• Computers and Concrete
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• v.12 no.4
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• pp.565-583
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• 2013

Existing numerical models for strain-hardening cement-based composites (SHCC) are short of providing sufficiently accurate solutions to the failure patterns of coupling beams of different designs. The objective of this study is to develop an effective model that is capable of simulating the nonlinear behavior of SHCC coupling beams subjected to cyclic loading. The beam model proposed in this study is a macro-scale plane stress model. The effects of cracks on the macro-scale behavior of SHCC coupling beams are smeared in an anisotropic model. In particular, the influence of the defined crack orientations on the simulation accuracy is explored. Extensive experimental data from coupling beams with different failure patterns are employed to evaluate the validity of the proposed SHCC coupling beam models. The results show that the use of the suggested shear stiffness retention factor for damaged SHCC coupling beams is able to effectively enhance the simulation accuracy, especially for shear-critical SHCC coupling beams. In addition, the definition of crack orientation for damaged coupling beams is found to be a critical factor influencing the simulation accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Steel and Composite Structures
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• v.10 no.1
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• pp.87-108
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• 2010

A Steel Plate Shear Wall (SPSW) is a lateral load resisting system consisting of an infill plate located within a frame. When buckling occurs in the infill plate of a SPSW, a diagonal tension field is formed through the plate. The study of the tension field behavior regarding the distribution and orientation patterns of principal stresses can be useful, for instance to modify the basic strip model to predict the behavior of SPSW more accurately. This paper investigates the influence of torsional and out-of-plane flexural rigidities of boundary members (i.e. beams and columns) on the buckling coefficient as well as on the distribution and orientation patterns of principal stresses associated with the buckling modes. The linear buckling equations in the sense of von-Karman have been solved in conjunction with various boundary conditions, by using the Ritz method. Also, in this research the effects of symmetric and anti-symmetric buckling modes and complete anchoring of the tension field due to lacking of in-plane bending of the beams as well as the aspect ratio of plate on the behavior of tension field and buckling coefficient have been studied.

• Journal of Satellite, Information and Communications
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• v.11 no.1
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• pp.55-57
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• 2016

In this paper, we present an algorithm to determine the abnormal behavior through a CCTV-based behavioral recognition and a pattern of hand using ConvexHull. In the existing way that using CCTV for crime prevention, facial recognition is mainly used. Facial recognition is the way that compares the faces that are seen on the screen and faces of criminals for determining how dangerous targets are, however, this way is hard to predict future criminal behavior. Therefore, to predict more various situations, abnormal behaviours are determined with targets' incline of arms, legs and bodys and patterns of hand movements. it can forecast crimes when an acting has been getting within common normality out, comparing whose acting patterns with the crime patterns.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.183-190
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• 2012

PURPOSES : The fundamental diagram provides basic information necessary in the analysis of traffic flow and highway operation. When traffic flow is congested, the density-flow points in the fundamental diagram are widely scattered and move in a stochastic manner. This paper investigates the pattern of density-flow point transitions and identifies car-following behaviors underlying the density-flow transitions. METHODS : From a microscopic analysis of 722 fundamental diagrams of NGSIM data, a total of 20 transition patterns of fundamental diagrams are identified. Prominent features of the transition patterns are explained by the behavior of the leader and follower. RESULTS : It is found out that the average speed and the speed difference between the leader and the follower critically determine the density-flow transition pattern. The density-flow path is very sensitive to the values of vehicle speed and spacing especially at low speed and high density such that most fluctuations in the fundamental diagram in the congested regime is due to the noise of speed and spacing variations. CONCLUSIONS : The result of this study suggests that the average speed, the speed difference between the leader and the follower, and the random variations of speed and spacing are dominant factors that explain the transition patterns of a fundamental diagram.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.61-74
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• 2012

As the retail industry has been challenged by stiff competition, the retailer becomes more interested in better understanding consumers' in-store behavior to gain and sustain competitive advantage. Consumers' shopping paths provide valuable clues to understanding customers' in-store behavior, which has been a long standing research issue in business. This study is to explore the shopping path patterns in a grocery using RFID technology and clustering method. To this end, we designed the RFID systems, affixing active RFID tags to the bottom of grocery carts. The tag emit signal that is received by receptors installed at various location throughout the store. The RFID systems provide the time and location of the cart while consumers shop around the store. The point of sale data are matched with the cart movement records to provide a complete picture of each shopping path. To find the distinctive patterns of consumers' shopping paths, we proposed the distance-index matrix using dijkstra method and normalization method to conduct the clustering in order to handle the problem in measuring the similarity among shopping paths, which is raised by the spatial nature of consumer movement in a grocery. After analyzing the RFID data obtained in one of the groceries in a major Korean retailer, we could successfully identify several distinctive patterns of shopping paths, which prove to provide the valuable implications for store management.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.3
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• pp.359-366
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• 1993

The purpose of this study was to classify college women according to the general life style, to examine the differences in the characteristics of time consumption, and clothing purchasing behavior, and ultimately to verify the importance of the time variable in life style analysis. 449 subjects were gathered through convenience sampling method and, frequency, precentage, average, $x^2$-test, t-test, ANOVA, Duncan's multiple range test, Factor Analysis, Cluster Analysis were conducted for data analysis. Results of the study were as follows : 1. College women were classified into five subgroups by cluster analysis : fashion-pursuit group, home-intended group, outer-intended group, conservative-economy group, and passive-inactive group. 2. In case of time consumption patterns, significant differences were found according to the different groups in patronage of library and extra off-campus educational programs, participation in household duties, consumption patterns of leisure time and participation in clubs. 3. In case of clothing purchasing behavior, significant differences were found according to the different groups in clothing purchasing motives, information sources, purchasing plan, the number of visited stores, and the confirmation of labels.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.467-471
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• 2005

In this paper, experimental methods about the flow behavior of thin polymer film by various edge patterns in the spin coating process for stable cover layer coating of a blu-ray disc is described. The blu-ray disc, a next-generation optical disc format over 25GB, consists of a 1.1m thick substrate and a 0.1mm tick cover layer. Generally, cover layer on the blu-ray disc is made by the polymer spin coating process. However, it is hard to secure sufficient coating uniformity around the rim on the cover layer. In order to get the uniform thickness deviation and to minimize the bead around the rim, the edge of the disc substrate can be modified into various patterns, such as normal plain, trench, step and chamfer pattern, etc, around the rim on the disc and experimented with various parameters, such as surface tension, viscosity, coating time, temperature and rotation speed, etc. And the optimal shape of the rim was tried to get by 3 dimensional computer simulation of the polymer expulsion process.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.520-537
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• 2019

This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.5
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• pp.116-124
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• 2008

In order to evaluate the behavior patterns of the ring beam by excavation at the vertical wall with circular section, the measured field datum were analyzed and evaluated in this study. Additionally, stress patterns of the ring beam with the change of excavation depth were estimated by using FE analysis. As the results, it was shown that the tendency of the measured values for the behavior patterns of the ring beam is similar to the analyzed values in FE analysis. From the tendency, it was confirmed that the behaviors of the ring beam due to change of excavation depth can predict by FE analysis using the suggested method in this study.