ARCHAEOLOGICAL HERITAGE OF PREHISTORIC CAVES IN BA THUOC DISTRICT, THANH HOA PROVINCE: PRESERVATION AND PROMOTION

Ba Thuoc is a mountainous district in western Thanh Hoa Province, where more than 20 archaeological sites have been discovered. One of the outstanding features here is the presence of Middle Pleistocene fauna in Lang Trang cave, including fossils of Gigantopithecus blacki, a giant ape. The appearance of early modern human fossils in the Late Pleistocene proves that Vietnam was the site of the earliest modern human evolution in Southeast Asia. In Ba Thuoc district, human communities were in continuous residence from 30,000 to 7,000 years BP and developed the Dieu stone craft industry. This industry merged with the Hoabinhian industry in the development process, creating a cultural nuance for the land of Ba Thuoc. By 7,000 years BP, the boundary between the two industries was virtually nonexistent. The prehistoric inhabitants here contributed to the formation of Middle Neolithic cultures in North Central Vietnam. In addition, the caves in Ba Thuoc were also places for the mountain-dwelling inhabitants of the Dong Son culture to visit and bury their dead in the centuries before and after the beginning of the common era. In this study, we systematized the documents on monuments and artifacts, and evaluated the outstanding cultural heritage of the prehistoric caves in Ba Thuoc district, which need to be preserved and promoted in the present day

Neuro-sliding mode multivariable control of a powered wheelchair.

This paper proposes a neuro-sliding mode multivariable control approach for the control of a powered wheelchair system. In the first stage, a systematic decoupling technique is applied to the wheelchair system in order to reduce the multivariable control problem into two independent scalar control problems. Then two Neuro-Sliding Mode Controllers (NSMCs) are designed for these independent subsystems to guarantee system robustness under model uncertainties and unknown external disturbances. Both off-line and on-line trainings are involved in the second stage. Real-time experimental results confirm that robust performance for this multivariable wheelchair control system under model uncertainties and unknown external disturbances can indeed be achieved

Optimal path-following control of a smart powered wheelchair.

This paper proposes an optimal path-following control approach for a smart powered wheelchair. Lyapunov's second method is employed to find a stable position tracking control rule. To guarantee robust performance of this wheelchair system even under model uncertainties, an advanced robust tracking is utilised based on the combination of a systematic decoupling technique and a neural network design. A calibration procedure is adopted for the wheelchair system to improve positioning accuracy. After the calibration, the accuracy is improved significantly. Two real-time experimental results obtained from square tracking and door passing tasks confirm the performance of proposed approach

Neural Network Based Diagonal Decoupling Control of Powered Wheelchair Systems

This paper proposes an advanced diagonal decou- pling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonaliza- tion technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plants Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects

Neural network based diagonal decoupling control of powered wheelchair systems

This paper proposes an advanced diagonal decoupling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonalization technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plant's Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects. © 2013 IEEE

HOABINHIAN IN VIETNAM AND ECONOMIC ACTIVITIES FROM 20,000 TO 7,000 YEARS BP

This article studies the basic economic activities of Hoa Binh cultural inhabitants in the period of 20,000 to 7,000 years BP, including tool making, hunting, gathering, and primitive agriculture. The research results have identified a number of key economic characteristics of Hoa Binh cultural residents and evaluated the effectiveness of human methods of finding and gathering food under the fluctuations of the natural environment during the late Pleistocene to early Holocene in northern Vietnam. Little evidence directly related to cultivation and animal husbandry has been found at Hoa Binh cultural sites, so the issue of Hoa Binh agriculture is still a working hypothesis that needs to be studied further

Development of a Bayesian recursive algorithm to find free-spaces for an intelligent wheelchair

This paper introduces a new shared control strategy for an intelligent wheelchair using a Bayesian recursive algorithm. Using the local environment information gathered by a laser range finder sensor and commands acquired through a user interface, a Bayesian recursive algorithm has been developed to find the most appropriate free-space, which corresponds to the highest posterior probability value. Then, an autonomous navigation algorithm will assist to manoeuvre the wheelchair in the chosen free-space. Experiment results demonstrate that the new method provides excellent performance with great flexibility and fast response. © 2011 IEEE

Effects of hyperglycemia on variability of RR, QT and corrected QT intervals in Type 1 diabetic patients

In this study, we evaluated the effects of hyperglycemia on the variability of RR (HRV), QT interval variability (QTV) and corrected QT interval variability (QTcV) during hyperglycemic and non-hyperglycemic conditions in six Type 1 diabetic patients at nights. The aim of this study was to investigate the association of high blood glucose levels with autonomic modulation of heart rate and variation in ventricular repolarization. Blood glucose level (BGL) threshold for defining hyperglycemia state was set at 8.33 mmol/l. Variability of RR, QT and corrected QT intervals during hyperglycemic and non-hyperglycemic were quantified using time and frequency domain measures. Hypomon® device was used to monitor ECG signals and acquire RR and QT intervals in Type 1 diabetic patients overnight. The results indicated that time and frequency domain HRV variables were significantly decreased under hyperglycemic condition and inversely correlated with BGL. QTV parameters also reduced when BGL increased and time domain measures of QTV were inversely associated with BGL. Variability in QTc interval was much less than in the QT interval and demonstrated a lower SDNN and LF power. We concluded that certain components of HRV, time-domain measures of QTV and QTc but not QTcV are strongly correlated to high blood glucose levels and can be good markers to identify hyperglycemic events in T1DM. © 2013 IEEE

Robust multivariable strategy and its application to a powered wheelchair

The paper proposes a systematic robust multivariable control strategy based on combination of systematic triangularization technique and robust control strategies. Two design stages are required. In the first design stage, multivariable control problem is reduced into a series of scalar control problems via triangularization technique. For each specific scalar system, two advanced control strategies are proposed and implemented in the second design stage. The first one is based on Model Predictive Control, which is an iterative, finite horizon optimization procedure. The second control strategy is known as Neuro-Sliding Mode Control, which integrates Sliding Mode Control (SMC) and Neural Network Design to achieve both chattering-free and system robustness. Real-time implementation on a powered wheelchair system confirms that robustness and desired performance of a multivariable system under model uncertainties and unknown external disturbances can indeed be achieved by the combination of triangularization technique and Neuro-Sliding Mode Control. ©2009 IEEE