人工智能在计算机辅助工艺设计中的应用

随着计算机软硬件技术的不断成熟,计算机辅助工艺设计的理论与方法已发生了质的飞跃。将人工智能理论应用于计算机辅助工艺设计是新近发展起来的研究热点之一,也是工业设计现代化发展趋势。它不仅可以把人工智能领域中的研究成果移植到计算机辅助工艺设计中,而且也扩大了人工智能的应用领域,使两者得到完美结合,促进共同发展。本文简要地叙述了计算机辅助工艺设计和人工智能的基本概念,探讨了人工智能在计算机辅助工艺设计中的应用。     

A collaborative and artificial intelligence approach for semiconductor cost forecasting

Forecasting the unit cost of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the unit cost. In order to effectively forecast the semiconductor unit cost, a collaborative and artificial intelligence approach is proposed in this study. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own fuzzy neural networks to forecast the semiconductor unit cost based on their viewpoints. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, a radial basis function (RBF) network is used. The effectiveness of the proposed methodology is shown with a case study.     

A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.     

医学影像人工智能辅助诊断的样本增广方法

针对不同领域人工智能（AI）应用研究所面临的采用常规手段获取大量样本时耗时耗力耗财的问题,许多AI研究领域提出了各种各样的样本增广方法。首先,对样本增广的研究背景与意义进行介绍;其次,归纳了几种公知领域（包括自然图像识别、字符识别、语义分析）的样本增广方法,并在此基础上详细论述了医学影像辅助诊断方面的样本获取或增广方法,包括X光片、计算机断层成像（CT）图像、磁共振成像（MRI）图像的样本增广方法;最后,对AI应用领域数据增广方法存在的关键问题进行总结,并对未来的发展趋势进行展望。经归纳总结可知,获取足够数量且具有广泛代表性的训练样本是所有领域AI研发的关键环节。无论是公知领域还是专业领域都进行样本增广,且不同领域甚至同一领域的不同研究方向,其样本获取或增广方法均不相同。此外,样本增广并不是简单地增加样本数量,而是尽可能再现小样本量无法完全覆盖的真实样本存在,进而提高样本多样性,增强AI系统性能。     

Developing an artificial retina by evolutionary cellular automata and self-organizing neural networks

Retinas are very important for human beings to get information about their environment. In this paper, we propose a new method to build artificial retinas which have many features similar to real ones. We use evolutionary cellular automata to extract some basic characteristics of objects, and use self-organizing neural networks to distinguish different objects. The results indicate a way to get computer vision by artificial life. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, Janaury 19–21, 1998     

Data differentiation and parameter analysis on the weight changes of premature babies with an artificial neuromolecular system

Prematurely born babies, whose weights are usually less than 2500 g, generally have nutritional deficiency problems, as most of their body functions have not developed completely. Total parental nutrition (TPN) has been one of the treatments commonly used by clinicians for improving their nutritional needs. This paper describes the application of an artificial neuromolecular system (ANM system), a self-organizing learning system, to investigate the factors (including TPN elements) that affect the weight changes of these babies. The system integrates intra- and inter-neuronal information processing that captures the gradual transformability feature of structure/function relationship embedded in biological systems. With this feature, the system is able to learn how to differentiate data in an autonomous manner. The system was applied to a database of prematurely born babies, comprising 274 records. Each record consisted of 30 parameters that might affect babies’ weights. Experimental results showed that the ANM system had better results than either the back-propagation neural networks or the SAS (a statistical tool). Contrary to our expectation, the result was even better than that of human judgment, suggesting that it could be used as tool to assist clinicians. Our parameter analysis showed that most of the parameters that the system identified as significant were identical to those employed by clinicians; however, some were not. The finding of the latter might provide another dimension of information to clinicians.     

An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks

This paper presents a new evolutionary artificial neural network (ANN) algorithm named IPSONet that is based on an improved particle swarm optimization (PSO). The improved PSO employs parameter automation strategy, velocity resetting, and crossover and mutations to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. IPSONet uses the improved PSO to address the design problem of feedforward ANN. Unlike most previous studies on only using PSO to evolve weights of ANNs, this study puts its emphasis on using the improved PSO to evolve simultaneously structure and weights of ANNs by a specific individual representation and evolutionary scheme. The performance of IPSONet has been evaluated on several benchmarks. The results demonstrate that IPSONet can produce compact ANNs with good generalization ability.     

Design optimization of a spatial six degree-of-freedom parallel manipulator based on artificial intelligence approaches

Optimizing the system stiffness and dexterity of parallel manipulators by adjusting the geometrical parameters can be a difficult and time-consuming endeavor, especially when the variables are diverse and the objective functions are excessively complex. However, optimization techniques that are based on artificial intelligence approaches can be an effective solution for addressing this issue. Accordingly, this paper describes the implementation of genetic algorithms and artificial neural networks as an intelligent optimization tool for the dimensional synthesis of the spatial six degree-of-freedom (DOF) parallel manipulator. The objective functions of system stiffness and dexterity are derived according to kinematic analysis of the parallel mechanism. In particular, the neural network-based standard backpropagation learning algorithm and the Levenberg–Marquardt algorithm are utilized to approximate the analytical solutions of system stiffness and dexterity. Subsequently, genetic algorithms are derived from the objective functions described by the trained neural networks, which model various performance solutions. The multi-objective optimization (MOO) of performance indices is established by searching the Pareto-optimal frontier sets in the solution space. Consequently, the effectiveness of this method is validated by simulation.     

Study on optimal design of pressure-adjusting spring using artificial neural networks

A preliminary discussion has been carried out on the traditional optimization design method for pressure-adjusting spring of relief valve. Based on the traditional optimization methods about the pressure-adjusting spring of the relief valve and combined with the advantages of neural network, this paper puts forward the optimization method with many parameters and a lot of constraints based on neural network in order to find the maximal inherent frequency. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure-adjusting spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure-adjusting spring. The specified technical performances of the relief valve are certified by experiments. The results of experiments showed that by configuring pressure-adjusting spring the dynamic performance and working stability of the relief valve are enhanced.     

Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.     

WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.     

Elite-guided multi-objective artificial bee colony algorithm

Multi-objective optimization has been a difficult problem and a research focus in the field of science and engineering. This paper presents a novel multi-objective optimization algorithm called elite-guided multi-objective artificial bee colony (EMOABC) algorithm. In our proposal, the fast non-dominated sorting and population selection strategy are applied to measure the quality of the solution and select the better ones. The elite-guided solution generation strategy is designed to exploit the neighborhood of the existing solutions based on the guidance of the elite. Furthermore, a novel fitness calculation method is presented to calculate the selecting probability for onlookers. The proposed algorithm is validated on benchmark functions in terms of four indicators: GD, ER, SPR, and TI. The experimental results show that the proposed approach can find solutions with competitive convergence and diversity within a shorter period of time, compared with the traditional multi-objective algorithms. Consequently, it can be considered as a viable alternative to solve the multi-objective optimization problems.     

基于RBF神经网络的涡轮性能预测

针对某型弹用航空发动机涡轮，建立了基于径向基函数神经网络的性能预测近似模型。由均匀设计提供训练样本，选取静叶叶身5个关键截面上的7个参数作为设计变量，涡轮效率作为输出变量，采用遗传算法对径向基网络进行训练，并和BP网络算法求解的模型进行了对比。结果表明：该算法能够广泛地利用样本空间，得到较高的训练和测试精度；构建的RBF网络具有较小的网络规模．较强的泛化能力。     

Using evolutionary artificial neural networks in monitoring binary and polytomous logistic profiles

A statistical profile is a relationship between a quality characteristic (a response) and one or more explanatory variables to characterize quality of a process or a product. Monitoring profiles or checking the stability of profiles over time, has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the non-normal response variable denoted by generalized linear models (GLM). Whereas, some of the potential applications of profile monitoring are cases where the response can be modelled using logistic profiles entailing binary, nominal and ordinal models. Also, most of existing control charts in this field have been developed by statistical approach and employing machine learning techniques have been rarely addressed in the related literature. Hence, to implement on-line process monitoring of logistic profiles, a novel artificial neural network (ANN) as a control chart with a heuristic training procedure is proposed in this paper. Performance of the proposed approach is investigated and compared using simulation studies in binary and polytomous models based on average run length (ARL) criterion. Simulation results revealed a good performance of the proposed approach. Nevertheless, to enhance the detection ability of the proposed approach more, the idea of combining run-rule which is a supplementary tool for making more sensitive control chart with final statistic is also implemented in this paper. Furthermore, a diagnostic method with machine learning schemes is employed to identify the shifted parameters in the profile. Results indicate the superior performance of the proposed approaches in most of the simulations. Finally, an example is used to illustrate the implementation of the proposed charting scheme.     

Least-cost design of singly and doubly reinforced concrete beam using genetic algorithm optimized artificial neural network based on Levenberg–Marquardt and quasi-Newton backpropagation learning techniques

In this work, least-cost design of singly and doubly reinforced beams with uniformly distributed and concentrated load was done by incorporating actual self-weight of beam, parabolic stress block, moment–equilibrium and serviceability constraint besides other constraints. Also, this design expertise was incorporated into a genetically optimized artificial neural network based on steepest descent, Levenberg–Marquardt, and quasi-Newton backpropagation learning techniques. The initial solution for the optimization procedure was obtained using limit state design as per IS: 456-2000.     

人工智能技术在网络空间安全防御中的应用

如何及时处理海量网络态势信息并有效应对动态演化的网络攻击是网络空间安全防御面临的主要挑战,人工智能技术由于具有传统方法所不具备的智能特性,近年来在网络空间安全防御中得到了广泛的关注,并取得了大量的研究成果。综述了近年来神经网络、多Agent系统以及专家系统等人工智能技术在网络空间安全防御中的主要应用和方法,分析比较了它们各自的应用特点,给出了未来研究与发展的趋势。     

A data clustering algorithm for stratified data partitioning in artificial neural network

The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANNs). Researchers have proposed optimized data partitioning (ODP) and stratified data partitioning (SDP) methods to partition of input data into training, validation and test datasets. ODP methods based on genetic algorithm (GA) are computationally expensive as the random search space can be in the power of twenty or more for an average sized dataset. For SDP methods, clustering algorithms such as self organizing map (SOM) and fuzzy clustering (FC) are used to form strata. It is assumed that data points in any individual stratum are in close statistical agreement. Reported clustering algorithms are designed to form natural clusters. In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean. Further, these algorithms are computationally expensive as well. We propose a custom design clustering algorithm (CDCA) to overcome these shortcomings. Comparisons are made using three benchmark case studies, one each from classification, function approximation and prediction domains. The proposed CDCA data partitioning method is evaluated in comparison with SOM, FC and GA based data partitioning methods. It is found that the CDCA data partitioning method not only perform well but also reduces the average CPU time.     

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.     

仿真技术在汽轮机组中的应用研究

该文利用人工神经网络模型结合模糊逻辑建立了汽轮机组的故障诊断系统，由于人工神经网络在实际应用中不涉及具体的物理模型，因而其结果具有很高的实际意义；该文利用模糊神经网络模型对某一实际的汽轮机组进行了故障诊断，诊断结果与检修实际情况是基本一致的，因此，该模型对动力系统的热力参数在线仿真，减少传感器的维护量，特别是对汽轮机组故障诊断技术水平的提高有很大的意义，与此同时使得基于参数采集的应用软件的可靠性程度也大大的增强了。