• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1051-1059
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• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

• Korean Journal of Culture and Social Issue
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• v.11 no.4
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• pp.83-95
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• 2005

The purpose of the present study was to investigate the relationship between spatial-erceptual ability and several aspects of driving-related situation awareness(in particular, recognition and prediction). Video clips of real driving were used in both recognition and prediction tasks, and the digit calculation task during driving the simulator was required as the integration task of recognition and prediction. The results showed that the subjects of higher spatial-perceptual ability performed better in recognition task, especially in terms of sensitivity measured in d'(as signal detection theory), prediction task, and digits calculation performance than those of lower spatial-perceptual ability.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.6
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• pp.760-771
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• 1998

The purpose of this study is to clarify the concept and construct of the problem recognition stage in consumer decision making process of apparel Purchase. This study was supplemented by the theoretical study and field interviews. 40 women were interviewed on their apparel purchase situation to identify problem recognition process. As a result, the concept of problem recognition in apparel purchase is the perceived difference between the ideal state of apparel affairs and the actual situation sufficient to arouse and activate the decision making process. And the problem recognition stage in apparel purchase is constituted of the following steps: gestation, categorization, problem definition, and purchase intention formation. In most cases, these four steps existed, but in some cases several steps were deleted or condensed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.523-528
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• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image to recognize facial expression. The DB has 6 kinds of facial expressions of human being that are basic facial expressions such as smile, sadness, surprise, anger, disgust, and fear expression. The proposed method produces good results of facial expression and discriminates risk situation well.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.163-170
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• 2014

The purpose of this study was to explore the effect of need for closure on college students' problem recognition and communication behavior. Specifically, this research examined differences about situation recognitions and communication behaviors across level of need for closure factors (preference for order and structure, preference for predictability, decisiveness, discomfort with ambiguity, closed-mindeness). The results show that the participants with higher level of discomfort with ambiguity and low level of closed-mindeness are more likely to high problem recognition, constraint recognition, involvement recognition, and doing active communication behaviors.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

• Archives of design research
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• v.15 no.1
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• pp.225-233
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• 2002

People's thinking and aging ways, which are spread in the abnormal situation called the reoognition panic, are different from those in the normal situation. So the design which is applied to this situation have the different points to be considered. The design in the abnormal situation is as important as that in the normal situation. This study is to evaluate the basic usability of the icons which are applied to this situation. The basic data about the recognition course, icon and metaphor have been collected and analyzed, and then the experiments based on this have been planned. The proprieties and conclusions about the ways have been generalized through accomplishing and analyzing the planned experiments. The most important meaning of this study is to suggest the examples of the usability estimating ways of the design works in the abnormal situation where the recognition panic may happen with high frequency. I hope that this study will be used for checking and improving in advance the difficulties of using the design which may happen in the real situation, by using actively the similar ways to this experiment.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.225-236
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• 2010

This study proposes a method for the recognition of emergency situation according to the bimodal information of camera image sensor and gravity sensor. This method can recognize emergency condition by mutual cooperation and compensation between sensors even when one of the sensors malfunction, the user does not carry gravity sensor, or in the place like bathroom where it is hard to acquire camera images. This paper implemented HMM(Hidden Markov Model) based learning and recognition algorithm to recognize actions such as walking, sitting on floor, sitting at sofa, lying and fainting motions. Recognition rate was enhanced when image feature vectors and gravity feature vectors are combined in learning and recognition process. Also, this method maintains high recognition rate by detecting moving object through adaptive background model even in various illumination changes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3412-3420
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• 2015

Smartphone has become an essential element in our daily life and built-in sensors of the smartphone can be utilized in order to recognize of user's situation. However, it is lack of research for safety and accident prevention by dynamic situation recognition. In this paper, we propose a technique that can be recognized risk situation dynamically using accelerometer, microphone and GPS sensor of mobile device. We propose an architecture and process for sensing techniques of Dynamic Recognition Technique, and develop the mobile application for verifying the suitability of the architecture.