Study on Chitooligosaccharide Application for in vitro Tissue Culture of Sweet Potato (Ipomea batatas L.)

( Nguyen Dinh Sy ),( Nguyen Anh Dzung ) 한국키틴키토산학회 2010 한국키틴키토산학회지 Vol.15 No.3

Study on tissue culture of sweet potato (Ipomea batatas L.) invitro and effects of chitooligosaccharide on growth of the plant were investigated. Effects of condition of sterilization and concentration of plant regulators such as: N(6) - benzyl adenine (BA), napthyl acetic acid (NAA) and kinetin (KN) on the rate of bud formation and the growth of root and shoot of the sweet potato were studied. The results showed that 20% sodium hypochlorite and 30 min for treatment was suitable for sterilization of the sweet potato tissue; the rate of contamination was only 3.3% and 76.7% bud formation; results also showed optimal concentration of BA and KN for the growth of the plantlets invitro was 2 mg/l and 1 mg/l. Effect of chitooligosaccharide supplement on the growth of sweet potato was also studied. Mugashige and Skoog (MS) medium containing BA and KN supplemented 0, 10, 20, 30 and 50 ppm chitooligosaccharide. Results showed that 20~30 ppm chitooligosaccharide was the best for growth of the root and shoot of sweet potato, number of the plantlet roots increased by 30%, the length of root also increased 40% and fresh and dry biomass of roots increased 100% compared to the control (no supplement of chitooligosaccharide). Chitooligosaccharide also increased in the number of leaf, height and fresh and dry biomass of the plantlet shoots.

An optimal design of interval type-2 fuzzy logic system with various experiments including magnetorheological fluid damper

Nguyen, Sy Dzung,Choi, Seung-Bok,Nguyen, Quoc Hung SAGE Publications 2014 Proceedings of the Institution of Mechanical Engin Vol.228 No.17

<P>This paper focuses on optimal design of an interval type-2 fuzzy logic system (IT-2FLS) to cope with uncertainty issue of training set and noisy data. Content of the solution is depicted based on the proposed algorithm to optimally design an IT-2FLS from a dataset, named OD-T2FLS. The major concept of the OD-T2FLS is a combination of a useful method of clustering data space to establish a type-1 fuzzy logic system (T-1FLS) and an appropriate way to transform the T-1FLS into an IT-2FLS as well as to optimally adjust parameters of the IT-2FLS. Firstly, an improved algorithm to establish an adaptive neuro-fuzzy inference system (ANFIS), named IM-ENFS, is presented. Based on the given dataset, clustering in the join input–output data space is realized to establish a cluster-data space. Using the IM-ENFS for this cluster-data space, together with the cluster-data space optimized, an ANFIS having a role as an optimal T1-FLS is also established. Parameters of the optimized T-1FLS are then used to build the initial structure of IT-2FLS. Subsequently, this IT-2FLS is optimally adjusted based on the well-known genetic algorithm. Finally, to demonstrate the effectiveness of the proposed OD-T2FLS, experiments including magnetorheological fluid damper are realized based on two different statuses of data sources, with and without noise.</P>

An Adaptive Input Data Space Parting Solution to the Synthesis of Neuro-Fuzzy Models

Sy Dzung Nguyen,Kieu Nhi Ngo 대한전기학회 2008 International Journal of Control, Automation, and Vol.6 No.6

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function Ψ and a penalty function τ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro-fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

Adaptive Fuzzy Sliding Control Enhanced by Compensation for Explicitly Unidentified Aspects

Sy Dzung Nguyen,서태일,최승복 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.6

Reality shows that 1) the effectiveness of compensators for uncertainty and disturbance (UAD) dependsdeeply on UAD’s time varying rate (TVR), and 2) controlling a system over a network introduces different constraintsand conditions, in which some of these are variable delays in control signal, packet losses, data quantization,safety, and security. This paper presents a new design of fuzzy sliding mode control (FSMC) enhanced by compensationfor UAD using a disturbance observer (DO), named DO-FSMC, for a class of nonlinear systems subjectedto UAD. First, in order to weaken partly the negative influence of TVR of UAD on the compensation effectiveness,we separate explicitly unidentified aspects into two groups, one related to the model error while the other comingfrom external disturbances, to distinctly consider. To stamp out the chattering status and reduce calculating cost,we propose an adaptive gain updated directly based on the sliding surface convergence status, to which two newcontrol laws, one for the FSMC and the other for the DO-FSMC, are given via Lyapunov stability analysis. In orderto evaluate the DO-FSMC, simulations as well as surveys based on a real semi-active suspension system using aMagnetorheological damper (MRD) with measured datasets are performed. The results obtained from the surveyscoincide with the theoretical analysis which show that the competence to stamp out vibration is the advantage ofthe proposed method compared with the other published methods.

SD-TCSs Control Deriving from Fractional-order Sliding Mode and Fuzzy-compensator

Sy Dzung Nguyen,Vien Quoc Nguyen 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.5

Uncertainty and disturbance (UAD) always exist and influence negatively on technical systems. Focusing on improving the effectiveness of smart dampers (SDs)-based semi-active train-car suspensions (SD-TCSs), we present the fuzzy-compensator-enhanced fractional-derivative (FD) order sliding control of a class of SD-TCSs subjected to UAD, in which the disturbance time-varying rate (DTVR) may be high but bounded. To reduce uncertainty related to the mathematical model error, we propose a fractional derivative (FD)-based sliding mode controller (FDSMC) for specifying the main control signal. Whereas, to estimate the compensation for external disturbance, first, we utilize the well-known DO to build an initial framework of the compensator. To avoid conflict between the update-laws of the DO and FDSMC, as well as to make the system dynamic response converge stably to the desired state even if the DTVR increasing but bounded, constraints along with a fuzzy-based adjusting mechanism are then discovered. Thus, we obtain an improved DO (imDO), update-laws of the imDO and FDSMC, and their combination model (imDO-FDSMC) of the proposed controller. The survey results reflect the positive capability of the method.

Bearing Fault Online Identification Based on ANFIS

Nang Toan Truong,Tae-Il Seo,Sy Dzung Nguyen 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.4

Effectiveness of online bearing status monitoring (OBSM) depends deeply on the online data processing ability and the sensitivity of data features used to recognize the mechanical-system dynamic response change. Focusing on these, we present a novel method of OBSM based on singular spectrum analysis (SSA) and adaptive neuro-fuzzy inference system (ANFIS) with the highlights as follows. A sensitive and stable multi-feature is discovered to better the ability to distill the valuable information in noisy and massive databases (NMDs) and process impulse-noise in them. The SSA-based high-frequency noise removal solution, the ANFIS’ interpolating and identifying capability, and the dual function of the proposed multi-feature are combined in a new algorithm named AfOBSM for building a system of OBSM through two phases, offline and online. The offline is to identify the mechanical-system in the presence of the typical kinds of bearing faults. The ANFIS is trained in this phase using a training dataset. Meanwhile, the online is to estimate online the real status of the bearing(s) based on the trained ANFIS and a monitoring dataset. Surveys from an experimental-system were performed. The obtained results showed the positive effects of the AfOBSM.