• Convergence Security Journal
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• v.11 no.3
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• pp.3-10
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• 2011

Recently, due to a series of DDoS attacks, government agencies have enhanced security measures and business-related legislation. However, service attack and large network violations or accidents are most likely to occur repeatedly in the near future. In order to prevent this problem, researches must be conducted to predict the vulnerability in advance. The existing research methods do not state the specific data used for the base of the prediction, making the method more complex and imprecise. Therefore this study was conducted using the vulnerability data used for the basis of machine learning technology prediction, which were retrieved from a reputable organization. Also, the study suggested ways to predict the future vulnerabilities based on the weaknesses found in prior methods, and certified the efficiency using experiments.

• ETRI Journal
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• v.42 no.2
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.11-21
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• 2018

In this paper we propose a novel log template discovery algorithm which achieves high quality of discovered log templates through iterative log filtering technique. Log templates are the static string pattern of logs that are used to produce actual logs by inserting variable values during runtime. Identifying individual logs into their template category correctly enables us to conduct automated analysis using state-of-the-art machine learning techniques. Our technique looks at the group of logs column-wise and filters the logs that have the value of the highest proportion. We repeat this process per each column until we are left with highly homogeneous set of logs that most likely belong to the same log template category. Then, we determine which column is the static part and which is the variable part by vertically comparing all the logs in the group. This process repeats until we have discovered all the templates from given logs. Also, during this process we discover the custom patterns such as ID formats that are unique to the application. This information helps us quickly identify such strings in the logs as variable parts thereby further increasing the accuracy of the discovered log templates. Existing solutions suffer from log templates being too general or too specific because of the inability to detect custom patterns. Through extensive evaluations we have learned that our proposed method achieves 2 to 20 times better accuracy.

• The Korean Journal of Rehabilitation Nursing
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• v.14 no.2
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• pp.95-102
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• 2011

Purpose: The purpose of this study was to analyze intervention studies related to Snoezelen (multisensory environment). Methods: Studies related to Snoezelen (multisensory environment) published between 1995 and 2010 in both Korean and International journals were systematically reviewed, and analyzed following guidelines. Based on inclusion criteria, 23 studies including 5 Korean and 18 International articles were selected. Results: Most studies were conducted in various area of research such as medicine, nursing, and occupational therapy. There was no publication related to Snoezelen (multisensory environment) in Korean nursing journals. In terms of target population, more than 65 % of the study subjects were patients with dementia, mental retardation, and learning disability. Intervention was implemented mostly in less than 30 minutes, once a week for 2 to 4 weeks. The effects on behavior, physical, and psychological contexts were assessed as outcome indicators. There was more 'positive' than 'no effect' in self-stimulatory behaviors, problem behaviors, heart rate, pain, mood state, and anxiety, whereas more 'no effect' than 'positive' in blood pressure, respiration, enjoyment, and relaxation. Conclusion: Future studies are needed to develop the protocol and outcome indicators for effective use of this new intervention in Korea.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.2
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• pp.215-233
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• 2015

Despite the continuous efforts of academic libraries to develop various user-centered outreach programs, services and new processes, library anxiety still remains a threat to university students' full use of academic library resources. Meanwhile, a new generation of students, called the "Net Generation," has grown up with developed information and communication technology enter university and must be persuaded to turn to the library. To serve this new group of patrons better, libraries need to adopt new technologies. However, since an initial introduction cost and labor efforts are involved in the integration of the technology, identifying the right time for introduction and the right scope of innovation is essential but difficult. The study proposes a not-yet-well-known, novel experimental design, Regression Point Displacement (RPD), to evaluate an orientation program applying Mobile Augmented Reality (MAR) for STEM students. Since this RPD design requires only one treatment group, the model is expected to be the incomparable and rational way to evaluate the new MAR technology. In the context of an informal learning experience, the findings of the study will determine the effectiveness of an orientation employing the MAR technology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.11
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• pp.1199-1205
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• 1992

In this paper, a Korean word recognition method which usese Neural Network and Hidden Markov Models(HMM) is proposed to improve a recognition rate with a small amount of learning data. The method reduces the fluctuation due to personal differences which is a problem to a HMM recognition system. In this method, effective recognizer is designed by the complement of each recognition result of the Hidden Markov Models(HMM) and Neural Network. In order to evaluate this model, word recognition experiment is carried out for 28 cities which is DDD area names uttered by two male and a female in twenties. As a result of testing HMM with 8 state, codeword is 64, the recognition rate 91[%], as a result of testing Neural network(NN) with 64 codeword the recognition rate is 89[%]. Finally, as a result of testing NN-HMM with 64 codeword which the best condition in former tests, the recognition rate is 95[%].

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.227-230
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• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.985-987
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.157-164
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• 2007

Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• The Journal of Pediatrics of Korean Medicine
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• v.21 no.3
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• pp.85-95
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• 2007

Objectives Attention Deficit Hyperactivity Disorder(ADHD) is a type of psychiatric disorder characterized with the primary symptoms of inattention and/or impulsivity and hyperactivity. The purpose of this study is to examine ADHD children who were treated with neurofeedback therapy. Methods We analyzed clinical report of 3 ADHD children who treated with neurofeedback therapy from January 2006 to June 2006. Results 1. All 3 children were diagnosed with predominantly inattention type of ADHD. 2. After treatment, cognitive strength, response, concentration, workload, left / right brain activity score were all different from each children. 3. After treatment, left and right brain activities were balanced. 4. After treatment, learning ability level was increased. 5. After treatment, the childrenwere in a better state referred to conner's scale and H.S.Q. score. Conclusions Further studies will be needed to get more clinical cases about the benefits of neurofeedback therapy with herbal medicine and acupuncture treatment.