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- 摘要
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- 实验方案
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[IEEE 2018 IEEE Congress on Evolutionary Computation (CEC) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 IEEE Congress on Evolutionary Computation (CEC) - Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models
摘要： Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.
关键词： solar forecasting,short-term forecasting,multigene genetic programming,intra-day forecasting
更新于2025-09-23 15:22:29
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Design of estimators for restoration of images degraded by haze using genetic programming
摘要： Restoring hazy images is challenging since it must account for several physical factors that are related to the image formation process. Existing analytical methods can only provide partial solutions because they rely on assumptions that may not be valid in practice. This research presents an e?ective method for restoring hazy images based on genetic programming. Using basic mathematical operators several computer programs that estimate the medium transmission function of hazy scenes are automatically evolved. Afterwards, image restoration is performed using the estimated transmission function in a physics-based restoration model. The proposed estimators are optimized with respect to the mean-absolute-error. Thus, the e?ects of haze are e?ectively removed while minimizing overprocessing artifacts. The performance of the evolved GP estimators given in terms of objective metrics and a subjective visual criterion, is evaluated on synthetic and real-life hazy images. Comparisons are carried out with state-of-the-art methods, showing that the evolved estimators can outperform these methods without incurring a loss in e?ciency, and in most scenarios achieving improved performance that is statistically signi?cant.
关键词： Image restoration,Haze removal,Image processing,Genetic programming
更新于2025-09-23 15:21:01
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Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques
摘要： Micro-drilling using lasers finds widespread industrial applications in aerospace, automobile, and bio-medical sectors for obtaining holes of precise geometric quality with crack-free surfaces. In order to achieve holes of desired quality on hard-to-machine materials in an economical manner, computational intelligence approaches are being used for accurate prediction of performance measures in drilling process. In the present study, pulsed millisecond Nd:YAG laser is used for micro drilling of titanium alloy and stainless steel under identical machining conditions by varying the process parameters such as current, pulse width, pulse frequency, and gas pressure at different levels. Artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to predict the performance measures, e.g. circularity at entry and exit, heat affected zone, spatter area and taper. Seventy percent of the experimental data constitutes the training set whereas remaining thirty percent data is used as testing set. The results indicate that root mean square error (RMSE) for testing data set lies in the range of 8.17–24.17% and 4.04–18.34% for ANFIS model MGGP model, respectively, when drilling is carried out on titanium alloy work piece. Similarly, RMSE for testing data set lies in the range of 13.08–20.45% and 6.35–10.74% for ANFIS and MGGP model, respectively, for stainless steel work piece. Comparative analysis of both ANFIS and MGGP models suggests that MGGP predicts the performance measures in a superior manner in laser drilling operation and can be potentially applied for accurate prediction of machining output.
关键词： Laser drilling,ANFIS,Genetic programming,Stainless steel,Artificial intelligence,Ti6Al4V,Surface cracks
更新于2025-09-12 10:27:22
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[Lecture Notes in Computer Science] Neural Information Processing Volume 11302 (25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part II) || Multi-Dimensional Optical Flow Embedded Genetic Programming for Anomaly Detection in Crowded Scenes
摘要： Anomaly detection is an important issue in the ?elds of video behavior analysis and computer vision. Genetic Programming (GP) performs well in those applications. Histogram of Oriented Optical Flow (HOOF), which is based on Optical Flow (OF), is a signi?cant method to extract features of frames in videos and has been widely used in computer vision. However, OF may produce a large number of features that will lead to high computational cost and poor performance. Moreover, HOOF accumulates optical ?ow values for moving objects in a region, so motion information of these objects may not be well represented. Especially in crowding scenes, common anomaly detection methods usually have a poor performance. Aiming to address the above issues, we propose a new feature called Multi-Dimensional Optical Flow (MDOF) and a new method GP-MDOF which embeds HOOF features and optimizes the structure of GP, and makes better use of the change information between consecutive frames. In this paper, we apply GP-MDOF to classify frames into abnormality or not. Experimental evaluations are conducted on the public dataset UMN, UCSD Ped1 and Ped2. Our experimental results indicate that the proposed feature extraction method MDOF and the new method GP-MDOF can outperform the popular techniques such as OF and Social Force Model in anomaly detection in crowded scenes.
关键词： Genetic Programming,Optical Flow,Anomaly detection
更新于2025-09-04 15:30:14