• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.6
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• pp.45-56
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• 1999

This paper presents a method for designing an autopilot of the BTT missile using 2DOF Wiener-Hopf control technique to improve tracking performance. Linear controllers are designed based on the linearized models which are obtained from the nonlinear missile dynamic equations at various operating points. The gain scheduling technique is used to implement the final autopilot. A simulation on the flight of missiles is carried out through the use of 6DOF equation program including exact nonlinear equations of the missile and the variations of aerodynamic variables in order to check applicability of the suggested method in real situation.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.264-273
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• 2013

Credit option is a policy that has been studied by many researchers in the area of supply chain management. This policy has been applied in practice to improve the profits of supply chain members. Usually, a credit option policy is proposed by the seller, and often under a symmetric information environment where members have complete information on each others' operations. In this paper, we investigate two scenarios: firstly, the seller offers a credit option to the buyer, and secondly, the buyer attempts to stretch the length of the credit period offered by the seller. The proposed model in both scenarios will be investigated under an asymmetric information structure where some information are private and are only known to the individual who has knowledge of this information. The interactions between buyer and seller will be modeled by non-cooperative Stackelberg games where the buyer and seller take turn as leader and follower. Among some of the numerical results obtained, the seller and buyer's profits obtained from symmetric information games are larger than those obtained from an asymmetric information game in both scenarios. Furthermore, both buyer and seller's profit in the second scenario are better than in the first scenario.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.5
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• pp.981-990
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• 2001

The purpose of this study is to review the concept of virtuality and analyze in which way virtuality is expressed in fashion. As for the research methodology, literary research was undertaken to study psychoanalytical and socioeconomic contexts in which virtuality has been formed. In addition, demonstrative studies on styles were undertaken through the analysis of photos in modern fashion magazine. With the explosive diffusion of the Internet since the 1990s, people have created a new identity in cyber space. Indeed, computers have made it possible for human beings to make virtual bodies in any way they want. Through the experiment of creating the figures that they dreamed of in their childhood buy could not embody in their actual life, people are making up for their narcissistic ego of their childhood. With the advent of the cyber society, dreams have been realized in cyber space, which in turn has influenced reality and finally had an effect on fashion. In cyber space, People try to break away from their bodies by combining elements of a different nature from them. They are dying hair and skin, and using holographic fabric for fashion, metallic color and geometric pattern for cosmetics. In pursuit of omnipotent beings, people have depicted models as flying in a weightless state and floating in the water within dress of undefined silhouette, so that they can be shown as transcending the law of nature. Furthermore, a variety of cultures newly appearing as dominant in cyber space have constantly interacted with actual life and formed an collage of heterogeneous cultures in fashion.

• Structural Engineering and Mechanics
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• v.31 no.5
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• pp.545-566
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• 2009

Bridge columns are subjected to combined actions of axial force, shear force and bending moment during earthquakes, caused by spatially-complex earthquake motions, features of structural configurations and the interaction between input and response characteristics. Combined actions can have significant effects on the force and deformation capacity of RC columns, resulting in unexpected large deformations and extensive damage that in turn influences the performance of bridges as vital components of transportation systems. This paper evaluates the seismic response of three prototype reinforced concrete bridges using comprehensive numerical models that are capable of simulating the complex soil-structural interaction effects and nonlinear behavior of columns. An analytical approach that can capture the shear-flexural interacting behavior is developed to model the realistic nonlinear behavior of RC columns, including the pinching behavior, strength deterioration and stiffness softening due to combined actions of shear force, axial force and bending moment. Seismic response analyses were conducted on the prototype bridges under suites of ground motions. Response quantities of bridges (e.g., drift, acceleration, section force and section moment etc.) are compared and evaluated to identify the effects of vertical motion, structural characteristics and the shear-flexural interaction on seismic demand of bridges.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1629-1639
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• 2016

This study is to estimate the production efficiency of abalone aquaculture and to find its determinants utilizing the survey data of operating expenses in 2015. The first part of the analysis applied both DEA and Super-DEA for the estimation of efficiency of each aquaculture household as DMU. We used wages, feeding costs and area as inputs and annual profits and sales as outputs of the model. The second part of the study applied both Tobit and OLS for the identification of determinants of the efficiency. We investigated cost-ratio, depreciation costs, careers, value of living seeds, cleaning costs of farming ground and a ratio of 1 and 2 year-old abalone at shipment as potential determinants. The estimation results show us that the average technical efficiency, pure technical efficiency, and scale efficiency score turn out to be 72%, 81% and 85% respectively. The Super-BCC and Super-CCR models reveal their average efficiency scores as 81% and 80%. All of the variables used to identify the determinants of the efficiency. The study results suggests that the production efficiency can be improved by cleaning farming ground and hence lowering the death rate of seeds.

• International Journal of CAD/CAM
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• v.1 no.1
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• pp.1-9
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• 2002

The STL or Stereolithography format, established by 3D systems, gathers the geometric data of the model in a number of triangular surfaces. It can be in ASCII or binary format, and is a de facto standard in the Rapid Prototyping (RP) world. RP has developed greatly over the last ten years. In particular, improvement in materials has meant greater part accuracy and strength, which in turn has increased the range of functional applications. Future applications of RP will focus on rapid tooling and direct manufacturing. Direct manufacturing in particular may see much benefit from the incorporation of color into models. For color RP, besides designing new hardware to add color into the prototypes, it is necessary to redefine the CAD software for adding and accurately positioning color onto the model. STL cannot effectively store this kind of information. Among the existing data file formats, VRML is an acceptable one that is complimentary to existing RP processes. This paper acts as a review to discuss several methods of using VRML for coloring model data. This paper will also discuss the problems occurred in coloring the layer contours of the RP model.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.4
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• pp.638-656
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• 2022

This study aimed to identify characteristics of Generation MZ's consumer purchase decision journey to develop the new fashion CDJ model. The initial stage was affected by habit, online community, social media, aesthetics, circumstantial need, and proxy. In the search and consideration stage, mobile channels were used actively. In the active search and evaluation stage, online media, experiential data, and personal information were employed. In the purchase stage, zoomers took plenty of time in search and evaluation before spending, contrary to millennials who made their purchases more quickly. In the post-purchase experience stage, zoomers actively displayed follow-up behaviors depending on their satisfaction, such as retaining or deleting the app. While, millennials did not turn away from the store or brand, but followed up on their purchases even when they had an unsatisfactory experience. Based on the characteristics of CDJ, iterative cycle CDJ models were developed. Zoomers CDJ model was presented as a search loop that consists of the search and evaluation process, in which information accumulates, and a purchase loop in which the actual purchase occurs. The iterative cycle CDJ model was presented connected to the loyalty loop as the main section, which is accelerated in millennials' CDJ model.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.125-150
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• 2022

Technology companies launched their intelligent personal assistants (IPAs). IPAs not only provide individuals with a convenient way to interact with technology but also offer them the opportunity to interact with AI in a useful and meaningful form. Therefore, the global IPAs have experienced tremendous growth over the past decade. But maintaining continuous usage intention is still a massive challenge for developers and marketers and previous technology adoption models are not enough to explain continuous usage intention of IPAs. Thus, we adopted the bi-dimensional perspectives of utilitarian and hedonic value in this research model, and investigated how three characteristics of IPAs - anthropomorphism, autonomy, and intelligence - affect utilitarian value and hedonic value, which in turn continuous usage intentions. 227 data were collected from IPA users. The results showed that IPAs' continuous usage intention is significantly determined by both utilitarian and hedonic value, with the hedonic value being more prominent. In addition, the results showed that anthropomorphism and intelligence are the most important antecedents of utilitarian and hedonistic value. The results also illustrated that autonomy is a crucial predictor of utilitarian value rather than hedonistic value. Our work contributes to current research by widening the theoretical understanding of the effect of IPA characteristics on continuous usage intention through bi-dimensional values. Our paper also provides IPAs' developer and marketer guidelines for enhancing continuous usage intention.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.1-8
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• 2006

Effective three dimensional modeling becomes essential in a wide range of drawings, such as construction, machinery and design. In particular, it has been developed as the tool enabling reverse design. Three dimensional modeling requires rapidity, accuracy and tangibility. Data acquisition methods for modeling including contact type coordinate measurement machine, LASER scanner, pattern scanner and digital photogrammetry. In this study, we try to analyze modeling techniques as well as introduce three dimensional modeling using pattern scanner. In addition, this study conducts three dimensional modeling using OPTO-Top pattern scanner with distinguished accuracy and rapidity, and then compare efficiency with digital photogrammetry. And, this study attempts to form environment that enables to turn around models on web in three dimensional ways.