采用标准数据接口SDAI的CAD／CAM系统集成

如何将ＳＴＥＰ的方法和规范应用于系统开发是解决计算机集成制造系统（ＣＩＭＳ）开发的关键问题之一。集成化ＣＡＤ／ＣＡＭ系统是ＣＩＭＳ的核心，实现将ＣＡＤ的工程设计功能和ＣＡＭ的制造功能的结合。本文讨论通过ＳＴＥＰ的标准数据接口ＳＤＡＩ实现系统集成的集成方法，采用ＳＴＥＰ的数据交换、建模思想，实现了一个产品模型为核心的ＣＡＤ、ＣＡＭ集成系统－ＧＨＣＡＤ（高化ＣＡＤ）。     

GHCAD：一个产品模型为基础的CAD／CAM集成系统

ＳＴＥＰ是关于产品数据表达和交换的国际标准，如何将ＳＴＥＰ的方法和规范应用于系统开发是解决计算机集成制造系统（ＣＩＭＳ）开发的关键问题之一。集成化ＣＡＤ／ＣＡＭ系统是ＣＩＭＳ的核心，实现将ＣＡＤ的工程设计功能和ＣＡＭ的制造功能的结合。本文介绍一个产品模型为基础的ＣＡＤ／ＣＡＭ集成系统－ＧＨＣＡＤ。系统采用ＳＴＥＰ的数据交换、建模思想，以产品模型为核心，通过ＳＴＥＰ的标准数据接口ＳＤＡＩ实现了系统的集成。     

基于PDM的CAD/CAPP、CAM集成

该文以８６３／ＣＩＭＳ示范工程的实践和开发为依据,对基于产品数据管理（ＰＯＭ）的ＣＡＤ／ＣＡＰＰ、ＣＡＭ集成进行了深入研究。文章首先给出了系统集成的原则,随后提出了ＰＤＭ的框架体系结构和基于ＰＤＭ的集成技术,最后讨论了基于ＳＴＥＰ的产品数据交换和基于ＳＤＡＩ标准数据接口技术。     

不连续生产系统的最大加工能力与最优生产安排的强多项式算法

不连续生产系统的最大加工能力与最优生产安排的强多项式算法杨承恩，梁枢里（长沙铁道学院）ＴＨＥＭＡＸＩＭＵＭＰＲＯＣＥＳＳＩＮＧＣＡＰＡＣＩＴＹＡＮＤＯＰＴＩＭＡＬＳＣＨＥＤＵＬＥＯＦＡＤＩＳＣＯＮＴＩＮＵＯＵＳＰＲＯＤＵＣＴＩＯＮＳＹＳＴＥＭ￥Ｙａｎ...     

CIMS系统集成接口的研究

本文讨论了ＣＩＭＳ系统中集成的数据接口和操作接口,并通过对ＣＩＭＳ系统集成接口的分析,提出了一种ＥＲＰ系统和ＰＤＭ系统以及３Ｃ系统（ＣＡＤ／ＣＡＰＰ／ＣＡＭ）集成的体系结构。     

并行计算模型及其算法设计

并行计算模型及其算法设计李晓梅，窦勇（国防科技大学计算机系）ＰＡＲＡＬＬＥＬＣＯＭＰＵＴＡＴＩＯＮＭＯＤＥＬＳＡＮＤＬＧＯＲＩＴＨＭＤＥＳＩＧＮ￥ＬｉＸｉａｏｍｅｉ；ＤｏｎＹｏｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅＣｈａｎｇｓｈ...     

I-DEAS GM加工——CAMAND的接力棒

ＳＤＲＣ公司是世界上最大的ＣＡＭ软件供应商之一,其ＣＡＭ产品除了享誉业界的ＣＡＭＡＮＤ之外,还有完全集成于Ｉ-ＤＥＡＳ工程环境中的加工模组ＧＭ（ Ｇｅｎｅｒａｔｉｖｅ Ｍａｃｈｉｎｉｎｇ　创成式加工）。ＧＭ不断汲取ＣＡＭＡＮＤ先进的数控技术,融汇其绝大部分功能和优点,形成了全新的刀具路径生成算法,成为ＭＤＡ集成环境下功能超强的加工模组。ＧＭ可以直接利用Ｉ－ＤＥＡＳ产生的实体或曲面几何,或由外部的ＣＡＤ系统输入的实体或曲面集合生成刀具路径;可以建立包含工件、毛坯、装夹和机床再内的完整的制造系统,从而…     

无约束优化的对角拟牛顿算法

无约束优化的对角拟牛顿算法林梦雄（中国科学院计算中心）首南祺（江西抚州师范专科学校）ＡＤＩＡＧＯＮＡＬＱＵＡＳＩ－ＮＥＷＴＯＮＡＬＧＯＲＩＴＨＭＦＯＲＵＮＣＯＮＳＴＲＡＩＮＥＤＯＰＴＩＭＩＺＡＴＩＯＮ￥ＬｉｎＭｅｎｇ－ｘｉｏｎｇ（ＣｏｍｐｕｔｉｎｇＣ...     

用VISUAL FOXPRO设计多媒体播放器

本文介绍了利用ＶＩＳＵＡＬＦＯＸＰＲＯ的应用程序接口ＦＯＸＴＯＯＬＳ调用ＷＩＮＤＯＷＳ的ＤＬＬ库开发多媒体播放器的原理及实现技术，利用该播放器可以对波形文件（．ＷＡＶ），数字音乐，（．ＭＩＤ），视频图象（．ＡＶＩ）动画（．ＦＬＡ）音频，压缩视频（．ＭＰＧ）Ｖ－ＣＤ视频，（．ＤＡＴ）等进行控制和播放。     

中国公用数据增值业务网

本文主要介绍中国公用传真存储转发业务网（ＣＨＩＮＡＦＡＸ）、电子数据互换业务网（ＣＨＩＮＡＥＤＩ）和电子信箱业务网（ＣＨＩＮＡＭＡＩＬ）的网络结构、规模、业务功能及用户接入方式。     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

Efficient representative pattern mining based on weight and maximality conditions

As a core area in data mining, frequent pattern (or itemset) mining has been studied for a long time. Weighted frequent pattern mining prunes unimportant patterns and maximal frequent pattern mining discovers compact frequent patterns. These approaches contribute to improving mining performance by reducing the search space. However, we need to consider both the downward closure property and patterns' subset checking process when integrating these different methods in order to prevent unintended pattern losses. Moreover, it is also essential to extract valid patterns with faster runtime and less memory consumption. For this reason, in this paper, we propose more efficient maximal weighted frequent pattern (MWFP) mining approaches based on tree and array structures. We describe how to handle these problems more efficiently, maintaining the correctness of our method. We develop two types of maximal weighted frequent mining algorithms based on weight ascending order and support descending order and compare these two algorithms to conclude which is more suitable for MWFP mining. In addition, comprehensive tests in this paper show that our algorithms are more efficient and scalable than state‐of‐the‐art algorithms, and they also have the correctness of the MWFP mining in terms of their pattern generation results.     

Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans

Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.     

Learning from positive and unlabeled examples

In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications.     

A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine

In order to reduce the energy consumption in the cloud data center, it is necessary to make reasonable scheduling of resources in the cloud. The accurate prediction for cloud computing load can be very helpful for resource scheduling to minimize the energy consumption. In this paper, a cloud load prediction model based on weighted wavelet support vector machine(WWSVM) is proposed to predict the host load sequence in the cloud data center. The model combines the wavelet transform and support vector machine to combine the advantages of them, and assigns weight to the sample, which reflects the importance of different sample points and improves the accuracy of load prediction. In order to find the optimal combination of the parameters, we proposed a parameter optimization algorithm based on particle swarm optimization(PSO). Finally, based on the WWSVM model, a load prediction algorithm is proposed for cloud computing using PSO-based weighted support vector machine. The Google cloud computing data set is used to verify the algorithm proposed in this paper by experiments. The experiment results indicate that comparing with the wavelet support vector machine, autoregressive integrated moving average, adaptive network-based fuzzy inference system and tuned support vector regression, the proposed algorithm is superior to the other four prediction algorithms in prediction accuracy and efficiency.     

Conditional Generative Adversarial Network Approach for Autism Prediction

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.     

Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones

Pervasive computing promotes the integration of smart devices in our living spaces to develop services providing assistance to people. Such smart devices are increasingly relying on cloud-based Machine Learning, which raises questions in terms of security (data privacy), reliance (latency), and communication costs. In this context, Federated Learning (FL) has been introduced as a new machine learning paradigm enhancing the use of local devices. At the server level, FL aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. Unfortunately, however, the most popular federated learning algorithms have been shown not to be adapted to some highly heterogeneous pervasive computing environments. In this paper, we propose a new FL algorithm, termed FedDist, which can modify models (here, deep neural network) during training by identifying dissimilarities between neurons among the clients. This permits to account for clients’ specificity without impairing generalization. FedDist evaluated with three state-of-the-art federated learning algorithms on three large heterogeneous mobile Human Activity Recognition datasets. Results have shown the ability of FedDist to adapt to heterogeneous data and the capability of FL to deal with asynchronous situations.     

基于HowNet概念获取的中文自动文摘系统

本文提出了一种中文自动文摘的方法。不同于其它的基于词频统计的一般方法,运用概念(词义)作为特征取代词语。用概念统计代替传统的词形频率统计方法,建立概念向量空间模型,计算出句子重要度,并对句子进行冗余度计算,抽取文摘句。对于文摘测试,采用两种不同的方法进行测试:一是用机器文摘和专家文摘进行比较的内部测试;二是对不同文摘方法进行分类,通过对分类正确率的比较的外部评测方法。     

Adaptive control of basis weight and moisture content for a paperboard machine

The development and industrial application of a MIMO adaptive control strategy for a paperboard machine are investigated. The control strategy incorporates a conventional regulatory control technique, multivariable k-incremental predictor and self-tuning control algorithm. The pulp consistency and flow rate, and the steam pressure are simultaneously manipulated to control the reel basis weight and moisture content. The control system demonstrates a satisfactory performance. The variations in reel basis weight and moisture content are greatly reduced.     

基于改进CGANs的入侵检测方法研究

近年来,机器学习算法在入侵检测系统(IDS)中的应用获得越来越多的关注。然而,传统的机器学习算法更多的依赖于已知样本,因此需要尽可能多的数据样本来对模型进行训练。遗憾地是,随着越来越多未知攻击的出现,且用于训练的攻击样本具有不平衡性,传统的机器学习模型会遇到瓶颈。文章提出一种将改进后的条件生成对抗网络(CGANs)与深度神经网络(DNN)相结合的入侵检测模型(CGANs-DNN),通过解决样本不平衡性问题来提高检测模型对未知攻击类型或只有少数攻击样本类型的检测率。深度神经网络(DNN)具有表征数据潜在特征的能力,而经过改进后的条件CGANs,能够通过学习已知攻击样本潜在数据特征分布,来根据指定类型生成新的攻击样本。此外,与生成对抗网络(GANs)和变分自编码器(VAE)等无监督生成模型相比,CGANsDNN经过改进后加入梯度惩罚项,在训练的稳定性上有了很大地提升。通过NSL-KDD数据集对模型进行评估,与传统算法相比CGANs-DNN不仅在整体准确率、召回率和误报率等方面有更好的性能,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。