智模数(IFN)在密码科学中的研究及应用

为了消除传统的纯数字加密技术(PNCT)保密性的缺陷,即泄密性、期限性和死密性,提出了一种新的基于变进数(VCN)智能特性的PNCT。传统的PNCT生成的准密码数均是恒进数(FCN),其变化规则(FCR)的单一性、机械性和难记性,造成其保密性缺陷。VCN则是FCN的拓展,是一种新的更为广义概念上的数,但其变化规则(VCR)的复杂性、智能性和灵活性,可以克服FCN的保密性缺陷。

基于VCR智能特性的系统信息数值处理技术

智能系统信息处理功能的结构要求,具有传统处理技术继承性、现代拓展模式多样性、未来新兴科技之兼容性和综合智能特点的集成性。21世纪信息处理技术的发展趋势是智能信息系统化。变进制(VCR)是基于传统数论中用恒进制(FCR)基础上创新拓展起来的更广义概念上的计数规则,其计数特性能给传统的智能信息数值处理技术带来更多创新和更好应用,并给出了ASCII码字符信息加密与解密的数值计算技术,空间三维立体真实图形表面(Surface)的视觉可视性计算,以及抗扰动数值计算技术等实例。

基于VCN的综合信息智能数值处理技术

21世纪信息处理技术的发展趋势是综合智能系统化。综合智能信息处理系统的功能结构要求,具有传统处理技术的继承性、现代拓展模式的多样性、未来新兴科技的兼容性和综合智能特点的集成性。变进数(VCN)是基于传统数论中在恒进规则(FCR)制基础上创新拓展起来的更广义概念上的数,它提出了其变进规则(VCR)制的计数特性能给传统的智能信息数值处理技术带来更多创新和更好应用的理论观点,并给出了文本信息加密与解密的数值计算技术,抗扰动数值计算的研究,3D实体表面视觉可视性的计算等实例。

神经网络计算与VCR智能融合技术

学习算法极易陷入局部极小值,很难获得全局极小值,并且其收敛速度极慢,这一直是实现BP网络快速算法的技术瓶颈。变进数(VCN)是基于传统数论中用恒进规则(FCR)制基础上创新拓展起来的更广义概念上的数,其变进规则(VCR)制的计数特性能给传统数值计算技术带来更多创新和更好应用的拓展空间。文章提出VCR智能计算用于3层BP网络快速算法和全局最优算法的融合技术,可以减少数字扰动误差,并较好地规避学习算法陷入局部极小值、找回全局极小值和降低搜索算法时间复杂性。

变进数VCN概述

文章"变进数VCN研究与应用"首发于1995年,可见文献[1][2]。本文介绍变进数VCN基本概念、理论处理技术及其研究、发展与应用的美丽前景。创新变进数VCN是恒进数FCN拓展,它是一种新的、人工智能AI而模糊的、广义概念上的数,其变化规则VCR复杂性、智能性和灵活性比较明显。但在国际数学和计算机科学领域,仍颇不为世人所知晓。当前,人们对数的研究、发展及使用深陷停滞不前多年的困惑境界的根本原因就是,局限于恒进数FCN"同一"恒进变化规则FCR里。同时指出了任意n位变进数VCN比相应位数恒进数FCN应用更加广泛,并给出一些实际应用研究例子。

Numerical simulation of intelligent compaction for subgrade construction

During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this paper establishes a relationship between the degree of compaction K and strain ε. The relationship between the compaction degree K and the shear strength of soil(cohesion c and frictional angle φ) was clearly established through indoor experiments. The subroutine UMAT in ABAQUS finite element numerical software was developed to realize an accurate calculation of the subgrade soil compaction quality. This value was compared and analyzed against the assumed compaction value of the model, thereby verifying the accuracy of the intelligent compaction calculation results for subgrade soil. On this basis, orthogonal tests of the influential factors(frequency, amplitude, and quality) for the degree of compaction and sensitivity analysis were carried out. Finally, the ‘acceleration intelligent compaction value’, which is based on the acceleration signal, is proposed for a compaction meter value that indicates poor accuracy. The research results can provide guidance and basis for further research into the accurate control of compaction quality for roadbeds and pavements.

A new accelerating algorithm for multi-agent reinforcement learning

In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.

Embedded integrated servo-controllers for intelligent modular actuators

This paper focuses on the development of an embedded integrated servo-controller （EISC） for servomotors. Comprising of mainly servo-controller and servo-amplifiers, this EISC is capable of controlling a wide range of servomotors to perform complieated tasks. Hence, integration of this EISC with a servomotor forms an intelligent modular actuator （IMA） that is essential to modern manufacturing industries. The development of such an EISC involves two major tasks： first, designing the hardware of a compact-sized and highly compatible EISC, and second, developing the software functions to facilitate its functionalities and capahilities. The developed EISC hence forms an integrated-servo-eontrol module, which determines the capability, functionality, flexibility and responsiveness of these IMAs.

Expert System for 3D Collar Intelligent Design

A method to set up 3D collar prototype is developed in this paper by using the technique of cubic spline and bicubic surface patch. Then the relationship between the parameters of 3D collar prototype and different collar styles are studied. Based on the relationship, we can develop some algorithms of transferring style requirements to the parameters value of the collar prototype, and obtain some generation rules for the design of 3D collar style. As such, the knowledge base can be constructed, and the intelligent design system of 3D collar style is built. Using the system, various 3D collar styles can be designed automatically to satisfy various style requirements.

CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.

Task Assignment Problem of Robots in a Smart Warehouse Environment

The task assignment problem of robots in a smart warehouse environment （TARSWE） based on cargo-to-person is investigated. Firstly, the sites of warehouse robots and the order picking tasks are given and the task assignment problem for picking one order is formulated into a mathematical model to minimize the total operation cost. Then a heuristic algorithm is designed to solve the task assignment problem for picking multiple orders. Finally, simulations are done by using the orders data of online bookstore A. The results show that using the heuristic algorithm of this paper to assign robots, the cost was reduced by 2% and it can effectively avoid far route and unbalanced workload of robots. The feasibility and validity of the model and algorithm are verified. The model and algorithm in this paper provide a theoretical basis to solve the TARSWE.

Data Recovery Based on Intelligent Pattern Matching

To solve the problem of data recovery on free disk sectors, an approach of data recovering based on intelligent pattern matching is proposed in this paper. Different from the methods based on the file directory, this approach utilizes the consistency among the data on the disk. A feature pattern library is established based on different types of fries according to the internal constructions of text. Data on sectors will be classified automatically by data clustering and evaluating. When the conflict happens on data classification, the digestion will be initiated by adopting context pattern. Based on this approach, the paper achieved the data recovery system aiming at pattern matching of txt, word and PDF fries. Raw and formatting recovery tests proved that the system works well.

Mathematical Model and Intelligent Control of a Quadcopter, with Non-conventional Membership Functions

This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-conventional membership functions for the most instable states, in order to get a fast and effective response.

Fast generation method of fuzzy rules and its application to flux optimization in process of matter converting

A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.

A new model of foraging behavior in ant system

Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swarm intelligence (SI), a mean field model is given and analyzed in foraging process with three sources in this paper. The distance of trails and the richness of each source are considered. Both of the theoretical numerical analysis and Monte Carlo simulation show the power law relationship between the completion time and the flux of foragers. The work presented here guides a better understanding on self-organization and swarm intelligence. It can be used to design more efficient, adaptive, and reliable intelligent systems.

On-line chatter detection using servo motor current signal in turning

Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.

One neural network approach for the surrogate turbulence model in transonic flows

With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.

Broken Wires Diagnosis Method Numerical Simulation Based on Smart Cable Structure

The smart cable with embedded distributed fiber optical Bragg grating （FBG） sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on cable force and stress distribution state of steel wires was put forward. By establishing the bridge-cable and cable-steel wires model, the broken wires sample database was simulated numerically. A method of the characterization cable state pattern which can both represent the degree and location of broken wires inside a cable was put forward. The training and predicting results of the sample database by the back propagation （BP） neural network showed that the proposed broken wires diagnosis method was feasible and expanded the broken wires diagnosis research area by using the smart cable which was used to be only representing cable force.