Fuzzy recognition of missile borne multi-line array infrared detection based on size calculating

In order to improve the infrared detection and discrimination ability of the smart munition to the dynamic armor target under the complex background,the multi-line array infrared detection system is established based on the combination of the single unit infrared detector.The surface dimension features of ground armored targets are identified by size calculating solution algorithm.The signal response value and the value of size calculating are identified by the method of fuzzy recognition to make the fuzzy classification judgment for armored target.According to the characteristics of the target signal,a custom threshold de-noising function is proposed to solve the problem of signal preprocessing.The multi-line array infrared detection can complete the scanning detection in a large area in a short time with the characteristics of smart munition in the steady-state scanning stage.The method solves the disadvantages of wide scanning interval and low detection probability of single unit infrared detection.By reducing the scanning interval,the number of random rendezvous in the infrared feature area of the upper surface is increased,the accuracy of the size calculating is guaranteed.The experiments results show that in the fuzzy recognition method,the size calculating is introduced as the feature operator,which can improve the recognition ability of the ground armor target with different shape size.

Discriminant Genetic Algorithm Extended (DGAE) model for seasonal sand and dust storm prediction

Here we use a Discriminant Genetic Algorithm Extended (DGAE) model to diagnose and predict seasonal sand and dust storm (SDS) activities occurring in Northeast Asia. The study employed the regular meteorological data, including surface data, upper air data, and NCEP reanalysis data, collected from 1980–2006. The regional, seasonal, and annual differences of 3-D atmospheric circulation structures and SDS activities in the context of spatial and temporal distributions were given. Genetic algorithms were introduced with the further extension of promoting SDS seasonal predication from multi-level resolution. Genetic probability was used as a substitute for posterior probability of multi-level discriminants, to show the dual characteristics of crossover inheritance and mutation and to build a non-linear adaptability function in line with extended genetic algorithms. This has unveiled the spatial distribution of the maximum adaptability, allowing the forecast field to be defined by the population with the largest probability, and made discriminant genetic extension possible. In addition, the effort has led to the establishment of a regional model for predicting seasonal SDS activities in East Asia. The model was tested to predict the spring SDS activities occurring in North China from 2007 to 2009. The experimental forecast resulted in highly discriminant intensity ratings and regional distributions of SDS activities, which are a meaningful reference for seasonal SDS predictions in the future.

Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data

The near-surface soil freeze–thaw(FT)transition is an important factor affecting land-atmosphere exchanges,hydrology and carbon cycles.Thus,effectively monitoring the temporal–spatial changes of soil FT processes is crucial to climate change and environment research.Several approaches have been developed to detect the soil FT state from satellite observations.The discriminant function algorithm(DFA)uses temperature and emissivity information from Advanced Microwave Scanning Radiometer Enhanced(AMSR-E)passive microwave satellite observations.Although it is well validated,it was shown to be insufficiently robust for all land conditions.In this study,we use in-situ observed soil temperature and AMSR-E brightness temperature to parameterize the DFA for soil FT state detection.We use the in-situ soil temperature records at 5 cm selected from available dense networks in the Northern Hemisphere as a reference.Considering the distinction between ascending and descending orbits,two different sets of parameters were acquired for each frequency pair.The validation results indicate that the overall discriminant accuracy of the new function can reach 90%.We further compared the Advanced Microwave Scanning Radiometer 2 discriminant results using the new function to the Soil Moisture Active Passive freeze/thaw product,and a reasonable consistency between them was found.

On the Legal Regulation of Algorithms

Increasingly,algorithms challenge legal regulations,and also challenge the right to explanation,personal privacy and freedom,and individual equal protection.As decision-making mechanisms for human-machine interaction,algorithms are not value-neutral and should be legally regulated.Algorithm disclosure,personal data empowerment,and anti-algorithmic discrimination are traditional regulatory methods relating to algorithms,but mechanically using these methods presents difficulties in feasibility and desirability.Algorithm disclosure faces difficulties such as technical infeasibility,meaningless disclosure,user gaming and intellectual property right infringement.And personal data empowerment faces difficulties such as personal difficulty in exercising data rights and excessive personal data empowerment,making it difficult for big data and algorithms to operate effectively.Anti-algorithmic discrimination faces difficulties such as non-machine algorithmic discrimination,impossible status neutrality,and difficult realization of social equality.Taking scenarios of algorithms lightly is the root cause of the traditional algorithm regulation path dilemma.Algorithms may differ in attributes due to specific algorithmic subjects,objects and domains involved.Therefore,algorithm regulation should be developed and employed based on a case-by-case approach to the development of accountable algorithms.Following these development principles,specific rules can be enacted to regulate algorithm disclosure,data empowerment,and anti-algorithmic discrimination.

Improvement of algorithms for digital real-time n-γ discrimination

Three algorithms（the Charge Comparison Method,n-γ Model Analysis and the Centroid Algorithm）have been revised to improve their accuracy and broaden the scope of applications to real-time digital n-γ discrimination.To evaluate the feasibility of the revised algorithms,a comparison between the improved and original versions of each is presented.To select an optimal real-time discrimination algorithm from these six algorithms（improved and original）,the figure-of-merit（FOM）,Peak-Threshold Ratio（PTR）,Error Probability（EP） and Simulation Time（ST） for each were calculated to obtain a quantitatively comprehensive assessment of their performance.The results demonstrate that the improved algorithms have a higher accuracy,with an average improvement of 10%in FOM,95%in PTR and 25%in EP,but all the STs are increased.Finally,the Adjustable Centroid Algorithm（ACA） is selected as the optimal algorithm for real-time digital n-γ discrimination.

Legal Regulation of Algorithmic Discrimination

Algorithmic discrimination is a kind of unreasonable,unequal treatment based on algorithms,which is widely existing in all areas of our social and economic life.Compared with traditional discrimination,algorithmic discrimination is inherent,professional and concealed.In practice,both market forces and government regulation have certain limitations in regulating algorithmic discrimination.On the one hand,enterprise autonomy often fails due to benefit considerations,while industry self-discipline lacks a set of mature and effective rules.On the other hand,in addition to the lack of complete algorithm review mechanisms and accountability systems,government supervision is also limited by technical regulatory difficulties.Therefore,it is necessary to further improve the legislation of algorithmic discrimination,set up algorithm review institutions,establish algorithm review mechanisms and strengthen industry and enterprise self-discipline.