• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• ETRI Journal
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• v.45 no.1
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• pp.93-104
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• 2023

Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks-based coreference resolution for Korean using multi-task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head-final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word- and sentence-level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre-trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.427-438
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• 2023

Plenty of works have indicated that single image super-resolution (SISR) models relying on synthetic datasets are difficult to be applied to real scene text image super-resolution (STISR) for its more complex degradation. The up-to-date dataset for realistic STISR is called TextZoom, while the current methods trained on this dataset have not considered the effect of multi-scale features of text images. In this paper, a multi-scale and attention fusion model for realistic STISR is proposed. The multi-scale learning mechanism is introduced to acquire sophisticated feature representations of text images; The spatial and channel attentions are introduced to capture the local information and inter-channel interaction information of text images; At last, this paper designs a multi-scale residual attention module by skillfully fusing multi-scale learning and attention mechanisms. The experiments on TextZoom demonstrate that the model proposed increases scene text recognition's (ASTER) average recognition accuracy by 1.2% compared to text super-resolution network.

• Korean Journal of Computational Design and Engineering
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• v.11 no.3
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• pp.172-182
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• 2006

This paper presents a method to convert 3D CAD model to an appropriate analysis model using wrap-around, smooth-out and thinning operators that have been originally developed to realize the multi-resolution modeling. Wrap-around and smooth-out operators are used to simplify 3D model, and thinning operator is to reduce the dimension of a target object with simultaneously decomposing the simplified 3D model to 1D or 2D shapes. By using the simplification and dimension-reduction operations in an appropriate way, the user can generate an analysis model that matches specific applications. The advantage of this method is that the user can create optimized analysis models of various simplification levels by selecting appropriate number of detailed features and removing them.

• Korean Journal of Artificial Intelligence
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• v.12 no.3
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• pp.1-7
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• 2024

In this paper, we propose an innovative super-resolution technique to address the issue of reduced accuracy in license plate recognition caused by low-resolution images. Conventional vehicle license plate recognition systems have relied on images obtained from fixed surveillance cameras for traffic detection to perform vehicle detection, tracking, and license plate recognition. However, during this process, image quality degradation occurred due to the physical distance between the camera and the vehicle, vehicle movement, and external environmental factors such as weather and lighting conditions. In particular, the acquisition of low-resolution images due to camera performance limitations has been a major cause of significantly reduced accuracy in license plate recognition. To solve this problem, we propose a Single Image Super-Resolution (SISR) model with a parallel structure that combines Multi-Scale and Attention Mechanism. This model is capable of effectively extracting features at various scales and focusing on important areas. Specifically, it generates feature maps of various sizes through a multi-branch structure and emphasizes the key features of license plates using an Attention Mechanism. Experimental results show that the proposed model demonstrates significantly improved recognition accuracy compared to existing vehicle license plate super-resolution methods using Bicubic Interpolation.

• Journal of Arbitration Studies
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• v.28 no.2
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• pp.3-43
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• 2018

The emergence of a multi-door courthouse is related with a couple of reasons as follows: First, a multi-door courthouse was originally initiated by the United States government that increasingly became impatient with the pace and cost of protracted litigation clogging the courts. Second, dockets of courts are overcrowded with legal suits, making it difficult for judges to handle those legal suits in time and causing delays in responding to citizens' complaints. Third, litigation is not suitable for the disputant that has an ongoing relationship with the other party. In this case, even if winning is achieved in the short run, it may not be all that was hoped for in the long run. Fourth, international organizations such as the World Bank, UNDP, and Asia Development Bank urge to provide an increased access to women, residents, and the poor in local communities. The generic model of a multi-door courthouse consists of three stages: The first stage includes a center offering intake services, along with an array of dispute resolution services under one roof. At the second stage, the screening unit at the center would diagnose citizen disputes, then refer the disputants to the appropriate door for handling the case. At the third stage, the multi-door courthouse provides diverse kinds of dispute resolution programs such as mediation, arbitration, mediation-arbitration (med-arb), litigation, and early neutral evaluation. This study suggests the extended model of multi-door courthouse comprised of five layers: intake process, diagnosis and door-selection process, neutral-selection process, implementation process of dispute resolution, and process of training and education. One of the major characteristics of extended multi-door courthouse model is the detailed specification of individual department corresponding to each process within a multi-door courthouse. The intake department takes care of the intake process. The screening department plays the role of screening disputes, diagnosing the nature of disputes, and determining a suitable door to handle disputes. The human resources department manages experts through the construction and management of the data base of mediators, arbitrators, and judges. The administration bureau manages the implementation of each process of dispute resolution. The education and training department builds long-term planning to procure neutrals and experts dealing with various kinds of disputes within a multi-door courthouse. For this purpose, it is necessary to establish networks among courts, law schools, and associations of scholars in order to facilitate the supply of manpower in ADR neutrals, as well as judges in the long run. This study also provides six case studies of multi-door courthouses across continents in order to grasp the worldwide picture and wide spread phenomena of multi-door courthouse. For this purpose, the United States and Latin American countries including Argentina and Brazil, Middle Eastern countries, and Southeast Asian countries (such as Malaysia and Myanmar), Australia, and Nigeria were chosen. It was found that three kinds of patterns are discernible during the evolution of a multi-door courthouse model. First, the federal courts of the United States, land and environment court in Australia, and Lagos multi-door courthouse in Nigeria may maintain the prototype of a multi-door courthouse model. Second, the judicial systems in Latin American countries tend to show heterogenous patterns in terms of the adaptation of a multi-door courthouse model to their own environments. Some court systems of Latin American countries including those of Argentina and Brazil resemble the generic model of a multi-door courthouse, while other countries show their distinctive pattern of judicial system and ADR systems. Third, it was found that legal pluralism is prevalent in Middle Eastern countries and Southeast Asian countries. For example, Middle Eastern countries such as Saudi Arabia have developed various kinds of dispute resolution methods, such as sulh (mediation), tahkim (arbitration), and med-arb for many centuries, since they have been situated at the state of tribe or clan instead of nation. Accordingly, they have no unified code within the territory. In case of Southeast Asian countries such as Myanmar and Malaysia, they have preserved a strong tradition of customary laws such as Dhammthat in Burma, and Shriah and the Islamic law in Malaysia for a long time. On the other hand, they incorporated a common law system into a secular judicial system in Myanmar and Malaysia during the colonial period. Finally, this article proposes a couple of factors to strengthen or weaken a multi-door courthouse model. The first factor to strengthen a multi-door courthouse model is the maintenance of flexibility and core value of alternative dispute resolution. We also find that fund raising is important to build and maintain the multi-door courthouse model, reflecting the fact that there has been a competition surrounding the allocation of funds within the judicial system.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.101-110
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• 2021

In computer vision, single-image super resolution has been an area of research for a significant period. Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration. Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges. Furthermore, previous works haven't placed enough emphasis on real-number scalability. Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size. In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations. It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space. Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model. Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.

• Korean Journal of Computational Design and Engineering
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• v.10 no.6
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• pp.432-443
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• 2005

Recent three-dimensional feature-based CAD systems based on solid or non-manifold modelling functionality have been widely used for product design in manufacturing companies. When product models associated with features are used in various downstream applications such as analysis, however, simplified and abstracted models at various levels of detail (LODs) are frequently more desirable and useful than the full detailed model. To provide multi-resolution models, the features need to be rearranged according to a criterion that measures the significance of the feature. However, if the features are rearranged, the resulting shape is possibly different from the original because union and subtraction Boolean operations are not commutative. To solve this problem, in this paper, the new concept of the effective zone of a feature is defined and identified using Boolean algebra. By introducing the effective zone, an arbitrary rearrangement of features becomes possible and arbitrary LOD criteria may be selected to suit various applications. Besides, because the effective zone of a feature is independent of the data structure of the model, the multi-resolution modelling algorithm based on the effective zone can be implemented on any 3D CAD system based on conventional solid representations as well as non-manifold topological (NMT) representations.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.101-107
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• 2010

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2498-2515
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• 2024

In this study, we propose a fully parallelized adaptive particle refinement (APR) algorithm for smoothed particle hydrodynamics (SPH) to construct a stable and efficient multi-resolution computing system for nuclear safety analysis. The APR technique, widely employed by SPH research groups to adjust local particle resolutions, currently operates on a serialized algorithm. However, this serialized approach diminishes the computational efficiency of the system, negating the advantages of acceleration achieved through high-performance computing devices. To address this drawback, we propose a fully parallelized APR algorithm designed to enhance both efficiency and computational accuracy, facilitated by a new adaptive smoothing length model. For model validation, we simulated both hydrostatic and hydrodynamic benchmark cases in 2D and 3D environments. The results demonstrate improved computational efficiency compared to the conventional SPH method and APR with a serialized algorithm, and the model's accuracy was confirmed, revealing favorable outcomes near the resolution interface. Through the analysis of jet breakup, we verified the performance and accuracy of the model, emphasizing its applicability in practical nuclear safety analysis.