基于Transformer网络的机载雷达多目标跟踪方法

传统的多目标跟踪数据关联算法需要提前知晓目标运动模型和杂波密度等先验信息,然而这些先验信息在跟踪之前无法及时准确地获取。针对这个问题,提出一种基于Transformer网络的多目标跟踪数据关联算法。首先,考虑到传感器会存在漏检的情况,引入虚拟量测来重新建立数据关联模型。在此基础上,提出基于Transformer网络的数据关联方法来解决多目标与多量测的匹配问题。同时,设计了一种掩蔽交叉熵损失与重叠度损失相结合的损失函数(MCD)用于优化网络参数。仿真和实测数据结果表明:在不同检测概率条件下,所提算法性能均优于经典的数据关联算法和基于双向长短时记忆网络的算法。      

An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things

With the development of the Internet of Things (IoT), the massive data sharing between IoT devices improves the Quality of Service (QoS) and user experience in various IoT applications. However, data sharing may cause serious privacy leakages to data providers. To address this problem, in this study, data sharing is realized through model sharing, based on which a secure data sharing mechanism, called BP2P-FL, is proposed using peer-to-peer federated learning with the privacy protection of data providers. In addition, by introducing the blockchain to the data sharing, every training process is recorded to ensure that data providers offer high-quality data. For further privacy protection, the differential privacy technology is used to disturb the global data sharing model. The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.     

A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating users. In this scheme, classification learning is used for identifying replicas and accessing granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.     

Chapter 11 Simulation studies of efficiency,returns to scale and misspecification with nonlinear functions in DEA

Using statistically designed experiments, 12,500 observations are generated from a 4-pieced Cobb-Douglas function exhibiting increasing and decreasing returns to scale in its different pieces. Performances of DEA and frontier regressions represented by COLS (Corrected Ordinary Least Squares) are compared at sample sizes ofn=50, 100, 150 and 200. Statistical consistency is exhibited, with performances improving as sample sizes increase. Both DEA and COLS generally give good results at all sample sizes. In evaluating efficiency, DEA generally shows superior performance, with BCC models being best (except at corner points), followed by the CCR model and then by COLS, with log-linear regressions performing better than their translog counterparts at almost all sample sizes. Because of the need to consider locally varying behavior, only the CCR and translog models are used for returns to scale, with CCR being the better performer. An additional set of 7,500 observations were generated under conditions that made it possible to compare efficiency evaluations in the presence of collinearity and with model misspecification in the form of added and omitted variables. Results were similar to the larger experiment: the BCC model is the best performer. However, COLS exhibited surprisingly good performances — which suggests that COLS may have previously unidentified robustness properties — while the CCR model is the poorest performer when one of the variables used to generate the observations is omitted.     

Determination of heavy metal concentration in feed and permeate streams of polymer enhanced ultrafiltration process

Polymer enhanced ultrafiltration (PEUF) is a newly developed method for the removal of heavy metals from aqueous solutions. This method was applied for the removal of mercury and cadmium with the presence of polyethyleneimine (PEI) as a water soluble polymer. After ultrafiltration experiments for metal–polymer mixtures, two separate streams, namely, retentate and permeate, former of which contains mainly metal–polymer complex and free polymer molecules while latter of which mainly contains free metal ions, were obtained. At the end of PEUF experiments, performance of operation was determined by concentration analyses which was achieved by atomic absorption spectroscopy (AAS) applied in a different way for permeate and retentate streams considering the effect of presence of polymer. For mercury analysis, cold vapor AAS was applied. It was observed that the presence of PEI did not affect the atomic absorption signal when 10% HCl was added to the sample solutions. For calcium and cadmium, flame AAS was used. It was observed that change in PEI concentration results in change in measured concentration of calcium and cadmium. Therefore, two new approaches were developed for accurate measurement of concentrations of calcium and cadmium. It was also observed that presence of other metals did not affect the accuracy of the measurement of a particular metal in the concentration range studied.     

数据库财务管理系统中的后台监督

本文借鉴某些大型机上的系统日记的思想,为自己的财务系统建立一个后台监督系统,以解决凭证数据库的数据修改问题以及由此引起的完整性和安全性问题。     

The least element property of center location on tree networks with applications to distance and precedence constrained problems

In the classicalp-center location model on a network there is a set of customers, and the primary objective is to selectp service centers that will minimize the maximum distance of a customer to a closest center. Suppose that thep centers receive their supplies from an existing central depot on the network, e.g. a warehouse. Thus, a secondary objective is to locate the centers that optimize the primary objective as close as possible to the central depot. We consider tree networks and twop-center models. We show that the set of optimal solutions to the primary objective has a semilattice structure with respect to some natural ordering. Using this property we prove that there is ap-center solution to the primary objective that simultaneously minimizes every secondary objective function which is monotone nondecreasing in the distances of thep centers from the existing central depot.Restricting the location models to a rooted path network (real line) we prove that the above results hold for the respective classicalp-median problems as well.     

Modern Analysis

The important advances being made in modern analytical methods are indicative of the fundamental changes that are occuring in the theory and practice of “analytical chemistry”. “Information optimization” demands a new approach in teaching and research, and calls for the intergration of chemistry with other scientific and technical disciplines.     

Application of similarity aggregation techniques to a population of paraplegic patients

We studied a population of paraplegic patients in order to give prominence to a possible relationship between the topography of their spinal lesion and the occurrence of special articular diseases (P.O.A.). According to the motor and sensory state of their spinal cord, we first tried to obtain a classification of these lesions (the usual one schematically separates ‘flaccid’ and ‘rigid’ paraplegics). We mainly put the emphasis on this clustering step of the study:

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