PARE:Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

Electric Multiple Unit Bogie Maintainability Allocation with Interval Analysis and Fuzzy Evaluation Method

Based on the failure rate and design features allocation method,considering the multiple influential factors which affect electric multiple unit( EMU) bogies,maintainability allocation on EMU bogie was presented by interval analytic hierarchy analysis and fuzzy comprehensive assessment. The maintainability allocation model was established. Weight based on the influence degree of each factor on maintenance was assigned. Fuzzy interval numbers were used to substitute real numbers and express uncertain information.The maintenance weighting factors for each subsystem were calculated by fuzzy comprehensive assessment. Then the allocation method was applied to EMU bogie. The results show that the method is feasible. The problem difficult to quantify for EMU bogie maintenance allocation is solved effectively.

Selection Method of Evaluation Indicators with Three-Parameter Interval Grey Number

The evaluation problem with three-parameter interval grey number (T-PIGN) widely exists in real world. To select effective evaluation indicators of the problem, this paper puts forward evaluation index system selection principle of T-PIGN based on distance entropy model, and gives out evaluation index system selection judgment criterion of T-PIGN. Furthermore, for the redundancy of evaluation index system with T-PIGN, a selection method of evaluation index system with T-PIGN is proposed. Finally, the applicability of the proposed method is verified by concrete examples.

Sustainability Evaluation of Modern Photovoltaic Agriculture Based on Interval Type-2 Fuzzy AHP-TOPSIS and Least Squares Support Vector Machine Optimized by Fireworks Algorithm

Photovoltaics(PV)has been combined with many other industries,such as agriculture.But there are many problems for the sustainability of PV agriculture.Timely and accurate sustainability evaluation of modern photovoltaic agriculture is of great significance for accelerating the sustainable development of modern photovoltaic agriculture.In order to improve the timeliness and accuracy of evaluation,this paper proposes an evaluation model based on interval type-2 Fuzzy AHP-TOPSIS and least squares support vector machine optimized by fireworks algorithm.Firstly,the criteria system of modern photovoltaic agriculture sustainability is constructed from three dimensions including technology sustainability,economic sustainability and social sustainability.Then,analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)methods are improved by using interval type-2 fuzzy theory,and the traditional evaluation model based on interval type-2 Fuzzy AHP-TOPSIS is obtained,and the improved model is used for comprehensive evaluation.After that,the optimal parameters of least squares support vector machine(LSSVM)model are obtained by Fireworks algorithm(FWA)training,and the intelligent evaluationmodel for the sustainability of modern photovoltaic agriculture is constructed to realize fast and intelligent calculation.Finally,an empirical analysis is conducted to demonstrate the scientificity and accuracy of the proposed model.This study is conducive to the comprehensive evaluation of the sustainability of modern photovoltaic agriculture,and can provide decision-making support for more reasonable development model in the future of modern photovoltaic agriculture.

Probability estimation based on grey system theory for simulation evaluation

In the evaluation of some simulation systems, only small samples data are gotten due to the limited conditions. In allusion to the evaluation problem of small sample data, an interval estimation approach with the improved grey confidence degree is proposed.On the basis of the definition of grey distance, three kinds of definition of the grey weight for every sample element in grey estimated value are put forward, and then the improved grey confidence degree is designed. In accordance with the new concept, the grey interval estimation for small sample data is deduced. Furthermore,the bootstrap method is applied for more accurate grey confidence interval. Through resampling of the bootstrap, numerous small samples with the corresponding confidence intervals can be obtained. Then the final confidence interval is calculated from the union of these grey confidence intervals. In the end, the simulation system evaluation using the proposed method is conducted. The simulation results show that the reasonable confidence interval is acquired, which demonstrates the feasibility and effectiveness of the proposed method.

Outsourced Privacy-Preserving Anomaly Detection in Time Series of Multi-Party

Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.Another problem is that privacy-preserving was not taken into account in these algorithms.In this paper,we propose a new data structure named Interval Hash Table(IHTable)to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table(ADIHT).The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast.Furthermore,to make our proposed algorithm fit for anomaly detection under multiple participation,we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption.Compared with existing anomaly detection schemes with privacy-preserving,OP-ADIHT needs less communication cost and calculation cost.Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants.Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC,and ADIHT needs the least time.

IFCEM based recognition method for target with interval-overlapped hybrid attributes

When the attributes of unknown targets are not just numerical attributes,but hybrid attributes containing linguistic attributes,the existing recognition methods are not effective.In addition,it is more difficult to identify the unknown targets densely distributed in the feature space,especially when there is interval overlap between attribute measurements of different target classes.To address these problems,a novel method based on intuitionistic fuzzy comprehensive evaluation model(IFCEM)is proposed.For numerical attributes,targets in the database are divided into individual classes and overlapping classes,and for linguistic attributes,continuous interval-valued linguistic term set(CIVLTS)is used to describe target characteristic.A cloud modelbased method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively.An improved inverse weighted kernel fuzzy c-means(IWK-FCM)algorithm is proposed for solution of attribute weight vector.The possibility matrix is applied to determine the identity and category of query target.Finally,a case study composed of parameter sensitivity analysis,recognition accuracy analysis.and comparison with other methods,is taken to verify the superiority of the proposed method.

Development of a Fuzzy Fire Risk Evaluation Model for Building Construction Sites in Hong Kong

Earlier research works on fire risk evaluation indicated that an objective,reliable,comprehensive,and practical fire risk evaluation model is essential for mitigating fire occurrence in building construction sites.Nevertheless,real empirical studies in this research area are quite limited.This journal paper gives an account of the second stage of a research study aiming at developing a fuzzy fire risk evaluation model for building construction sites in Hong Kong.The empirical research findings showed that the overall fire risk level of building construction sites is 3.6427,which can be interpreted as“moderate risk”.Also,the survey respondents perceived that“Restrictions for On-Site Personnel”is the most vital fire risk factor;with“Storage of Flammable Liquids or Dangerous Goods”being the second;and“Attitude of Main Contractor”the third.The proposed fuzzy fire risk evaluation model for building construction sites can be used to assess the overall fire risk level for a building construction site,and to identify improvement areas needed.Although the fuzzy fire risk evaluation model was developed domestically in Hong Kong,the research could be reproduced in other nations to develop similar models for international comparisons.Such an extension would provide a deeper understanding of the fire risk management on building construction sites.

Hybrid interval AHP-entropy method for electricity user evaluation in smart electricity utilization

Smart electricity utilization(SEU) is one of the most important components in a smart grid. It is crucial to evaluate efficiency, safety, and demand response capability of electricity users to achieve the smart use of electricity.The analytic hierarchy process(AHP) uses subjective criteria to determine index weights in multi-criteria decisionmaking problems, while the entropy method provides objectivity in determining index weights. Taking into account the uncertainty of expert scoring and user data, a hybrid interval analytic hierarchy process(IAHP) and interval entropy(IE) method is proposed for electricity user evaluation(EUE). Specifically, in the proposed method,electricity users are evaluated in terms of energy efficiency,safety monitoring, and demand response. The weights of EUE indices are calculated under uncertainty. The proposed approach derives subjective weights of EUE indicesby the IAHP with expert scoring as input data, and determines objective weights of EUE indices by the IE method with user data as inputs. In order to obtain the optimal combined index weights, the two weights are normalized by a selected weight factor. Numerical case studies illustrate the effectiveness and advantages of the proposed approach, which combines subjective and objective information to derive the optimal combined index weights.

Mathematical Statistics Method of Evaluating Application Effects of a New Type of Gas Anchor

Based on the basic formula of the confidence interval and the sampling error of mathematical statistics, the mathematical statistics method of evaluating application effects of a new type of gas anchor was given in this paper. By the method mentioned above, the confidence interval and the sampling errors of the relevant mean value differences of Daqing Oilfield S block’s 150 wells, according to the mean value differences of the liquid producing capacity per day, the oil production per day, the submergence depth of the 10 sampling test wells, in which before and after a new type of gas anchor were laid down, were calculated. The calculation results show that a new type of gas anchor has a better effect of increasing oil production of oil well and enhancing pump efficiency. Through the real value differences analysis of the liquid producing capacity per day, the oil production per day, the submergence depth of 150 wells mentioned above, in which before and after a new type of gas anchor were laid down, it was verified. By using the confidence interval and the sampling errors of the liquid producing capacity per day, the oil production per day, the submergence depth mentioned above, in which before and after a new type of gas anchor were laid down, the application effects of a new type of gas anchor could be evaluated. And a mathematical statistics method of evaluation application effects of a new type of gas anchor is presented.

区间函数型数据构权方法研究

针对由数据量过大而引起的函数型综合评价信息损失或是计算复杂度过大等问题,文中提出了一种基于区间函数型数据的综合评价方法,并针对区间函数型数据表支持的综合评价问题的特殊性,提出了一种新的确定权重系数的"全局"拉开档次法.相较于函数型综合评价,区间函数型综合评价在呈现连续的函数特征同时,增加了区间化的步骤,能够更好地挖掘数据信息,提升综合评价的效率.最后通过以义乌小商品景气指数为例,分别使用函数型数据形式和区间函数型数据形式下的"全局"拉开档次法对其进行赋权,进而进行评价研究.结果表明该文提出的区间函数型综合评价方法更具优势.

基于行驶特征区间划分的重型载货汽车实际道路能耗评价方法

提出一种基于行驶特征区间划分的能耗评价方法。通过对中国货车行驶工况的工况点分布特征进行分析,确定了速度和加速度区间的数量与边界的划分方案。考虑道路坡度的影响,进行实际道路试验数据筛选,并用于计算不同行驶特征区间内的燃油消耗量平均值。结合中国货车行驶工况在不同行驶特征区间内的累计行驶里程,计算能耗评价结果。为了验证该方法的有效性,开展了3次底盘测功机试验和2次实际道路行驶试验,结果表明,所提出的能耗评价方法能提高重型载货汽车实际道路能耗评价结果的复现性。

面向物理约束的机器人运动学标定最优位姿集规划方法研究

在物理约束下的工业机器人运动学标定过程中,标定精度受到位姿集的影响,而位姿集的选取又受到标定装置的约束,针对以上问题,提出了一种采样区间评价结合位姿集优选的最优位姿集规划方法。首先建立了机器人运动学模型及距离约束标定模型,计算了机器人系统参数误差约束方程及误差雅可比矩阵;然后对机器人工作空间进行空间网格划分,应用拉丁超立方采样结合可观测指标对各个网格区间进行评价,得到最优采样区间;再次基于离线数据建立标定精度预测模型,在最优采样区间内实现最优位姿集的搜索;最后对中瑞RT-608机器人进行最优位姿集的规划及验证,结果表明:基于最优位姿集标定后的平均拟合球半径为0.3947 mm,较随机位姿集减小了57.98%。

高强度间歇训练应用于冠心病患者康复效果的系统评价再评价

目的 对于高强度间歇训练(high-intensity interval training,HIIT)应用于冠心病(coronary heart disease,CHD)患者康复效果的系统评价/Meta分析再评价,为CHD患者进行康复运动提供循证依据。方法 计算机检索PubMed,Embase,Web of Science,Cochrane Library,CNKI,万方,维普和中国生物医学文献服务系统,搜集CHD患者HIIT康复效果的系统评价/Meta分析,时间从建库至2023年11月4日。经过循证护理培训考核合格的2名研究者进行文献筛选和资料提取,采用系统评价方法学质量评价工具(assessment of multiple systematic reviews 2,AMSTAR 2)量表、系统综述和Meta分析优先报告规范(preferred reportingitems for systematic reviews and Meta-analyses,PRISMA)声明、证据质量和推荐强度分级系统(grading of recommendations assessment,development and evaluation,GRADE)对纳入文献进行方法学质量评价、文献报告质量评价以及证据质量评价。结果 共纳入13篇系统评价。AMSTAR 2结果显示,1篇文献质量高,2篇低,10篇极低;PRISMA得分为16~26.5分,6篇报告相对完整,7篇报告存在一定缺陷;13篇系统评价的48条结局指标证据中,12条证据为中等质量,9条证据为低等质量,27条证据为极低等质量。与中等强度持续训练(moderate continuous training,MICT)比较,HIIT在6项主要结局指标:峰值摄氧量、无氧阈值、峰值心率、静息心率、左室射血分数和血压均得到更佳的康复效果。结论 HIIT在CHD患者康复中表现出更大的优势且安全性尚可,考虑到相关系统评价/Meta分析的方法学质量、文献报告质量以及证据质量级别较低,未来仍需开展多中心大样本的随机对照研究以进一步验证。

迈瑞CX-9000全自动凝血分析仪性能评估

目的:对迈瑞CX-9000全自动凝血分析仪的性能进行验证评估。方法:参照行业标准和国家标准,通过凝血酶原时间(prothrombin time,PT)、活化部分凝血活酶时间(activated partial thromboplastin time,APTT)、纤维蛋白原(fibrinogen,FIB)、凝血酶时间(thrombin time,TT)、D-二聚体5个凝血常规检查项目对迈瑞CX-9000全自动凝血分析仪进行性能验证,评估该仪器的精密度、准确度、线性范围、携带污染率及参考区间能否满足临床需求。结果:5个凝血常规检查项目的批内精密度变异系数(coefficient of variation,CV)均<3%,日间精密度CV均<5%,准确度相对偏差在±10%以内。FIB的线性回归方程斜率k为1.0060,相关系数r为0.9993,D-二聚体的线性回归方程k为1.0132,r为0.9975,线性良好。样本浓度携带污染率CR1为4.6%,项目携带污染率CR2均<2%,参考区间R值均为100%,均符合行业标准要求,验证通过。结论:迈瑞CX-9000全自动凝血分析仪是一款精密度好、准确度高、线性范围宽、携带污染率低的检测系统,能够满足临床需求。

基于切片采样-马尔科夫链蒙特卡洛模拟的高比例新能源电力系统等效惯量概率评估

风电、光伏新能源发电功率的不确定性给高比例新能源电力系统的等效惯量评估带来了新的挑战。基于确定性的惯量评估方法不能有效反映全分析时段高比例新能源电力系统惯量的时序变化态势。为了评估新能源波动所引起的系统惯量不确定性,该文提出了一种基于切片采样–马尔科夫链蒙特卡洛模拟(slice sample Markov chain Monte Carlo,S-MCMC)的系统等效惯量概率评估方法。首先,对高比例新能源电力系统惯量组成及特性进行了分析,并对同步旋转设备的有效机械惯量、风电光伏电源的有效虚拟惯量进行量化计算。然后,基于Copula函数建立风速和光照强度之间的相关性模型,利用S-MCMC对系统等效惯量进行概率评估,从而得到系统等效惯量时序变化区间。最后,以改进的IEEE 39节点系统进行仿真分析,验证了所提出方法及分析结果的有效性。

基于IT2F-BWM-MABAC方法的应急物流设施选址方案评价

针对不确定环境下应急物流设施选址方案评价问题,提出一种基于区间二型模糊集(IT2F)-最优最劣法(BWM)-多属性边界近似区域比较法(MABAC)的应急物流设施选址方案评价方法。首先,针对应急物流设施选址方案评价过程中评价信息的模糊不确定性问题,采用IT2F处理,并利用MABAC对选址方案排序;然后,针对传统的MABAC中属性权重需要从外部获得的问题,采用基于IT2F的BWM确定属性的权重;最后,将所提方法应用于某城市应急物流设施选址方案评价研究,并验证所提选址方法的可行性和有效性。结果表明:所提方法能够解决不确定环境下应急物流设施选址问题,同时能够保证方案评价的质量。

赣南生土历史建筑安全评价研究

为了对赣南生土历史建筑的结构安全进行准确评价,结合现有规范以及相关研究成果,提出了基于改进的模糊层次分析法的评价方法。首先,建立赣南生土历史建筑结构安全的多维度评价模型;其次,使用区间层次分析法确定相关指标权重;然后,采用多层次模糊综合评价对所建模型进行评价;最后,通过实际案例对建立的评价方法进行验证,结构表明评价模型贴合实际,可以为历史建筑保护工作提供安全现状依据。

区间粗糙数群组G1法的随机聚合求解及应用

针对复杂不确定环境下的群体评价问题,采用区间粗糙数表征专家偏好,在G1法的基础上,结合蒙特卡洛仿真技术,提出了一种随机模拟聚合算法.首先,通过随机抽样的方式模拟权重系数,并依据序相关性和区间粗糙数贴近度确定专家权重.其次,综合所有专家意见得到指标的最终权重,并将其与预处理后的指标值线性集结,求得一次模拟的综合评价值,据此判断被评价对象间的优劣.通过充分模拟得出优胜度矩阵,并从中推导出包含优胜概率的可能性排序,弥补了不确定信息环境下绝对排序呈现的不足.最后,通过算例说明了方法的有效性,并与现有方法对比阐述了所提方法的优势.

基于PPRR理论的化工罐区定量应急管理能力研究

化工储罐区是危险源数量极多的场所,通过传统的应急管理方法无法量化并表征储罐区的动态风险。因此,基于PPRR理论构建了化工罐区火灾应急管理能力定量评价模型;利用区间层次分析法确定了评估指标的权重,使用改进的模糊综合评价法,对化工罐区火灾应急管理能力进行了定量评价;运用基于PPRR理论的化工罐区火灾应急管理能力定量评价模型,对M石化公司化工罐区火灾应急管理能力进行评价,并对此模型的准确性进行了验证。结果表明,M石化公司化工罐区火灾应急管理能力的评价得分为3.35,与定性评价结果相吻合,验证了此模型的准确性;该模型能很好地解决指标重要程度不确定的问题,而改进的模糊综合评价法能指出化工罐区的欠缺之处及欠缺程度,可为完善和提高化工罐区火灾应急管理能力提供新思路。