• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1033-1043
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• 2007

The multi layer perceptron for segmenting objects in images only uses the input windows that are made from a image in a fixed size. These windows are recognized so each independent learning data that they make the performance of the multi layer perceptron poor. The poor performance is caused by not considering the position information and effect of input windows in input images. So we propose a new approach to add the position information and effect of input windows to the multi layer perceptron#s input layer. Our new approach improves the performance as well as the learning time in the multi layer perceptron. In our experiment, we can find our new algorithm good.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.14-20
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• 2017

In this paper, we discuss physical layer security techniques in downlink networks, including eavesdroppers. The main objective of using physical layer security is delivering a perfectly secure message from a transmitter to an intended receiver in the presence of passive or active eavesdroppers who are trying to wiretap the information or disturb the network stability. In downlink networks, based on the random feature of channels to terminals, opportunistic user scheduling can be exploited as an additional tool for enhancing physical layer security. We introduce user scheduling strategies and discuss the corresponding performances according to different levels of channel state information (CSI) at the base station (BS). We show that the availability of CSI of eavesdroppers significantly affects not only the beamforming strategy but also the user scheduling. Eventually, we provide intuitive information on the effect of CSI on the secrecy performance by considering three scenarios: perfect, imperfect, and absence of eavesdropper's CSI at the BS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.627-630
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• 2002

Bi$_2$Sr$_2$CuOx(Bi-2201) thin films were fabricated by atomic layer-by-layer deposition using an ion bean sputtering method. 10 wt% and 90 wt% ozone mired with oxygen were used with ultraviolet light irradiation to assist oxidation. XRD and RHEED investigations revealed that a buffer layer is formed at the early stage of deposition (less than 10 unit cell), and then c-axis oriented Bi-2201 grows on top of it.

• Journal of Communications and Networks
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• v.7 no.1
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• pp.45-53
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• 2005

In this paper, we present a cross-layer analysis of wireless TCP systems over correlated channels. The effects of error correlation on the behavior of link retransmission strategy and the end-to-end throughput of TCP layer are investigated. Based on the cross-layer analysis, an efficient refinement of link layer protocol is proposed by consciously utilizing the information of channel correlations, which leads to the performance improvement of wireless TCP systems.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1348-1351
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• 2007

We demonstrated that the threshold voltage shift owing to a gate-bias stress is originated from the trapped charges at the interface between semiconductor layer and dielectric layer, and such drawback can be settled by applying long-term delay time to the gate electrode.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1189-1197
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• 2006

Ultra-wideband(UWB) is a killer technology for short-range wireless communications. In the past, most of the UWB research focused on physical layer but the unique characteristics of UWB make it different to design the upper layer protocols than conventional narrow band systems. Cross-layer protocols have received high attention for UWB networks. In this paper, we investigate the performance of two physical layer schemes: Time Hopping Binary Pulse Position Modulation(TH-BPPM) and Time Hopping Binary Pulse Amplitude Modulation (TH-BPAM) with proposed private MAC protocol for UWB ad-hoc networks. From pulse level to packet level simulation is done in network simulator ns-2 with realistic network environments for varying traffic load, mobility and network density. Our simulation result shows TH-BPAM outperforms TH-BPPM in high traffic load, mobility and dense network cases but in a low traffic load case identical performance is achieved.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1347-1360
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• 2018

Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size, end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path which contains the information such as optimal power vector, remaining battery energy vector and number of packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving can be accomplished with the proposed cross layer power control algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.135-142
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• 2012

DSRC system is a communication system that consists of road side equipment and on board equipment to provide services of communication technology for intelligent transportation systems. In this paper, we carry out a short-range dedicated high-speed wireless communications via DSRC system based on board equipment that is installed in the vehicle and road side equipment through wireless channels of communication. on board equipment is system that have a memory which initialization information is stored, it loads physical layer and MAC layer, LLC layer, L7 layer in turn. In the upper, it should analyze the various commands that are sent from roadside base stations, and carry out the operation which is in accordance with the command. and also it designs the structure of protocol stack which is TRM Layer loaded that is to initialize on L7 layer and MAC layer and efficiently designs operation between on board equipment and the road side equipment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.233-236
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• 2012

In this paper, the output characteristics of GaN-based LED considering blocking layer design variables are analyzed. The basic structure of the LED consists of active region of GaN barrier and InGaN quantum well between AlGaN EBL(Electron Blocking Layer) and AlGaN HBL(Hole Blocking Layer) on GaN buffer layer. The output power, internal quantum efficiency characteristics of LED active region considering Al mole fraction of EBL, thickness of EBL, Al mole fraction of HBL and doping concentration of HBL are analyzed using ISE-TCAD.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.633-635
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• 2016

Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used to obtain image representations for transfer learning. The basic pipeline for transfer learning is to first train a ConvNet on a large dataset (source task) and then use feed-forward units activation of the trained ConvNet as image representation for smaller datasets (target task). Our key contribution is to demonstrate superior performance of multiple ConvNet layer features over single ConvNet layer features. Combining multiple ConvNet layer features will result in more complex feature space with some features being repetitive. This requires some form of feature selection. We use AdaBoost with single stumps to implicitly select only distinct features that are useful towards classification from concatenated ConvNet features. Experimental results show that using multiple ConvNet layer activation features instead of single ConvNet layer features consistently will produce superior performance. Improvements becomes significant as we increase the distance between source task and the target task.