Quantum Logic Networks for Probabilistic Teleportation of an Arbitrary Three-Particle State

The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an arbitrary three-particle state are constructed.

Quantum Logic Network for Probabilistic Cloning Quantum States

We construct efficient quantum logic network for probabilistic cloning the quantum states used in imple mented tasks for which cloning provides some enhancement in performance.

Probabilistic seismic hazard assessment of Nepal using multiple seismic source models

The potential for devastating earthquakes in the Himalayan orogeny has long been recognized. The 2015 MW7.8 Gorkha, Nepal earthquake has heightened the likelihood that major earthquakes will occur along this orogenic belt in the future. Reliable seismic hazard assessment is a critical element in development of policy for seismic hazard mitigation and risk reduction. In this study, we conduct probabilistic seismic hazard assessment using three different seismogenic source models(smoothed gridded, linear, and areal sources)based on the complicated tectonics of the study area. Two sets of ground motion prediction equations are combined in a standard logic tree by taking into account the epistemic uncertainties in hazard estimation. Long-term slip rates and paleoseismic records are also incorporated in the linear source model. Peak ground acceleration and spectral acceleration at 0.2 s and 1.0 s for 2% and 10%probabilities of exceedance in 50 years are estimated. The resulting maps show significant spatial variation in seismic hazard levels. The region of the Lesser Himalaya is found to have high seismic hazard potential. Along the Main Himalayan Thrust from east to west beneath the Main Central Thrust, large earthquakes have occurred regularly in history; hazard values in this region are found to be higher than those shown on existing hazard maps. In essence, the combination of long span earthquake catalogs and multiple seismogenic source models gives improved seismic hazard constraints in Nepal.

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.

Temperature-induced logical resonance in the Hodgkin–Huxley neuron

Logical resonance has been demonstrated to be present in the Fitz Hugh-Nagumo(FHN)neuron,namely,the FHN neuron can operate as a reliable logic gate within an optimal parameter window.Here we attempt to extend the results to the more biologically realistic Hodgkin-Huxley(HH)model of neurons.In general,biological organisms have an optimal temperature at which the biological functions are most effective.In view of this,we examine if there is an optimal range of temperature where the HH neuron can work like a specific logic gate,and how temperature influences the logical resonance.Here we use the success probability P to measure the reliability of the specific logic gate.For AND logic gate,P increases with temperature T,reaches the maximum in an optimal window of T,and eventually decreases,which indicates the occurrence of the temperature-induced logical resonance phenomenon in the HH neuron.Moreover,single and double logical resonances can be induced by altering the frequency of the modulating periodic signal under the proper temperatures,suggesting the appearance of temperature-controlled transition of logical resonance.These results provide important clues for constructing neuron-based energy-efficient new-fashioned logical devices.

A REALIZATION OF FUZZY LOGIC BY A NEURAL NETWORK

This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.

Employing fuzzy logic in the diagnosis of a clinical case

Fuzzy logic is a logical calculus which operates with many truth values (while classical logic works with the two values of true and false). Since fuzzy logic considers the truth of scientific statements like something softened, it is fruitfully applied to the study of biological phenomena, biology is indeed considered the field of complexity, uncertainty and vagueness. In this paper fuzzy logic is successfully applied to the clinical diagnosis of a patient who suffers from different diseases bound by a complex causal chain. In this work it is presented a mathematical foundation of fuzzy logic (with connectives and inference rules) and then the application of fuzzy reasoning to the study of a clinical case. Probabilistic logic is widely considered the unique logical calculus useful in clinical diagnosis, thus the usefulness of fuzzy logic and its relation with probabilistic logic is here explored. The presentation of the case is supplied with all the features necessary to affect a clinical diagnosis: physical exam, anamnesis and tests.

基于概率逻辑推理的高阶互补云API推荐方法

云时代,云API作为服务交付、数据交换和能力复制的最佳载体,已成长为当今面向服务软件开发和企业数字化转型不可或缺的核心要素.然而动态开放网络中持续增长的云API在给开发者提供了更多选择的同时,也将其淹没在海量的云API选择之中,设计有效的云API推荐方法就此成为API经济健康发展中迫切要解决的现实问题.但是,现有研究主要利用搜索关键词、服务质量和调用偏好进行建模,生成质量高功能单一的云API推荐列表,没有考虑服务化软件实际开发中开发者对多元化高阶互补云API的客观需要.高阶互补云API推荐旨在为多个查询云API生成多元互补云API列表,要求推荐结果与查询云API均互补,以满足开发者的联合需求.针对此问题,本文提出基于概率逻辑推理的高阶互补云API推荐方法(Probabilistic Logic Reasoning for High-order Complementary Cloud API Recom⁃mendation,PLR4HCCR).首先,通过云API生态真实数据分析论证云API互补推荐需求的必要性和互补关系建模中替补噪声的客观存在,为云API互补推荐问题研究提供动机和数据支持.其次,采用Beta概率嵌入对云API及其之间的关系约束进行编码,以刻画云API间互补关系的不确定性和支持互补逻辑推理.接着,设计由投影、取反和交并三个基本逻辑算子构建的互补关系逻辑推理网络,使查询集中的每个云API获得非对称互补关系感知和替补噪声消解约束下的互补云API表示.然后,引入注意力机制为查询云API的互补云API分配不同权重,增强高阶互补云API基向量的表征能力.在此基础上,采用KL散度度量高阶互补云API基向量与候选云API之间的距离,并根据KL散度排序生成高阶互补性可感知下的云API推荐结果.最后,我们利用两个真实云API数据集在不同阶互补推荐场景下进行实验,实验表明,与传统启发式推荐方法和深度学习推荐方法相比,PLR4HCCR在互补关系感知推理和替补噪声消解方面均具有较大的优势,继而使其在低阶、高阶和混合阶互补云API推荐中均展示出更优的推荐效果和更强的泛化能力.进一步,超参数敏感性实验、实例分析和用户调查验证了方法的有效性、实用性和可行性,这使结合高阶互补关系的云API推荐方法PLR4HCCR不仅更有可能生成开发者满意的结果,而且可有效提升云API服务提供者的收益.

融合物理理解与模糊逻辑的分类强对流客观短期预报系统:(2)表现评估

本文对分类强对流客观短期概率预报系统2022年6月13日强对流过程预报产品的表现进行分析,基于2022年的雷暴、短时强降水、雷暴大风及冰雹客观概率预报产品和可用的分类强对流监测实况资料,结合强对流预报业务中使用的空间检验方法和常用的确定性及概率性检验指标,对该短期预报系统提供的四类强对流天气客观概率预报产品进行了详细的性能评估。用于评估的预报资料是时段为2022年4月1日至9月30日每天08时(北京时)起报,96 h内逐12 h间隔的预报产品。预报个例分析显示,四类产品均可提前24 h指示需要关注的强对流天气区域。统计检验结果表明,短时强降水各方面性能最好,其次是雷暴,雷暴大风也有一定的可参考性。四类强对流天气预报产品均存在预报概率与实况频率相比偏高的过度预报问题。雷暴、短时强降水和雷暴大风预报产品均存在与预报覆盖时效有关的日变化。评估结果为预报模型和系统后续改进发展奠定了基础,为应用基于融合物理理解与模糊逻辑人工智能方法的分类强对流预报产品提供了有益参考。

基于SOA的光子脉冲LIF神经元研究

脉冲神经网络与传统人工神经网络相比,具有硬件友好性和低能耗的优点。而光子脉冲神经网络与电脉冲神经网络相比具有速度快、能耗低、传输容量大和抗电磁干扰能力强等优点。基于半导体光放大器(SOA)设计了一种新的光子脉冲神经元模型—LIF(泄漏积分与发射)模型,实现了信号的加权、延迟、积分和阈值判决等功能,并在此基础上探索了该LIF模型在数字逻辑中的应用。实现了基于该模型的“异或”逻辑功能,并改进了一种具有兴奋性和抑制性神经元,通过光通信仿真软件得到LIF模型、“异或”逻辑和具有兴奋性和抑制性神经元良好的输出。

Agent-Oriented Probabilistic Logic Programming

Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.

一种模糊时间序列概率预测方法

在时序预测任务中,历史观测值的不确定性给预测带来了困难。而模糊时间序列预测方法在处理数据不确定性方面具有独特的优势。概率预测则能够提供预测目标的分布情况,从而量化预测结果的不确定性。因此,为了减少不确定性对预测任务的影响,提出了一种基于概率加权策略的模糊时间序列概率预测方法。该方法利用预测目标的历史观测值建立概率加权的模糊时间序列预测模型,通过引入额外的观测值对预测模型的模糊规则库进行细化。在细化过程中,使用2种计算成本较低的算子重构模糊逻辑关系。具体地,交算子用于剔除干扰的信息,并算子则融合所有信息,从而得到2个不同的模糊逻辑关系组集合。当前时刻观测值在2个集合中对应的模糊逻辑关系组即为对下一时刻模糊集的预测,最后经过解模糊输出下一时刻的概率分布。在公开时间序列数据集上验证了该方法的准确性和有效性,与近期提出的PWFTS预测方法相比,预测精度有显著提高。

基于改进概率软逻辑模型的高校学生状态推理方法

随着高等教育的普及,各类管理决策需求增加,基于数据驱动和知识驱动的决策方法成为新的发展趋势。基于此,提出了一种通过改进概率软逻辑模型挖掘知识规律和推理学生状态的方法,该方法通过引入规则挖掘算法,赋予概率软逻辑模型自动挖掘规则的能力。在真实学生状态数据上的实验结果表明,该方法在多项目标关系的推理上,准确率能达到80%以上,并且多层次推理结构的实验效果优于非层次推理结构的实验效果。对于学生状态类非完整知识库数据的挖掘和推理,该方法减少了人工工作量,降低了因人为认知偏差带来的不确定性。

贝叶斯程序分析

程序分析在软件开发和维护中发挥着关键作用.然而,传统基于逻辑的程序分析方法在处理现代复杂、大规模和动态特性丰富的软件系统时往往效果有限,其根源在于软件系统中的不确定性.研究人员针对具体的程序分析问题提出了一系列新的技术,其特征是在传统逻辑分析的基础上结合概率信息来捕获软件系统中的不确定性.通过总结和抽象这些已有工作,本文提出了贝叶斯程序分析框架,其核心思想是结合程序分析和贝叶斯统计推断,通过建模和更新关于程序的概率分布来推断有关程序行为的信息.贝叶斯程序分析采用概率逻辑编程来同时处理概率信息和逻辑信息,用统一的方式捕获了现有的多项不同工作,也能泛化到程序缺陷定位和差异调试等非传统程序静态分析任务上.本文给出了贝叶斯程序分析框架的定义,展示了该框架在程序分析和相关领域的应用,并展望了未来发展方向.

Finite-Time Observability of Probabilistic Logical Control Systems

This study investigates finite-time observability of probabilistic logical control systems(PLCSs)under three definitions(i.e.,finite-time observability with probability one,finite-time singleinput sequence observability with probability one,and finite-time arbitrary-input observability with probability one).The authors adopt a parallel extension technique to recast the finite-time observability problem of a PLCS as a finite-time set reachability problem.Then,the finite-time set reachability problem can be transferred to stabilization problem of a logic dynamical system by using the state transfer graph reconstruction method.Necessary and sufficient conditions for finite-time observability under the three definitions are derived respectively.Finally,the proposed methods are illustrated by numerical examples.

用于Buck变换器监测的嵌入式概率数字孪生控制器

目前,数字孪生技术已经具备对给定物理系统进行测试和诊断的能力,可以完成对给定物理系统的预测和健康管理。该技术依靠给定物理系统实时更新的数据建立起相应的数字模型,并将数字模型作为与物理系统进行比较的并行参考,通过监测确定物理系统的运行状况。然而在电力电子功率变换器领域,使用此项技术进行控制器监测的研究相对较少,针对此问题,借助概率数字孪生和广义多项式混沌理论,提出一种用于Buck变换器监测的嵌入式概率数字孪生控制器。通过实验验证当功率变换器的占空比和元器件参数发生改变的情况下,所提方案的有效性和可行性。

Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and data.MLN can solve the problems of complexity and uncertainty,and has good knowledge expression ability.However,MLN structure learning is relatively weak and requires a lot of computing and storage resources.Essentially,the MLN structure is derived from sensor data in the current scene.Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be obtained.To this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning.Such a rulebase can reduce the time required for MLN structure learning.We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time.In addition,we evaluate the parameters of the rulebase building scheme to check its stability.

基于模糊规则和神经网络的新能源消纳数据清洗研究与应用

为了充分挖掘和高效利用新能源的消纳空间,需要在调度控制时考虑广域量测、环境、气象以及社会等多源信息。数据清洗是使用这些数据的前提,提出了一种数据清洗技术,分析了新能源消纳能力计算的数据来源以及数据间的隐含关联,将其转换为PSL形式的模糊规则,构建了面向新能源消纳数据清洗的神经网络。以张家口市张北县2020年风电数据为例进行数据清洗。结果表明:数据清洗并修复后的数据正确率达到98.3%,该技术能够有效地利用多源信息的关联映射关系,实现对不良数据的有效甄别。研究结果可为各方参与新能源建设和开展新能源价值挖掘提供可靠的数据支撑。

基于概率特征根分析的电力系统稳定器参数设计

多运行方式下的系统特征根将以一定的概率分布在某些区间内。正态假定下的均值、方差可用来描述这种分布特性。当使用电力系统稳定器（ＰＳＳ）改善系统阻尼时，主要应考虑ＰＳＳ对临界特征根的均值、方差的影响。文中将传统的特征根灵敏度分析方法扩展为对特征根均值的灵敏度和方差的灵敏度。由此进行的ＰＳＳ选址和参数设计，将使ＰＳＳ具有多运行方式下的整体合理性。

电力系统连锁性发展事件的概率评估与预防控制

电力系统偶然事件安全性评估的目的是为了提高系统安全性,但当偶然事件本身具有随机性时,如何恰当选择偶然事件会存在很多困难,如果选得过于冒进往往会导致预防控制费用过高,如果选得过于保守又可能会造成大面积停电事故的发生。文中应用证据理论对电力系统连锁发展复杂偶然事件的概率进行评估,并在此基础上对用户期望停电费用和预防控制的功率限值费用进行了分析,给出了优化选择预防控制的方法。所提出的方法不仅可以减少不必要的预防控制费用支出,还可以减少发生大面积停电事故的风险,提高系统运行的安全性。算例讨论了它的有效性。