• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.771-788
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• 2020

Detection of abandoned objects for smart video surveillance should be robust and accurate in various situations with low computational costs. This paper presents a new algorithm for abandoned object detection based on the dual background model. Through the template registration of a candidate stationary object and presence authentication methods presented in this paper, we can handle some complex cases such as occlusions, illumination changes, long-term abandonment, and owner's re-attendance as well as general detection of abandoned objects. The proposed algorithm also analyzes video frames at specific intervals rather than consecutive video frames to reduce the computational overhead. For performance evaluation, we experimented with the algorithm using the well-known PETS2006, ABODA datasets, and our video dataset in a live streaming environment, which shows that the proposed algorithm works well in various situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1262-1270
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• 2020

Recently, the smart video surveillance research, which has been receiving increasing attention, has mainly focused on the intruder detection and tracking, and abandoned object detection. On the other hand, research on real-time detection of stolen objects is relatively insufficient compared to its importance. Considering various smart surveillance video application environments, this paper presents two different types of stolen object detection algorithms. We first propose an algorithm that detects theft of statically and dynamically registered surveillance objects using a dual background subtraction model. In addition, we propose another algorithm that detects theft of general surveillance objects by applying the dual background subtraction model and Mask R-CNN-based object segmentation technology. The former algorithm can provide economical theft detection service for pre-registered surveillance objects in low computational power environments, and the latter algorithm can be applied to the theft detection of a wider range of general surveillance objects in environments capable of providing sufficient computational power.

• The Korean Journal of Pain
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• v.35 no.2
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• pp.173-182
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• 2022

Background: Neurokinin-1 (NK1) and calcitonin gene-related peptide (CGRP) play a vital role in pain pathogenesis, and these proteins' antagonists have attracted attention as promising pharmaceutical candidates. The authors investigated the anti-nociceptive effect of co-administration of the CGRP antagonist and an NK1 antagonist on pain models compared to conventional single regimens. Methods: C57Bl/6J mice underwent sciatic nerve ligation for the neuropathic pain model and were injected with 4% formalin into the hind paw for the inflammatory pain model. Each model was divided into four groups: vehicle, NK1 antagonist, CGRP antagonist, and combination treatment groups. The NK1 antagonist aprepitant (BIBN4096, 1 mg/kg) or the CGRP antagonist olcegepant (MK-0869, 10 mg/kg) was injected intraperitoneally. Mechanical allodynia, thermal hypersensitivity, and anxiety-related behaviors were assessed using the von Frey, hot plate, and elevated plus-maze tests. The flinching and licking responses were also evaluated after formalin injection. Results: Co-administration of aprepitant and olcegepant more significantly alleviated pain behaviors than administration of single agents or vehicle, increasing the mechanical threshold and improving the response latency. Anxiety-related behaviors were also markedly improved after dual treatment compared with either naive mice or the neuropathic pain model in the dual treatment group. Flinching frequency and licking response after formalin injection decreased significantly in the dual treatment group. Isobolographic analysis showed a meaningful additive effect between the two compounds. Conclusions: A combination pharmacological therapy comprised of multiple neuropeptide antagonists could be a more effective therapeutic strategy for alleviating neuropathic or inflammatory pain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Korean Security Journal
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• no.41
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• pp.123-151
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• 2014

The purpose of this study was focused on the relationship between private guards' job embeddedness and dual commitment. In this study, job embeddedness is selected as an independent variable and dual commitment is selected as a dependent variable one. job embeddedness was divided into three sub-factors such as "fit", "links", and "sacrifice", and dual commitment is again composed with organizational commitment, and career commitment. Moreover sex, age, academic background, service period, and income were selected as a control variable. To test the hypotheses, survey data from private guards in Kyungpook are collected and analyzed. Principal component method is used to see which items cluster together in each factor and to calculate factor scores. Multiple regression analysis identifies several factors which have significant effects on dual commitment. Key finding can be summarized as follow. Fist, the factor of "fit" have significant effects on organizational commitment, and career commitment. Second, the factor of "links" have significant effects on organizational commitment, and career commitment. Third the factor of "sacrifice" have significant effects on organizational commitment, and career commitment. Finally, when all the variables with significant effects are included in the final model, "links" disappear, while "fit" and "sacrifice" remain statistically significant. Based on these finding, this study suggests some policy issues to promote private guards' dual commitment.

• Korean Institute of Interior Design Journal
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• v.24 no.1
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• pp.93-103
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• 2015

This study set out to figure out the tendencies of the staff members participating in the space design of Blade Runner and compare and analyze its set and location characteristics with its narrative based on Gilles Deleuze' Simulacrum, one of the basic theories of Post modernism, thus identifying the characteristics of postmodern space inherent in it. The findings were as follows: first, the spaces in a Late modernism tendency in Blade Runner seem to have been created by the cinematic imagination of Syd Mead and Douglas Trumbull rather than being influenced by the old Late modernism architecture. Second, the postmodern spaces of the movie were designed to depict a more realistic future by reinforcing the old ornamental elements or adding the mechanical aesthetics of Late modernism based on a prediction of future cities. Third, the characters representing Late modernism and Post modernism in the narrative of the movie embrace the tendencies of the parties objected by Model and Simulacrum in the scenes where they deny the tendencies of the spaces to which they belong, thus exhibiting a dual trend. Fourth, the dual narrative of Model and Simulacrum holds duality even in the space and architecture of the movie, which is the reason why the movie chose postmodern spaces reflecting historical contexts instead of inner spaces in the tendency of minimalism, which was in vogue when SF movies were made those days. Finally, the spaces of the movie can be categorized according to the Late modernism and Post modernism tendencies from the perspective of the 1980s and be understood to show the architecture and space of future Post modernism feasible through the layering of historicity, locality, and mechanical aesthetics from ancient Maya to a future city in Los Angeles, the background of the movie, from the perspective of 2019.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3721-3737
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• 2015

3D reconstruction based on binocular data is significant to machine vision. In our method, we propose a new and high efficiency 3D reconstruction approach by using a consumer camera aiming to: 1) address the configuration problem of dual camera in the binocular reconstruction system; 2) address stereo matching can hardly be done well problem in both time computing and precision. The kernel feature is firstly proposed in calibration stage to rectify the epipolar. Then, we segment the objects in the camera into background and foreground, for which system obtains the disparity by different method: local window matching and kernel feature-based matching. Extensive experiments demonstrate our proposed algorithm represents accurate 3D model.

• Journal of Oriental Neuropsychiatry
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• v.33 no.2
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• pp.133-141
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• 2022

Objectives: Brainspotting is a relatively new type of brain-body psychotherapeutic approach discovered and developed by David Grand. The objective of this study was to introduce possible clinical application of Brainspotting in Korean medicine. Methods: The background, basic tools, standard processes, and principles of Brainspotting are presented mainly in reference to "Brainspotting: The revolutionary new therapy for rapid and effective change" published by David Grand. Results: There are many similarities between Brainspotting and Korean medicine, such as Mind-Body holism, non-prejudiced attitude of therapist like Tao and wu-wei, and the importance of the eyes to the mind. They also share similarities in methods such as Iijungbyunqi and Qigong. Conclusions: Brainspotting is expected to be applied to Korean medicine in various forms. It needs to be researched more in the future.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1272-1280
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• 2013

In this paper, we propose an audio surveillance system which can detect and localize dangerous sounds in real-time. The location information about dangerous sounds can render a PTZ camera to be directed so as to catch a snapshot image about the dangerous sound source area and send it to clients instantly. The proposed audio surveillance system firstly detects foreground sounds based on adaptive Gaussian mixture background sound model, and classifies it into one of pre-trained classes of foreground dangerous sounds. For detected dangerous sounds, a sound source localization algorithm based on Dual delay-line algorithm is applied to localize the sound sources. Finally, the proposed system renders a PTZ camera to be oriented towards the dangerous sound source region, and take a snapshot against over the sound source region. Experiment results show that the proposed system can detect foreground dangerous sounds stably and classifies the detected foreground dangerous sounds into correct classes with a precision of 79% while the sound source localization can estimate orientation of the sound source with acceptably small error.