• Convergence Security Journal
• /
• v.5 no.4
• /
• pp.1-6
• /
• 2005

Today the malicious approach and information threat against a network system increase and, the demage about this spread to persnal user from company. The product which provides only unit security function like an infiltration detection system and an infiltration interception system reached the limits about the composition infiltration which is being turn out dispersion anger and intelligence anger Necessity of integrated security civil official is raising its head using various security product about infiltration detection, confrontation and reverse tracking of hacker. Because of the quantity to be many analysis of the event which is transmitted from the various security product and infiltration alarm, analysis is difficult. So server is becoming the charge of their side. Consequently the dissertation will research the method to axis infiltration alarm data to solve like this problem.

• Science of Emotion and Sensibility
• /
• v.25 no.1
• /
• pp.21-28
• /
• 2022

People differ greatly in their capacity to persist in the face of challenges. Despite significant research, relatively little is known about cognitive factors that might be involved in perseverance. Building upon human threat-management mechanism, we predicted that perseverant people would be characterized by reduced sensitivity (i.e., longer detection latency) to threat cues. Our data from 5,898 job applicants showed that highly perseverant individuals required more time to correctly identify anger in faces, regardless of stimulus type (dynamic or static computer-morphed faces). Such individual differences were not observed in response to other facial expressions (happiness, sadness), and the effect was independent of gender, dispositional anxiety, or conscientiousness. Discussions were centered on the potential role of threat sensitivity in effortful pursuit of goals.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.41-51
• /
• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

• The Journal of the Acoustical Society of Korea
• /
• v.25 no.8
• /
• pp.375-382
• /
• 2006

This paper is about how to identify emotional information and how to detect a specific emotion from speech signals. For emotion identification and detection task. we use long-term acoustic feature parameters and select the optimal Parameters using the feature selection technique based on F-score. We transform the conventional SVM into probabilistic output SVM for our emotion identification and detection system. In this paper we propose three approximation methods for log-likelihoods in a hypothesis test and compare the performance of those three methods. Experimental results using the SUSAS database showed the effectiveness of both feature selection and Probabilistic output SVM in the emotion identification task. The proposed methods could detect anger emotion with 91.3% correctness.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.917-924
• /
• 2018

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3820-3841
• /
• 2018

Most of the accidents occur due to drowsiness while driving, avoiding road signs and due to driver's distraction. Driver's distraction depends on various factors which include talking with passengers while driving, mood disorder, nervousness, anger, over-excitement, anxiety, loud music, illness, fatigue and different driver's head rotations due to change in yaw, pitch and roll angle. The contribution of this paper is two-fold. Firstly, a data set is generated for conducting different experiments on driver's distraction. Secondly, novel approaches are presented that use features based on facial points; especially the features computed using motion vectors and interpolation to detect a special type of driver's distraction, i.e., driver's head rotation due to change in yaw angle. These facial points are detected by Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). Various types of classifiers are trained and tested on different frames to decide about a driver's distraction. These approaches are also scale invariant. The results show that the approach that uses the novel ideas of motion vectors and interpolation outperforms other approaches in detection of driver's head rotation. We are able to achieve a percentage accuracy of 98.45 using Neural Network.

• Journal of Advanced Navigation Technology
• /
• v.14 no.2
• /
• pp.191-197
• /
• 2010

Many does an application and application but the image analysis of face detection considerably is difficult. In order for with effect of the illumination which is irregular in the present paper America the illumination to range evenly in the face which is detected, detects a face territory, Complemented the result which detects only the front face of existing. With LAB color illumination revisions compared in Adaboost face detection of existing and 32% was visible the face detection result which improves. Bought two images which are input and executed Glassfire label rings. Compared Area critical price and became the area of above critical value and revised from RGB smooth anger and LAB images with LCFD system algorithm. The operational conversion image which is extracted like this executed a face territory detection in the object. In order to extract the feature which is necessary to a face detection used AdaBoost algorithms. The face territory remote login with the face territory which tilts in the present paper, until Multi-view face territory detections was possible. Also relationship without high detection rate seems in direction of illumination, With only the public PC application is possible was given proof user authentication field etc.

• The Journal of Korea Robotics Society
• /
• v.5 no.1
• /
• pp.7-13
• /
• 2010

This paper presents the sound-based emotion estimation method and the growing HRI (human-robot interaction) system for a Mon-E robot. The method of emotion estimation uses the musical element based on the law of harmony and counterpoint. The emotion is estimated from sound using the information of musical elements which include chord, tempo, volume, harmonic and compass. In this paper, the estimated emotions display the standard 12 emotions including Eckman's 6 emotions (anger, disgust, fear, happiness, sadness, surprise) and the opposite 6 emotions (calmness, love, confidence, unhappiness, gladness, comfortableness) of those. The growing HRI system analyzes sensing information, estimated emotion and service log in an edutainment robot. So, it commands the behavior of the robot. The growing HRI system consists of the emotion client and the emotion server. The emotion client estimates the emotion from sound. This client not only transmits the estimated emotion and sensing information to the emotion server but also delivers response coming from the emotion server to the main program of the robot. The emotion server not only updates the rule table of HRI using information transmitted from the emotion client and but also transmits the response of the HRI to the emotion client. The proposed system was applied to a Mon-E robot and can supply friendly HRI service to users.

• Journal of Digital Convergence
• /
• v.12 no.12
• /
• pp.243-248
• /
• 2014

In this paper, we propose a method to recognize the facial expressions by selecting face elements and finding its status. The face elements are selected by using image area segmentation method and the facial expression is decided by using the normal distribution of the change rate of the face elements. In order to recognize the proper facial expression, we have built database of facial expressions of 90 people and propose a method to decide one of the four expressions (happy, anger, stress, and sad). The proposed method has been simulated and verified by face element detection rate and facial expressions recognition rate.

• Nuclear Medicine and Molecular Imaging
• /
• v.43 no.4
• /
• pp.253-258
• /
• 2009

Bone scintigraphy using $^{99m}$Tc-labeled phosphate agents has long been the standard evaluation method for whole skeletal system. However, recent shortage of $^{99m}$Tc supply and advanced positron emission tomography (PET) technology evoked the attention to surrogate radiopharmaceuticals and imaging modalities for bone. Actually, fluorine-18 ($^{18}$F) was the first bone seeking radiotracer before the introduction of $^{99m}$Tc-labeled agents even though its clinical application failed to become pervasive anymore after the rapid spread of Anger type gamma camera systems in early 1970s. However, rapidly developed PET technology made us refocus on the usefulness of $^{18}$F as a PET tracer. Early study comparing $^{18}$F-Na PET scan and planar bone scintigraphy reported that PET has higher sensitivity and specificity in the diagnosis of metastatic bone lesions than planar bone scan. Subsequent reports comparing between PET and both planar and SPECT bone image also revealed better results of PET scan in similar study groups. Rapid clinical application of PET/CT also accumulated considerable amount of experiences in skeletal evaluation and this modality is known to have better diagnostic power than stand alone PET system as well as bone scan. Furthermore $^{18}$F-Na PET/CT revealed better or at least equal results in detection of primary and metastatic bone lesions compared with CT and MRI. Therefore, it is obvious that $^{18}$F-Na PET/CT has potential to become new imaging modality for practical skeletal evaluation so continuous and careful evaluation of this modality and radiopharmaceutical must be required.