• The Research Journal of the Costume Culture
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• v.6 no.2
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• pp.203-216
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• 1998

In the study, conceiving that signifying processes like model, setting, advertisement and description are important to promote the purchase of clothes that would satisfy consumer's desire by their expressing mode, I considered the processes of components of which fashion ads consist. As for the methods to study, I regarded the results presented from prior researches of clothing & textiles and other disciplines for the components of fashion ads and objectified their image that may be interpreted subjectively: and then, I adopted to analyse them using advertisement-semiological method to make clear the signifying processes. The results are as follow: 1. Fashion ad, one of visual symbols to transfer brand image, conveys the image with which various components are combined like model, clothes, setting and description as signs. ① the image of clothes amy be differently expressed according to social, cultural norm and individual characteristics, in the case of clothes, therefore, the signified can be regarded as the transferred image by design of the clothes① sign, and the abstract conception which may be rise to mind by the image in a ceratin culture. ② Each signifier such as countenance, line of vision, attitude and hairstyle of a model conveys different image, or the signified, respectively, and it amy operate as a sign that can express the brand image symbolically. ③ The signifiers like background, color and property symbolize the advertised merchandise of clothes and define it attribute.. 2. In the case of fashion ads, key referent systems are fashion phenomena, contemporary role image, social psychology, common morality, and social, economical and milieu.

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.377-393
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• 2016

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

• Steel and Composite Structures
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• v.35 no.5
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• pp.701-715
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• 2020

Steel storage racks are lightweight structures, made of thin-walled cold-formed members, whose behaviour is remarkably influenced by local, distortional and overall buckling phenomena, frequently mutually combined. In addition, the need of an easy and rapid erection and reconfiguration of the skeleton frame usually entails the presence of regular perforations along the length of the vertical elements (uprights). Holes and slots strongly influence their behaviour, whose prediction is however of paramount importance to guarantee an efficient design and a safe use of racks. This paper focuses on the behaviour of isolated uprights subjected to both axial load and bending moments, differing for the cross-section geometry and for the regular perforation systems. According to the European standards for routine design, four alternatives to evaluate the bending moment-axial load resisting domains are shortly discussed and critically compared in terms of member load carrying capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.464-475
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• 2017

In this paper, we present a new texture image retrieval method which combines color and texture features extracted from images by a set of multi-resolution multi-direction (MRMD) filters. The MRMD filter set chosen is simple and can be separable to low and high frequency information, and provides efficient multi-resolution and multi-direction analysis. The color space used is HSV color space separable to hue, saturation, and value components, which are easily analyzed as showing characteristics similar to the human visual system. This experiment is conducted by comparing precision vs. recall of retrieval and feature vector dimensions. Images for experiments include Corel DB and VisTex DB; Corel_MR DB and VisTex_MR DB, which are transformed from the aforementioned two DBs to have multi-resolution images; and Corel_MD DB and VisTex_MD DB, transformed from the two DBs to have multi-direction images. According to the experimental results, the proposed method improves upon the existing methods in aspects of precision and recall of retrieval, and also reduces feature vector dimensions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5006-5022
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• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• Smart Structures and Systems
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• v.15 no.2
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• pp.425-445
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• 2015

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.742-762
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• 2015

In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN's entangled data distribution due to high levels of appearance variations in unconstrained environments, DML's goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN's performance on faces with large variances, such as pose and expression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.872-885
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• 2015

The rapid increase of smart mobile devices and mobile applications has led to explosive growth of data traffic in cellular network. Offloading data traffic becomes one of the most urgent technical problems. Recent work has proposed to exploit opportunistic communications to offload cellular traffic for mobile data dissemination services, especially for accepting large delayed data. The basic idea is to deliver the data to only part of subscribers (called target-nodes) via the cellular network, and allow target-nodes to disseminate the data through opportunistic communications. Human mobility shows temporal and spatial characteristics and predictability, which can be used as effective guidance efficient opportunistic communication. Therefore, based on the regularity of human mobility we propose NodeRank algorithm which uses the encounter characteristics between nodes to choose target nodes. Different from the existing work which only using encounter frequency, NodeRank algorithm combined the contact time and inter-contact time meanwhile to ensure integrity and availability of message delivery. The simulation results based on real-world mobility traces show the performance advantages of NodeRank in offloading efficiency and network redundant copies.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1023-1047
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• 2015

In this study, the optimal location of the MR fluid segments in a partially treated laminated composite sandwich plate has been identified to maximize the natural frequencies and the loss factors. The finite element formulation is used to derive the governing differential equations of motion for a partially treated laminated composite sandwich plate embedded with MR fluid and rubber material as the core layer and laminated composite plate as the face layers. An optimization problem is formulated and solved by combining finite element analysis (FEA) and genetic algorithm (GA) to obtain the optimal locations to yield maximum natural frequency and loss factor corresponding to first five modes of flexural vibration of the sandwich plate with various combinations of weighting factors under various boundary conditions. The proposed methodology is validated by comparing the natural frequencies evaluated at optimal locations of MR fluid pockets identified through GA coupled with FEA and the experimental measurements. The converged results suggest that the optimal location of MR fluid pockets is strongly influenced not only by the boundary conditions and modes of vibrations but also by the objectives of maximization of natural frequency and loss factors either individually or combined. The optimal layout could be useful to apply the MR fluid pockets at critical components of large structure to realize more efficient and compact vibration control mechanism with variable damping.