• ETRI Journal
• /
• v.43 no.3
• /
• pp.524-537
• /
• 2021

High-performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities of computing systems and has been used as a standard to compare the performance of computing systems since the early 1980s. In the initial system-design stage, it is critical to estimate the capabilities of a system quickly and accurately. However, the original HPL mathematical model based on a single core and single communication layer yields varying accuracy for modern processors and accelerators comprising large numbers of cores. To reduce the performance-estimation gap between the HPL model and an actual system, we propose a mathematical model for multi-communication layered HPL. The effectiveness of the proposed model is evaluated by applying it to a GPU cluster and well-known systems. The results reveal performance differences of 1.1% on a single GPU. The GPU cluster and well-known large system show 5.5% and 4.1% differences on average, respectively. Compared to the original HPL model, the proposed multi-communication layered HPL model provides performance estimates within a few seconds and a smaller error range from the processor/accelerator level to the large system level.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.06a
• /
• pp.1311-1314
• /
• 2022

DNN을 사용하여 객체 인식 과정에서 객체를 잘 분류하기 위해서는 시각적 설명성이 요구된다. 시각적 설명성은 object class에 대한 예측을 pixel-wise attribution으로 표현해 예측 근거를 해석하기 위해 제안되었다, Scale-invariant한 특징을 제공하도록 설계된 pyramidal features 기반 backbone 구조는 object detection 및 classification 등에서 널리 쓰이고 있으며, 이러한 특징을 갖는 feature pyramid를 trainable attention mechanism에 적용하고자 할 때 계산량 및 메모리의 복잡도가 증가하는 문제가 있다. 본 논문에서는 일반적인 FPN에서 객체 인식 성능과 설명성을 높이기 위한 피라미드-주의집중 계층네트워크 (FPN-Attention Layered Network) 방식을 제안하고, 실험적으로 그 특성을 평가하고자 한다. 기존의 FPN만을 사용하였을 때 객체 인식 과정에서 설명성을 향상시키는 방식이 객체 인식에 미치는 정도를 정량적으로 평가하였다. 제안된 모델의 적용을 통해 낮은 computing 오버헤드 수준에서 multi-level feature를 고려한 시각적 설명성을 개선시켜, 결괴적으로 객체 인식 성능을 향상 시킬 수 있음을 실험적으로 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.6
• /
• pp.1869-1880
• /
• 2014

Wireless communication is one of most active areas in modern communication researches, QoS (Quality of Service) assurance is very important for wireless communication systems design, especially for applications such as streaming video etc., which requires strict QoS assurance. The modern wireless networks multi-layer protocol stack structure results in QoS metrics layered and acting in cascade and QoS metrics vertical mapping between protocol layers. Based on virtual buffer between protocol layers and queuing technology, a unified layered QoS mapping framework is proposed in this paper, in which we first propose virtual queue concept, give a novelty united neighboring protocol layers QoS metric mapping framework, and analysis method based on dicerete-time Markov chain, and numerical results show that our proposed framework represents a significant improvement over previous model.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.11 no.3
• /
• pp.169-181
• /
• 2011

In this present paper, a comprehensive drain current model incorporating the effects of channel length modulation has been presented for multi-layered gate material engineered trapezoidal recessed channel (MLGME-TRC) MOSFET and the expression for linearity performance metrics, i.e. higher order transconductance coefficients: $g_{m1}$, $g_{m2}$, $g_{m3}$, and figure-of-merit (FOM) metrics; $V_{IP2}$, $V_{IP3}$, IIP3 and 1-dB compression point, has been obtained. It is shown that, the incorporation of multi-layered architecture on gate material engineered trapezoidal recessed channel (GME-TRC) MOSFET leads to improved linearity performance in comparison to its conventional counterparts trapezoidal recessed channel (TRC) and rectangular recessed channel (RRC) MOSFETs, proving its efficiency for low-noise applications and future ULSI production. The impact of various structural parameters such as variation of work function, substrate doping and source/drain junction depth ($X_j$) or negative junction depth (NJD) have been examined for GME-TRC MOSFET and compared its effectiveness with MLGME-TRC MOSFET. The results obtained from proposed model are verified with simulated and experimental results. A good agreement between the results is obtained, thus validating the model.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.2
• /
• pp.432-438
• /
• 2011

This paper describes an analysis of optimal design conditions of multi-mode LDPC(low density parity check) decoder which supports three block lengths (648, 1296, 1944) and four code rates (1/2, 2/3, 3/4, 5/6) for IEEE 802.11n WLAN system. A fixed-point model of LDPC decoder, which adopts min-sum algorithm and layered decoding scheme, is implemented using Matlab. From fixed-point simulation results for various bit-width parameters such as internal bit-width, integer/fractional part bit-widths, optimal design conditions and decoding performance of LDPC decoder are analyzed.

• IE interfaces
• /
• v.16 no.spc
• /
• pp.28-32
• /
• 2003

In this paper, the fuzzy group method data handling-type(GMDH) neural networks and their application to the forecasting of mobile communication system are described. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neuro-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the neuro-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. The computer program is developed and successful applications are shown in the field of estimating problem of mobile communication with the number of factors considered.

• Journal of the Korea Convergence Society
• /
• v.11 no.4
• /
• pp.33-39
• /
• 2020

Interest in 5G communication security has been growing recently amid growing expectations for 5G technology with faster speed and stability than LTE. However, 5G has so far included disparate areas, so it has not yet fully supported the issues of security. This paper proposes a blockchain-based IoT management model in order to efficiently provide the authentication of users using IoT in 5G In order to efficiently fuse the authentication of IoT users with probabilistic theory and physical structure, the proposed model uses two random keys in reverse direction at different layers so that two-way authentication is achieved by the managers of layers and layers. The proposed model applied blockchain between grouped IoT devices by assigning weights to layer information of IoT information after certification of IoT users in 5G environment is stratified on a probabilistic basis. In particular, the proposed model has better functions than the existing blockchain because it divides the IoT network into layered, multi-layered networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.05a
• /
• pp.523-526
• /
• 2002

This paper presents the simulation of Au / $Ba_{x}$S $r_{1-x}$ Ti $O_3$(BSTO) / Magnesium oxide (MgO) multi-layered and electrically tunable band-pass filters (BPFs) by using high frequency structure simulator (HFSS). This model is a two-pole microstrip edge coupled filter. The filter was designed fur a center frequency about 5.8 GHz. The tunabillity of the filter is achieved using the nonlinear dc electric-field dependence on the relative dielectric constant of BSTO frroelectric thin film. This work seems very promising for future wireless communication systems....

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.1
• /
• pp.41-49
• /
• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.2
• /
• pp.55-60
• /
• 2002

This paper presents a controller for the multiple DC motors using the CAN(Controller Area Network). The controller has a benefit of reducing the cable connections and making the controller boards compact through the network including expansibility. CAN, among the field buses, is a serial communication methodology which has the physical layer and the data link layer in the ISO's OSI (Open System Interconnect) 7 layered reference model. It provides the user with many powerful features including multi-master functionality and the ability to broadcast / multicast telegrams. When we use a microprocessor chip embedding the CAN function, the system becomes more economical and reliable to react shortly in the data transmission. The controller, we proposed, is composed of two main controllers and a sub controller, which have built with a one-chip microprocessor having CAN function. The sub controller is plugged into the Pentium PC to perform a CAN communication, and connected to the main controllers via the CAN. Main controllers are responsible for controlling two motors respectively. Totally four motors, actuators for the biped robot in our laboratory, are controlled in the experiment. We show that the four motors are controlled properly to actuate the biped robot through the network in real time.