• Management Science and Financial Engineering
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• v.17 no.1
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• pp.1-18
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• 2011

The present study provides a theoretical investigation on pioneering advantage based on reference dependence and loss aversion effect under prospect theory (Kahneman and Tversky, 1979). Behavioral explanations for pioneering advantage are provided from two different perspectives: one based on the prototypicality and the other on the utility uncertainty of the option. A pioneer brand creates the product category and makes a strong impression in customers' mind, and thus becomes the most representative or prototypical option of the category. In addition, the pioneer brand becomes the first option to be experienced by the majority of consumers in the product category, thus has the lowest level of utility uncertainty compared with the late movers. This study integrates the previous accounts for pioneering advantage by showing that consumers have higher preferences for the most prototypical and the least uncertain option based on loss aversion and reference dependence effect. This study suggests that firms should carefully analyze the consumers' loss aversion and perceived uncertainty and prototypicality of their products in order to develop effective market entry strategies.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.23-34
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• 2014

With a view to analyzing the influential factors and their prioritization in association with the loss from construction disasters, this study has presented relative weighted value and importance for each category of loss by making a systematic classification of costs for non-insured categories (indirect costs) and conducting AHP analysis based on results of a survey of specialists. Through the study, first, I have divided the larger classification of loss factors into human loss factor, financial loss factor, special cost factor, and managerial loss factor, and, second, have presented prioritization of loss categories by allotting scores based on weighted values after calculating weighted value through pairwise comparison of loss levels. Based on these results of the study, we should be able to qualitatively calculate the loss costs that construction disasters inflict on business, promote rational decision-making and efficiency in spending related to a disaster, and compare it against safety investment designed to reduce disaster loss from the perspective of business strategy.

• Food Science of Animal Resources
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• v.41 no.6
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• pp.1012-1021
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• 2021

This study evaluated the effect of freezing rate on the quality characteristics of pork loin to establish an objective standard for rapid freezing. To generate various freezing rates, three air flow rates (0, 1.5, and 3.0 m/s) were applied under three freezing temperatures (-20℃, -30℃, and -40℃). Based on the results, freezing rates ranged from 0.26-1.42 cm/h and were graded by three categories, i.e, slow (category I, >0.4 cm/h), intermediate (category II, 0.6-0.7 cm/h) and rapid freezing (category III, >0.96 cm/h). Both temperature and the air flow rate influenced the freezing rate, and the freezing rate affected the ice crystal size and shear force in pork loin. However, the air flow rate did not affect thawing loss, drip loss or the color of pork loins. In the comparison of freezing rates, pork belonging to category II did not show a clear difference in quality parameters from pork in category I. Furthermore, pork in category III showed fresh meat-like qualities, and the quality characteristics were clearly distinct from those of category I. Although the current standard for rapid freezing rate is 0.5 cm/h, this study suggested that 0.96 cm/h is the lowest freezing rate for achieving meat quality distinguishable from that achieved with conventional freezing, and further increasing the freezing rate did not provide advantages from an energy consumption perspective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.245-265
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• 2022

In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

• Journal of Communications and Networks
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• v.5 no.4
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• pp.303-308
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• 2003

We present a statistical model for the path loss of ultrawideband (UWB) channels in indoor environments. In contrast to our previously reported measurements, the data reported here are for a bandwidth of 6GHz rather than 1.25GHz; they encompass commercial buildings in addition to single-family homes (20 of each); and local spatial averaging is included. As before, the center frequency is 5.0GHz. Separate models are given for commercial and residential environments and, within each category, for lineof sight (LOS) and non-line-of-sight (NLS) paths. All four models have the same mathematical structure, differing only in their numerical parameters. The two new models (LOS and NLS) for residences closely match those derived from the previous measurements, thus affirming the stability of our path loss modeling. We find, also, that the path loss statistics for the two categories of buildings are quite similar.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.91-99
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• 2018

Sketch-based 3D object retrieval is a convenient way to search for various 3D data using human-drawn sketches as query. In this paper, we propose a new method of using Sketch CNN, Wasserstein CNN and Wasserstein center loss for sketch-based 3D object search. Specifically, Wasserstein center loss is a method of learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. To do this, the proposed 3D object retrieval is performed as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we learn the features of the extracted 3D object and the features of the sketch using the proposed Wasserstein center loss. In order to demonstrate the superiority of the proposed method, we evaluated two sets of benchmark data sets, SHREC 13 and SHREC 14, and the proposed method shows better performance in all conventional metrics compared to the state of the art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3913-3934
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• 2021

Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.