Optimal design of reinforced concrete plane frames using artificial neural networks

Chin-Sheng Kao,I-Cheng Yeh 사단법인 한국계산역학회 2014 Computers and Concrete, An International Journal Vol.14 No.4

To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.

Optimal design of plane frame structures using artificial neural networks and ratio variables

Chin-Sheng Kao,I-Cheng Yeh 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.52 No.4

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

Optimal design of plane frame structures using artificial neural networks and ratio variables

Kao, Chin-Sheng,Yeh, I-Cheng Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.52 No.4

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

Fusion of hydrologic and geophysical tomographic surveys

Tian-Chyi Jim Yeh,Cheng-Haw Lee,Kuo-Chin Hsu,Jet-Chau Wen 한국지질과학협의회 2008 Geosciences Journal Vol.12 No.2

In this paper, we argue the need for high-resolution characterization of the subsurface and discuss difficulties of traditional characterization approaches to meet this need. Necessary and sufficient conditions are then presented for well-posedness of groundwater inverse problems associated with identifying spatially distributed parameters. Non-uniqueness and large uncertainty in model calibration are subsequently attributed to difficulties in collecting information to meet these conditions. Using an example, we show that a tomographic survey can make problems of identification of spatially distributed parameters better posed. We subsequently present some recent advances in hydrologic/geophysical characterization of the subsurface using information fusion based on tomographic survey concepts. This paper includes hydraulic and electrical resistivity tomographic surveys as well as fusion of hydraulic and resistivity tomography and fusion of hydraulic and tracer tomography

Combined Assessment of Serum Alpha-Synuclein and Rab35 is a Better Biomarker for Parkinson’s Disease

Hung-Li Wang,Chin-Song Lu,Tu-Hsueh Yeh,Yu-Ming Shen,Yi-Hsin Weng,Ying-Zu Huang,Rou-Shayn Chen,Yu-Chuan Liu,Yi-Chuan Cheng,Hsiu-Chen Chang,Ying-Ling Chen,Yu-Jie Chen,Yan-Wei Lin,Chia Chen Hsu,Huang-Li 대한신경과학회 2019 Journal of Clinical Neurology Vol.15 No.4

Background and Purpose It is essential to develop a reliable predictive serum biomarker for Parkinson’s disease (PD). Te accumulation of alpha-synuclein (αSyn) and up-regulated expression of Rab35 participate in the etiology of PD. Te purpose of this investigation was to determine whether the combined assessment of serum αSyn and Rab35 is a useful predictive biomarker for PD. Methods Serum levels of αSyn or Rab35 were determined in serum samples from 59 sporadic PD patients, 19 progressive supranuclear palsy (PSP) patients, 20 multiple system atrophy (MSA) patients, and 60 normal controls (NC). Receiver operating characteristics (ROC) curves were calculated to determine the diagnostic accuracy of αSyn or/and Rab35 in discriminating PD patients from NC or atypical parkinsonian patients. Results The levels of αSyn and Rab35 were increased in PD patients. The serum level of Rab35 was positively correlated with that of αSyn in PD patients. Compared to analyzing αSyn or Rab35 alone, the combined analysis of αSyn and Rab35 produced a larger area under the ROC curve and performed better in discriminating PD patients from NC, MSA patients, or PSP patients. When age was dichotomized at 55, 60, 65, or 70 years, the combined assessment of αSyn and Rab35 for classifying PD was better in the group below the cutof age than in the group above the cutof age. Conclusions Combined assessment of serum αSyn and Rab35 is a better biomarker for discriminating PD patients from NC or atypical parkinsonian patients, and is a useful predictive biomarker for younger sporadic PD patients.

On-line Error-matching Measurement and Compensation Method for a Precision Machining Production Line

Shih-Ming Wang,Chun-Yi Lee,Hariyanto Gunawan,Chin-Cheng Yeh 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.2

When producing a matching part for good mating with its counterpart (assembly part), good matching accuracy between the matching part and assembly part is highly required. The matching errors are influenced by not only the errors of the matching part but also the errors of the assembly part. Because the errors of an assembly part could be different from others, it is very difficult to have very good mating accuracy, if the manufacturing of a matching part does not take the actual errors of the assembly into account. Furthermore, it will need additional off -line assembly mating tests to find a good mating pair. This study proposes an error-matching compensation method to solve the not-good-mating problem for manufacturing assembly modules with good matching accuracy. In this method, the actual errors of an assembly part are directly in-line measured and used to compensate the machining process of a matching part so that a dedicated part with the good mating condition can be on-line made for the error-measured assembly part. In addition to having better matching accuracy, this method also provides advantages of high production efficiency and cost saving, because it will not need the off -line mating test to find good mating pairs. This method also includes a function of in-line auto-generation of error compensation NC programs to support continuous production. In this study, error analysis of two mating types (planar mating and triangle mating) were explored and discussed. The Human–Machine Interface was built with the use of C# language. Finally, machining experiments with in-line measurements were conducted to verify the proposed method. The experimental results have shown the feasibility and effectiveness of the proposed method.