IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.

Residential Parking Service Model Based on Resource Sharing

Aiming at solving pressing parking issues in the urban environment, a residential parking spaces sharing model was proposed in this study. In this model, firstly, the residential community pattern, the status of idle parking spaces, and the temporal and spatial characteristics of sharing parking had been analyzed. Next,in the convenience of modeling,medical institutions that have the most prominent parking problems were selected as the subject of study. Based on the K-S statistical analysis results and the actual parking sharing situation,it was observed that the residential parking sharing time satisfied the shifted negative exponential distribution( SNED). Finally,a probability model of shared service capacity based on the SNED and critical time condition was established. By applying the statistical analysis method,the time of vehicles passing in and out of parking spaces, the idle time of parking spaces, the shifted distribution parameters, and other important model parameters had been calibrated,which was leading to the algorithm of model. In addition,considering the feasibility of model without sufficient data,the vehicle travel probability,the stagnation rate of parking space,and the status of parking spaces were defined and the reference data were also provided. The results of case studies indicate that it is very promising to solve urban parking issues if the residential community shares its rich parking resources with adjacent commercial buildings.

Modelling the variation of demersal fi sh distribution in Yellow Sea under climate change

Climate change can aff ect fi sh individuals or schools,and consequently the fi sheries.Studying future changes of fi sh distribution and abundance helps the scientifi c management of fi sheries.The dynamic bioclimate envelope model(DBEM)was used to identify the“environmental preference profi les”of the studied species based on outputs from three Earth system models(ESMs).Changes in ocean conditions in climate change scenarios could be transformed by the model into those in relative abundance and distribution of species.Therefore,the distributional response of 17 demersal fi shes to climate change in the Yellow Sea could be projected from 1970 to 2060.Indices of latitudinal centroid(LC)and mean temperature of relative abundance(MTRA)were used to represent the results conducted by model.Results present that 17 demersal fi sh species in the Yellow Sea show a trend of anti-poleward shift under both low-emission scenario(RCP 2.6)and high-emission scenario(RCP 8.5)from 1970 to 2060,with the projected average LC in three ESMs shifting at a rate of-1.17±4.55 and-2.76±3.82 km/decade,respectively,which is contrary to the previous projecting studies of fi shes suggesting that fi shes tend to move toward higher latitudes under increased temperature scenarios.The Yellow Sea Cold Water Mass could be the major driver resulting in the shift,which shows a potential signifi cance to fi shery resources management and marine conservation,and provides a new perspective in fi sh migration under climate change.

BEST WEIGHT PATTERN EVALUATION BASED SECURITY CONSTRAINED POWER DISPATCH ALGORITHM

This paper presents a methodology which determines the allocation of power demand among the committed generating units while minimizes number of objectives as well as meets physical and technological system constraints. The procedure considers two decoupled problems based upon the dependency of their goals on either active power or reactive power generation. Both the problems have been solved sequentially to achieve optimal allocation of active and reactive power generation while minimizes operating cost, gaseous pollutants emission objectives and active power transmission loss with consideration of system operating constraints along with generators prohibited operating zones and transmission line flow limits. The active and reactive power line flows are obtained with the help of generalized generation shift distribution factors （GGDF） and generalized Z-bus distribution factors （GZBDF）, respectively. First problem is solved in multi-objective framework in which the best weights assigned to objectives are determined while employing weighting method and in second problem, active power loss of the system is minimized subject to system constraints. The validity of the proposed method is demonstrated on 30-bus IEEE power system.