• Earthquakes and Structures
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• v.16 no.1
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• pp.83-96
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• 2019

The seismic behavior of PRC coupling beam-hybrid coupled shear wall system is analyzed by using the finite element software ABAQUS. The stress distribution of steel plate, reinforcing bar in coupling beam, reinforcing bar in slab and concrete is investigated. Meanwhile, the plastic hinges developing law of this hybrid coupled shear wall system is also studied. Further, the effect of coupling ratio, section dimensions of coupling beam, aspect ratio of single shear wall, total height of structure and the role of slab on the seismic behavior of the new structural system. A fitting formula of plate characteristic values for PRC coupling beams based on different displacement requirements is proposed through the experimental date regression analysis of PRC coupling beams at home and abroad. The seismic behavior control method for PRC coupling beam-hybrid coupled shear wall system is proposed based on the continuous connection method and through controlling the coupling ratio, the roof displacement, story drift angle of hybrid coupled shear wall system, displacement ductility of coupling beam.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.627-633
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• 1999

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a ？3-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robot using communication (immune network). Finally much stimulated strateby is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of optimal swarm strategy. Adaptation ability of robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.445-453
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• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• Journal of the Korean Home Economics Association
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• v.34 no.4
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• pp.295-308
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• 1996

The purpose of this study was to explore how family types clustered based on the intra system dynamics explained housewive's managerial behavior. The data were collected by means of questionnaire distributed to a stratified sample of 544 housewives in Seoul who lived with husband and children. The questionnaires included FACES Ⅱ and Ⅲ, Communication Scale, Managerial behavior Scale and Life Satisfaction Scale. Frequency, percentile, mean, correlation, factor analysis, cluster analysis, One-way ANOVA with Scheffe test, and multiple regression were used to analyze the data. This study had resulted in three major findings. The first was that families were clustered by four types, named structed-separated family, flexible-connected family, change oriented emashed, and rigid-disengaed family. The second finding was that a difference in managerial behavior was found among four types of family. Housewives whose family were more connected each other and adapted more easily to changing situations showed better managerial behavior. The last one was that the managerial behavior of housewives was better explained by family types than socio-demographic variables. The recommendations for future research and the better ways to lead effective managerial behavior were suggested.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.532-532
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• 2000

Conventional artificial intelligence systems are not properly responding under dynamically changing environments. To overcome this problem, reactive planning systems implementing new Al principles, called behavior-based Al or emergent computation, have been proposed and confirmed their usefulness. As another alternative, biological information processing systems may provide many feasible ideas to these problems. Immune system, among these systems, plays important roles to maintain its own system against dynamically changing environments. Therefore, immune system would provide a new paradigm suitable for dynamic problem dealing with unknown environments. In this paper, a new approach to behavior-based Al by paying attention to biological immune system is investigated. The feasibility of this method is confirmed by applying to behavior control of an autonomous mobile robot in cluttered environment.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.233-244
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• 2021

Recently, several ICT-based cat studies have produced some successful results, according to academic and industry sources. However, research on the level of simply identifying the cat's condition, such as the behavior and sound classification of cats based on images and sound signals, has yet to be found. In this paper, based on the veterinary scientific knowledge of cats, a practical and academic cat monitoring and disease diagnosis system is proposed to monitor the health status of the cat 24 hours a day by automatically categorizing and analyzing the behavior of the cat with location information using LSTM with a beacon sensor and a raspberry pie that can be built at low cost. Validity of the proposed system is verified through experimentation with cats in actual custody (the accuracy of the cat behavior classification and location identification was 96.3% and 92.7% on average, respectively). Furthermore, a rule-based disease analysis system based on the veterinary knowledge was designed and implemented so that owners can check whether or not the cats have diseases at home (or can be used as an auxiliary tool for diagnosis by a pet veterinarian).

• Journal of Power System Engineering
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• v.11 no.1
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• pp.121-126
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• 2007

This study aims at investigating the effect of heat treatment conditions on the dry sliding wear behavior of thermally sprayed Ni-based self-flux alloy coatings. Ni-based self-flux alloy powders were sprayed onto a carbon steel substrate and then heat-treated at 700, 800, 900 and $1000^{\circ}C$ for 30 minutes in a vacuum furnace. Dry sliding wear tests were performed using sliding speed of 0.4 m/s and applied load of 6 N. AISI 52100 ball(diameter 8 mm) was used as counterparts. Microstructure and wear behavior of both as-sprayed and heat-treated Ni-based self-flux alloy coatings were studied using a scanning electron microscope(SEM), energy dispersive X-ray spectroscopy(EDX), electron probe micro-analysis(EPMA) and X-ray diffraction(XRD). It was revealed that microstructure and wear behavior of thermally sprayed Ni-based self-flux alloy coatings were much influenced by heat treatment conditions.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.44-50
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• 2017

To understand whether Falling, which is one of the causes of injuries, occurs, various behavior recognition research is proceeding. However, in most research recognize only the fact that Falling has occurred and provide the service. As well as the occurrence of the Falling, the risk varies greatly based on the type of Falling and the situation before and after the Falling. Therefore, when Falling occurs, it is necessary to infer the user's current situation and provide appropriate services. In this paper, we propose to base on Fog Computing and Cloud Computing to design Context-aware System using analysis of behavior data and process sensor data in real-time. This system solved the problem of increase latency and server overload due to large capacity sensor data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.903-908
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• 2003

In this paper, we present an observer-based robust controller which achieves not only robust stability but also an performance robustness for linear uncertain discrete-time systems. The performance robustness means that comparing the transient behavior of the uncertain system with a desired one generated by the nominal system, the deterioration of control performance (i.e. the error between the real response and the desired one) is suppressed without excessive control input. The control law consists of a state feedback law for the nominal system and a compensation input given by a feedback form of an estimated error signal. In this paper, we show that conditions for the existence of the observer-based controller are given in terms of linear matrix inequalities (LMIs). Finally, a numerical example is given to illustrate the proposed technique.