• Journal of the Korean Society of Safety
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• v.13 no.4
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• pp.230-239
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• 1998

Though limited, Model Human Processor (MHP) has been used to explain the complex users' behaviors during human-computer interactions in a simplified manner. MHP consists of perceptual, cognitive and motor systems, each with processors and memories interacting with each other in serial or parallel mode. The important parameters of memory include the storage capacity, the decay time, and the code type of a memorized item. The important parameter of a processor is the cycle time. Using these features of the model, this study developed a computerized cognitive process simulator to predict the cognitive process time of a class match task process. An experimental validity test result showed that the mean prediction time for cognitive process of the class match task simulated 50 times by the simulator was consistent with the mean cognitive process time of the same task performed by 37 subjects. Animation of the data flow during the class match task simulation will help understand the invisible human cognitive process.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.329-332
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• 1999

Currently, the most important technology is compression methods in the multimedia society. In audio compression, the method using human auditory nervous property is used. This method using psychoacoustical model is applied to perceptual audio coding, because human's audibility is limited. MPEG-II AAC(Advanced Audio Coding) is the most advanced coding scheme that is of benefit to high quality audio coding. The compression ratio is 1.4 times compared with MPEG-I layer-III. In this paper, the vector processor for MDCT/IMDCT(Modified Discrete Cosine Transform /Inverse Modified Discrete Cosine Transform) of MPEG-II AAC is designed.

• Journal of Semiconductor Engineering
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• v.2 no.2
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• pp.130-135
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• 2021

Recently, deep neural networks (DNNs) are actively used for action control so that an autonomous system, such as the robot, can perform human-like behaviors and operations. Unlike recognition tasks, the real-time operation is essential in action control, and it is too slow to use remote learning on a server communicating through a network. New learning techniques, such as reinforcement learning (RL), are needed to determine and select the correct robot behavior locally. In this paper, we propose an energy-efficient DNN processor with a LUT-based processing engine and near-zero skipper. A CNN-based facial emotion recognition and an RNN-based emotional dialogue generation model is integrated for natural HRI system and tested with the proposed processor. It supports 1b to 16b variable weight bit precision with and 57.6% and 28.5% lower energy consumption than conventional MAC arithmetic units for 1b and 16b weight precision. Also, the near-zero skipper reduces 36% of MAC operation and consumes 28% lower energy consumption for facial emotion recognition tasks. Implemented in 65nm CMOS process, the proposed processor occupies 1784×1784 um2 areas and dissipates 0.28 mW and 34.4 mW at 1fps and 30fps facial emotion recognition tasks.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.05a
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• pp.163-168
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment was designed in within-subject design with two factors; vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods; the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.192-200
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• 2012

In order for humans and robots to interact in daily life, robots need to understand human speech and link it to their actions. This paper proposes a hierarchical behavior model for robot action control using natural language commands. The model, which consists of episodes, primitive actions and atomic functions, uses a sentential cognitive system that includes multiple modules for perception, action, reasoning and memory. Human speech commands are translated to sentences with a natural language processor that are syntactically parsed. A semantic parsing procedure was applied to human speech by analyzing the verbs and phrases of the sentences and linking them to the cognitive information. The cognitive system performed according to the hierarchical behavior model, which consists of episodes, primitive actions and atomic functions, which are implemented in the system. In the experiments, a possible episode, "Water the pot," was tested and its feasibility was evaluated.

• Journal of the Ergonomics Society of Korea
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• v.21 no.3
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• pp.25-34
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment the cognition time of the road sign in dynamic environment. This experiment was designed in with-subject design with two factors: vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods: the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• International Journal of Automotive Technology
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• v.5 no.1
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• pp.33-46
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• 2004

A finite element model development of a 50th percentile male cervical spine is presented in this paper. The model consists of rigid, geometrically accurate vertebrae held together with deformable intervertibral disks, facet joints, and ligaments modeled as a series of nonlinear springs. These deformable structures were rigorously tuned, through failure, to mimic existing experimental data; first as functional unit characterizations at three cervical levels and then as a fully assembled c-spine using the experimental data from Duke University and other data in the NHTSA database. After obtaining satisfactory validation of the performance of the assembled ligamentous cervical spine against available experimental data, 22 cervical muscle pairs, representing the majority of the neck's musculature, were added to the model. Hill's muscle model was utilized to generate muscle forces within the assembled cervical model. The muscle activation level was assumed to be the same for all modeled muscles and the degree of activation was set to correctly predict available human volunteer experimental data from NBDL. The validated model is intended for use as a post processor of dummy measurement within the simulated injury monitor (SIMon) concept being developed by NHTSA where measured kinematics and kinetic data obtained from a dummy during a crash test will serve as the boundary conditions to "drive" the finite element model of the neck. The post-processor will then interrogate the model to determine whether any ligament have exceeded its known failure limit. The model will allow a direct assessment of potential injury, its degree and location thus eliminating the need for global correlates such as Nij.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.822-829
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• 2009

This paper proposes a new method of improving the quality of High Efficiency Advanced Audio Coding (HE-AAC). HE-AAC encodes input source by allocating bits for each scalefactor bands appropriately according to human ear's psychoacoustic property. As a result, insufficient bits are assigned to the bands which have relatively low energy. This imbalance between different energy bands can cause decreasing of sound quality like musical noise. In the proposed system, a Linear Prediction (LP) module is combined with HE-AAC as a pre-processor to improve sound quality by even bits distribution. To apply accurate human being's psychoacoustic property, the psychoacoustic model uses Fast Fourier Transform (FFT) spectrum of original input signal to make masking threshold. In its implementation, masking threshold of psychoacoustic model is normalized using the LP spectral envelope in prior to quantization of the LP residual. Experimental result shows that, the proposed algorithm allocates bits appropriately for insufficient bits condition and improves the performance of HE-AAC.

• Science of Emotion and Sensibility
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• v.6 no.1
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• pp.55-62
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• 2003

This study was an exploratory approach to the cognitive model of textile designers on the stage of motif development in textile design process. Prior to the main research, several previous studies adopting methods of video/audio protocol analysis were reviewed. On the basis of the review, the categories of design action were derived as an analysis frame by application of top-down access method, meanwhile the sub-groups of each category of design action were identified through a bottom-up access method. To summarize the research result, total three categories of textile design action appeared based on the theory of ‘Human processor’ model : ‘motor action’, ‘perceptual action’ and 'cognitive action'. In next, a new coding scheme suitably explaining these three categories of fertile design action was developed. Finally, a cognitive model of textile designer on the stage of motif development, employing the new coding scheme, was suggested in this study.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.531-535
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• 1999

Automatic human face detection in a complex background is one of the difficult problems. In this paper, we propose an effective and robust automatic face detection approach that can locate the face region in natural scene images when the system is used as a pre-processor of a face recognition system . We use two natural and powerful visual cues, the skin color and the eyes. In the first step of the proposed system, the method based on the human skin color space by selecting flesh tone regions using normalized r-g space in color images. In the next step, we extract eye features by calculating moments and using geometrical face model. Experimental results demonstrate that the approach can efficiently detect human faces and satisfactory deal with the problems caused by bad lighting condition, skew face orientation.