Papers by Keyword: Recognition

Abstract: According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Abstract: Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

Abstract: To functionalize the smart nanocomposites, the nanocomposites of CNTs/polyaniline with pending calix [8] arene were prepared. A series of characterizations were performed by SEM (scanning electron microscopy), the Fourier-Transform Infrared (FTIR) spectra, The UV-Vis (Ultra-violet visible spectroscopy), et al. The photoconductivity response to visible light and 808 nm laser with low-power were studied based on interdigital electrodes of Au on flexible PET (polyethylene terephthalate) film substrate with casting method. The results showed that the nanocomposites of CNTs/polyaniline with pending calix [8] arene exhibited good photoresponse to visible light and weak 808 nm laser, but its recoverability was very slow, it needed several hours, and the film-forming property of nanocomposite was not very good. This may be attributed to the results of increased hydrophobicity of nanocomposite because of introducing the calix [8] arene ring. In order to increase the film-forming technology of nanocomposites, the grapheme oxide were obtained with unzipping method of carbon nanotube (CNTs) for enhancing the hydrophilcity of carbon nanomaterials. The nanocomposites of grapheme oxide/polyaniline with pending calix [8] arene were obtained with similar methods, which showed improved film-forming property. The photoresponses to weak visible light and 808 nm laser also showed the similar results. It may develop the nanocomposite with external stimuli response, and have good potential applications in sensors, organic photocatalyst, et al.

Abstract: In a manufacturing process it may be necessary to distinguish the parts of various types produced by different machine-tools and placed on a conveyor. Using an artificial vision system represents a possible solution. Images of the parts can be periodically taken using a video camera. It is considered that all parts are positioned with one of the faces which defines its type on the upper side. Thus, parts’ recognition can be solved by recognizing the shapes from the images. In addition, information about position and orientation of parts can be determined using the captured images. All these information are listed in a text file which can be used by a decision algorithm. This algorithm can choose which parts are useful to assemble different objects. An industrial robot can be commanded using a program written in RobotStudio environment’s programming language (RAPID) to pick the needed parts and place them in a storage area.This paper describes a recognition and measurement of position and orientation method of the different parts produced. In addition, details about implementing this method in MATLAB environment using Image Processing Toolbox and geometrical relations are provided gradually.

Abstract: This paper describes the algorithm for classifying flat projections on the two images to three-dimensional objects. The formulas, images of projection of test three-dimensional objects, research results and conclusions are presented.

Abstract: During the resistance spot welding quality control process, the traditional quality control methods can’t compensate all the affecting factors effectively. By welding 316LVM stainless steel wire to the surface of sheet of the same material, three kinds of process parameters, including electrode voltage, dynamic resistance and electrode displacement which affected by many affecting factors during the spot welding process were measured and analyzed. The results show that, by comparing with the process parameters during the normal situation, the dynamic change of the these process parameters during different stage appears distinguishable characteristics, these characteristics would supply a research basis for recognizing whether the affecting factors exist.

Abstract: This paper proposes a new solution to recognize and segment adhering bars. A support vector machine (SVM) is constructed according to the feature vectors of training samples to recognize the adhesions type of bars. The geometric feature values and moment feature values based on Blob regions in the image are extracted, which are the input feature vectors of support vector machine. The trained classifier is used for identifying the adhesions type of bars in the image. Finally, classification and recognition are realized by support vector machine. The experimental result shows that the recognition accuracy based RBF kernel achieves 100%. The method is feasible and effective for the recognition and segmentation of the adhering bars.

Abstract: Shaizhudong spring is the largest karst spring in the central of north of the Weihe River, Shaanxi Province, China. A lot of studies have been done for investigating the recharge source of it. The most common understanding was that the leakage of Jinghe was the main recharge source for Shaizhudong Spring which is the concentrated discharge point of hidden karst system in the catchment area of Shaizhudong Spring. However, with the method of hydrogen and oxygen isotopes, and combining the condition of hydro-geochemistry and karst hydro-geology, we have different opinions on the recharge source of Shaizhudong Spring. This study shows the result that the recharge of Shaizhudong Spring is given priority by karst groundwater which is outside the southwest of the Shaizhudong Spring area.

Abstract: First disease spot color and texture features were extracted from barley field images in Gansu, and the feature vectors were used as input vector to establish barley diseases classifier model. Then the neural network was applied to rain classified model with collected images as training set. Finally, two groups of random selected images as test sets were used to perform classified verification experiments. The experimental results show that the overall accuracy of barley dis-eases recognition model is above 86.7%. Therefore, Barley disease image recognition based on neural net-work provides a new technology for the classified treatment of barley diseases.

Abstract: An efficient facial representation is a crucial step for successful and effective performance of cognitive tasks such as object recognition, fixation, facial recognition system, etc. This paper demonstrates the use of Gabor wavelets transform for efficient facial representation and recognition. Facial recognition is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Gabor wavelet transform is used for facial features vector construction due to its powerful representation of the behavior of receptive fields in human visual system (HVS). The method is based on selecting peaks (high-energized points) of the Gabor wavelet responses as feature points. This paper work introduces the use of Gabor wavelets transform for efficient facial representation and recognition. Compare to predefined graph nodes of elastic graph matching, the approach used in this paper has better representative capability for Gabor wavelets transform. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. Based on the experiment, the proposed method performs better compared to the graph matching and eigenface based methods. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. The proposed system is validated using four different face databases of ORL, FERRET, Purdue and Stirling database.