• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.771-780
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• 2016

Detecting a set of longest paths is one of the crucial steps in static timing analysis and optimization. Recently, the process variation during manufacturing affects performance of the circuit design due to nanometer feature size. Measuring the performance of a circuit prior to its fabrication requires a considerable amount of computation time because it requires multi-corner and multi-mode analysis with process variations. An efficient algorithm of detecting the K-most critical paths in multi-corner multi-mode static timing analysis (MCMM STA) is proposed in this paper. The ISCAS'85 benchmark suite using a 32 nm technology is applied to verify the proposed method. The proposed K-most critical paths detection method reduces about 25% of computation time on average.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.434-442
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• 2015

In this paper, we propose an integrated spatial and view scalable video codec based on high efficiency video coding (HEVC). The proposed video codec is developed based on similarity and uniqueness between the scalable extension and 3D multi-view extension of HEVC. To improve compression efficiency using the proposed scalable multi-view video codec, inter-layer and inter-view predictions are jointly employed by using high-level syntaxes that are defined to identify view and layer information. For the inter-view and inter-layer predictions, a decoded picture buffer (DPB) management algorithm is also proposed. The inter-view and inter-layer motion predictions are integrated into a consolidated prediction by harmonizing with the temporal motion prediction of HEVC. We found that the proposed scalable multi-view codec achieves bitrate reduction of 36.1%, 31.6% and 15.8% on the top of ${\times}2$, ${\times}1.5$ parallel scalable codec and parallel multi-view codec, respectively.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.357-361
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• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1848-1868
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• 2014

Multiple-in multiple-out (MIMO) in long-term evolution (LTE) is an essential factor in achieving high speed data rates and spectral efficiency. The unexpected growth in data rate demand has pushed researchers to extend the benefits of multi-user MIMO. The multi-user MIMO system can take full advantage of channel conditions by employing efficient adjustment techniques for scheduling, and by assigning different modulation and coding rates. However, one of the critical issues affecting this feature is the appropriate estimation of channel quality indicator (CQI) to manage the allocated resources to users. Therefore, an accurate CQI estimation scheme is required for the multi-user MIMO transmission to obtain significant improvements on spectral efficiency. This paper presents overviews of multi-user MIMO in LTE/LTE-advanced systems. The link adaptation, scheduling process, and different factors that affect the reliability of CQI measurements are discussed. State-of-the-art schemes for the post-processing CQI estimation, and the comparisons of various CQI estimation schemes to support multi-user MIMO are also addressed.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.295-306
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• 2007

It is well known that mathematical solutions for multi-agent planning problems are very difficult to obtain due to the complexity of mutual interactions among multi-agents. Most of the past research results are thus based on the probabilistic completeness. However, the practicality and effectiveness of the solution from the probabilistic completeness is significantly reduced by heavy computational burden. In this paper, we propose a practically applicable solution technique for multi-agent planning problems, which assures a reasonable computation time and a real world application for more than 3 multi-agents, for the case of general shaped paths in agent movement. First, to reduce the computation time, an extended collision map is developed and utilized for detecting potential collisions and obtaining collision-free solutions for multi-agents. Second, a priority for multi-agents is considered for successive and interactive modifications of the agent movements with lower priority. Various solutions using speed reduction and time delay of the relevant agents are investigated and compared in terms of the computation time. A practical implementation is finally provided for three different types of agents to emphasize the effectiveness of the proposed interactive approach to multi-agent planning problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.897-900
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• 2011

최근 새로운 소프트웨어 배포 방식인 SaaS가 주목을 받고 있다. SaaS는 원활하고 효율적인 서비스 제공을 위하여 소프트웨어 서비스를 제공하는 SaaS 어플리케이션과 과금, 사용자 인증과 같은 공통 서비스들을 제공하는 SaaS 플랫폼으로 역할이 분리되어 있다. 이런 배경을 바탕으로 본논문에서는 효율적인 사용자인증을 위한 SaaS 플랫폼의 사용자인증 서비스인 Multi-Authentication Server를 설계 한다. 본논문에서 설계되는 Multi-Authentication Server는 OpenID, OAuth, CAS, X.509 프로토콜을 통합하여 웹기반의 사용자인증 서비스를 구현한다. 마지막으로 웹기반 사용자 인증의 보안성 한계 극복과, 최근 하드웨어 보안기능을 이용하여 RADIUS 프로토콜과 TPM칩을 통합한 하드웨어 기반 네트워크 인증서비스를 제공한다.

• Proceedings of the KIEE Conference
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• 2004.07e
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• pp.88-91
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• 2004

In order to improve ozone generation, we experimentally investigated the silent discharge plasma and ozone generation characteristics of a multi-discharge type ozonizer. Ozone in a multi-discharge type ozonizer is generated by superposition of a silent discharge plasma, which is simultaneously generated in separated discharge spaces. A multi-discharge type ozonizer is composed of three different kinds of superposed silent discharge type ozonizers, depending on the method of applying power to each electrode. We observed that the discharge period of the current pulse for a multi-discharge type ozonizer can be longer than that of silent discharge type ozonizer with two electrodes and one gap.Hence, ozone generation is improved up to 17185 ppm and 783 g/kwh for the superposed silent discharge type ozonizer for which an AC high voltages with a 180 phase difference were applied to the internal electrode and the external electrode, respectively, with the central electrode being ground

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.954-957
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• 2015

In this paper, the android platform based on the morning call system using multi-point group communication is proposed. Implemented multi-point group communication was applied by fusing a variety of techniques such as JAVA NIO, JSP, MySQL, DBMS Pool, GCM and JSON. Some experiments are conducted so as to verify the proposed method, and as a result, morning-call application based multi-point group communication is well performed.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.707-720
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• 2021

To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual "TripAdvisor" dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

• 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.