• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.3
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• pp.60-66
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• 2012

This paper aims to constitute a feedback vibration control system using wireless sensor networks and experiment it on a model structure to verify its effectiveness. For the purpose, we set up a feedback vibration control system composed of a wireless input/output(I/O) sensor node based on bluetooth, a home-made shear type MR damper, a shaker which generates a constant size of sine wave, and a simple beam model structure. The vibration control experiment was performed by shaking the 1/4 point of beam with a shaker. At the moment of shaking, we controled the vibration with MR damper which was placed vertically on the center of beam. Simultaneously, by acquiring acceleration response at the 2/4 point of beam, we evaluated the effectiveness of control capability. The control command was set to send a voltage signal to MR damper when the acceleration response, acquired from the wireless I/O sensor node placed at the center of beam, was more than a certain amount. Although the realtime feedback vibration control system constituted in this paper is effective only within a limited command system, it has been proven that the system was able to effectively decrease the vibration of structure by generating a control command aimed for realtime purpose. The system also showed a possibility to be used as a structural response control system adapting a variety of semi-active control algorithm.

• Geotechnical Engineering
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• v.8 no.2
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• pp.5-20
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• 1992

The physical-based and lumped-parameter hydrologic groundwater flow model for predicting the rainfall-triggered rise of groundwater levels in hillside slopes is developed in this paper to assess the risk of landslides. The developed model consists of a vertical infiltration model for unsaturated zone linked to a linear storage reservoir model(LSRM) for saturated zone. The groundwater flow model has uncertain constants like soil depttL slope angle, saturated permeability, and potential evapotranspiration and four free model parameters like a, b, c, and K. The free model parameters could be estimated from known input-output records. The BARD algorithm is uses as the parameter estimation technique which is based on a linearization of the proposed model by Gauss -Newton method and Taylor series expansion. The application to examine the capacity of prediction shows that the developed model has a potential of use in forecast systems of predicting landslides and that the optimal estimate of potential 'a' in infiltration model is the most important in the global optimum analysis because small variation of it results in the large change of the objective function, the sum of squares of deviations of the observed and computed groundwater levels. 본 논문에서는 가파른 산사면에서 산사태의 발생을 예측하기 위한 수문학적 인 지하수 흐름 모델을 개발하였다. 이 모델은 물리적인 개념에 기본하였으며, Lumped-parameter를 이용하였다. 개발된 지하수 흐름 모델은 두 모델을 조합하여 구성되어 있으며, 비포화대 흐름을 위해서는 수정된 abcd 모델을, 포화대 흐름에 대해서는 시간 지체 효과를 고려할 수 있는 선형 저수지 모델을 이용하였다. 지하수 흐름 모델은 토층의 두께, 산사면의 경사각, 포화투수계수, 잠재 증발산 량과 같은 불확실한 상수들과 a, b, c, 그리고 K와 같은 자유모델변수들을 가진다. 자유모델변수들은 유입-유출 자료들로부터 평가할 수 있으며, 이를 위해서 본 논문에서는 Gauss-Newton 방법을 이용한 Bard 알고리즘을 사용하였다. 서울 구로구 시흥동 산사태 발생 지역의 산사면에 대하여 개발된 모델을 적용하여 예제 해석을 수행함으로써, 지하수 흐름 모델이 산사태 발생 예측을 위하여 이용할 수 있음을 입증하였다. 또한, 매개변수분석 연구를 통하여, 변수 a값은 작은 변화에 대하여 목적함수값에 큰 변화를 일으키므로 a의 값에 대한 최적값을 구하는 것이 가장 중요한 요소라는 결론을 얻었다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.127-133
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• 2014

In order to protect information asset from various malicious code, Honeypot system is implemented. Honeypot system is designed to elicit attacks so that internal system is not attacked or it is designed to collect malicious code information. However, existing honeypot system is designed for the purpose of collecting information, so it is designed to induce inflows of attackers positively by establishing disguised server or disguised client server and by providing disguised contents. In case of establishing disguised server, it should reinstall hardware in a cycle of one year because of frequent disk input and output. In case of establishing disguised client server, it has operating problem such as procuring professional labor force because it has a limit to automize the analysis of acquired information. To solve and supplement operating problem and previous problem of honeypot's hardware, this thesis suggested hybrid honeypot. Suggested hybrid honeypot has honeywall, analyzed server and combined console and it processes by categorizing attacking types into two types. It is designed that disguise (inducement) and false response (emulation) are connected to common switch area to operate high level interaction server, which is type 1 and low level interaction server, which is type 2. This hybrid honeypot operates low level honeypot and high level honeypot. Analysis server converts hacking types into hash value and separates it into correlation analysis algorithm and sends it to honeywall. Integrated monitoring console implements continuous monitoring, so it is expected that not only analyzing information about recent hacking method and attacking tool but also it provides effects of anticipative security response.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.368-376
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• 2006

This paper describes a Fcore_GenSim(Parameterized FFT Core Generation & Simulation Program), which can be used as an essential If(Intellectual Property) in various OFDM modem designs. The Fcore_Gensim is composed of two parts, a parameterized core generator(PFFT_CoreGen) that generates Verilog-HDL models of FFT cores, and a fixed-point FFT simulator(FXP_FFTSim) which can be used to estimate the SQNR performance of the generated cores. The parameters that can be specified for core generation are FFT length in the range of 64 ~2048-point and word-lengths of input/output/internal/twiddle data in the range of 8-b "24-b with 2-b step. Total 43,659 FFT cores can be generated by Fcore_Gensim. In addition, CBFP(Convergent Block Floating Point) scaling can be optionally specified. To achieve an optimized hardware and SQNR performance of the generated core, a hybrid structure of R2SDF and R2SDC stages and a hybrid algorithm of radix-2, radix-2/4, radix-2/4/8 are adopted according to FFT length and CBFP scaling.

• Journal of the Korean Institute of Gas
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• v.12 no.2
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• pp.38-41
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• 2008

In this research, we propose a new method for tracking the release rate using the concentration data obtained from the sensor. We used a sensor network that has already been set surrounding the area where hazardous gas releases can occur. From the real-time sensor data, we detected and analyzed releases of harmful materials and their concentrations. Based on the results, the release rate is estimated using the neural network. This model consists of 14 input variables (sensor data, material properties, process information, meteorological conditions) and one output (release rate). The dispersion model then performs the simulation of the expected dispersion consequence by combining the sensor data, GIS data and the diagnostic result of the source term. The result of this study will improve the safety-concerns of residents living next to storage facilities containing hazardous materials by providing the enhanced emergency response plan and monitoring system for toxic gas releases.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.161-172
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• 2005

This study is to develop Bus Transit Route Analysis and Evaluation Model that can product the quantitative performance measures for Bus Transit Route Network Design. So far, in Korea, there are no so many models that evaluate a variety of other performance measures or service quality that are of concern to both the transit users and operator because of lower-level bus database system and transit route network analysis algorithm's limit. The BTRAEM in this research differ from the previous approach in that the BTRAEM employs a multiple path transit trip assignment model that explicitly considers the transfer and different travel time after boarding. And we develop input-output data structure and quantitative performance measure for the BTRAEM. In the numerical experimental applying BTRAEM to Mandl transit network, We got the meaningful results on performance measure of bus transit route network. In the future, we expect BTRAEM to give a good solution in real transit network.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.149-157
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• 2012

In this thesis, the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. It is shown formally that neither approximation can be addressed effectively for a sliding-window join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequency based model of stream arrival. There exists a sampling problem for this model. More importantly, it is shown that a broad class of applications for which an age-based model of stream arrival is more appropriate, and both approximation scenarios under this new model are addressed. Finally, for the case of multiple joins being executed with an overall memory constraint, an algorithm for memory allocation across the join that optimizes a combined measure of approximation in all scenarios considered is provided.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• Journal of IKEEE
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• v.25 no.2
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• pp.388-398
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• 2021

This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system (ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption, calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds the input. In addition, in order to compensate for the additional power, the process of allocating power to the determined point is performed, and an optimization that minimizes the average of the load expected at the active power allocations using PSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme, case study of small and large load prediction errors was conducted by reflecting actual load data and load prediction algorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduce electricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak load reduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.

• The Journal of the Acoustical Society of Korea
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• v.29 no.5
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• pp.303-313
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• 2010

This paper describes an acoustic communication receiver structure, which is designed for QPSK (Quadrature Phase Shift Keying) signal with 25 kHz carrier frequency and 5 kHz symbol rate, and takes samples from received signal at 100 kHz sampling rate. Based on the described receiver structure, optimum design parameters, such as number of taps of FF (Feed-Forward) and FB (Feed-Back) filters and forgetting factor of RLS (Recursive Least-Square) algorithm, of joint equalizer are determined to minimize the BER (Bit Error Rate) performance of the joint equalizer output symbols when the acquisition data in shallow water using implemented acoustic transducers is decimated at a rate of 2:1 and then enforced to the input of receiver. The transmission distances are 1.4 km, 2.9 km, and 4.7 km. Analysis results show that the optimum number of taps of FF and FB filters are different according to the distance between source and destination, but the optimum or near optimum value of forgetting factor is 0.997. Therefore, we can reach a conclusion that the proper receiver structure could change the number of taps of FF and FB filters with the fixed forgetting factor 0.997 according to the transmission distance. Another analysis result is that there are an acceptable performance degradation when the 16-tap-length simple filter is used as a low-pass filter of receiver instead of 161-tap-length matched filter.