• Korean Journal of Breeding Science
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• v.43 no.1
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• pp.62-67
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• 2011

This study was carried out to gain basic informations about amylogram, antioxidant activity function and physicochemical properties for kernel of the newly developed waxy corn hybrids. The used materials were produced in 2008 and cultivated at breeding farm of CNU in 2009. From amylogram analysis, peak and minimum viscosity of the used hybrids were appeared in CNU08H-71 and CNU08H-69 hybrid, respectively. DPPH free radical scavenging effect marked as election donating ability was highly appeared in CNU08H-15 and CNU08H-69 hybrid, while CNU08H-h102 hybrid was the lowest. Hardness of kernel was highly appeared in CNU08H-35 hybrid, and chewiness and gumminess were also the highest in this hybrid, but those of CNU08H-h105 hybrid were the lowest. In these facts, we confirmed that the used hybrids were very different among traits related to amylogram, functions and table qualities. Accordingly, development of the new waxy corn hybrid will be profitable to select and develop as a crossing combination including many excellent traits.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.16-26
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• 2019

Wetlands that provide diverse ecosystem services, such as habitat provision and hydrological control of flora and fauna, constitute ecosystems through interaction between wetlands existing in a wetlandscape. Therefore, to evaluate the wetland functions such as resilience, it is necessary to analyze the ecological connectivity that is formed between wetlands which also show hydrologically dynamic behaviors. In this study, by defining wetlands as ecological nodes, we generated ecological networks through the connection of wetlands according to the dispersal model of wetland species. The characteristics of these networks were then analyzed using various network metrics. In the case of the dispersal based on a threshold distance, while a high local clustering is observed compared to the exponential dispersal kernel and heavy-tailed dispersal model, it showed a low efficiency in the movement between wetlands. On the other hand, in the case of the stochastic dispersion model, a low local clustering with high efficiency in the movement was observed. Our results confirmed that the ecological network characteristics are completely different depending on which dispersal model is chosen, and one should be careful on selecting the appropriate model for identifying network properties which highly affect the interpretation of network structure and function.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Knowledge Management Research
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• v.24 no.1
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• pp.245-271
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• 2023

The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.61-69
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• 2005

TCP and UDP is a transport layer protocol of Internet. TCP is a connection oriented protocol which supports a reliable data transfer by offering error and flow control, but it bring a transmission delay. On the other hand, the UDP is a connectionless protocol which does not carry out error and flow control, but it guarantees a realtime transmission. There are hardly any protocols which supports not only realtime functions but also data reliability. In this paper, we have designed and implemented a new TCP mode option which supports reliable realtime transmission. Our designed TCP performs an error recovery process during a fixed amount of time. This time is negotiated during the connection establishment phase. Our designed TCP is tested in real environments, and we find that it is relatively faster than the standard TCP and more reliable than the UDP. It can be used for the reliable transfer of realtime multimedia data.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.132-137
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• 2004

On-Board Diagnostic(OBD) systems are in most cars and light trucks on the load today. During the 1970's and early 1980's manufacturers started using electronic means to control engine functions and diagnose engine problems. The CARB's diagnostic requirements to meet EPA emission standards have been designated as OBD with a goal of monitoring all of the emissions-related components, as well as the chassis, body, accessory devices and the diagnostic control network of the vehicle for proper operation. In this paper, we present a remote measurement system for the wireless monitoring of diagnosis signal and sensors output signals of ECU adopted KWP2000, united the OBD communication protocol, on OBD-compliant vehicle using the wirless communication technique of Bluetooth. In order to measure the ECU signals, the interface circuit is designed to communicate ECU and designed terminal wirelessly according to the ISO, SAE regulation of communication protocol standard. A microprocessor S3C3410X is used for communicating ECU signals. The embedded system's software is programmed to measure the ECU signals using the ARM compiler and ANCI C based on MicroC/OS kernel to communicate between bluetooth modules using bluetooth stack. The diagnostic system is developed using Visual C++ MFC and protocol stack of bluetooth for Windows environment. The self-diagnosis and sensor output signals of ECU is able to monitor using PC with bluetooth board connected in serial port of PC. The algorithms for measuring the ECU sensor output and self-diagnostic signals are verified to monitor ECU state. At the same time, the information to fix the vehicle's problem can be shown on the developed monitoring software. The possibility for remote measurement of self-diagnosis and sensor signals of ECU adopted KWP2000 in embedded system verified through the developed systems and algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.133-138
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• 2020

This paper introduces a method for improving accuracy using the first convolution layer, which is not used in most modified CNN(: Convolution Neural Networks). In CNN, such as GoogLeNet and DenseNet, the first convolution layer uses only the traditional methods(3×3 convolutional computation, batch normalization, and activation functions), replacing this with RGB-csb. In addition to the results of preceding studies that can improve accuracy by applying RGB values to feature maps, the accuracy is compared with existing CNN using a limited number of images. The method proposed in this paper shows that the smaller the number of images, the greater the learning accuracy deviation, the more unstable, but the higher the accuracy on average compared to the existing CNN. As the number of images increases, the difference in accuracy between the existing CNN and the proposed method decreases, and the proposed method does not seem to have a significant effect.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.63-72
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• 2005

This paper describes a new approach for the operating system level power management to reduce the energy consumed in the IO devices in a robot platform, which provides various functions such as navigation, multimedia application, and wireless communication. The policy proposed in the paper, which was named the Energy-Aware Job Schedule (EAJS), rearranges the jobs scattered so that the idle periods of the devices are clustered into a time period and the devices are shut down during their idle period. The EAJS selects a schedule that consumes the minimum energyamong the schedules that satisfy the buffer and time constraints. Note that the burst job execution needs a larger memory buffer and causes a longer time delay from generating the job request until to finishing it. A prototype of the EAJS is implemented on the Linux kernel that manages the robot system. The experiment results show that a maximum $44\%$ power saving on a DSP and a wireless LAN card can be obtained with the EAJS.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.3
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• pp.291-305
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• 2010

A three-dimensional time-domain calculation method is of crucial importance in prediction of the motions and wave loads of a ship advancing in a severe irregular sea. The exact solution of the free surface wave-ship interaction problem is very complicated because of the essentially nonlinear boundary conditions. In this paper, an approximate body nonlinear approach based on the three-dimensional time-domain forward-speed free-surface Green function has been presented. The Froude-Krylov force and the hydrostatic restoring force are calculated over the instantaneous wetted surface of the ship while the forces due to the radiation and scattering potentials over the mean wetted surface. The time-domain radiation and scattering potentials have been obtained from a time invariant kernel of integral equations for the potentials which are discretized according to the second-order boundary element method (Hong and Hong 2008). The diffraction impulse-response functions of the Wigley seakeeping model advancing in transient head waves at various Froude numbers have been presented. A simulation of coupled heave-pitch motion of a long rectangular barge advancing in regular head waves of large amplitude has been carried out. Comparisons between the linear and the approximate body nonlinear numerical results of motions and wave loads of the barge at a nonzero Froude number have been made.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.149-154
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• 2014

The cross-correntropy concept can be expressed with inner products of two different probability density functions constructed by Gaussian-kernel density estimation methods. Blind algorithms based on the maximization of the cross-correntropy (MCC) and a symbol set of randomly generated N samples yield superior learning performance, but have a huge computational complexity in the update process at the aim of weight adjustment based on the MCC. In this paper, a method of reducing the computational complexity of the MCC algorithm that calculates recursively the gradient of the cross-correntropy is proposed. The proposed method has only O(N) operations per iteration while the conventional MCC algorithms that calculate its gradients by a block processing method has $O(N^2)$. In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.