• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2365-2382
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• 2014

While single-user spatial multiplexing multiple-input multiple-output (SU-MIMO) allows spatially multiplexed data streams to be transmitted to one node at a time, multi-user spatial multiplexing MIMO (MU-MIMO) enables the simultaneous transmission to multiple nodes. However, if the transmission time required to send packets to each node varies considerably, MU-MIMO may fail to utilize the available MIMO capacity to its full potential. The transmission time typically depends upon two factors: the link quality of the selected channel and the data length (packet size). To utilize the cumulative capacity of multiple channels in MIMO applications, the assignment of channels to each node should be controlled according to the measured channel quality or the transmission queue status of the node.A MAC protocol design that can switch between MU-MIMO and multiple SU-MIMO transmissions by considering the channel quality and queue status information prior to the actual data transmission (i.e., by exchanging control packets between transmitter and receiver pairs) could address such issues in a simple but in attractive way. In this study, we propose a new MAC protocol that is capable of performing such switching and thereby improve the system performance of very high throughput WLANs. The detailed performance analysis demonstrates that greater benefits can be obtained using the proposed scheme, as compared to conventional MU-MIMO transmission schemes.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.448-457
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• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.218-224
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• 1990

A motor group control system is developed for continuous manufacturing processes such as rolling process or electrolytic tinning process. The control system consists of four subsystems ; Multi-Function Controller (MFC), Flexible Motor Drive (FMD), Bulky Input/Output (BIO), Graphic Console and Simulator (GCS). A graphic control language, called Function Block Language, is used to configure the control algorithms for each subsystem. All subsystem are linked together thru a field bus to communicate data with each other.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2006년도 하계학술대회
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• pp.215-218
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• 2006

In this paper, a small size, $7{\times}6mm^2$, Low Noise Amplifier(LNA) using LTCC process was fabricated with multi-layer structure for 2.4GHz wireless LAN. The measured results demonstrate that the bandwidth is 130 MHz, and the operating frequency is from 2.39GHz to 2.52GHz. The power gain is above 7.3 dB in the operating frequency range and the gain flatness is 0.5 dB. The maximum S11 is -4 dB and the maximum S22 is -7.5 dB. The noise figure is less than 1.83 dB. The measured power gain, S11 and S22 were had poorer performance than the simulation results. The reason for this discrepancy is that the input and output matching was not performed exactly. However, the noise figure of the LTCC low noise amplifier is better than simulation result. It is found that it is possible to fabricate a LTCC low noise amplifier in a small size.

• 한국정보처리학회논문지
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• 제6권8호
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• pp.2098-2115
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• 1999

신경망 회로의 해석에서 아직 해결하지 못하는 부분이 은닉층(hidden layer)의 해석이다. 본 논문에서는 신경망 회로의 기본적인 구성회로로써 하나의 입력(p levels)과 하나의 출력(q levels)을 갖는 2-layer Core-Net를 정의하고, 이 Core-Net의 처리 가능 용량(the capacity)은 2차원 무게값 공간(weight space)을 나눌 수 있는 영역의 수로, {{{{ {a}_{p,q} = {{q}^{2}} over {2}p(p-1)- { q} over {2 } (3 { p}^{2 } -7p+2)+ { p}^{2 }-3p+2}}}}임을 수학적 귀납법으로 증명하였다. 이 Core-Net로 신경망 회로의 중간층 해석이 가능함을 시뮬레이션 예제를 통하여 보였다.

• Journal of Power Electronics
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• 제8권4호
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권8호
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.372-372
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• 2000

In this paper, we propose Neuro Fuzzy Polynomial Networks(NFPN) based on Polynomial Neural Network(PNN) and Neuro-Fuzzy(NF) for model identification of complex and nonlinear systems. The proposed NFPN is generated from the mutually combined structure of both NF and PNN. The one and the other are considered as the premise part and consequence part of NFPN structure respectively. As the premise part of NFPN, NF uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. As the consequence part of NFPN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. NFPN is available effectively for multi-input variables and high-order polynomial according to the combination of NF with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. In order to evaluate the performance of proposed models, we use the nonlinear function. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously.

• 한국통신학회논문지
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• 제31권10A호
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• pp.968-975
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• 2006

고속 데이터 전송을 위하여, 송수신단에 다중 안테나를 사용함으로써 독립적인 페이딩 채널을 다수개 형성하여 다이버시티 이득과 코딩 이득을 동시에 얻는 MIMO(Multiple-Input Multiple-Output) 방식에 대한 연구가 활발히 진행되고 있다. 하지만 아직 UWB(Ultra Wide Band) 시스템과의 간섭에 대한 분석은 이루어지고 있지 않다. 이에 본 논문에서는 공간 다이버시티 특성을 가진 블록 코드를 OFDM 시스템에 적용하여, MIMO-OFDM 수신기에 인접한 UWB 기기로부터 SFBC(Space Frequency Block Code)-OFDM 시스템과 SFTC(Space Frequency Trellis Code)-OFDM 시스템에 .미치는 간섭 영향을 MIMO 채널 환경에서 성능을 분석하였다. 시뮬레이션 결과 SFTC-OFDM 시스템이 MB(Multi Band)-OFDM UWB 간섭에 대해 SFBC-OFDM 시스템보다 더 강인한 성능을 보여주었다.

• 환경영향평가
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• 제9권3호
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• pp.185-202
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• 2000

Uncertainty and variability in Life Cycle Assessment(LCA) have been significant key issues in LCA methodology with techniques in other research area such as social and political science. Variability is understood as stemming from inherent variations in the real world, while uncertainty comes from inaccurate measurements, lack of data, model assumptions, etc. Related articles in this issues were reviewed for classification, distinguish and elaboration of probabilistic/stochastic health risk analysis application in LCA. Concept of focal zone, streamlining technique, scenario modelling and Monte Carlo/Latin Hypercube risk analysis were applied to the uncertainty/variability analysis of health risk in LCA. These results show that this general framework of multi-disciplinary methodology between probabilistic health risk assessment and LCA was of benefit to decision making process by suppling information about input/output data sensitivity, health effect priority and health risk distribution. There should be further research needs for case study using this methodology.