利益相关者视角下农地整治项目绩效测度目标分析

测度目标是农地整治项目绩效测度体系运行的指南针,是开展农地整治项目绩效测度的重要基础。从农地整治项目利益相关者入手剖析农地整治项目绩效测度主体,探讨测度主体的价值观、测度主体对农地整治项目建设的利益诉求,根据价值观和利益诉求诊断农地整治项目绩效的测度目标,提供农村公共产品和农村公共服务,满足社会公共需求,维护社会公平公正,改善社会经济发展环境,提高农民生活水平。在农地整治项目绩效测度中需要以社会公众满意为准则,有效协调政府、项目区社会公众、周围社区对农地整治项目的建设要求,确定合理的农地整治项目绩效测度目标。

一种度量目标散射相似性的新参数

针对目标相似性参数度量分布式目标与典型目标散射相似程度不准确的问题,本文利用规范散射Pau li基矢量和分布式目标相干矩阵,定义了一个度量目标散射相似性程度的新参数——目标散射相似性。由于该参数综合考虑了分布式目标主散射机制、次要散射机制与规范散射的相似性,以及不同散射机制对应的发生概率,因而它能准确反映分布式目标与规范目标散射的平均近似程度。与相似性参数一样,新参数具有目标旋转不变性、尺度无关性等特性。作为一种实际应用,给出了分布式目标与金属球、角反射器等典型目标的散射相似性。结合旧金山地区AIRSAR极化数据,讨论了这些相似性测度在地物分类方面的应用。

Improved YOLOv8-Based Target Detection Algorithm for UAV Aerial Image

In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

利用多目标分析法进行供热设计方案优选研究

多目标综合效果测度法是一种非常有效的系统优化评价方法。本文建立以经济、技术和人文环保指标为内容的供热热源方案评价指标体系,提出了方案优选综合效果测度法,结合工程实例介绍了该方法的计算过程。

进化规划在Multi-UCAV合作攻击伪装目标中的研究

在多无人作战飞机攻击隐蔽伪装目标的任务调度规划中,针对静态武器分配方法的不足,提出了一种目标可侦测度相关模型(TDRM),该模型体现了UCAV对不同目标的侦测能力与无人作战机群整体射击效率之间的联系。将基于混合概率分布的进化规划(EPBHPD)引入到对隐蔽伪装目标的攻击规划中,能在问题求解的精确性与实时性两者不断变化的战场环境中,通过调整进化规划中变异算子的参数和问题空间与可行解空间的映射关系可以获得合理的折中。仿真结果表明了目标可侦测度相关模型的有效性。

基于一类新的胞腔排除遗传算法求解迭代函数系逆问题

提出求解迭代函数系 ( IFS)逆问题的一类有效遗传算法 .该算法基于新发展的可拼接 /可分解编码 ,并结合使用胞腔排除技巧 .对于典型图像的应用表明 :该方法可有效应用于基于矩匹配表示的 IFS逆问题求解 ,从而为

“生态脚印”指标及其评价

The paper introduces to “zoological footprint” measuring method and further tries to analyse and evaluate the method. The author points out that the indicators cannot be used to convert the concept of sustainable development into feasible management model and provides policy makers with a substantial standard to make decision and technical choice.

基于球面散射相似性的POLSAR图像分类方法

针对基于H-Alpha平面的目标分类方法运算量偏大的问题,本文研究了基于散射相似性的极化合成孔径雷达(POLSAR)图像分类。首先在分析现有目标散射相似性度量参数的不足基础上,利用目标相干矩阵定义了目标散射相似性的新度量参数。该参数综合考虑了目标主散射机制、次要散射机制与规范目标的相似性,以及不同散射机制对应的发生概率,因而它能准确反映目标与规范目标的散射相似程度。接着,考虑到表面散射在自然界中是一种普遍的散射类型,利用新参数提取了表面散射相似性参数,并从理论上分析了该参数表征目标散射类型的可行性及它与平均Alpha角、极化散射熵的约束关系。最后,利用San Francisco地区AIRSAR极化数据验证了表面散射相似性替代平均Alpha角的有效性,并讨论了表面散射相似性参数分别与现有极化散射熵替代参数的组合分类效果。实验结果表明,基于简单线性近似参数+表面散射相似性的H-Alpha替代方法更具实用性。

Decision-Making Method for Problem of Goal Type Based on Evaluation Criterion

The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we found some defects of the method and pointed out these defects. To overcome these defects we absorbed the spirit and exploited concepts of evaluation criterion and the fault measure of evaluation criterion. We proposed and applied a method with an evaluation criterion, after which we also p...

Multi Objective Decision Making Method Based on Evaluation Criterion for Problems with Linear Value Function

A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole information of weights for objectives. The concept of fault measure of the evaluation criterion is proposed to measure the deviation of the evaluation criterion from the DMs preference structure.The approach to obtain an upper boundary of fault measure of an evaluation criterion,and the approach to modify the evaluation criterion to be one with smaller fault measure,and the approach to obtain a pre optimized objective set by evaluation criterion with certain fault measure are also proposed.

矿产资源规划环境影响综合评价研究

针对矿产资源开发导致的主要环境问题及其环境效应,设计出矿产资源规划环境影响评价指标体系的基本框架与内容,探讨了熵技术支持下的评价指标体系权系数的层次分析法和专家群民主决策支持下定性和定量转化的赋权值方法。以四川省第二轮矿产资源规划环境影响评价为例,利用多层次多目标模糊综合测度模型,对矿产资源规划环境影响进行综合评价。评价结果显示,该模型具有较好的可操作性。

Enhancement Dataset for Low Altitude Unmanned Aerial Vehicle Detection

In recent years,the number of incidents involved with unmanned aerial vehicles(UAVs)has increased conspicuously,resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high detection accuracy with respect to low altitude UAVs are put forward. In addition,the methods of UAV detection based on deep learning are of great potential in low altitude UAV detection. However,such methods need high-quality datasets to cope with the problem of high false alarm rate(FAR)and high missing alarm rate(MAR)in low altitude UAV detection,special high-quality low altitude UAV detection dataset is still lacking. A handful of known datasets for UAV detection have been rejected by their proposers for authorization and are of poor quality. In this paper,a comprehensive enhanced dataset containing UAVs and jamming objects is proposed. A large number of high-definition UAV images are obtained through real world shooting, web crawler, and data enhancement.Moreover,to cope with the challenge of low altitude UAV detection in complex backgrounds and long distance,as well as the puzzle caused by jamming objects,the noise with jamming characteristics is added to the dataset. Finally,the dataset is trained,validated,and tested by four mainstream deep learning models. The results indicate that by using data enhancement,adding noise contained jamming objects and images of UAV with complex backgrounds and long distance,the accuracy of UAV detection can be significantly improved. This work will promote the development of anti-UAV systems deeply,and more convincing evaluation criteria are provided for models optimization for UAV detection.

Measurement of single-fish target strength in the South China Sea

We measured the target strength （TS） of three commercial fish species： whitespotted spinefoot （Siganus canaliculatus）, black porgy （Acanthopagrus schlegelii）, and creek red bream （Lutjanus argentimaculatus）, in the South China Sea. The TS of caged or tethered fish （n=76 total） was measured using a Simrad EY60 portable scientific echosounder at 120 kHz. We evaluated the relationship between TS and total length （TL, cm） for the three species. This is the first attempt to use split-beam acoustics to measure single-fish TS in the South China Sea by Chinese researchers. Our results will improve the accuracy and precision of acoustic abundance estimates of commercially important species and fi.trther the development of underwater acoustic survey techniques in fisheries in the South China Sea.

Spatio-temporal Compensation Based Object Detection for Video Surveillance Systems

Moving object detection in video surveillance is an important step. This paper addresses an automatic object detection algorithm based on spatio-temporal compensation for video surveillance. Temporal difference of the pairs of two frames with a k-frame distance is utilized to obtain coarse object masks. Usually, object regions in these coarse masks have discontinuous boundaries and some holes. Region growing with the distance constraint is proposed to compensate these coarse object regions in spatial domain, followed by filling holes. The added distance constraint can prevent object regions from growing infinitely. The proposed filling holes method is simple and effective. To solve the temporarily stopping problem of moving objects, temporal compensation is proposed to compensate the object mask by utilizing temporal coherence of moving objects in temporal domain. The proposed detection algorithm can extract moving objects as completely as possible. Experimental results have successfully demonstrated the validity of the proposed algorithm.

Object detection of artifact threaded hole based on Faster R-CNN

In order to improve the accuracy of threaded hole object detection,combining a dual camera vision system with the Hough transform circle detection,we propose an object detection method of artifact threaded hole based on Faster region-ased convolutional neural network(Faster R-CNN).First,a dual camera image acquisition system is established.One industrial camera placed at a high position is responsible for collecting the whole image of the workpiece,and the suspected screw hole position on the workpiece can be preliminarily selected by Hough transform detection algorithm.Then,the other industrial camera is responsible for collecting the local images of the suspected screw holes that have been detected by Hough transform one by one.After that,ResNet50-based Faster R-CNN object detection model is trained on the self-built screw hole data set.Finally,the local image of the threaded hole is input into the trained Faster R-CNN object detection model for further identification and location.The experimental results show that the proposed method can effectively avoid small object detection of threaded holes,and compared with the method that only uses Hough transform or Faster RCNN object detection alone,it has high recognition and positioning accuracy.

A NEW METHOD OF MOVING OBJECT DETECTION AND SHADOW REMOVING

This paper presents an adaptive method of objects and shadows detection in video streams. Models of background are firstly set up and adaptively updated in Hue Saturation Intensity (HSI) color space to detect motion regions. Then, detection errors are dealt with by motion continuity and velocity consistency. Finally, cast shadows are removed by the generic properties of luminance, chrominance and gradient density. Experimental results and their evaluation are presented to verify the effectiveness of this new method.

Infrared radiation measurement of the aerial target based on temperature calibration and target images

The gray level values of target pixels of infrared images processed by the SUSAN noise filtering algorithm and the background filtering algorithm are calculated,assuming that the target distance is given in image acquisition.The equivalent blackbody temperature is obtained by the temperature calibration model that represents the quantitative relation,at a certain integration time,between the gray level value and the equivalent blackbody temperature.The temperature calibration model has been acquired by the curve fitting with the experimental data of blackbody temperature calibration using Origin 6.0.Then the target radiance can be determined by the radiant flux equivalence model built on condition that the target radiant flux received by the infrared detector is equivalent to that of the blackbody placed in front of the infrared detector at a close distance.This measuring method is based on the temperature calibration parameters and real image information,so it can avoid subjectivity of the traditional theory modeling methods.

Passive target tracking with intermittent measurement based on random finite set

In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.

An algorithm for moving target detection in IR image based on grayscale distribution and kernel function

A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.