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。     

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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Fluorescence monitoring of ultrasound degradation processes

Ultrasound-based water treatment is often applied for degradation of stable organic pollutants, such as polycyclic aromatic hydrocarbons and halogenated compounds. Monitoring the degradation process, during the application of ultrasound radiation, is of considerable economical interest. In this work, the possibility of performing on-line spectral analysis during sonication was examined and it was found that direct absorption or fluorescence readings are misleading. Optical monitoring is strongly affected by the absorption and scattering of light by cavitation micro-bubbles and ultrasound induced particulates. A model was developed to account for these effects and to allow for on-line fluorescence analysis. The model takes into account the absorption and scattering coefficients of the micro-bubbles and particulates, as well as their time dependent concentration. The model parameters are found from independent measurements where the pollutants are added to already sonicated pure water. Then, the model is tested for predicting the actual fluorescence behavior during the sonication process. It has been shown that the model allows for recovery of the true degradation data, as obtained by off-line HPLC measurements.     

Optimization of a GFAAS method for determination of total inorganic arsenic in drinking water

The new 10 μg l⁻¹ arsenic standard in drinking water has been a spur to the search for reliable routine analytical methods with a limit of detection at the μg l⁻¹ level. These methods also need to be easy to handle due to the routine analyses that are required in drinking water monitoring. Graphite furnace atomic absorption spectrometry (GFAAS) meets these requirements, but the limit of detection is generally too high except for methods using a pre-concentration or separation step. The use of a high-intensity boosted discharge hollow-cathode lamp decreases the baseline noise level and therefore allows a lower limit of detection. The temperature program, chemical matrix modifier and thermal stabilizer additives were optimized for total inorganic arsenic determination with GFAAS, without preliminary treatment. The optimal furnace program was validated with a proprietary software. The limit of detection was 0.26 μg As l⁻¹ for a sample volume of 16 μl corresponding to 4.2 pg As. This attractive technique is rapid as 20 samples can be analysed per hour. This method was validated with arsenic reference solutions. Its applicability was verified with artificial and natural groundwaters. Recoveries from 91 to 105% with relative standard deviation <5% can be easily achieved. The effect of interfering anions and cations commonly found in groundwater was studied. Only phosphates and silicates (respectively at 4 and 20 mg l⁻¹) lead to significant interferences in the determination of total inorganic arsenic at 4 μg l⁻¹.