SPATIAL TRAJECTORY PREDICTION OF VISUAL SERVOING

Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly required. A position-based visual servo parallel system is presented for tracking target with high speed. A local Frenet frame is assigned to the sampling point of spatial trajectory. Position estimation is formed by the differential features of intrinsic geometry, and orientation estimation is formed by homogenous transformation. The time spent for searching and processing can be greatly reduced by shifting the window according to features location prediction. The simulation results have demonstrated the ability of the system to track spatial moving object.

TCL: a taxi trajectory prediction model combining time and space features

Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM) was proposed, which is named trajectory-CNN-LSTM(TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution(T-CONV), multi-layer perceptron(MLP), and recurrent neural network(RNN) model, respectively.

Prediction of network public opinion features in urban planning based on geographical case-based reasoning

As a significant part of sustainable urban development proposed by the United Nations,urban planning is related to the ecological environment and transportation,especially affecting quality of life and social well-being. In the process of urban planning,the public express their opinions on open network platforms,resulting in large quantities of network public opinion data,which has important implications for evaluating urban planning. Based on the idea of geographical case-based reasoning (CBR),this paper constructs an expression framework for urban planning cases in the form of a ‘case problem–case attribute–case result’ triad. On this basis,this paper proposes a similarity calculation method of urban planning cases that integrates case attribute. Finally,based on an improvement to the traditional k-nearest neighbors method,the proposed public opinion feature calculation model considers similarity weights,which allow us to predict network public opinion features,including viewpoint-level emotional tendency and concerned groups of urban planning cases. The experimental result shows similarity weights (SWs) model could effectively improve the prediction accuracy,where the average MIC-F1 score reached more than 74%. Based on CBR,the proposed method can predict the development trends of public opinion in future planning cases,and provide scientific and reasonable decision support for urban planning.

Pattern recognition of seismogenic nodes using Kohonen selforganizing map: example in west and south west of Alborz region in Iran

Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5＋ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ （land zoning method） method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps （SOFM）. Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.

Advance in Significant Wave Height Prediction:A Comprehensive Survey

The significant wave height prediction holds critical value for marine energy development,coastal infrastructure planning,and ensuring safety in maritime operations.The precision of such predictions carries substantial the oretical and practical weight.This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction,serving as a methodical guidepost for academicians.The study introduces an all-encompassing predictive framework for significan wave height,which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs.The framework is structured into four principal layers i...feature selection,basic prediction,data decomposition,and parameter optimization.The ensuing sections meticulously dissect the methodologies within these strata,elucidating their core concepts,distinctive features merits,and constraints,and their applicability to significant wave height prediction.To wrap up,the study delves into fresh research inguiries and avenues pertinent to the discipline,thereby broadening the comprehension of significant wave height prediction.In essence,this scholarly article imparts critical knowledge beneficial to the realm of marine technology.

Analyzing Electricity Consumption via Data Mining

This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified successfully through five classi-fiers using the selected feature subset,and the best model can be determined further.The effects on analyzing electricity consump-tion of the other three attributes,including months,businesses,and meters,can be estimated using the chosen model.The data used for the project is provided by Beijing Power Supply Bureau.We use WEKA as the machine learning tool.The models we built are promising for electricity scheduling and power theft detection.