Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process

An energy-efficient intelligent manufacturing system could significantly save energy compared to traditional intelligent manufacturing systems that do not consider energy issues. Intelligent energy estimation of machining processes is the foundation of the energy-efficient intelligent manufacturing system. This paper proposes a method for machining activity extraction and energy attributes inheritance to support the intelligent energy estimation of machining processes. Fifteen machining activities and their energy attributes are defined according to their operating and energy consumption characteristics. Activities and energy attributes are extracted mainly from NC program supplemented with blank dimensional information. An effective extraction method of activities and energy attributes is the basis for the intelligent energy calculating of machining process. Based on an investigation on the extraction procedure of activities and energy attributes, energy attributes inheritance method is further discussed. Four types of energy attribute inheritance rules are summarized according to the different inheritance characteristics. Based on these four types of inheritance rules, the energy attributes can be transmitted from activity to Therblig as effective inputs of Therblig-based energy model of machining processes. The proposed methodology is finally demonstrated through two machining cases.     

Robust bundle adjustment for large-scale structure from motion

Bundle adjustment (BA) is the problem of refining a visual reconstruction to produce jointly optimal 3D structure and viewing parameter (camera pose and or calibration) estimates, and it is almost always used as the last step of feature-based 3D reconstruction algorithm. Generally, the result of Structure from Motion (SFM) mainly relies on the quality of BA. The problem of BA is often formulated as a nonlinear least squares problem, where the data arises from keypoints matching. For 3D reconstruction, mismatched keypoints may cause serious problems, even a single mismatch will affect the entire reconstruction. Therefore, to further impove the robustness of BA algorithm is very necessary. In this paper, we propose a robust Bundle Adjustment (RBA) algorithm to optimize the initial 3D point-clouds and camera parameters which are produced by the SFM system. In the proposed RBA algorithm, we firstly use the Huber loss function to potentially down-weight outliers. Secondly, we split a large-scale bundle adjustment problem into some small ones by making use of the sparsity between 3D points and the cameras for reducing the requirements of memory. Thirdly, according to the inherent property of the matrix after it spare decompose, we use a fast matrix factorization algorithm to solve the normal equation to avoid calculating the inverse of large-scale matrix. Finally, we evaluate the proposed RBA method and compare it with the state-of-the-art methods on the synthetic dataset, BAL benchmark and real image datasets, respectively. Experimental results show that the proposed RBA method clearly outperforms the state-of-the-art methods on both computational cost and precision.     

A fast inter-frame encoding scheme using the edge information and the spatiotemporal encoding parameters for HEVC

Multimedia Tools and Applications - High Efficiency Video Coding (HEVC), as a novel video coding standard, has shown a better coding efficiency than all existing standards, such as H.264/AVC. It...     

Ordering states with Tsallis relative $$\alpha $$-entropies of coherence

In this paper, we study the ordering states with Tsallis relative \(\alpha \)-entropies of coherence and \(l_{1}\) norm of coherence for single-qubit states. Firstly, we show that any Tsallis relative \(\alpha \)-entropies of coherence and \(l_{1}\) norm of coherence give the same ordering for single-qubit pure states. However, they do not generate the same ordering for some high-dimensional states, even though these states are pure. Secondly, we also consider three special Tsallis relative \(\alpha \)-entropies of coherence for \(\alpha =2, 1, \frac{1}{2}\) and show these three measures and \(l_{1}\) norm of coherence will not generate the same ordering for some single-qubit mixed states. Nevertheless, they may generate the same ordering if we only consider a special subset of single-qubit mixed states. Furthermore, we find that any two of these three special measures generate different ordering for single-qubit mixed states. Finally, we discuss the degree of violation of between \(l_{1}\) norm of coherence and Tsallis relative \(\alpha \)-entropies of coherence. In a sense, this degree can measure the difference between these two coherence measures in ordering states.     

Entangled state fusion with Rydberg atoms

We propose a scheme for preparation of large-scale entangled GHZ states and W states with neutral Rydberg atoms. The scheme mainly depends on Rydberg antiblockade effect, i.e., as the Rydberg–Rydberg interaction strength and the detuning between the atom transition frequency and the classical laser frequency satisfies some certain conditions, the effective Rabi oscillation between the two ground states and the two excitation Rydberg states would be generated. The prominent advantage is that both two multiparticle GHZ states and two multiparticle W states can be fused in this model, especially the success probability for fusion of GHZ states can reach unit. In addition, the imperfections induced by the spontaneous emission is also discussed through numerical simulation.     

基于霍尔效应的土沉降监测方法及装置

针对我国目前土沉降监测的自动化程度及仪器可靠性低、监测数据不及时、测量精度不高等问题,提出了一种新型基于霍尔效应的科学监测方法;这种方法采用多个霍尔器件组成阵列,将沉降磁环分布在土中,土的沉降会带动沉降磁环的跟随沉降,于是便会引起霍尔器件周围磁场强度发生相应的变化;利用霍尔效应监测霍尔器件周围磁场强弱并转化为相应的电信号,采用RS485总线通讯方式将采集到的数据实时上传至实验主机,主机对实验数据进行科学建模处理找到沉降磁环沉降位移与电信号的关系,并设计监测管理平台以实现对土沉降实时在线监测;通过实验验证这种监测方法监测精度高最大误差在0.4 mm、稳定性好,可以对多点实时在线监测,从而实现对土沉降的智能化高精度监测;在设计中可结合当前远程传输控制技术实现数据远程共享,具有更加广泛的应用前景。     

运载火箭飞行力学环境虚拟试验及可视化系统设计与实现

运载火箭的飞行力学环境随着飞行过程不断发生变化,而当前无论是基于实物试验还是基于数值分析,火箭的飞行力学环境分析大多针对特定飞行状态和工况而无法给出动态变化信息,因此开展了飞行力学环境虚拟试验及可视化技术研究。基于特征代理模型采用Fortran语言设计了场数据快速预测算法,在采样数值仿真的基础上实现了火箭在大气飞行过程中的动态气动力与气动热环境虚拟试验;采用C++开发了实时分站载荷算法,实现了火箭飞行期间的载荷环境虚拟试验。开发了与运载火箭飞行仿真配合的总线通信接口,并基于EnSight开发了分布式动态可视化系统,通过共享内存的进程间通信方式实现了飞行力学环境的动态显示。结果表明,该系统可以给出火箭飞行力学环境的全局和关注点信息的动态变化,为飞行力学环境精细化分析和直观可视化研究提供了手段。     

基于异常数据驱动的WSN簇内数据融合方法

引入数据驱动的思想,提出了一种基于异常数据驱动的簇内数据融合方法.在节点数据采集过程中,仅当异常数据发生时才发送给簇头,减少了监测网络的数据传输量.在簇头数据融合过程中,建立了各传感器之间的相互支持度矩阵,支持度值较低的监测数据将被剔除,支持度值较高的监测数据进行最优加权融合,从而保证了融合结果的准确性和有效性.仿真实验结果表明,与算术平均值法及自适应加权融合法相比,本文方法能有效去除冗余信息,在融合精度、能量消耗方面具有明显的优势.     

区块链技术在中国慈善事业中的应用分析和研究

我国慈善事业因受负面消息影响,需要一种有公信力的技术机制来重建公众信任.区块链技术有着可靠透明、不可更改撤销的本质特征,建立"互联网+区块链+慈善"的新模式,将有助我国慈善事业的发展.     

Global adaptive stabilization for high‐order uncertain time‐varying nonlinear systems with time‐delays

This paper focuses on the adaptive stabilization problem for a class of high‐order nonlinear systems with time‐varying uncertainties and unknown time‐delays. Time‐varying uncertain parameters are compensated by combining a function gain with traditional adaptive technique, and unknown multiple time‐delays are manipulated by the delicate choice of an appropriate Lyapunov function. With the help of homogeneous domination idea and recursive design, a continuous adaptive state‐feedback controller is designed to guarantee that resulting closed‐loop systems are globally uniformly stable and original system states converge to zero. The effectiveness of the proposed control scheme is illustrated by the stabilization of delayed neural network systems. Copyright © 2016 John Wiley & Sons, Ltd.