Study to Cost of Air Spares Support Based on IPSO

Air spares support is general term of using and repairing of aircrafts which is the material foundation of aero technical support, its effectiveness influences operational effectiveness and equipments of aircrafts directly. Based on particle swarm optimization algorithm, a new model is proposed to optimize the distribution of the cost of air spares, it take the funds as resource and the improvement of performance efficiency as objective and deduces the expressions to get the best distribution plan. The results of experiments indicate that this model can make full use of the limited funds and obtain the highest efficiency of air spares support.

A Perspective on Rhythmic Gymnastics Performance Analysis Powered by Intelligent Fabric

Performance analysis is an important tool for gymnasts and coaches to assess the techniques,strengths,and weaknesses of rhythmic gymnasts during training.To have an accurate insight about the motion and postures can help the optimization of their performance and offer personalized suggestions.However,there are three primary limitations of traditional perfor-mance analysis systems applied in rhythmic gymnastics:(1)Inability to quantify anthropometric data in an imperceptible way,(2)labor-intensive nature of data labeling and analysis,and(3)lack of monitoring of all-round and multi-dimensional perspectives of the target.Thus,an advanced performance analysis system for rhythmic gymnastics is proposed in this paper,powered by intelligent fabric.The system uses intelligent fabric to detect the physiological and anthropometric data of the gymnasts.After a variety of data are collected,the analysis component is implemented by artificial intelligence techniques resulting in behavior recognition,decision-making,and other functions assisting performance improvement.A feasible solution to implementing the analysis component is the use of the hyperdimensional computing technique.In addition,four typical applications are presented to improve training performance.Powered by intelligent fabric,the proposed advanced performance analysis system exhibits the potential to promote innovative technologies for improving training and competi-tive performance,prolonging athletic careers,as well as reducing sports injuries.

Dense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDAR

Accurate and efficient three-dimensional(3D)streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks.Recently,remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging(LiDAR),but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing.To address these issues,this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time.Firstly,the pose of vehicle is estimated by visual and laser odometry(VLO)and the state-of-the-art pyramid stereo matching network(PSMNet)is introduced to estimate depth information.Then,incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization.Finally,redundant and noise points are removed through multiple filtering,resulting good quality of dense reconstruction.Comprehensive experiments were undertaken to check the visual effect,trajectory pose error and multi-scale model to model cloud comparison(M3C2)based on reference trajectories and reconstructions provided by the state-of-the-art method,showing the precision,recall and F-score of sampling core points(SCPs)are over 80.42%,71.68%and 77.19%,respectively,which verified the proposed method.