Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

Abstract

In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.     

The Influence of Heat on the Sensitivity of a Photographic Film to Low Intensity Light

A fast radiation monitoring film, the lIford PM I, was exposed at 0 per cent relative humidity to low intensity u-v light from a plastic scintillator, the exposure temperoture being varied between +10 and +75°C. Films were also heated at 60°C. and 0 per cent RH and subsequently exposed to the scintillator light at 30O°C, the results indicating the presence of latent sub-image in the heated film at the end of the heating. From the temperature dependence of the net film density obtained with constant heating time the activation energy of 126 eV was derived, this corresponding to the formation energy of a Frenkel defect in silver bromide. The results suggest that the effect of increased temperature in increasing light absorption is a minor one in this instance.     

A feature extraction method for galvanized steel sheet powdering rates classification

Abstract

This paper describes a feature extraction method for classifying galvanized steel sheet powdering rates. By combining machine vision with statistics, and using the standard deviation's difference between powdered and normal regions, the range of powdered areas is recognized. The method exhibits a number of interesting features: it uses mathematical statistics for image feature analysis, and develops an effective method for analyzing particle size which cannot be measured by manual detection. The experiment result shows that the correction rate of this method to acquire galvanized steel sheet powdered regions is up to 99%, which satisfies the requirements of the application.     

Rheology Feature of Simple Metal Melt

The rheology feature of Sb, Bi melt and alloys was studied using coaxial cylinder high-temperature viscometer. The results showed that the curve of torsion-rotational speed for Sb melt presents a linear relation in all measured temperature ranges, whereas for the Bi melt, the curve presents obvious non-Newtonian feature within the low temperature range and at relative high shear stress. The rheology feature of Sb80Bi20 and Sb20Bi80 alloy melts was well correlated with that of Sb and Bi, respectively. It is considered that the rheology behavior of Sb melt plays a crucial role in Sb80Bi20 alloy and that of Bi melt plays a crucial role in Sb20Bi80 alloy.     

Emotional Feature Extraction Method Based on the Concentration of Phoneme Influence for Human–Robot Interaction

Depending on the emotion of speech, the meaning of the speech or the intention of the speaker differs. Therefore, speech emotion recognition, as well as automatic speech recognition is necessary to communicate precisely between humans and robots for human–robot interaction. In this paper, a novel feature extraction method is proposed for speech emotion recognition using separation of phoneme class. In feature extraction, the signal variation caused by different sentences usually overrides the emotion variation and it lowers the performance of emotion recognition. However, as the proposed method extracts features from speech in parts that correspond to limited ranges of the center of gravity of the spectrum (CoG) and formant frequencies, the effects of phoneme variation on features are reduced. Corresponding to the range of CoG, the obstruent sounds are discriminated from sonorant sounds. Moreover, the sonorant sounds are categorized into four classes by the resonance characteristics revealed by formant frequency. The result shows that the proposed method using 30 different speakers' corpora improves emotion recognition accuracy compared with other methods by 99% significance level. Furthermore, the proposed method was applied to extract several features including prosodic and phonetic features, and was implemented on 'Mung' robots as an emotion recognizer of users.     

General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data

This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments.     

The Absorption Cross Section for Light of the Silver Grains in Photographic Deposits

Estimations of projection area of silver grains in photographic deposits were made on photomicrographs as well as counts of grains per unit area. Density of the deposit was calculated from these data employing known formulae. These were found smaller by a factor of 2-3 when compared with densities measured in diffuse light. The absorption cross section of grains for light considerably greater than their projective area is concluded. The results are in agreement with those obtained by other authors, which were, however, interpreted in a different way.     

人体创口工具残留物的扫描电镜能谱检验法

本文通过设置合适的参数,使用扫描电镜X射线能谱GSR(即FEATURE)软件检验人体附着微小可疑残留物,借以形成人体致伤工具微量残留物检验方法,为人体损伤致伤物推断研究建立重要的技术方法.结果表明使用GSR(FEATURE)软件可定量检测到人体创口内微量工具残留物；通过对人体创伤工具残留物颗粒成分进行分析,可以全面解决人体残留致伤工具成份分析问题,为人体损伤致伤物推断研究建立关键理论技术基础;GSR(FEATURE)软件对于建立法医微量物证研究方法具有重要意义.     

Modelling weld bead geometry using neural networks for GTAW of austenitic stainless steel

Abstract

The authors analyse the importance of different weld control parameters on the weld pool geometry of gas tungsten arc welding using an online feature selection technique that suggests weld voltage and vertex–angle pair as more important than the weld voltage and torch speed pair. Using the selected features multi layer perception and radial basis function networks are developed for prediction of bead width, penetration depth, and bead area. With cross-validation the authors have extensively studied the performance of composite models (one model for all outputs) and individual models (one model for each output). The individual models are found to work better than composite models. Usually, radial basis function networks are found to work better than the multi layer perception networks. To assess the influence of weld control parameters the authors have studied the performance of both networks using different combination of inputs. Overall, the performance of the proposed models is found to be quite satisfactory.     

Integrated On-Line Localization,Mapping and Coverage Algorithm of Unknown Environments for Robotic Vacuum Cleaners Based on Minimal Sensing

This paper presents a new complete coverage algorithm of a robotic vacuum cleaner (RVC) with a low-cost sensor in an unknown environment. To achieve complete coverage, the RVC must have navigation systems for precise position estimation with localization and a prior map or a map using information that has been continuously collected from the environment. To do this, two-dimensional laser range finders and vision sensors are becoming increasingly popular in mobile robotics, and various methods using sensors like these have been introduced by many researchers. However, it is difficult to apply the methods to sensors used in most RVCs due to their constraints. In this paper, we present a new method applied to most RVCs. For developing the method, we considered the two main problems of maintaining low computational load, and coping with low-cost sensor systems with limited range, detection uncertainty and measurement error. To solve the problems, we propose an assumption that major structures of an indoor environment are rectilinear, and can be represented by sets of parallel and perpendicular lines. Then we derive an algorithm that uses this assumption to map the environment, localize the robot and plan the coverage path with a new cellular decomposition approach. Simulation and experiments verify that the proposed method guarantees complete coverage.