Parenchymal-sparing anatomical hepatectomy based on portal ramification of the right anterior section: A prospective multicenter experience with short-term outcomes

Truong Giang Nguyen,Thanh Khiem Nguyen,Ham Hoi Nguyen,Hong Son Trinh,Tuan Hiep Luong,Minh Trong Nguyen,Van Duy Le,Hai Dang Do,Kieu Hung Nguyen,Van Minh Do,Quang Huy Tran,Cuong Thinh Nguyen 한국간담췌외과학회 2024 Annals of hepato-biliary-pancreatic surgery Vol.28 No.1

Backgrounds/Aims: Parenchymal-sparing anatomical hepatectomy (Ps–AH) based on portal ramification of the right anterior section (RAS) is a new technique to avoid unnecessarily transecting too much liver parenchyma, especially in cases of major anatomical hepatectomy. Methods: We prospectively assessed 26 patients with primary hepatic malignancies having undergone major Ps–AH based on portal ramification of the RAS from August 2018 to August 2022 (48 months). The perioperative indications, clinical data, intra-operative index, pathological postoperative specimens, postoperative complications, and follow-up results were retrospectively evaluated. Results: Among the 26 patients analyzed, there was just one case that had intrahepatic cholangiocarcinoma The preoperative level of α– Fetoprotein was 25.2 ng/mL. All cases (100%) had Child–Pugh A liver function preoperatively. The ventral/dorsal RAS was preserved in 19 and 7 patients, respectively. The mean surgical margin was 6.2 mm. The mean surgical time was 228.5 minutes, while the mean blood loss was 255 mL. In pathology, 5 cases (19.2%) had microvascular invasion, and in the group of HCC patients, 92% of all cases had moderate or poor tumor differentiation. Six cases (23.1%) of postoperative complications were graded over III according to the Clavien–Dindo system, including in three patients resistant ascites or intra-abdominal abscess that required intervention. Conclusions: Parenchymal-sparing anatomical hepatectomy based on portal ramification of the RAS to achieve R0-resection was safe and effective, with favorable short-term outcomes. This technique can be used widely in clinical practice.

Prediction of Heating-Line Paths in Induction Heating Process using the Artificial Neural Network

Truong-Thinh Nguyen,양영수,김기성,현충민 한국정밀공학회 2011 International Journal of Precision Engineering and Vol. No.

This paper presents the development of a back propagation neural network model for the prediction of heating-line positions in induction heating process. The vertical displacements of plate have been considered as the input parameters and the selected induction heating lines as output parameters to develop the model. The training patterns of neural network are obtained using an analytical solution that predicts plate deformations in induction heating process. The feasibility test reveals that the developed method can be used to determine the heating-line positions in line heating process.

Adaptive neuro-fuzzy control of ionic polymer metal composite actuators

Thinh, Nguyen Truong,Yang, Young-Soo,Oh, Il-Kwon Institute of Physics Publishing 2009 Smart materials & structures Vol.18 No.6

<P>An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation. </P>

The Development of an Artificial Neural Network Model to Predict Heating-Line Positions for Plate Forming in Induction Heating Process

Nguyen, Truong-Thinh,Yang, Young-Soo,Bae, Kang-Yul Taylor Francis 2009 MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES Vol.37 No.2

<P> An artificial neural network model can help manufacturers determine the positions of induction heating lines and their heating parameters to form a desired shape of plate. The vertical displacements of a deformed plate are considered as the input parameters and the selected induction heating lines as output parameters to develop the model. The training patterns of neural network are obtained using an analytical solution that is derived from the plate theory to predict plate deformations in induction heating process. The plastic region in the analytical solution of the angular deformation of a steel plate is obtained from the thermal analysis of the plate with the heat input calculated from the electro-magnetic analysis of the induction heating process.</P>

Prediction of deformations of steel plate by artificial neural network in forming process with induction heating

Truong-Thinh Nguyen,양영수,배강열,최성남 대한기계학회 2009 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.23 No.4

To control a heat source easily in the forming process of steel plate with heating, the electro-magnetic induction process has been used as a substitute of the flame heating process. However, only few studies have analyzed the deformation of a workpiece in the induction heating process by using a mathematical model. This is mainly due to the difficulty of modeling the heat flux from the inductor traveling on the conductive plate during the induction process. In this study, the heat flux distribution over a steel plate during the induction process is first analyzed by a numerical method with the assumption that the process is in a quasi-stationary state around the inductor and also that the heat flux itself greatly depends on the temperature of the workpiece. With the heat flux, heat flow and thermo-mechanical analyses on the plate to obtain deformations during the heating process are then performed with a commercial FEM program for 34 combinations of heating parameters. An artificial neural network is proposed to build a simplified relationship between deformations and heating parameters that can be easily utilized to predict deformations of steel plate with a wide range of heating parameters in the heating process. After its architecture is optimized, the artificial neural network is trained with the deformations obtained from the FEM analyses as outputs and the related heating parameters as inputs. The predicted outputs from the neural network are compared with those of the experiments and the numerical results. They are in good agreement.

An Artificial Neural Network System for Predicting the Deformation of Steel Plate in Triangle Induction Heating Process

Truong Thinh Nguyen,김재웅,양영수 한국정밀공학회 2013 International Journal of Precision Engineering and Vol. No.

This paper presents the development of an artificial neural network (ANN) model to predict the positions and sizes of induction triangle heating based on laminated plate theory combined with Finite Element Method (FEM) solutions. The vertical displacements and transverse shrinkage of nodes on the plate are used as inputs to the network and the position of the heating triangles on the plate,heated surface, and size of the heating triangles are the outputs of the models. The developed feed-forward ANN with 242-100-72arrangement is capable of estimating all necessary conditions for the induction triangle heating process. The training patterns of the neural network are obtained using an analytical solution derived from the plate laminate theory to predict the plate deformations in the induction heating process. The developed neural network model is tested to demonstrate its feasibility for determining the heating positions on the surface of a flat steel plate in the triangle induction heating process for forming a desired shape.

Relaxor characteristics of PSLZT-BMT-based ferroelectric material ceramics

Nguyen Van Thinh,Le Dai Vuong,Do Viet On,Truong Van Chuong,Le Vu Truong Son,Trinh Ngoc Dat,Le Van Thanh Son,Vo Thanh Tung 한양대학교 청정에너지연구소 2023 Journal of Ceramic Processing Research Vol.24 No.3

(1-x)[(Pb0.94Sr0.05La0.01)(Zr0.54Ti0.46)0.9975O3]-x[Bi(Mn1/2Ti1/2)O3] (PSLZT-BMT) ferroelectric material ceramics with x in therange of 0-0.05 mol were successfully synthesized following the conventional solid-phase route. The materials were thoroughlyinvestigated to study their structural phase, microstructure, ferro-piezoelectric characteristics, and dielectric behavior. Theexperimental results show that the density of the samples decreased from 7.75 to 7.58 g/cm3, and the relative density decreasedin the range of 98.48%-96.28%. However, with increasing contents of BMT, the dielectric and ferroelectric properties ofPSLZT-BMT ceramics tend to decrease, specifically the maximum dielectric constant (εmax) of PSLZT-BMT decreased in therange of 23579-9991 and the residual polarization (Pr) decreases in the range of 22.54-7.87 μC/cm2 when the doping contentincreased in the range of 0.0-0.05 mol. The diffusivity values (γ) of the PSLZT-BMT material are 1.74, 1.78, 1.82, 1.84, 1.79,and 1.77 when the doping content x varies as 0.0, 0.01, 0.02, 0.03, 0.04, and 0.05 mol, respectively, which is characteristic ofrelaxor ferroelectric materials. Besides, the Tm, TB, TC-W, and C values depend on BMT concentration, and the trend decreases.

An Artificial Neural Network System for Heating-Path Prediction in Induction Heating Process for Concave Curved Surface Forming

Nguyen, Truong-Thinh,Yang, Young-Soo,Kim, Jae-Woong 한국정밀공학회 2014 International Journal of Precision Engineering and Vol.15 No.2

This paper presents the development of an artificial neural network (ANN) model for the prediction of heating-line or heating-triangle positions and their parameters in induction heating process to form a desired shape of plate, especially a concave curved surface. This model of ANN can help manufacturers to determine the positions of induction heating lines or triangles and their parameters to form a desired shape of plate. The vertical displacements and transverse shrinkage of nodes on the plate are used as inputs to the network and the selected induction heating positions and the parameters are the output of the models. The training data for the ANN model are obtained using an analytical solution based on laminated plate theory combined with Finite Element Method (FEM) solutions. A feed-forward ANN with 242-400-490 arrangement was developed for predicting the induction heating conditions. The developed neural network model is tested to show its feasibility to determine the heating positions on the surface of a flat steel plate in the induction heating process for forming a desired concave curved surface.

Using a Novel CNN Model for Predicting the Induction Heating Lines to Obtain a Desired Deformed Shape of Steel Plate

Nguyen Truong Thinh,Kang-Yul Bae,Young-Soo Yang 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.10

Induction heating is a fast and energy-efficient method of heating steel plates in the shipyard industry due to the increasing demand for steel plate deformation. In this study, deep learning models are proposed with abstract representations of heatmap images allowing efficient recognition of complex and nonlinear patterns. The model for determining the heating type for the plastic zone is a fairly simple and effective approach to perform the heating type determination with the choice of weight optimization of network. A novel Convolutional Neural Network (CNN) is trained to predict heating regions from heatmap images with different vertical displacements of the required steel plate. We have proposed a Region based CNN (R-CNN) model using a sparse group to classify heating lines based on heatmap images of desired complex shape as input. The rotated region of interest pool layers and the rotated bounding box regression are also suggested to recognize inclined slender shapes of heating lines. The R-CNN is trained to detect heating regions bounded by labelled boxes within the confines of the steel plate. A frame-based event predictor for deformation areas is trained independently to analyse each individual box in the regions proposed. Then a post-processing step is based on the output of the R-CNN to determine the actual heating zones. By evaluating through the data sets, we have experimentally found that the proposed algorithm has performed well and achieved better results than other conventional methods.

Robot Supporting for Deaf and Less Hearing People

Nguyen Truong Thinh,Tuong Phuoc Tho,Tran Thi Thuy Nga 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10

This paper discusses the development of a service robot for translating spoken language text into signed languages and vice versa. The motivation for our study is the improvement of accessibility to public information announcements for deaf and less hearing people. The robot can translate Vietnamese sign language into speech and recognize Vietnamese/English speech to suitable gesture/sign language. The paper describes the use of service robot in a sign language machine translation system. Several sign language visualization methods were evaluated on the robot. In order to perform this study a machine translation service robot that uses display screen on robot as service-delivery device was developed as well as a 3D avatar. It was concluded that service robot are suitable service-delivery platforms for sign language machine translation systems.