• The Korean Journal of Vision Science
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• v.20 no.4
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• pp.505-511
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• 2018

Purpose : This study investigated the visual functions of drivers aged over 65 year olds and drivers aged 50~65 year olds. In addition, difference of visual functions between professional drivers and normal drivers were investigated. Methods : Forty Driver aged over 65 year olds and 67 drivers aged less than 67 year olds were participated. All participants had more than 5 years of driving experiences and had no ocular pathology. Demographic data(gender, job, age, body condition) and visual functions such as contrast sensitivity(CS), stereopsis, glare recovery time and discomfort glare index were measured. Results : Constrast sensivity under photopic condition was higher with bus driver group. In addition, difference of CS at 12cpd and 18cpd were signifcantly different between normal drivers(1.57) and bus drivers(1.70) (p<0.05). There was no significant difference for glare recovery time, despite of trend of longer recovery time with age. Discomfort glare index was significantly different that normal drivers with more than 65 year olds had 3, taxi and truck driver presented almost 5 index score (p<0.05). Conclusion : Analysis of visual function of elderly drivers, it was confirmed that their visual functions decreased with age. Therefore, visual function tests such as CS, discomfort glare index and stereopsis in addition to current available test may need to be considered for drivers aged over 65.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.91-104
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• 2021

This study explored users' emotional states identified from the wake-up words -"Hi, KIA!"- using a machine learning algorithm considering the user interface of passenger cars' voice. We targeted four emotional states, namely, excited, angry, desperate, and neutral, and created a total of 12 emotional scenarios in the context of car driving. Nine college students participated and recorded sentences as guided in the visualized scenario. The wake-up words were extracted from whole sentences, resulting in two data sets. We used the soundgen package and svmRadial method of caret package in open source-based R code to collect acoustic features of the recorded voices and performed machine learning-based analysis to determine the predictability of the modeled algorithm. We compared the accuracy of wake-up words (60.19%: 22%~81%) with that of whole sentences (41.51%) for all nine participants in relation to the four emotional categories. Accuracy and sensitivity performance of individual differences were noticeable, while the selected features were relatively constant. This study provides empirical evidence regarding the potential application of the wake-up words in the practice of emotion-driven user experience in communication between users and the artificial intelligence system.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.729-735
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• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.

• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.15-26
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• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

• Journal of the Korean Society of Radiology
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• v.6 no.1
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• pp.73-77
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• 2012

Recently, Interest to the photoconductor, which is used to flat form X-ray detector such as a-Se, $HgI_2$, PbO, CdTe, $PbI_2$ etc. is increasing. In this study, the film layer by using the photoconductive material with particle sedimentation was fabricated and evaluated. The quantization efficiency of the continuous X-ray with the 70 kVp energy bandwidth was analyzed by using the Monte Carlo simulation. With the results, the thickness of film with 64 % quantization efficiency was 180 ${\mu}m$ which is similar to the efficiency of 500 ${\mu}m$ a-Se film. And $HIg_2$ film has the high quantization efficiency of 74 % on 240 ${\mu}m$ thickness. The electrical characteristics of the 239 ${\mu}m$ $Hgl_2$ films produced by particle sedimentation were shown as very low dark current(under 10 $pA/mm^2$), and high sensitivity(19.8 mC/mR-sec) with 1 $V/{\mu}m$ input voltage. The SNR, which is influence to the contrast of X-ray image, was shown highly as 3,125 in low driving voltage on 0.8 $V/{\mu}m$. With the results of this study, the development of the low-cost, high-performance image detector with film could be possible by replacing the film produced by particle sedimentation instead to a-Se detector.