement expression activity of intellectually disabled people is effective in the learning process of LMA motion recognition based on Kinect sensor. We performed an ICT motion recognition games for intellectually disabled based on movement learning of LMA. The characteristics of the movement through Laban's LMA include the change of time in which movement occurs through the human body that recognizes space and the tension or relaxation of emotion expression. The design and implementation of the motion recognition model will be described, and the possibility of using the proposed motion recognition model is verified through a simple experiment. As a result of the experiment, 24 movement expression activities conducted through 10 learning sessions of 5 participants showed a concordance rate of 53.4% or more of the total average. Learning motion games that appear in response to changes in motion had a good effect on positive learning emotions. As a result of study, learning motion games that appear in response to changes in motion had a good effect on positive learning emotions

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.292-302
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• 2022

The radar tomographic imaging is based on the Radar Cross-Section "RCS" of the materials of a shape under examination and investigation. The RCS varies as the conductivity and permittivity of a target, where the target has a different material profile than other background objects in a scene. In this research paper, we use Hierarchical Performance Modeling "HPM" and a framework developed earlier to determine/spot bottleneck(s) for pattern recognition of materials using a combination of the Single Layer Perceptron (SLP) technique and tomographic images in radar systems. HPM provides mathematical equations which create Objective Functions "OFs" to find an average performance metric such as throughput or response time. Herein, response time is used as the performance metric and during the estimation of it, bottlenecks are found with the help of OFs. The obtained results indicate that processing images consumes around 90% of the execution time.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.137-140
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• 2002

This study was conducted to examine the korean word length effects on auditory word recognition. Linguistically, word length can be defined by several sublexical units such as letters, phonemes, syllables, and so on. In order to investigate which units are used in auditory word recognition, lexical decision task was used. Experiment 1 and 2 showed that syllable length affected response time, and syllable length interacted with word frequency. As a result, in recognizing auditory word syllable length was important variable.

• Journal of the Korea Convergence Society
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• v.8 no.3
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• pp.15-22
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• 2017

People generally believe that disaster forecast and warning systems and response systems are well established in the age of cutting edge technology. As a matter of fact, reliable systems to respond to disasters are not properly equipped, as we witnessed the Sewol ferry disaster in 2014. The existing forecast and warning systems are based on sensor information with low efficiency, and image information is only operated by monitoring staff manually. In addition, the interconnection between a warning system and a response system in order to decide how to cope with the recognized disaster is very insufficient. This paper introduces the CCTV based disaster recognition and real time crisis response system composed of the CCTV image recognition engine and the crisis response technique. This system has brought the possibility to overcome the limitations of existing sensor based forecast and warning systems, and to resolve the problems in the absence of monitoring staff when responding to crisis.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of the Ergonomics Society of Korea
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• v.18 no.3
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• pp.105-125
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• 1999

As speech is the human being's most natural communication medium, using it gives many advantages. Currently, most user interfaces of a computer are using a mouse/keyboard type but the interface using speech recognition is expected to replace them or at least be used as a tool for supporting it. Despite the advantages, the speech recognition interface is not that popular because of technical difficulties such as recognition accuracy and slow response time to name a few. Nevertheless, it is important to optimize the human-computer system performance by improving the usability. This paper presents a set of guidelines for designing speech recognition interfaces and provides a method for evaluating the usability. A total of 113 guidelines are suggested to improve the usability of speech-recognition interfaces. The evaluation method consists of four major procedures: user interface evaluation; function evaluation; vocabulary estimation; and recognition speed/accuracy evaluation. Each procedure is described along with proper techniques for efficient evaluation.