• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.89-96
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• 2013

The graphic processor unit (GPU) has been developed to process not only graphic data but also general system data. It shows a better performance than CPU in algorithm for 3D graphics and parallel program. In order to execute algorithm for CPU on GPU, we should understand about GPU architectures and rewrite program considering parallel processing capability and new memory model of GPU. For this reasons, a performance prediction model for the algorithm and its predicted performance through GPU system are required. These can predict problems in GPU application development or construct a performance evaluation standard for GPU. In this paper, we applied the AES encryption algorithms on our performance model and accomplished performance prediction with high accuracy under a heavy workload.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.692-698
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• 2016

Recently, the facility extension construction and maintenance market portion has increased instead of decreased the newly facility construction. In this context, it is important to examine the reverse engineering of MEP (Mechanical Electrical and Plumbing) facilities, which have the high operation and management cost in the architecture domains. The purpose of this study was to suggest the Large Point Cloud-based Pipe Shape Reverse Engineering Method. To conduct the study, the related researches were surveyed and the reverse engineering automation method of the pipe shapes considering large point cloud was proposed. Based on the method, the prototype was developed and the results were validated. The proposed method is suitable for large data processing considering the validation results because the rendering performance standard deviation related to the 3D point cloud massive data searching was 0.004 seconds.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.753-758
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• 2009

Recently the high-end CMOS image sensors are developed, conforming to the SMIA CCP2 specification, which is a high-speed low-power serial interface based on LVDS technology. But this kind of technology trend makes the existing equipments are no longer useful, although their capability is still good enough to handle the recent image sensors if there was no interfacing problem. In this paper, we propose and realize a data protocol conversion system that translates the SMIA CCP2 serial signals into the existing 10-bit parallel signals. The proposed system is composed of a de-serializer and a FPCA chip, and thus can be constructed on a small PCB which enables easy integration between the existing equipments and the new high-end image sensors. Besides, the maximum transfer rate by the SMIA specification is also achieved on the implemented system. So it is expected that the implemented system can be used as a general-purpose protocol converter in a variety of sensor-related application fields.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1571-1576
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• 2005

DCS has many processing components and various communication elements. And its communication delay characteristic is affected diverse operating situation and context. Especially, binding signal which traversed from one control-node to another control-node undergo all sort of delay conditions. So its delay value has large deviation with the lapse of time, and the measurement of delay statistics during long time is very difficult by using general oscilloscope or other normal instruments. This thesis introduces the design and implementation of PC-based BDAS(Binding Delay Analysis System) System developed to overcomes these hardships. The system has signal-generator, IO-card, data-acquisition module, delay-calculation and analyzer module, those are implemented on industrial standard PC/Ethernet hardware and Windows/Linux platforms. This system can detect accurate whole-system-wide delay time including io, control processing and network delay, in the resolution of msec unit, and can analyze each channel's delay-historic data which is maintained by realtime database. So, this system has strong points of open system architecture, for example, user-friendly environment, low cost, high compatibility, simplicity of maintenance and high extension ability. Of all things, the measuring capability of long-time delay-statistics obtained through historic-DB make the system more valuable and useful, which function is essential to analyze accurate delay performance of DCS system. Using this system, the verification of delay performance of DCS for nuclear power plants is succeeded in KNICS(Korea Nuclear Instrumentation & Control System) projects

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• KIEAE Journal
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• v.8 no.4
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• pp.11-18
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• 2008

This research case-study places are chosen Seoul Metropolitan City and Yong-In City where the distinction characteristics of outdoor spaces are introduced in Apartment Complex. In this study, the standard of waterscape facilities will be set through preliminary researches and detail design factors are prepared for each characteristics. Analyze and research the characteristics of design factors through the field research of places. The Group data from Cluster Analysis, which is about waterscape facilities, is analyzed and classified by types of waterscape facilities in common residence. and then each type of characteristics and representatives of waterscape facilities founded. Waterscape facilities are charactered in 4 main types. I Type is appeared to the natural artificially mountain stream, slope and a rest space type, and feelings type. This type has twenty sites. II Type is appeared to the fewest sites(11 sites). This type is appeared to the Eco-pond, Border planting next to the waterscape facilities. III Type is appeared to the largest sites(28 sites), that is, play style water facilities of no-plantation patterns. IV Type is appeared to the retaining wall type, the rest space of bench type. Research result led, detailed plan element 64 are selected with design elements Seoul Metropolitan City and the Yong-In City at the time of the fact that well is only reflected commonly from external spaces, the type by quality which leads a statistical analysis the type quality was well reflected relatively was judged.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.53-58
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• 2008

This paper designs sigma-delta D/A Converter with a high resolution and low power consumption. It reorganizes the input data along LJ, RJ, I2S mode and bit mode to the output data of A/D converter. The D/A converter decodes the original analog signal through HBF, Hold and 5th CIFB(Cascaded Integrators with distributed Feedback as well as distributed input coupling) sigma-delta modulation blocks. It uses repeatedly the addition operation in instead of the multiply operation for the chip area and the performance. Also, the half band filters of similar architecture composed the one block and it used the sample-hold block instead of the sinc filter. We supposed simple D/A Converter decreased in area. The filters of the block analyzed using the matlab tool. The top block designed using the top-down method by verilog language. The designed block is fabricated using Samsung 0.35um CMOS standard cell library. The chip area is 1500*1500um.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8A
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• pp.640-647
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• 2009

IEEE 802.11i is an amendment to the original IEEE 802.11/b,a,g standard specifying security mechanism by stipulating RSNA for tighter security. The RSNA uses TKIP(Temporal Key Integrity Protocol) and CCMP(Counter with CBC-MAC Protocol) instead of old-fashioned WEP(Wired Equivalent Privacy) for data encryption. This paper describes a design of a communication security engine for IEEE 802.11i MAC layer. The design includes WEP and TKIP modules based on the RC4 encryption algorithm, and CCMP module based on the AES encryption algorism. The WEP module suffices for compatibility with the IEEE 802.11 b,a,g MAC layer. The CCMP module has about 816.7Mbps throughput at 134MHz, hence it satisfies maximum 600Mbps data rate described in the IEEE 802.11n specifications. We propose a pipelined AES-CCMP cipher core architecture, which has lower hardware cost than existing AES cores, because CBC mode and CTR mode operate at the same time.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.