• The Korean Journal of Nuclear Medicine
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• v.38 no.2
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• pp.161-170
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• 2004

Molecular Nuclear Neuro-Imaging in "CNS" drug discovery and development tan be divided into four categories that are clearly inter-related.(1) Neuroreceptor mapping to examine the involvement of specific neurotransmitter system in CNS diseases, drug occupancy characteristics and perhaps examine mechanisms of action;(2) Structural and spectroscopic imaging to examine morphological changes and their consequences;(3) Metabolic mapping to provide evidence of central activity and "CNS fingerprinting" the neuroanatomy of drug effects;(4) Functional mapping to examing disease-drug interactions. In addition, targeted delivery of therapeutic agents could be achieved by modifying stem cells to release specific drugs at the site of transplantation('stem cell pharmacology'). Future exploitation of stem cell biology, including enhanced release of therapeutic factors through genetic stem cell engineering, might thus constitute promising pharmaceutical approaches to treating diseases of the nervous system. With continued improvements in instrumentation, identification of better imaging probes by innovative chemistry, molecular nuclear neuro-imaging promise to play increasingly important roles in disease diagnosis and therapy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.91-97
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• 2011

In this paper we present an approach to the structure identification based on genetic algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy-genetic hybrid system in order to predicate the Mackey-Glass Chaotic time series. In this scheme the basic idea consists of two steps. One is the construction of a fuzzy rule base for the partitioned input space via genetic algorithm, the other is the corresponding parameters of the fuzzy control rules adapted by the backpropagation algorithm. In an attempt to test the performance the proposed system, three patterns, x(t-3), x(t-6) and x(t-9), was prepared according to time interval. It was through lots of simulation proved that the initial small error of learning owed to the good structural identification via genetic algorithm. The performance was showed in Table 2.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.46-51
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• 2014

The most important process of engineering system optimal design is to identify the relationship between the design variables and system response. In case of the system optimization, Response Surface Method (RSM) is widely used. The optimization process of RSM generates the design space using the typical alternative candidates and finds the optimal design point in the generated design space. By changing the optimal point depending on the configuration of the design space, it is important to generate the design space. Therefor in this study, the design space is generated by using the relationship between design variables and system response based on Neuro-Response Surface Method (NRSM). And I try to construct the framework for optimal shape design based on NRSM that the optimum shape can be predicted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) within the generated design space. In order to verify the usefulness of the constructed framework, we applied the nonlinear mathematical function problem. In this study, we can solve the constraints of time in the optimization process for the engineering problem and effective to determine the optimal design was possible. by using the generated framework for optimal shape design based on NRSM. In the future research, we try to apply the optimization problem for Naval Architectural & Ocean Engineering based on the results of this study.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.6871-6876
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• 2015

Background: The meningeal hemangiopericytoma (MHPC) is a vascular tumor arising from pericytes. Most intracranial MHPCs resemble meningiomas (MNGs) in their clinical presentation and histological features and may therefore be misdiagnosed, despite important differences in prognosis. Materials and Methods: We report 8 cases of MHPC and 5 cases of MNG collected from 2007 to 2011 from the Neuro-Surgery and Histopathology departments. All 13 samples were re reviewed by two independent pathologists and investigated by immunohistochemistry (IHC) using mesenchymal, epithelial and neuro-glial markers. Additionally, we screened all tumors for a large panel of chromosomal alterations using multiplex ligation probe amplification (MLPA). Presence of the NAB2-STAT6 fusion gene was inferred by immunohistochemical staining for STAT6. Results: Compared with MNG, MHPCs showed strong VIM (100% of cases), CD99 (62%), bcl-2 (87%), and p16 (75%) staining but only focal positivity with EMA (33%) and NSE (37%). The p21 antibody was positive in 62% of MHPC and less than 1% in all MNGs. MLPA data did not distinguish HPC from MNG, with PTEN loss and ERBB2 gain found in both. By contrast, STAT6 nuclear staining was observed in 3 MHPC cases and was absent from MNG. Conclusions: MNG and MHPC comprise a spectrum of tumors that cannot be easily differentiated based on histopathology. The presence of STAT6 nuclear positivity may however be a useful diagnostic marker.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.37-46
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• 2005

To date, many viable smart base isolation systems have been proposed and investigated. In this study, a novel friction pendulum system (FPS) and an MR damper are employed as the isolator and supplemental damping device, respectively, of the smart base isolation system. A fuzzy logic controller (FLC) is used to modulate the MR damper because the FLC has an inherent robustness and ability to handle non linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. The main purpose of employing a GA is to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. To this end, a GA with a local improvement mechanism is applied. This method is efficient in improving local portions of chromosomes. Neuro fuzzy models are used to represent dynamic behavior of the MR damper and FPS. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can find optimal fuzzy rules and the GA optimized FLC outperforms not only a passive control strategy but also a human designed FLC and a conventional semi active control algorithm.

• The Korean Journal of Nuclear Medicine
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• v.38 no.2
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• pp.111-114
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• 2004

Molecular imaging provides a visualization of normal as well as abnormal cellular processes at a molecular or genetic level rather than at a anatomical level. Conventional medical imaging methods utilize the imaging signals produced by nonspecific physico-chemical interaction. However, molecular imaging methods utilize the imaging signals derived from specific cellular or molecular events. Because molecular and genetic changes precede anatomical change in the course of disease development, molecular imaging can detect early events in disease progression. in the near future, through molecular imaging we can understand basic mechanisms of disease, and diagnose earlier and, subsequently, treat earlier intractable diseases such as cancer, neuro-degenerative diseases, and immunologic disorders. In beginning period, nuclear medicine started as a molecular imaging, and has had a leading role in the field of molecular imaging. But recently molecular imaging has been rapidly developed. Besides nuclear imaging, molecular imaging methods such as optical imaging, magnetic resonance imaging are emerging. Each imaging modalities have their advantages and weaknesses. The opportunities from molecular imaging look bright. We should try nuclear medicine continues to have a leading role in molecular imaging.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.740-743
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• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.606-609
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• 2015

본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

• Journal of Korean Neurosurgical Society
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• v.61 no.3
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• pp.402-406
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• 2018

In contrast to many of the malignant tumors that occur in the central nervous system in adults, the management, responses to therapy, and future perspectives of children with malignant lesions of the brain hold considerable promise. Within the past 5 years, remarkable progress has been made with our understanding of the basic biology of the molecular genetics of several pediatric malignant brain tumors including medulloblastoma, ependymoma, atypical teratoid rhabdoid tumour, and high grade glioma/diffuse intrinsic pontine glioma. The recent literature in pediatric neuro-oncology was reviewed, and a summary of the major findings are presented. Meaningful sub-classifications of these tumors have arisen, placing children into discrete categories of disease with requirements for targeted therapy. While the mainstay of therapy these past 30 years has been a combination of central nervous system irradiation and conventional chemotherapy, now with the advent of high resolution genetic mapping, targeted therapies have emerged, and less emphasis is being placed on craniospinal irradiation. In this article, the present and future perspective of pediatric brain malignancy are reviewed in detail. The progress that has been made offers significant hope for the future for patients with these tumours.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.238-242
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• 2013

We consider the problem of constructing observers for nonlinear systems with unknown inputs. Connectionist networks, also called neural networks, have been broadly applied to solve many different problems since McCulloch and Pitts had shown mathematically their information processing ability in 1943. In this thesis, we present a genetic neuro-control scheme for nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.