REACHABILITY/CONTROLLABILITY OF HIGH ORDER MIX-VALUED LOGICAL NETWORKS

This paper addresses the teachability/controllability of high order mix-valued logical control networks by using the semi-tensor product method, and presents some necessary and sufficient conditions for the reachability/controllability. The high order mix-valued logical network is converted into an algebraic form first, baaed on which the reachability/controllability of the system is then investigated, and several necessary and sufficient conditions are established. The study of several illustrative examples shows that our new method is very effective in dealing with the reachability/controllability of high order mix-valued logical control networks.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

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.

Robust Feedback Set Stabilization of Logic Networks with State-Dependent Uncertain Switching and Control Constraints

This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs)with state-dependent uncertain switching and control constraints.First,based on the properties of the semi-tensor product of matrices and the vector representation of logic,an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form.Second,an input transformation and a switching model are constructed to transfer the original SLCN into one with a free control input and arbitrary switching.The equivalence between the set stabilizability of the original SLCN and that of the resulting SLCN is established.Based on such equivalence,the authors propose a necessary and sufficient condition for robust feedback set stabilizability.Finally,an example is presented to demonstrate the application of the results obtained.

Research on Action Recognition and Content Analysis in Videos Based on DNN and MLN

In the current era of multimedia information,it is increasingly urgent to realize intelligent video action recognition and content analysis.In the past few years,video action recognition,as an important direction in computer vision,has attracted many researchers and made much progress.First,this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network.Second,we analyze the characteristics of each method and the performance from the experiment results.Then compare the emphases of these methods and discuss the application scenarios.Finally,we consider and prospect the development trend and direction of this field.

On-line Tool Wear Classification in Unmanned-machining Environments

One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system composed of multi-sensors, signal processing devices and intelligent decision making pla ns is a necessary requirement for automatic manufacturing processes. An intellig ent tool wear monitoring system will be introduced in this paper. The system is equipped with power consumption, vibration, AE and cutting force sensors, signal transformation and collection apparatus and a microcomputer. Tool condition monitoring is a pattern recognition process in which the characte ristics of the tool to be monitored are compared with those of the standard mode ls. The tool wear classification process is composed of the following parts: fea ture extraction; determination of the fuzzy membership functions of the features ; calculation of the fuzzy similarity; learning and tool wear classification. Fe atures extracted from the time domain and frequency domain for the future patter n recognition are as follows. Power consumption signal: mean value; AE-RMS sign al: mean value, skew and kutorsis; Cutting force, AE and vibration signal: mean value, standard deviation and the mean power in 10 frequency ranges. These signa l features can reflect the tool wear states comprehensively. The fuzzy approachi ng degree and the fuzzy distance between corresponding features of different obj ects are combined to describe the closeness of two fuzzy sets more accurately. A unique fuzzy driven neural network based pattern recognition algorithm has bee n developed from this research. The combination of Artificial Neural Networks (A NNs) and fuzzy logic system integrates the strong learning and classification ab ility of the former and the superb flexibility of the latter to express the dist ribution characteristics of signal features with vague boundaries. This methodol ogy indirectly solves the automatic weight assignment problem of the conventiona l fuzzy pattern recognition system and let it have greater representative power, higher training speed and be more robust. The introduction of the two-dimensio nal weighted approaching degree can make the pattern recognition process more re liable. The fuzzy driven neural network can effectively fuse multi-sensor i nformation and successfully recognize the tool wear states. Armed with the advan ced pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for different machini ng conditions, robust to noise and tolerant to faults. Cooperated with the contr ol system of the machine tool, the optimized machining processed can be achieved .

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.

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.

On observability of Galois nonlinear feedback shift registers over finite fields

Observability ensures that any two distinct initial states can be uniquely determined by their outputs,so the stream ciphers can avoid unobservable nonlinear feedback shift registers(NFSRs)to prevent the occurrence of equivalent keys.This paper discusses the observability of Galois NFSRs over finite fields.Galois NFSRs are treated as logical networks using the semi-tensor product.The vector form of the state transition matrix is introduced,by which a necessary and sufficient condition is proposed,as well as an algorithm for determining the observability of general Galois NFSRs.Moreover,a new observability matrix is defined,which can derive a matrix method with lower computation complexity.Furthermore,the observability of two special types of Galois NFSRs,a full-length Galois NFSR and a nonsingular Galois NFSR,is investigated.Two methods are proposed to determine the observability of these two special types of NFSRs,and some numerical examples are provided to support these results.

State space optimization of finite state machines from the viewpoint of control theory

Motivated by the inconvenience or even inability to explain the mathematics of the state space optimization of finite state machines(FSMs)in most existing results,we consider the problem by viewing FSMs as logical dynamic systems.Borrowing ideas from the concept of equilibrium points of dynamic systems in control theory,the concepts of t-equivalent states and t-source equivalent states are introduced.Based on the state transition dynamic equations of FSMs proposed in recent years,several mathematical formulations of t-equivalent states and t-source equivalent states are proposed.These can be analogized to the necessary and sufficient conditions of equilibrium points of dynamic systems in control theory and thus give a mathematical explanation of the optimization problem.Using these mathematical formulations,two methods are designed to find all the t-equivalent states and t-source equivalent states of FSMs.Further,two ways of reducing the state space of FSMs are found.These can be implemented without computers but with only pen and paper in a mathematical manner.In addition,an open question is raised which can further improve these methods into unattended ones.Finally,the correctness and effectiveness of the proposed methods are verified by a practical language model.

Linearization of Multi-Valued Nonlinear Feedback Shift Registers

Nonlinear feedback shift registers(NFSRs) have been used in many stream ciphers for cryptographic security. The linearization of NFSRs is to describe their state transitions using some matrices. Such matrices are called their state transition matrices. Compared to extensive work on binary NFSRs, much less work has been done on multi-valued NFSRs. This paper uses a semi-tensor product approach to investigate the linearization of multi-valued NFSRs, by viewing them as logical networks. A new state transition matrix is found for a multi-valued NFSR, which can be simply computed from the truth table of its feedback function. The new state transition matrix is easier to compute and is more explicit than the existing results. Some properties of the state transition matrix are provided as well, which are helpful to theoretically analyze multi-valued NFSRs.

Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm

In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.

KeEL: knowledge enhanced entity linking in automatic biography construction

Biography is a direct and extensive way to know the representation of well known peoples, however, for common people, there is poor knowledge for them to be recognized. In recent years, information extraction （IE） technologies have been used to automatically generate biography for any people with online information. One of the key challenges is the entity linking （EL） which can link biography sentence to corresponding entities. Currently the used general EL systems usually generate errors originated from entity name variation and ambiguity. Compared with general text, biography sentences possess unique yet rarely studied relational knowledge （RK） and temporal knowledge （TK）, which could sufficiently distinguish entities. This article proposed a new statistical framework called the knowledge enhanced EL （KeEL） system for automated biography construction. It utilizes commonsense knowledge like PK and TK to enhance Entity Linking. The performance of KeEL on Wikipedia data was evaluated. It is shown that, compared with state-of-the-art method, KeEL significantly improves the precision and recall of Entity Linking.

DLP Learning from Uncertain Data

Description logic programs （DLP） are an expressive but tractable subset of OWL. This paper ana-lyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncer-tainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.