The objective of this study was to identify the maturity and position of tomatoes in greenhouse. Three parts have been included in this study: building the model of image capturing and object detection, position identification of mature fruits and prediction of the size of the mature fruits. For the first part, image capturing in different time and object detection will be conducted in the greenhouse for identification of mature fruits. For the second part, the relative 3D position of the mature fruits calculated by the binocular vision was compared with the actual measured position. For the third part, the size of the bounding box from the object detection was compared with the actual size of the mature fruit, and the correlation was calculated in order to pre-adjust the width of the gripper for plucking operation in the future. The precision and the recall of the mature fruits of this study are over 95%. The average error of the 3D position is 0.5 cm. The actual size of the fruits and the R-squared of the size of the bounding box are over 0.9.
Accurate and timely short‐term traffic flow forecasting is an essential component for intelligent traffic management systems. However, developing an effective and robust forecasting model is challenging due to the inherent randomness and nonlinear characteristic of the traffic flow. In this paper, a gravitational search algorithm optimized extreme learning machine, termed GSA‐ELM, is proposed to unlock the potential performance for short‐term traffic flow forecasting. The extreme learning machine avoids the tedious backpropagation by analytically determining the optimal solution. The gravitational search algorithm globally searches the optimal parameters for the extreme learning machine. The forecasting performance of the GSA‐ELM is evaluated on four benchmark datasets by comparing several state‐of‐the‐art models. The four benchmark datasets are real‐world traffic flow data from highways A1, A2, A4, A8 near the ring road of Amsterdam. The MAPEs of the GSA‐ELM model are 11.69%, 10.25%, 11.72% and 12.05% on four benchmark datasets, respectively, whereas the RMSEs of the GSA‐ELM model are 287.89, 203.04, 221.39 and 163.24, respectively. The experimental results demonstrate the superior performance of the proposed model.
Credible and accurate traffic flow forecasting is critical for deploying intelligent traffic management systems. Nevertheless, it remains challenging to develop a robust and efficient forecasting model due to the nonlinear characteristics and inherent stochastic traffic flow. Aiming at the nonlinear relationship in the traffic flow for different scenarios, we proposed a two-stage hybrid extreme learning model for short-term traffic flow forecasting. In the first stage, the particle swarm optimization algorithm is employed for determining the initial population distribution of the gravitational search algorithm to improve the efficiency of the global optimal value search. In the second stage, the results of the previous stage, rather than the network structure parameters randomly generated by the extreme learning machine, are used to train the hybrid forecasting model in a data-driven fashion. We evaluated the trained model on four real-world benchmark datasets from highways A1, A2, A4, and A8 connecting the Amsterdam ring road. The RMSEs of the proposed model are 288.03, 204.09, 220.52, and 163.92, respectively, and the MAPEs of the proposed model are 11.53%, 10.16%, 11.67%, and 12.02%, respectively. Experimental results demonstrate the superior performance of our proposed model.
<abstract><p>The neuropsychiatric systemic lupus erythematosus (NPSLE), a severe disease that can damage the heart, liver, kidney, and other vital organs, often involves the central nervous system and even leads to death. Magnetic resonance spectroscopy (MRS) is a brain functional imaging technology that can detect the concentration of metabolites in organs and tissues non-invasively. However, the performance of early diagnosis of NPSLE through conventional MRS analysis is still unsatisfactory. In this paper, we propose a novel method based on genetic algorithm (GA) and multi-agent reinforcement learning (MARL) to improve the performance of the NPSLE diagnosis model. Firstly, the proton magnetic resonance spectroscopy ($ ^{1} $H-MRS) data from 23 NPSLE patients and 16 age-matched healthy controls (HC) were standardized before training. Secondly, we adopt MARL by assigning an agent to each feature to select the optimal feature subset. Thirdly, the parameter of SVM is optimized by GA. Our experiment shows that the SVM classifier optimized by feature selection and parameter optimization achieves 94.9% accuracy, 91.3% sensitivity, 100% specificity and 0.87 cross-validation score, which is the best score compared with other state-of-the-art machine learning algorithms. Furthermore, our method is even better than other dimension reduction ones, such as SVM based on principal component analysis (PCA) and variational autoencoder (VAE). By analyzing the metabolites obtained by MRS, we believe that this method can provide a reliable classification result for doctors and can be effectively used for the early diagnosis of this disease.</p></abstract>
The traffic flow forecasting proposed for a series of problems, such as urban road congestion and unreasonable road planning, aims to build a new smart city, improve urban infrastructure, and alleviate road congestion. The problems encountered in traffic flow forecasting are also relatively difficult; the reason is that traffic flow forecasting is uncertain, dynamic, and nonlinear. It is challenging to build a reliable and safe model. Aiming at this complex and nonlinear traffic flow forecasting problem, this paper proposes a solution of an ABC-ELM model optimized by an artificial bee colony algorithm to solve the above problem. It uses the characteristics of the artificial bee colony algorithm to optimize the model so that the model can better and faster find the optimal solution in space. Moreover, it also uses the characteristics of the limit learning machine to quickly deal with this nonlinear specific problem. Experimental results on the Amsterdam road traffic flow dataset show that the traffic flow prediction model proposed in this paper has higher prediction accuracy and is more sensitive to data changes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.