Information Security Evaluation of Industrial Control Systems Using Probabilistic Linguistic MCDM Method

Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.

Resolving the Information Paradox with Probabilistic Spacetime

It has been 50 years since Hawking described the black hole (BH) information paradox. The combination of BH radiation and subsequent BH evaporation was found to take trapped information into oblivion contrary to the law of conservation of quantum information. Numerous attempts have been made since to resolve this paradox. A brief review herein documents how all these attempts have significant shortcomings, meaning the paradox is still unresolved. A relatively new cosmological theory offers a resolution despite not being developed for that purpose. The theory, entitled the probabilistic spacetime theory (PST), starts with an alteration in one basic assumption compared to all current cosmological theories. Spacetime, instead of being seen as a void or container of other entities, is viewed as the most fundamental entity in the universe, composed of energy fragments, and (in keeping with the conservation principle) impermeable to destruction. The potential contribution of the PST in resolving the information paradox is delineated, with the finding that the single change in the conceptualization of spacetime results in the disappearance of the paradox and not information.

The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems

For the moment, the representative and hot research is decision-theoretic rough set (DTRS) which provides a new viewpoint to deal with decision-making problems under risk and uncertainty, and has been applied in many fields. Based on rough set theory, Yao proposed the three-way decision theory which is a prolongation of the classical two-way decision approach. This paper investigates the probabilistic DTRS in the framework of intuitionistic fuzzy information system (IFIS). Firstly, based on IFIS, this paper constructs fuzzy approximate spaces and intuitionistic fuzzy (IF) approximate spaces by defining fuzzy equivalence relation and IF equivalence relation, respectively. And the fuzzy probabilistic spaces and IF probabilistic spaces are based on fuzzy approximate spaces and IF approximate spaces, respectively. Thus, the fuzzy probabilistic approximate spaces and the IF probabilistic approximate spaces are constructed, respectively. Then, based on the three-way decision theory, this paper structures DTRS approach model on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. So, the fuzzy decision-theoretic rough set (FDTRS) model and the intuitionistic fuzzy decision-theoretic rough set (IFDTRS) model are constructed on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. Finally, based on the above DTRS model, some illustrative examples about the risk investment of projects are introduced to make decision analysis. Furthermore, the effectiveness of this method is verified.

Extending Qualitative Probabilistic Network with Mutual Information Weights

Bayesian network (BN) is a well-accepted framework for representing and inferring uncertain knowledge. As the qualitative abstraction of BN, qualitative probabilistic network (QPN) is introduced for probabilistic inferences in a qualitative way. With much higher efficiency of inferences, QPNs are more suitable for real-time applications than BNs. However, the high abstraction level brings some inference conflicts and tends to pose a major obstacle to their applications. In order to eliminate the inference conflicts of QPN, in this paper, we begin by extending the QPN by adding a mutual-information-based weight (MI weight) to each qualitative influence in the QPN. The extended QPN is called MI-QPN. After obtaining the MI weights from the corresponding BN, we discuss the symmetry, transitivity and composition properties of the qualitative influences. Then we extend the general inference algorithm to implement the conflict-free inferences of MI-QPN. The feasibility of our method is verified by the results of the experiment.

Probabilistic and Hierarchical Quantum Information Splitting Based on the Non-Maximally Entangled Cluster State

With the emergence of classical communication security problems,quantum communication has been studied more extensively.In this paper,a novel probabilistic hierarchical quantum information splitting protocol is designed by using a non-maximally entangled four-qubit cluster state.Firstly,the sender Alice splits and teleports an arbitrary one-qubit secret state invisibly to three remote agents Bob,Charlie,and David.One agent David is in high grade,the other two agents Bob and Charlie are in low grade.Secondly,the receiver in high grade needs the assistance of one agent in low grade,while the receiver in low grade needs the aid of all agents.While introducing an ancillary qubit,the receiver’s state can be inferred from the POVM measurement result of the ancillary qubit.Finally,with the help of other agents,the receiver can recover the secret state probabilistically by performing certain unitary operation on his own qubit.In addition,the security of the protocol under eavesdropping attacks is analyzed.In this proposed protocol,the agents need only single-qubit measurements to achieve probabilistic hierarchical quantum information splitting,which has appealing advantages in actual experiments.Such a probabilistic hierarchical quantum information splitting protocol hierarchical is expected to be more practical in multipartite quantum cryptography.

Nonbinary ldpc-coded probabilistic shaping scheme for MIMO systems based on signal space diversity

This paper first describes a binary Low-Density Parity-Check(LDPC)-coded Probabilistic Shaping(PS)scheme for Multiple-Input Multiple-Output(MIMO)systems based on Signal Space Diversity(SSD).Second,a Nonbinary(NB)LDPC-coded PS scheme for MIMO systems based on SSD is proposed.The first scheme can be used to obtain a shaping gain,whereas the second can also realize a coding gain.The theoretical average mutual information of the optimized rotated quadrature amplitude modulation constellations is analyzed and the simulated error per-formance with 22 and 44 MIMO schemes is investigated.The theoretical average mutual information analysis and simulation results show that the proposed NB LDPC-coded PS scheme for MIMO systems based on SSD is reliable and robust,and is therefore suitable for future wireless communication systems.

基于策略梯度Informer模型的滚动轴承剩余寿命预测

典型的编码器-解码器——Transformer存在二次时间复杂度、高内存使用及模型结构复杂等固有限制,造成Transformer用于滚动轴承剩余寿命(RUL)预测会表现出较低预测精度和较低计算效率的问题。为此,提出一种新型编解码器——策略梯度Informer(PG-Informer)模型,并将其应用于滚动轴承RUL预测。首先,在PG-In-former的新型编解码器体系结构——Informer中设计了概率稀疏自注意力(PSSA)机制替代Transformer中原有的自注意力机制,以提高非线性逼近能力并减少时间和空间复杂度;然后,PG-Informer采用自注意力蒸馏(SAD)操作减少参数维度和参数量,并提高了对时间序列的预测鲁棒性;此外,PG-Informer的生成式解码器对解码输入进行一步解码输出预测结果,无需动态多步解码,提升了对时间序列的预测速度;最后,构造了策略梯度学习算法来提高对PG-Informer参数的训练速度。PG-Informer的以上优势使所提出的基于PG-Informer模型的滚动轴承RUL预测方法可以获得较高的预测精度、较好的鲁棒性和较高的计算效率。对辛辛那提大学智能维护系统中心的1号滚动轴承的RUL预测实验结果表明,所提出方法预测得到的RUL值为963min,其RUL预测误差仅为6.50%,比基于Transformer的RUL预测方法预测精度更高、预测误差更小、鲁棒性更好;所提出方法所耗费的RUL预测时间仅为132.37s,比基于Transformer的RUL预测方法的预测时间更短。以上实验结果验证了所提出方法的有效性。

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

Probabilistic Teleportation of an Unknown One-Particle State by a Three-Particle General W State

Two schemes for teleporting an unknown one-particle state are proposed when a general W state is utilized as quantum channel. In the first scheme, after the sender （Alice） makes a Bell-state measurement on her particles, the recipient （Bob） performs a Von Neumann measurement and introduces an auxiliary particle, and carries out a unitary transformation on his particle and the auxiliary particle, and performs a Von Neumann measurement on the auxiliary particle to confirm whether the teleportation succeeds or not. In the second scheme, the recipient （Bob） does not need to perform the first Von Neumann measurement or introduce the auxiliary particle, which is necessary in the first scheme. It is shown that the maxima/probabilities of successful teleportation of the two schemes are identical if the recipient （Bob） performs an appropriate unitary transformation and adopts a proper particle on which he recovers the quantum information of state to be teleported.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs

The use of agent technology in a dynamic environment is rapidly growing as one of the powerful technologies and the need to provide the benefits of the Intelligent Information Agent technique to massive open online courses, is very important from various aspects including the rapid growing of MOOCs environments, and the focusing more on static information than on updated information. One of the main problems in such environment is updating the information to the needs of the student who interacts at each moment. Using such technology can ensure more flexible information, lower waste time and hence higher earnings in learning. This paper presents Intelligent Topic-Based Information Agent to offer an updated knowledge including various types of resource for students. Using dominant meaning method, the agent searches the Internet, controls the metadata coming from the Internet, filters and shows them into a categorized content lists. There are two experiments conducted on the Intelligent Topic-Based Information Agent: one measures the improvement in the retrieval effectiveness and the other measures the impact of the agent on the learning. The experiment results indicate that our methodology to expand the query yields a considerable improvement in the retrieval effectiveness in all categories of Google Web Search API. On the other hand, there is a positive impact on the performance of learning session.

Robust Optimization Operation of Power System Considering the Non-probabilistic Uncertainty of Parameter

Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which contains unsufficient information.To solve this problem,a robust optimization operation method based on information gap decision theory(IGDT) is presented considering the non-probabilistic uncertainties of parameters.By the proposed method the maximum resistance to the disturbance of uncertain parameters is achieved and the optimization strategies with uncertain parameters are presented.Finally,numerical simulation is performed on the modified IEEE-14 bus system.Numerical results show the effectiveness of the proposed approach.

基于改进BNN-LSTM的风电功率概率预测

针对确定性的风电功率预测难以提供预测结果的波动区间和支撑风险决策的问题,以贝叶斯网络为基础,通过将先验分布置于LSTM网络层权重参数之上,构建了贝叶斯LSTM神经网络(BNN-LSTM)。以时间卷积神经网络(TCNN)处理风电功率预测的历史时序数据,提取时序数据的关联特征。使用互信息熵方法分析了风电功率的气象数据集,剔除关联性小的变量,对气象数据集进行降维处理。并采用嵌入(embedding)结构学习风电功率时间分类特征。随后将TCNN处理后的时序数据、降维后的气象数据以及时间分类特征数据一起送入BNN-LSTM预测模型,通过在某风电数据集不同算法的概率预测指标pinball损失和Winkler评分的对比验证,可知,本文所提方法能从可对风电功率波动做出较为准确的响应,预测效果更好。

三角模糊不完备三支群决策及其在糖尿病诊断中的应用

为同时应对不确定信息表示与风险信息融合对群决策带来的挑战,构建一种三角模糊不完备三支群决策方法,并将其应用于糖尿病诊断决策。首先,针对信息不确定性蕴含的模糊性和不完备性,分别引入三角模糊集和不完备信息系统的概念。通过与多粒度三支决策结合,构建了可调多粒度三角模糊概率粗糙集模型。然后,根据离差最大化法计算属性权重与专家权重,结合ELECTRE(elimination et choice translating reality)方法建立了三角模糊多属性群决策方法。最后,通过对糖尿病患者数据的案例分析和评估,验证了所提方法的可行性和有效性。该方法不仅从不确定信息表示、风险信息融合和最优粒度选择的视角丰富了多粒度三支群决策理论,而且推动了糖尿病智能诊断方面的应用。

具有最小信息延迟的多无人机路径规划方法

在一些实时性要求高的监测任务中,为提升多无人机协同工作的效能,提出了具有最小信息延迟的多无人机路径规划方法。首先定义最大信息延迟描述监测信息的时效性,综合考虑了无人机能量、禁飞区等多种约束,以无人机信息延迟和飞行总距离为优化目标,建立多无人机路径规划模型。然后,提出一种改进的多目标灰狼算法,引入交叉算子完成灰狼位置更新以增强其全局搜索能力,引入大规模邻域算法以提高其局部搜索能力。最后,利用概率路图算法对得到的飞行方案进行局部避障优化,从而得到最终的路径规划结果。仿真和实验结果表明,所提算法不仅可以得到很好的路径规划结果,而且对比NSGA-Ⅱ算法,本文算法得到的飞行路径总距离分别缩短了3.34%、5.09%,最大延迟时间降低了11.02%、15.66%,验证了所提算法的可行性和有效性。

考虑消费者信息更新的概率销售策略

概率销售模式下,部分商家不披露其产品抽中的概率信息,消费者需通过产品在线评论等收集信息以更新对抽取概率的预估,从而产生信息更新行为。此外,概率销售往往导致抽中不满意产品的消费者将产品转售。综合考虑消费者的信息更新和产品转售行为,本文分别构建了信息披露和不披露情形下的概率销售企业策略优化模型,求解并对比了不同信息披露情形下的产品定价及零售商利润,总结了信息精度等重要因素对零售商的定价、信息披露策略以及利润的影响。结论表明,信息不披露会降低概率产品价格。此外,当消费者获得的信息精度较低或转售价格较高时,零售商倾向于不披露产品的抽取概率信息;反之,则披露信息更有利。

基于概率主题模型的软件开发数据库隐私数据泄露识别方法研究

为了提升软件开发数据库的隐私数据安全性,提出基于概率主题模型的软件开发数据库隐私数据泄露识别方法。挖掘“主题-词”与“主题-文档”之间的概率分布,建立概率主题模型,通过该模型将数据转化为恶意代码并提取特征;同时结合概率检测算法与指纹对比判断恶意代码配置文件是否为敏感文件,将敏感恶意代码配置文件主题上的概率均值作为确定隐私数据泄漏的指标,实现隐私数据泄露识别。实验结果表明,该方法可100%识别软件开发数据库的隐私数据是否泄漏,而且针对不同欺骗率和失真率的网络攻击导致的软件开发数据库的隐私数据泄露,识别准确率依旧在98%以上,泄露识别优势显著。

基于深度学习与PMF的个性化学习推荐算法研究

针对概率矩阵分解(Probabilistic Matrix Factorization,PMF)模型仅利用用户对象历史行为数据进行预测估计导致估计值准确率不高的缺点,提出一种基于深度学习的cnnPMF算法。新的算法先通过卷积神经网络(Convolutional Neural Networks,CNN)对用户与对象的边信息进行特征提取,其次根据提取出来的特征向量与用户对象的历史行为数据进行融合,最后利用融合后的数据采用概论矩阵分解模型进行预测估计。仿真结果表明,与经典的PMF算法相比,cnnPMF算法在估计值的准确率方面具有明显优势。

二维变量信息视角下的概率犹豫模糊集决策方法研究

将概率犹豫模糊集中的隶属度与概率看作二维变量信息,在二维变量信息条件下研究概率犹豫模糊集多属性决策问题。首先,将概率犹豫模糊数书写形式改为点坐标形式,并在此形式下建立概率犹豫模糊元几何距离函数模型与离差程度系数模型;其次,属性的评价值为概率犹豫模糊元,在考虑其内部元素的离差程度与相互之间离差程度基础上,采用熵值法确定属性权重;再次,使用新定义的几何距离函数与离差程度系数函数计算由概率犹豫模糊元组成的属性值,得到属性的综合值,并利用Maclaurin对称平均算子对各方案属性的综合值进行集结,通过比较集结的方案属性综合值大小对各方案进行排序;最后用一个数值算例对本文方法进行验证分析,结果表明,该方法能够快速取得有效的排序结果。