面向大规模认知诊断的DINA模型快速计算方法研究

在教育教学的过程中,如何诊断学生的知识水平是一个重要的问题.传统方法大多由教师根据学生的表现和成绩进行人工判断,存在效率低、主观性强的问题,且难以做到针对大量学生的个性化诊断.近年来,认知诊断模型中的DINA模型被广泛应用于诊断学生个性化知识掌握程度.然而传统DINA模型大多基于小样本数据,当面对在线教育带来的大规模数据处理需求时,存在收敛速度慢的问题,难以实际应用.针对DINA模型计算时间过长的问题,本文首先给出了DINA模型的收敛性证明,并提出了三种能够加速DINA求解的算法：（1）增量算法,它将学生数据划分为多个学生块,每次迭代只访问其中一个学生块;（2）最大熵方法,它只访问在极大化模型熵的过程中影响较大的学生数据;（3）基于前两者的混合方法.最后,本文通过真实数据和模拟数据上的实验,分析证明了三种方法均能在保证DINA模型有效性的情况下,达到几倍至几十倍的加速效果,有效地改善了DINA模型的计算效率.     

制导滑翔炸弹虚拟比例末制导律研究

对于不同的地面目标,以不同的着角命中目标,可大幅提高制导滑翔炸弹的毁伤效能,虚拟比例末制导律是实现这一目的的有效途径。其实现方法是:按照距离和角度约束公式,计算出虚拟目标的坐标,采用比例制导律引导制导滑翔炸弹飞向虚拟目标。距离约束保证了制导滑翔炸弹、虚拟目标和目标在命中时刻重合,角度约束保证了前两者以给定着角接近目标,最终保证脱靶量和着角满足要求。通过数字仿真,最大着角超过80°,着角误差小于5°。此方法只需要制导滑翔炸弹的坐标、速度以及目标坐标信息就可实现,所需信息量小,且形式简单、计算量小、工程上易于实现。     

Flexible and Air-Stable Near-Infrared Sensors Based on Solution-Processed Inorganic–Organic Hybrid Phototransistors

Flexible and air-stable phototransistors are highly demanded for wearable near-infrared (NIR) image sensors. However, advanced NIR sensors via low-cost, solution-based processes remained a challenge. Herein, high-performance inorganic–organic hybrid phototransistors are achieved based on solution processed n-type metal oxide/polymer semiconductor heterostructures of In₂O₃/poly{5,5′-bis[3,5-bis(thienyl)phenyl]-2,2′-bithiophene-3-ethylesterthiophene]} (PTPBT-ET). The In₂O₃/PTPBT-ET hybrid phototransistor combines the advantages of both fast electron transport in In₂O₃ and high photoresponse in PTPBT-ET, showing high saturation mobility of 7.1 cm² V⁻¹ s⁻¹ and large current on/off ratio of >10⁷. As a result, the phototransistor exhibits high performance towards NIR light sensing with a responsivity of 200 A W⁻¹, a specific detectivity of 1.2 × 10¹³ Jones, and fast photoresponse with rise/fall time of 5/120 ms. Remarkably, the hybrid phototransistor, without any passivation, demonstrates excellent electrical stability without performance degradation even after 160 days in air. A 10 × 10 phototransistor array is also enabled by virtue of the high device uniformity. Lastly, flexible In₂O₃/PTPBT-ET phototransistor on polyimide substrate is attained, exhibiting outstanding mechanical flexibility up to 1000 bending/releasing cycles at a bending radius of 5 mm. These achievements pave the way for constructing air-stable hybrid phototransistors for flexible NIR image sensor applications.     

Medical image segmentation based on non-parametric mixture models with spatial information

Because of too much dependence on prior assumptions, parametric estimation methods using finite mixture models are sensitive to noise in image segmentation. In this study, we developed a new medical image segmentation method based on non-parametric mixture models with spatial information. First, we designed the non-parametric image mixture models based on the cosine orthogonal sequence and defined the spatial information functions to obtain the spatial neighborhood information. Second, we calculated the orthogonal polynomial coefficients and the mixing ratio of the models using expectation-maximization (EM) algorithm, to classify the images by Bayesian Principle. This method can effectively overcome the problem of model mismatch, restrain noise, and keep the edge property well. In comparison with other methods, our method appears to have a better performance in the segmentation of simulated brain images and computed tomography (CT) images.     

零知识的理想功能和零知识的序列组合

为了降低零知识的错误概率,通常采用协议的组合操作,独立运行安全的协议,在组合情形,其安全性却并非平凡的。利用UC安全密码元件可以在协议组合过程中作为一个模块安全调用的特性,在UC框架下,通过定义理想功能Fwzk,在Fwzk-混合模型下,讨论零知识协议的组合安全性问题。得到结果:基本Blum协议在序列合成时零知识性具有封闭性。     

宽角合成孔径雷达二维缺失数据自适应幅相估计成像方法

宽角合成孔径雷达(WASAR)由于数据获取时间长,强电磁体、无线频段及方位扰动等因素会造成接收数据出现缺失,采用常规FFT处理方法会造成图像混叠,模糊及高旁瓣等问题。该文首先将自适应幅相估计(ATAPES)方法扩展至2维,其次,将tuning因子引入缺失数据外推过程,提出一种2维缺失数据自适应幅相估计(ATMAPES)方法,算法将最优外推估计结果填补回原始数据得到完整2维频域数据,通过循环迭代得到目标位置及幅度的准确估计结果。仿真实验验证了算法的有效性。     

行业特色高校研究生党建模式探析

具有行业特色高校的研究生规模一般不大,但是却承担着培养行业精英的重任,许多行业的中高层领导和技术骨干均来自于这些高校,在新的社会和高校大发展的形势下,行业特色高校必须与时俱进,按照国家和行业发展的需要,加大创新力度,不断探析构建适合本校和本行业研究生培养的党建模式。     

基于环结构分析的准循环LDPC码构造

准循环奇偶校验(Quasi-Cyclic Low-Density Parity-Check,QC-LDPC)码在通信工程领域具有重要的应用价值,因此它的构造算法一直是LDPC码研究领域的一个热点内容.根据现有的QC-LDPC码构造算法,特别是基于渐进边增长(Progressive Edge Growth,PEG)算法的QC-LDPC码构造方法,提出了一种新的移位矩阵构造方法.该方法有效减少了随机搜索带来的时间损耗,并改进了二次同余、等差数列等算法仅能除去四环的情况,进一步消去了六环、八环和十环结构,确保QC-LDPC码的围长不小于12.仿真结果表明,所构造的QC-LDPC码具有更优的环结构特点和纠错性能.     

Congestion warning method based on the Internet of vehicles and community discovery of complex networks

The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles(IOV) and community discovery of complex networks. The communities in complex network model of traffic flow reflect the local aggregation of vehicles in the traffic system, and it is used to predict the upcoming congestion. The real-time information of vehicles on the roads is obtained from the IOV, which includes the locations, speeds and orientations of vehicles. Then the vehicles are mapped into nodes of network, the links between nodes are determined by the correlations between vehicles in terms of location and speed. The complex network model of traffic flow is hereby established. The communities in this complex network are discovered by fast Newman(FN) algorithm, and the congestion warnings are generated according to the communities selected by scale and density. This method can detect the tendency of traffic aggregation and provide warnings before congestion occurs. The simulations show that the method proposed in this paper is effective and practicable, and makes it possible to take action before traffic congestion.