• Proceedings of the Korea Society for Simulation Conference
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• 2003.11a
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• pp.3-10
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• 2003

Developments in science and technology based on computer technology influenced military fields and created up-to-date weapons and equipment, and as a result, which is changing the war accomplishing methods of the future warfare. Due to these changes in the war accomplishing methods, the army command centers are requested to make changes in their decision-making process. In other words, they need to apply more scientific methods rather than just build a scheme by the mere analysis of commanders and the staffs as in the past. Consequently, we propose a model, Vision 21 we developed as a war game model for division-size unit analysis use, in the COA development process, which is the most important part in establishing the OPLAN for mission accomplishment. Vision 21, with a comparative analysis of the other COA built in the COA development process, can be applied to making the best COA. Model employment concept can let us choose the best COA, operating war games on condition that the COA of the opposite forces is fixed and ours sequentially opposed against, and with a comparative analysis also. Moreover, if the time available is limited, before establishing several courses, we can apply the COA to the process for making the best decision, analysing in stages or by main phases and not establishing several courses for a special purpose.

• Wind and Structures
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• v.21 no.6
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• pp.709-726
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• 2015

In this study, a novel vision-based bolt-loosening monitoring technique is proposed for bolted joints connecting tubular steel segments of the wind turbine tower (WTT) structure. Firstly, a bolt-loosening detection algorithm based on image processing techniques is developed. The algorithm consists of five steps: image acquisition, segmentation of each nut, line detection of each nut, nut angle estimation, and bolt-loosening detection. Secondly, experimental tests are conducted on a lab-scale bolted joint model under various bolt-loosening scenarios. The bolted joint model, which is consisted of a ring flange and 32 sets of bolt and nut, is used for simulating the real bolted joint connecting steel tower segments in the WTT. Finally, the feasibility of the proposed vision-based technique is evaluated by bolt-loosening monitoring in the lab-scale bolted joint model.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.261-266
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• 2021

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

• Journal of Korean Academy of Nursing
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• v.23 no.4
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• pp.678-693
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• 1993

The purposes of this study were to describe the level of vision and hearing impairments, depression and functional capacity, among Korean institutionalized elderly and to examine the relation-ship between sensory impairments, depression, and functional capacity in these people. The final pupose was to test the cognitive function path model using sensory competencies as predictors. A convenience sample of thirty nine male and 90 female subjects with a mean age of 80.5 were the subjects of this study. The subjects were tested for cognitive function, and vision and hearing impairments. Physical function and social function were measured by observation of designated task performance by the subjects. Their level of de-pression was measured using a Geriatric Depression Scale administered through an interview. Individual subjective ratings of hearing and vision were marked by the subjects, on a ladder scale. The results of the study showed that 48.8% of the subjects had a hearing impairment, 63.5% had a vision impairement, and 36.4% had both a vision and hearing impairement. The four sensory groups (no sensory impairement, hearing impairement, vision impairement, hearing and vision impairement) were tested for differences in depression, physical function, social behavior and cognitive function. The only significant difference that was found was in cognitive function, between the no sensory impairement group and the hearing and vision impairement group(F=3.25, P<.05), Subjective ratings of hearing showed a significant correlation with cognitive function(r=.34, p<.001) and with social behavior(r=.31, p<.001). There was no correlation between subjective vision ratings and cognitive function or social behavior. However there was a significant correlation between vision and hearing(r=.49, p<.001). There was also a significant negative correlation between age and vision(r=-.21, p<.01) and between age and hear-ing(r=-.34, p<.001). There was a significant correlation between depression and physical function (r=-.32, p<.001) but there was no correlation between depression and cognitive function or social behavior. Based on the literature review and the result, this study, a path model of sensory competence-> cognitive function- >social behavior was developed and tested : Perceived vision and perceived hearing were the exogenous variahles and cognitive function and social behavior were the endogeneous variables in the model. The path analysis result demonstrated an accept-able fit (GFI=.997, AGFI=.972, X$^2$=.72 (p=.396), RMSR=.019) between the data and the model. There was a significant direct effect($\beta$=.38) of perceived hearing on cognitive function. There was a significant direct effect ($\beta$=.32) of cognitive function on social behavior. The total effect of hearing on social behavior was $\beta$=.32 including the indirect effect ($\beta$=.12) . However perceived vsion had little effect ($\beta$=-.08) on cognitive function. The result of path analysis confirms that hearing levels influence cognitive function, and both hearing and cognitive function levels influence social behavior. However, vision has little effect on cognitive function or on social behavior. For the next study, a combined model of the pre viously developed environment - >depression- > physical and social function model, and the present cognitive function model, should be tested to further refine the functional capacity model. There also a need for longitudinal study of functional capacity and sencory competence in order to better understand how declining sensory competence influences functional capacity and how it effects in-creasing dependency and nursing needs in the elderly.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1089-1099
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• 2015

This paper proposes vision-based collision risk estimation method for an unmanned surface vehicle. A robust image-processing algorithm is suggested to detect target obstacles from the vision sensor. Vision-based Target Motion Analysis (TMA) was performed to transform visual information to target motion information. In vision-based TMA, a camera model and optical flow are adopted. Collision risk was calculated by using a fuzzy estimator that uses target motion information and vision information as input variables. To validate the suggested collision risk estimation method, an unmanned surface vehicle experiment was performed.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.31-35
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• 2003

In the late 1990s, ROK Army started developing a simulation model(ChangJo21) for division/corps level battle command training and finished it successfully. The CJ2l model provides realistic representation of Korean characteristics in doctrine, weapon systems, terrain, and climate etc. The successful development of CJ2l implanted us with confidence on high-technology model development and this has been our motive for development of JeonToo21 for battalion/regiment level battle command training and other war-game models like Hwarang21 (Rear Area Ops. Model) and Vision21 (Division Combat Analysis Model). Eventually, ROK Army was able to establish M&S system by echelons, from battalion to corps. Moreover interoperability between ROK-US simulation systems are on the progress. In this paper, we introduce recently developed 3 war-game simulation models and mention on the future directions of ROK Army Modeling & Simulation.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.16-21
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• 2003

In the agricultural field, a machine vision system has been widely used to automate most inspection processes especially in quality grading. Though machine vision system was very effective in quantifying geometrical quality factors, it had a deficiency in quantifying color information. This study was conducted to evaluate color of beef using machine vision system. Though measuring color of a beef using machine vision system had an advantage of covering whole lean tissue area at a time compared to a colorimeter, it revealed the problem of sensitivity depending on the system components such as types of camera, lighting conditions, and so on. The effect of color balancing control of a camera was investigated and multi-layer BP neural network based color calibration process was developed. Color calibration network model was trained using reference color patches and showed the high correlation with L*a*b* coordinates of a colorimeter. The proposed calibration process showed the successful adaptability to various measurement environments such as different types of cameras and light sources. Compared results with the proposed calibration process and MLR based calibration were also presented. Color calibration network was also successfully applied to measure the color of the beef. However, it was suggested that reflectance properties of reference materials for calibration and test materials should be considered to achieve more accurate color measurement.