Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Enhanced detection algorithm for apple bruises using structured light imaging

Bruising reduces the edibility and marketability of fresh apples,inevitably causing economic losses for the apple industry.However,bruises lack obvious visual symptoms,which makes it challenging to detect them using imaging techniques with uniform or diffuse illumination.This study employed the structured light imaging(SLI)technique to detect apple bruises.First,the grayscale reflection images were captured under phase-shifted sinusoidal illumination at three different wavelengths(600,650,and 700 nm)and six different spatial frequencies(0.05,0.10,0.15,0.20,0.25,and 0.30 cycles mm−1).Next,the grayscale reflectance images were demodulated to produce direct component(DC)images representing uniform diffuse illumination and amplitude component(AC)images revealing bruises.Then,by quantifying the contrast between bruised regions and sound regions in all AC images,it was found that bruises exhibited the optimal contrast when subjected to sinusoidal illumination at a wavelength of 700 nm and a spatial frequency of 0.25 mm−1.In the AC image with optimal contrast,the developed h-domes segmentation algorithm to accurately segment the location and range of the bruised regions.Moreover,the algorithm successfully accomplished the task of segmenting central bruised regions while addressing the challenge of segmenting edge bruised regions complicated by vignetting.The average Intersection over Union(IoU)values for the three types of bruises were 0.9422,0.9231,and 0.9183,respectively.This result demonstrated that the combination of SLI and the h-domes segmentation algorithm was a viable approach for the effective detection of fresh apple bruises.

Tricuspid regurgitation following pacemaker implantation for bradycardia:a two-year study comparing different pacing strategies

To the Editor:Tricuspid regurgitation(TR)is a known complication of cardiac implantable electrical device(CIED)implantation with a prevalence of up to 10–30%.Due to the detrimental effects of right ventricular pacing(RVP)on cardiac function,His bundle pacing(HBP)and left bundle branch area pacing(LBBAP)have been increasingly used as physiological pacing modalities.This observational study aimed to assess the lead-related significant TR(moderate and severe)in patients with HBP,LBBAP,right ventricular apical pacing(RVAP),and right ventricular septal pacing(RVSP)in bradycardia patients during the two-year follow-up.