• Bulletin of the Korean Space Science Society
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• 2010.04a
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• pp.34.3-34.3
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• 2010

We present the results of flux monitoring of BL Lac object 0716+71 and 0954+65 at 22GHz and 43GHz. Both of the flat spectrum radio sources are known as Intraday variables (IDVs) which are characterized by fast flux variation on time scales of a day or less. In general, the IDV phenomenon is interpreted as the effect of refractive scintillation in the interstellar medium or the evidence of source intrinsic flux variation. The observations were made simultaneously at 22GHz and 43GHz with KVN Yonsei 21m radio telescope.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.1
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• pp.43-50
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• 2007

In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.60.2-60.2
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• 2011

We introduce the preliminary results of flux monitoring of BL Lac object 0716+714 with the KVN Ulsan 21m radio telescope. This radio source is well known as the intraday variable (IDV) source which is characterized by the rapid flux variation on the time scale of a day or less. In general, the IDV phenomenon is interpreted as the effect of refractive scintillation in the interstellar medium or the evidence of intrinsic flux variation. In previous observations that took a few days, however, it had not been detected the flux variation of short time scale but the monotonic increase and decrease. Therefore, to investigate the longer time scale of 0716+714, we had the flux variation monitoring at 22GHz and 43GHz simultaneously for 9 months from October 2010 to June 2011. We present here the structure functions and the cross correlation functions between different frequencies as well as the light curves.

• Journal of the Korean Professional Engineers Association
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• v.28 no.3
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• pp.121-134
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• 1995

To operate the automatic devices of manufacturing process more effectively and to solve the needs of the resource sharing, network technology is applied to the unit con-trol devices located In common manufacturing zone and operated by connecting them. The network management system, which controls and supervises the network resources in the communication network, is the function that is necessarily required in this net-work. For the stability in this network, this paper measures the variation of data packet length and node number and analyzes the variated value of the token rotation time and waiting time for the network operation. On the evalution of the performance, the data packet length varies from 10 bytes to 100 bytes in the operation of the group manage-ment network, the variation of the token rotation time and waiting time is less than 10 msec. When the variation of the data packet lenhth is less than 50 bytes, differences in token rotation time or waiting time is unnoticable. Since the waiting time in this case is less than 10 msec, response time Is fast enough.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.102-111
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• 1997

In this paper, a patern recognition method of surface mount device(SMD) IC using wavelet transform and neural network is proposed. We chose the feature parameter according to the characteristics of coefficient matrix which is obtained from four level discrete wavelet transform (DWT). These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Experimental results show that when the same form of feature pattern, as is used for learning, is put into neural network and gained 100% rate ofrecognition irrespective of SMD IC kinds, location and variation of illumination. In the case of unused feature pattern for learning, the recognition rate is 85.9% under the similar surroundings, where as an average recognition rate is 96.87% for the case of reregulated value of illumination. Proosed method is relatively simple compared with the traditional space domain method in extracting the feature parameter and is also well suited for recognizing the pattern's class, position and existence. It can also shorten the processing tiem better than method extracting feature parameter with the use of discrete cosine transform(DCT) and adapt the surroundings such as variation of illumination, the arrangement and the translation of SMD IC.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.58-62
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• 2003

Silicon carbide (SiC) was etched in a $NF_3/CH_4$ inductively coupled plasma. The etch process was modeled by using a neural network called generalized regression neural network (GRNN). For modeling, the process was characterized by a $2^4$ full factorial experiment with one center point. To test model appropriateness, additional test data of 16 experiments were conducted. Particularly, the GRNN predictive capability was drastically improved by a genetic algorithm (GA). This was demonstrated by an improvement of more than 80% compared to a conventionally obtained model. Predicted model behaviors were highly consistent with actual measurements. From the optimized model, several plots were generated to examine etch rate variation under various plasma conditions. Unlike the typical behavior, the etch rate variation was quite different depending on the bias power Under lower bias powers, the source power effect was strongly dependent on induced dc bias. The etch rate was strongly correated to the do bias induced by the gas ratio. Particularly, the etch rate variation with the bias power at different gas ratio seemed to be limited by the etchant supply.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.171-174
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• 1999

The torque of SRM is developed by phase currents and inductance variation. Phase currents and inductance variation. Phase current is often the controlled variable in electrical motor drives, so it seems natural to use closed loop current controllers. However, the highly nonlinear nature of switched reluctance motors makes optimisation of closed loop current controlled difficult because of saturation effect in magnetic circuit. Therefore, torque generation region is nonlinearly varied according to phase current and rotor position. This paper describes the torque control scheme with neural network that can control varied with load torque. The torque control is simulated by PSIM.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.75-81
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• 2019

Though recent advancement of deep learning methods have provided satisfactory results from large data domain, somehow yield poor performance on few-shot classification tasks. In order to train a model with strong performance, i.e. deep convolutional neural network, it depends heavily on huge dataset and the labeled classes of the dataset can be extremely humongous. The cost of human annotation and scarcity of the data among the classes have drastically limited the capability of current image classification model. On the contrary, humans are excellent in terms of learning or recognizing new unseen classes with merely small set of labeled examples. Few-shot learning aims to train a classification model with limited labeled samples to recognize new classes that have neverseen during training process. In this paper, we increase the backbone depth of the embedding network in orderto learn the variation between the intra-class. By increasing the network depth of the embedding module, we are able to achieve competitive performance due to the minimized intra-class variation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.05a
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• pp.202-205
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• 1996

In this paper it is researched a relation between network structures and electrical properties - especially dielectric characteristics with changing of network structure. It is resulted that the specimens which have single network structures have smaller dielectric loss than SIN specimens but have relatively larger dependency to variation of temperature and frequency. For that reason formation of structures is attained by introducing of SIN to insulating materials. therefore it is counted that introduction of multiple structure including SIN is necessary to improve heat proof and electrical properties.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.43-46
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• 2003

We consider influences of the aperture variation and the ambient groundwater flow on the migration of DNAPL within a fracture network. In context of a modified invasion percolation (MIP) growth algorithm, we formulate a mechanistic model that includes capillary and gravity forces as well as viscous forces within the DNAPL and the ambient groundwater. The MIP model is verified against laboratory experiments, which is conducted using a two-dimensional random fracture network model. The results show that the aperture variation and ambient groundwater flow can be significant factors controlling DNAPL migration path within fracture networks.