Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

A new family of proximity graphs: Class cover catch digraphs

Motivated by issues in machine learning and statistical pattern classification, we investigate a class cover problem (CCP) with an associated family of directed graphs—class cover catch digraphs (CCCDs). CCCDs are a special case of catch digraphs. Solving the underlying CCP is equivalent to finding a smallest cardinality dominating set for the associated CCCD, which in turn provides regularization for statistical pattern classification. Some relevant properties of CCCDs are studied and a characterization of a family of CCCDs is given.     

风力发电机组变速恒频控制系统研究

全面介绍了一种最新应用于风力发电中的新型电机—无刷双馈电机，并分析了这种新型电机作变速恒频运行的原理并对这种电机进行了动态特性仿真研究，给出了形式简洁的控制方程和易于实现的控制方案。     

Motion Perception Using Analog VLSI

Motion perception is arguably a fundamental mechanism used by natural species to accomplish a number of tasks, such as navigating freely in an unknown environment. Traditional motion perception methods tend to be computationally intensive, requiring powerful computers and large memories. However, by copying biological mechanisms, such as elementary motion discrimination at the early stages of the visual processing paths, it should be possible to build small and efficient motion perception systems. This paper describes the manner in which a simple motion perception model based on the insect visual system has been implemented using mixed analog/digital VLSI. The device has been fabricated in a 2 micron double metal, double polysilicon process, and comprises 61 photo-detectors, and associated analog and digital circuitry. While not entirely successful in that component mismatches hamper the detection of dark-to-bright changes in contrast, the results clearly show the feasibility of using such a device in autonomous control systems.     

高精度视觉贴片机拾放程序设计编程

探讨了SMT高精度视觉贴片机难点之一拾放程序设计编程 ,重点介绍示教编程和CAD输入编程设计方法。     

具有两台专用机,两台通用机的Q4／／Cmax问题的近似算法

本文讨论具有两台专用机、两台通用机的两组工件的同种类平行机的Q4／／Cmax问题，对这类特殊的排序问题，提出一种启发式算法，得到了最差情况下性能指标的严格的界．     

SINGLE MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES AND COMPRESSION COSTS （PART I：EQUAL TIMES AND COSTS）

Most papers in scheduling research have treated individual job processing times as fixed parameters. However, in many practical situations, a manager may control processing time by realloeating resources. In this paper, authors consider a machine scheduling problemwith controllable processing times. In the first part of this paper, a special case where the pro-cessing times and compression costs are uniform among jobs is discussed. Theoretical results are derived that aid in developing an O（n^2) algorithm to slove the problem optimally. In the second part of this paper, authors generalize the discussion to general case, An effective heuristic to the genera/ problem will be presented.     

夜视系统鉴别率测试的新方法

用室内低光照高对比鉴别率取代野外探测目标距离作为衡量夜视系统性能优劣的标准,将给验收工作带来方便。本文给出一种实验室内测试夜视系统鉴别率的方法。文中导出了夜视系统鉴别率的一种新的表达式,从而提出了一种改进后的物理模型及其实验方案。分析表明,采用新方案,即在实验室中人为增加环境照度,由此测得的系统工作鉴别率,用以取代野外探测目标距离,其可信度可望得到明显的提高。     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contributions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.