• 소성∙가공
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• 제29권2호
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• 소성∙가공
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• 제21권8호
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• pp.479-484
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• 2012

The objective of this study is to compensate the elastic recovery in the flexible forming process using the predictive models. The target shape was limited to two-dimensional shape having only one curvature radius in the longitudinal-direction. In order to predict the shape error the regression and neural network models were established based on the finite element (FE) simulations. A series of simulations were conducted considering input variables such as the elastic pad thickness, the thickness of plate, and the objective curvature radius. Then, at sampling points in the longitudinal-direction, the shape errors between formed and objective shapes could be calculated from the FE simulations as an output variable. These shape errors were expressed to a representative error value by the root mean square error (RMSE). To obtain the correct objective shape the die shape was adjusted by the closed-loop using the neural network model since the neural network model shows a higher capability of estimating the shape error than the regression model. Finally the experimental result shows that the formed shape almost agreed with the objective shape.

• 소성∙가공
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• 제29권5호
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• pp.272-281
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• 2020

As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

• BMB Reports
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• 제53권11호
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• pp.551-564
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• 2020

Proper development of the nervous system is critical for its function, and deficits in neural development have been implicated in many brain disorders. A precise and predictable developmental schedule requires highly coordinated gene expression programs that orchestrate the dynamics of the developing brain. Especially, recent discoveries have been showing that various mRNA chemical modifications can affect RNA metabolism including decay, transport, splicing, and translation in cell type- and tissue-specific manner, leading to the emergence of the field of epitranscriptomics. Moreover, accumulating evidences showed that certain types of RNA modifications are predominantly found in the developing brain and their dysregulation disrupts not only the developmental processes, but also neuronal activities, suggesting that epitranscriptomic mechanisms play critical post-transcriptional regulatory roles in development of the brain and etiology of brain disorders. Here, we review recent advances in our understanding of molecular regulation on transcriptome plasticity by RNA modifications in neurodevelopment and how alterations in these RNA regulatory programs lead to human brain disorders.

• Biomolecules & Therapeutics
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• 제14권2호
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• pp.75-82
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• 2006

Synaptic plasticity, which is a long lasting change in synaptic efficacy, underlies many neural processes like learning and memory. It has long been acknowledged that new protein synthesis is essential for both the expression of synaptic plasticity and memory formation and storage. Most of the research interests in this field have focused on the events regulating transcriptional activation of gene expression from the cell body and nucleus. Considering extremely differentiated structural feature of a neuron in CNS, a neuron should meet a formidable task to overcome spatial and temporal restraints to deliver newly synthesized proteins to specific activated synapses among thousands of others, which are sometimes several millimeters away from the cell body. Recent advances in synaptic neurobiology has found that almost all the machinery required for the new protein translation are localized inside or at least in the vicinity of postsynaptic compartments. These findings led to the hypothesis that dormant mRNAs are translationally activated locally at the activated synapse, which may enable rapid and delicate control of new protein synthesis at activated synapses. In this review, we will describe the mechanism of local translational control at activated synapses focusing on the role of cytoplasmic polyadenylation of dormant mRNAs.

• PNF and Movement
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• 제7권1호
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• pp.33-46
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• 2009

Purpose : N.A.P.(Neuromuscular Skeletal Plasticity) an integrative neuro-orthopedic concept to facilitate motor strategies in daily life. The primary thesis is, that treatment of body functions and structural impairments should be integrated within goal-oriented activities. The purpose of this article is to demonstrate that the functional activity itself, determines the structure. Material and Methods : A case report of a dentist with brachial plexus lesion after a motor vehicle accident is presented. The necessity for training body functions within relevant tasks is undermined by references which emphasize the importance of training realistic activities to enhance long-term changes in neural representation. Results : The treatment methods presented in this case show significant effects for the patient's ability to participate in his profession within less than a year's time after his motor vehicle accident. Conclusions : Current evidence supports the treatment methods of this concept. The inability to flex his elbow and supinate his forearm placed a considerable doubt to his ability to ever be able to participate in his profession again. Structural reorganization is possible and depends on functional demands, which need to be trained task-specifically. Single case reports may serve as the basis for further randomized controlled studies to support the efficacy of the treatment methods within the N.A.P. concept.

• Molecules and Cells
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• 제23권3호
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• pp.259-271
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• 2007

Investigation of molecular and cellular mechanisms of synaptic plasticity is the major focus of many neuroscientists. There are two major reasons for searching new genes and molecules contributing to central plasticity: first, it provides basic neural mechanism for learning and memory, a key function of the brain; second, it provides new targets for treating brain-related disease. Long-term potentiation (LTP), mostly intensely studies in the hippocampus and amygdala, is proposed to be a cellular model for learning and memory. Although it remains difficult to understand the roles of LTP in hippocampus-related memory, a role of LTP in fear, a simplified form of memory, has been established. Here, I will review recent cellular studies of LTP in the anterior cingulate cortex (ACC) and then compare studies in vivo and in vitro LTP by genetic/pharmacological approaches. I propose that ACC LTP may serve as a cellular model for studying central sensitization that related to chronic pain, as well as pain-related cognitive emotional disorders. Understanding signaling pathways related to ACC LTP may help us to identify novel drug target for various mental disorders.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2006년도 춘계학술대회 논문집
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• pp.429-433
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• 2006

High Temperature deformation behavior of newly developed beta-gamma TiAl alloy was investigated in this study. The optimum processing condition was investigated with the aid of Dynamic Materials Model (DMM). Processing maps representing the efficiency of power dissipation for microstructural evolution and instability were constructed utilizing the results of hot compression test at temperatures ranging from $1000^{\circ}C$ to $1200^{\circ}C$ and strain rate ranging from $10^{-4}/s$ to $10^2/s$. The Artificial Neural Network (ANN) simulation was adopted to consider the deformation heating. With the help of processing map and microstructural analysis, the optimum processing condition was presented and the role of $\beta$ phase was also discussed in this study.

• 인지과학
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• 제27권2호
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• pp.159-219
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• 2016

수학적 사고력은 STEM(science, technology, engineering, mathematics) 분야에서의 학업적인 성취와 과학기술의 혁신에서 중요한 역할을 하고 있다. 본 연구에서는 학제 간 연구 분야인 수 인지(numerical cognition) 및 수학적 인지와 관련된 최근의 인지신경학적 연구 결과들을 종합하여 개관하였다. 첫째로 수학적 사고의 기초가 되는 뇌 기제의 위치와 정보처리 메커니즘을 확인하였다. 수학적 사고는 영역 특정적(domain specific)인 기능인 수 감각과 시공간적 능력뿐만 아니라 영역 일반적(domain general)인 기능인 언어, 장기기억, 작업 기억(working memory) 등을 기초로 하며 이를 토대로 추상화, 추론 등의 고차원적인 사고를 한다. 이 중에서 수 감각과 시공간적 능력은 두정엽(parietal lobe)을 기반으로 한다. 두 번째로는 수학적 사고 능력에서 관찰되는 개인 차이에 대하여 고찰하였다. 특히 수학 영재들의 신경학적인 특성을 신경망 효율성(neural efficiency)의 관점에서 고찰해 보았다. 그 결과 높은 지능이란 두뇌가 얼마나 많이 일하느냐가 아니라 얼마나 효율적으로 일하는가에 달렸다는 사실을 확인하였다. 수학 영재들의 또 다른 특성은 좌반구와 우반구 간의 연결과 반구 내에서 전두엽과 두정엽의 연결이 뛰어나다는 사실이다. 세 번째로는 학습과 훈련, 그리고 성장에 따른 변화 및 발전에 대한 분석이다. 개인이 성장하며, 수학 학습과 훈련을 하게 될 때 이에 따라 두뇌 피질에서도 변화가 반영되어 나타난다. 그 변화를 피질에서의 활성화 수준의 변화, 재분배, 구조적 변화라는 관점에서 해석하였다. 이 중에서 구조적 변화는 결국 신경 가소성(neural plasticity)을 의미한다. 마지막으로 수학적 창의성은 수학적 지식(개념)을 기초로 하여 수학적 개념들을 결합하는 단계가 요구되며, 그 후 결합된 개념들 중에서 심미적인 선택을 통해 수학적 발명(발견)으로 연결된다. 전문성이 높아질수록 결합과 선택이라는 두 단계가 더욱 중요해진다.

• 인지과학
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• 제7권3호
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• pp.37-60
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• 1996

생물심리학적 관점에서,학습은 우리가 환경으로부터 얻은 정보를 뇌내 신경세표의 회로망으로 전이시키는 과정이라 할 수 있다. 학습과 기억의 생물할적 실체를 찾고자 하는 연구들에는,기억 또는 정보의 저장이 신경계내 시냅스수정의 방식으로 이루어진다는 가정하에,특정 유형의 학습과 관련된 신경회로를 규명하고 신경가소성의 기초를 밝히려는 노력들이 있었다.이와는 달리 신경계내 뉴련들로 연결된 복잡한 신경망의 형태들이 특정정보를 표상한다고 보고,학습과 기억에 관련된 신경구조물들의 상호작용 기초를 분석 하려는 노력들이 있었다.본 연구는,전자의 입장에서,학습과 기억에 필수적인 엔그램의 부위를 찾기 위하여 사용된느 연구방법과 주요 실험동물 모델체계들의 특성,그리고 이러한 모델체계들을 사용한 연구결과들을 개관하였다.즉,본 논고는 실험동물 모델체계를 사용하여 학습과 기억에 필수적으로 관여하는 기억흔적회로를 찾아내고,그 신경회로내에서 학습과 기억에 결정적인 신경의 가소적 변화가 일어나는 부위를 규명하며,학습과정중에 신경수준에서 일어나는 시냅스의 수정에 대한 신경생리적,생화학적 기제를 밝히고자 한 연구들을 개관하였다.