算法审计内容论

以经典审计理论为基础,提出算法审计内容的一个理论框架。算法审计对象是算法责任履行情况,主要包括:代理人(算法使用者)的算法全面责任、算法开发者的算法开发责任、算法营运者的算法营运责任和算法监管者的算法监管责任。算法审计对象要落实到算法审计主题,算法全面责任、算法开发责任、算法营运责任的审计主题都是算法系统,算法监管责任的审计主题包括算法监管信息、算法监管行为和算法监管制度。基于不同的算法审计主题形成不同类型的算法审计业务,基于算法系统形成算法系统审计,基于算法监管信息、算法监管行为和算法监管制度分别形成算法监管信息审计、算法监管行为审计和算法监管制度审计。在算法审计业务实施中,必须将各个审计业务的审计主题分解为审计标的,算法系统的审计标的包括输入数据、算法设计和算法影响,每一个有独立计算方法的算法监管信息都可以成为独立的审计标的,每一个有独立规则的算法监管行为都可以成为独立的审计标的,每一项有独立规范对象的算法监管制度都可以成为独立的审计标的。

卫星测控信号的调制类型自动识别算法

研究了应用于卫星测控信号的自动调制识别算法 ,提出了FM和PM测控信号识别的两种算法 ,分析并仿真验证了载频偏移对正确识别率的影响 ,研究了载频偏移估计算法对消除载频偏移影响的效果。计算机仿真实验表明 :在基于瞬时相位标准偏差σdp为关键特征的算法中 ,采用最小二乘原理估计频偏可以有效地消除载频偏移和部分噪声的影响 ;基于瞬时折叠相位偏差σlwp的算法在载频偏移为 1 0 0Hz,信噪比高于 - 6dB时 ,其识别率可达 1 0 0 %。

算法治理:体系建构与措施进路

如果说数据是数字经济时代的核心生产要素,那么算法则是推进这一核心生产要素资产化和价值化的运行基础。当数字技术赋能各个领域,催生了新产品、新组织模式、新商业模式和新产业业态的同时,也带来了诸如算法歧视、算法操纵、算法协同、算法黑箱等潜在风险。不同类型的算法经由设计和实施,其带来的算法应用潜在风险不同,并可能导致政府失灵与市场失灵。围绕“治理主体-治理对象-治理工具措施”探索算法治理的体系架构,可以融合多元主体协同、利益均衡和敏捷治理等三种机制,在元规制和全过程管理理念的基础上优化算法治理措施,推动三种机制并行。

锥束CT图像重建算法

随着螺旋多层面ＣＴ的出现，医用ＣＴ正在向着螺旋锥束ＣＴ转变。从螺旋锥束数据来重建图像有许多优点，但是这种成像方式在数学上比较复杂，技术实现也有相当的难度。本文介绍这一领域的重要成果，特别是近年文献中的基本思想。文中涉及各种主要重建算法，包括准确重建，近似重建和迭代重建算法。简言之，当数据是完整的、无噪音的，应选择准确重建方法；当数据是有噪音的和／或受物体运动干扰，近似重建会有较好的性能；而当数据是不可靠的和被截断时，迭代方法可以发挥作用。今后主要研究方向是对这些算法进行改进，比较它们的特性，并将其优点结合到一起。

网络消费领域算法滥用行为的法律规制

算法在网络消费领域的适用既能提高消费决策效率,又能降低消费活动成本,但算法滥用行为导致的侵害消费者权益现象也不容忽视。网络消费领域可能被不当使用算法主要为推荐型算法、限制型算法、评价型算法、定价型算法以及概率型算法,不同算法类型导致的侵害消费者权益现象不同。算法本身所具有的不透明、难解释的技术特征,法律制度供给不足以及监督管理机制不完善加剧了算法滥用行为的规制难度。应以促进算法公开,完善制度供给,创新算法监督管理机制为抓手,实现算法滥用行为的法律规制。

Annoyance-type speech emotion detection in working environment

In order to recognize people＇s annoyance emotions in the working environment and evaluate emotional well- being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a Mandarin database with two thousands samples is built. In searching for annoyance-type emotion features, the prosodic feature and the voice quality feature parameters of the emotional statements are extracted first. Then an improved back propagation （BP） neural network based on the shuffled frog leaping algorithm （SFLA） is proposed to recognize the emotion. The recognition capability of the BP, radical basis function （RBF） and the SFLA neural networks are compared experimentally. The results show that the recognition ratio of the SFLA neural network is 4. 7% better than that of the BP neural network and 4. 3% better than that of the RBF neural network. The experimental results demonstrate that the random initial data trained by the SFLA can optimize the connection weights and thresholds of the neural network, speed up the convergence and improve the recognition rate.

Optimizing of large-number-patterns string matching algorithms based on definite-state automata

Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC++algorithm, by optimizing the Aho-Corasick (i.e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC++and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc., the optimizing method discussed is significant in practice.

Particle swarm optimization for train schedule of Y-type train routing in urban rail transit

The train schedule usually includes train stop schedule,routing scheme and formation scheme.It is the basis of subway transportation.Combining the practical experience of transport organizations and the principle of the best match between transport capacity and passenger flow demand,taking the minimum value of passenger travel costs and corporation operating costs as the goal,considering the constraints of the maximum rail capacity,the minimum departure frequency and the maximum available electric multiple unit,an optimization model for city subway Y-type operation mode is constructed to determine the operation section of mainline as well as branch line and the train frequency of the Y-type operation mode.The particle swarm optimization(PSO)algorithm based on classification learning is used to solve the model,and the effectiveness of the model and algorithm is verified by a practical case.The results show that the length of branch line in Y-type operation affects the cost of waiting time of passengers significantly.

农业机械装配分组配置优化研究

针对现阶段农业机械领域的相关产品在装配过程、优化分组的配置流程中缺乏相关约束条件考虑、数学算法的收敛性不理想、产品生产效率低及性能较差等情况,给出一类以改善粒子群数学算法为原理的农业领域机械类产品在分组装配优化过程中的配置办法。设置产品零件装配时的边界条件,以公差等级、尺寸链结构、公差函数及成本函数等有机结合,生成装配过程的约束函数系统,将其当成农业领域机械分组装配的数学模型。

PSO type-reduction method for geometric interval type-2 fuzzy logic systems

In a special case of type-2 fuzzy logic systems （FLS）, i.e. geometric inteIval type-2 fuzzy logic systems （GIT-2FLS）, the crisp output is obtained by computing the geometric center of footprint of uncertainly （FOU） without type-reduction, but the defuzzifying method acts against the corner concepts of type-2 fuzzy sets in some cases. In this paper, a PSO type-reduction method for GIT-2FLS based on the particle swarm optimization （PSO） algorithm is presented. With the PSO type-reduction, the inference principle of geometric interval FLS operating on the continuous domain is consistent with that of traditional interval type-2 FLS operating on the discrete domain. With comparative experiments, it is proved that the PSO type-reduction exhibits good performance, and is a satisfactory complement for the theory of GIT-2FLS.

Brain MRI Segmentation Using KFCM and Chan-Vese Model

To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.

An Iterative Clustering-Based Localization Algorithm for Wireless Sensor Networks

In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.

Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems

Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.

A Novel Approach for Unsupervised Segmentation of Homogeneous Regions in Gray-scale Images

An improved approach for JSEG is presented for unsupervised segmentation of homogeneous regions in gray-scale images. Instead of intensity quantization, an automatic classification method based on scale space-based clustering is used for nonparametric clustering of image data set. Then EM algorithm with classification achieved by space-based classification scheme as initial data used to achieve Gaussian mixture modelling of image data set that is utilized for the calculation of soft J value. Original region growing algorithm is then used to segment the image based on the multiscale soft J-images. Experiments show that the new method can overcome the limitations of JSEG successfully.

A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST

Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space Transformation （CBEST） to pro- duce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clus- tered by combining CBEST and K-means in each pre-defined ecological zone （50 in total for China）. Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test sam- pies indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products （i.e., Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） and GlobCover 2009） also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.

Research on Performance of Centrifugal Pump with Different-type Open Impeller

To investigate the influence of impeller type on the performance and inner flow of centrifugal pump, the numeri- cal simulation and experimental research were carried out on the same centrifugal pump with straight-blade and curved-blade open impeller. Based on SIMPLEC algorithm, time-averaged N-S equation and the standard k-e turbulence model, the numerical results are obtained. The pressure distribution in the different type impellers is uniform, while the low pressure area in straight-blade inlet is larger. The vortexes in the passage of impeller exist in both cases. Relative to curved-blade impeller, there are larger vortexes in most of the flow passages except the passage near the tongue in straight-blade impeller. Also some small backflow regions are found at the blade inlet of two impellers. The characteristic curves achieved by numerical simulation basieaUy agree with those by experiment, and straight-blade open impeller centrifugal pump has a better hydraulic performance.

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.

A TWO-STAGE ESTIMATION ALGORITHM FOR A TYPE OF CURRENT STATUS DATA

Success-failure life tests are widely used in reliability engineering research to evaluate the storage life of products, where the observed data are the current status data, usually summarized as the form of ＂binomial life data＂. For this type of data, this paper proposes a two-stage algorithm to estimate some commonly used lifetime distributions. This Mgorithm is automatic, intuitively appealing and simple to implement. Simulation studies show that compared with some existing methods, the proposed algorithm is more stable and efficient, especially in small sample situations, and it can also be extended to deM with some complicated lifetime distributions.

Non-Algorithmically Integrating Land Use Type with Spatial Interpolation of Surface Soil Nutrients in an Urbanizing Watershed

Integrating land use type and other geographic information within spatial interpolation has been proposed as a solution to improve the performance and accuracy of soil nutrient mapping at the regional scale. This study developed a non-algorithm approach, i.e., applying inverse distance weighting （IDW） and ordinary kriging （OK）, to individual land use types rather than to the whole watershed, to determine if this improved the performance in mapping soil total C （TC）, total N （TN）, and total P （TP） in a 200-km2 urbanizing watershed in Southeast China. Four land use types were identified by visual interpretation as forest land, agricultural land, green land, and urban land. One hundred and fifty soil samples （0-10 cm） were taken according to land use type and patch size. Results showed that the non-algorithm approach, interpolation based on individual land use types, substantially improved the performance of IDW and OK for mapping TC, TN, and TP in the watershed. Root mean square errors were reduced by 3.9% for TC, 10.770 for TN, and 25.9% for TP by the application of IDW, while the improvements by OK were slightly lower as 0.9% for TC, 7.7% for TN, and 18.1% for TP. Interpolations based on individual land use types visually improved depiction of spatial patterns for TC, TN, and TP in the watershed relative to interpolations by the whole watershed. Substantial improvements might be expected with denser sampling points. We suggest that this non-algorithm approach might provide an alternative to algorithm-based approaches to depict watershed-scale nutrient patterns.

On the algorithmic construction of classifying spaces and the isomorphism problem for biautomatic groups

We show that the isomorphism problem is solvable in the class of central extensions of word-hyperbolic groups,and that the isomorphism problem for biautomatic groups reduces to that for biautomatic groups with finite centre.We describe an algorithm that,given an arbitrary finite presentation of an automatic group Γ,will construct explicit finite models for the skeleta of K(Γ,1) and hence compute the integral homology and cohomology of Γ.