Papers by Author: Hong Men

Abstract: The transparent conductive film made of Indium Tin Oxide (ITO) is widely used in many fields because of its excellent electrical conductivity and optical properties. At present, the information in biological detection field has not satisfied the people's needs, which are obtained based on some kind of sensors with traditional rigid substrate preparation. The contact surface for creatures perceive and stimulation are flexible surfaces. The kinds of electrode with ITO conductive film as the base for electrode preparation have a great application prospect in biological information collection.

Abstract: The disadvantages of infrared image are low resolution, bad stereoscopic sense, fuzzy image and low SNR, according to the application of infrared image in fault diagnosis of electronic power equipment, in this paper ,we make a comparative research on pre-processing technique of image de-noising and enhancement, and propose an infrared image enhancement algorithm based on platform histogram equalization combined with enhanced high-pass filtering, the algorithm can effectively improve the contrast by comparison, it is obvious to the noise effect, highlighting the objectives and details, and makes a good foundation for the subsequent target identification and fault diagnosis.

Abstract: This paper proposes a reasonable methodology applied in classification and quantification techniques based on the voltammetric electronic tongue. We designed voltammetric electronic tongue oil sample pretreatment system with petroleum ether organic solvent. Through three-electrode system and cyclic voltammetry method processing blending soybean oil sample to get waveform output. Extracting crest methods as its feature extraction Pattern recognition use kernel principal component analysis and factor analysis blending on a different level of soybean oil, Partial Least Square (PLS) techniques was applied for data management and prediction models building, the prediction models are the blending ratio, the results show that voltammetric electron tongue can distinguish the quality of soybean oil.

Abstract: This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.

Abstract: Electronic Tongue is a kind of intelligent equipment which is used to distinguish tastes. An electronic tongue composed of a sensor array of ion-selective electrodes has been developed and used for the qualitative analysis of five different brands of mineral water. The acquired original data has been optimized by principal component analysis (PCA) and then the probabilistic neural network (PNN) model is designed to process the data. The application results show that the performance of the proposed method has surpasses the traditional BP neural network algorithm, the speed of recognition is fast and the accuracy rate can reach 100%, which gives the electronic tongue system good practicability and feasibility.

Abstract: Five kinds of vinegars were measured by a gas sensor array composed of six TGS gas sensors. The sensor array should be optimized by the minimal Wilks statistic value, then, the four best sensor array used to detect the type of vinegars were formed, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were applied to analyze the data of primary and optimized sensor array. The results indicated that optimization sensor array could be more adaptable to recognize the five kinds of vinegars. Thereby the given optimization method is effective.

Abstract: An electronic tongue which composed of selective electrodes is applied to mineral water recognition (optimization of sensor array). The task of the system is to distinguish among five brands of mineral water. For this purpose, various pattern recognition (PARC) procedures are employed: principle components analysis (PCA), independent component analysis, linked-like adaptive genetic algorithm (LAGA), et al. LAGA networks are proved to exhibit the best performance both in array optimization and mineral water recognition. Their further advantages, such as fast training and robustness, make them the suggested pattern classifiers for sensor array data.

Abstract: In this paper, we used three working electrodes of platinum, gold, glassy carbon which constituted the microbial electronic tongue to identify microorganisms. The main purpose of this article is to classify sulfate-reducing bacteria and iron bacteria with the reference of broth culture. The accuracy of classification was compared based on directly processing (raw data processed by Back- Propagation Neural Network (BPNN), raw data processed by Partial LeastSquares (PLS) and raw data processed by Principal Components Analysis (PCA)) and two-stage processing (Principal Components Analysis (PCA) outputs processed by BPNN,PLS(score) outputs processed by BPNN and PLS(ypred) outputs processed by BPNN). The result showed that the performance of both PLS (ypred) and BPNN were optimum, which proved that the combine of linear and nonlinear model will get the best result for classification of bacterial.