深颈压力机进给系统的数控化研究与应用

分析了普通深颈压力机在生产实际应用中存在的问题，提出数控化改造方案。较详细地分析了数控化改造的基本原理和方法，成功地应用国产数控系统和德国百格拉公司三相混合式步进电机及驱动器对国产深颈压力机进行数控化改造，在生产实际应用中得到充分的证明和肯定。     

基于十瓶保龄球的断言优化

在软件需求增加和规模增大,却又要能做出及时交付给用户并且让用户满意的软件的时期,传统的软件工程方法显得有些力不从心,且已经遭到人们的质疑,而最近兴起的一种软件开发过程相关的技术,提供一些比较高效、实用的软件过程开发方法,这就是敏捷软件开发方法。测试驱动开发TDD(Test-Driven Development)是极限编程过程(一种敏捷开发)的重要特征,其对控制软件开发进度和开发质量有非常重要的作用。它的基本思想就是在开发前根据对将要开发的程序的要求,先写好所有测试代码,并且在开发过程中不时地通过运行测试代码来获得所开发的代码与所要求的结果之间的差距。这些活动的进行很大程度上依赖着程序的断言代码。因此断言代码的全面性及完整性深深地影响着一个程序的开发进度和质量。     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems

This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.

     

推进绿色制造 建设生态文明——中国绿色制造战略研究

我国正处于制造大国迈向制造强国的重要历史阶段,在绿色发展理念的指导下,如何推进制造业与生态环境的协调发展,促进工业文明与生态文明和谐共生,是当前亟待解决的现实问题。绿色制造是制造强国建设的必有之意,是生产方式转变的重要着力点,更是绿色发展的重要组成部分,涵盖了低碳发展和循环发展的全部内容。本文在明晰绿色制造与生态文明建设关系的基础上,深入阐述了绿色制造的科学内涵、目标和评价体系,基于我国绿色制造已取得的成绩和现实发展困境,提出了推进我国绿色制造的战略方向,最后从顶层设计、队伍建设、创新体系、服务平台、体制机制及国际合作等方面给出了政策建议。     

Reproducible Enhancement of Fluorescence by Bimetal Mediated Surface Plasmon Coupled Emission for Highly Sensitive Quantitative Diagnosis of Double‐Stranded DNA

Plasmonic enhancement of fluorescence from SYBR Green I conjugated with a double‐stranded DNA (dsDNA) amplicon is demonstrated on polymerase chain reaction (PCR) products. Theoretical computation leads to use of the bimetallic (Au 2 nm–Ag 50 nm) surface plasmons due to larger local fields (higher quality factors) than monometallic (Ag or Au) ones at both dye excitation and emission wavelengths simultaneously, optimizing fluorescence enhancement with surface plasmon coupled emission (SPCE). Two kinds of reverse Kretschmann configurations are used, which favor, in signal‐to‐noise ratio, a fluorescence assay that uses optically dense buffer such as blood plasma. The fluorescence enhancement (12.9 fold at maximum) with remarkably high reproducibility (coefficient of variation (CV) < 1%) is experimentally demonstrated. This facilitates credible quantitation of enhanced fluorescence, however unlikely to obtain by localized surface plasmons. The plasmon‐induced optical gain of 46 dB due to SPCE‐active dye molecules is also estimated. The fluorescence enhancement technologies with PCR enables LOD of the dsDNA template concentration of ≈400 fg µL^?1 (CV < 1%), the lowest ever reported in DNA fluorescence assay to date. SPCE also reduces photobleaching significantly. These technologies can be extended for a highly reproducible and sufficiently sensitive fluorescence assay with small volumes of analytes in multiplexed diagnostics.     

一类积分不等式的机器判定

将一类积分不等式转化为Tarski模型外的齐次对称多项式不等式,该类齐次对称多项式的次数是给定的,变元个数可以是任意多个,并且多项式的系数是与变元个数相关的变系数.这些特点与杨路等人最近提出的几个公开问题密切相关,是比较有代表性的一类齐次对称多项式.然后利用Timofte关于对称多项式不等式判定的降维方法,结合不等式证明软件BOTTEMA及差分代换方法,给出对应的一类Tarski模型外的齐次对称多项式不等式的机器判定算法,从而实现原积分不等式的机器判定.当给定的积分不等式及齐次对称多项式不等式不成立时,可给出具体不成立的数值反例.应用例子表明问题的广泛性及算法的有效性.     

Salient structural elements based texture synthesis

Salient structural elements are ubiquitous in natural textures, and their distribution exhibits some stochastic distribution features. Current texture synthesis algorithms can neither preserve the integrity of the elements nor capture this distributive information. We present an algorithm to treat this high-level visual information. Here, we address the issue by taking specific care of the structural elements. Our texture synthesis process grows the target texture one structural element at a time. A Markov ...     

A reversible data hiding scheme using complementary embedding strategy

Obtaining good visual quality and high hiding capacity with reversible data hiding systems is a technically challenging problem. In this paper, we propose a simple reversible data hiding scheme that uses a complementary hiding strategy. The proposed method embeds one secret bit horizontally and vertically into one cover pixel of a grayscale cover image by decreasing odd-valued pixels and increasing even-valued pixels by one. Experimental results show that the hiding capacity measured by bit per pixel (bpp) of the proposed scheme is at least 1.21 bpp with a PSNR (peak signal-to-noise ratio) value greater than 52 dB for all standard test images. Especially in the case of four-layer embedding, the PSNR value of the proposed method is still greater than 51 dB at a hiding capacity of about 5 bpp for all standard test images. In addition, the proposed method is quite simple because it primarily uses additions and subtractions. These results indicate that the proposed scheme is superior to many existing reversible data hiding schemes introduced in the literature.