Parametric modeling of microwave passive components using combined neural networks and transfer functions in the time and frequency

A novel parametric modeling technique is proposed to develop combined neural network and transfer function models for both time and frequency (TF) domain applications of passive components, where the neural network is trained to map geometrical variables to the coefficients of transfer functions. Built on our previous work, a new order‐changing module is developed to enforce stability of transfer functions and simultaneously guarantee continuity of coefficients. A constrained optimization strategy is introduced to enforce passivity of transfer functions through a neural network training process. A general equivalent circuit for two‐port passive components is generated directly from coefficients of arbitrary‐order transfer functions. Once trained, the parametric model can provide accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to our previous work, the proposed method enables models to work well in the time domain providing good accuracy in challenging modeling applications. Two parametric modeling examples of spiral inductors and interdigital capacitors, and their application in both time and frequency domain simulations of a power amplifier are examined to demonstrate the validity of the proposed technique. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2013.     

Computing the Maximum Degree of Minors in Matrix Pencils via Combinatorial Relaxation

   Abstract. This paper presents a new algorithm for computing the maximum degree δ _k (A) of a minor of order k in a matrix pencil A(s) . The problem is of practical significance in the field of numerical analysis and systems control. The algorithm adopts a general framework of ``combinatorial relaxation' due to Murota. It first solves the weighted bipartite matching problem to obtain an estimate

Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.     

Hopping on Even Ground and Up Stairs with a Single Articulated Leg

This paper presents a method for generating gaits for a one-legged articulated hopping robot. A static optimization procedure produces the initial joint velocities for the flight phase, using the principle of conservation of angular momentum and assuming (nearly) passive flight. Two novel objective functions for this static optimization enable one to choose different gaits by simply changing a few parameters. A dynamic optimization procedure yields a solution for the flight trajectory that minimizes control effort. The stance phase (when the foot is touching the ground) becomes a standard two point boundary value problem, also solved with a dynamic optimization procedure. During the stance phase, the physical joint limitations, ground reaction forces, and the trajectory of the zero-moment point all constrain the solution. After these single-phase optimizations, a complete-cycle optimization procedure, incorporating both flight and stance phases, further reduces the control effort and balances the motion phases. In simulation, the leg hops on even ground and up stairs, exhibiting energy-efficient and intuitively satisfying gaits.     

基于Chan算法的无源雷达定位精度分析

该文对无源雷达时差定位中的Chan算法进行分析研究,主要分析了非线性定位方程组的求解技术及其与之相关的定位精度、定位模糊问题,讨论不同布站对定位模糊的影响,时差测量与站址测量误差对定位精度的影响。仿真结果表明,时差测量误差,站址测量误差．布站形式、目标辐射源与各接收站几何位置关系都对定位精度有很大影响。     

社会网络信息的本体论建模与可视化

本体论是共享概念模型的明确的形式化规范说明,基于本体论对社会网络信息建模能够提供客观存在的本质认识,发挥本体论在信息表示与组织上的优势.提出一种针对社会网络信息的领域本体模型,将社会网络信息领域的客观存在抽象为3个主要本体:行动者、关系网络和群组,能够方便地定义各种社会网络分析与可视化方法,为社会网络信息可视化应用提供支持.着眼于社会网络结构分析中的需求,在上述模型框架下提出了基于规则对等组的群组分析方法和基于群组、角色分析的力导引布点算法.最后结合恐怖活动信息可视化实例,阐述了文中提出的领域模型与可视化方法的应用.     

A static view of localities

This paper proposes alternative, effective characterizations for nets of automata of the location equivalence and preorder presented by Boudol et al. in the companion paper [BCHK]. Contrary to the technical development in the above given reference, where locations are dynamically associated to the subparts of a process in the operational semantics, the equivalence and preorder we propose are based on a static association of locations to the parallel components of a net. Following this static approach, it is possible to give these distributed nets a standard operational semantics which associates with each net a finite labelled transition system. Using this operational semantics for distributed nets, we introduce effective notions of equivalence and preorder which are shown to coincide with those proposed in [BCHK].     

差分无源器件的设计

在现代无线通信系统中,差分电路相比单端电路而言具有更强的抗干扰能力,因此受到了国内外学者的广泛关注。文章介绍了混合模散射参数、差分器件设计中常用的微带线和缝隙线及两种传输线之间的相互转化,并介绍了基于微带线和缝隙线转换结构的两种全差分带通滤波器。两种滤波器分别实现了多频段和宽带的差模传输特性以及宽带的共模抑制特性。文章还给出了差分耦合器、差分功分器和差分天线等差分器件的设计。仿真与实测结果吻合较好,验证了设计方法的正确性。     

Ultrafast Switchable Passive Radiative Cooling Smart Windows with Synergistic Optical Modulation

Switchable passive radiative cooling (PRC) smart windows can modulate sunlight transmission and spontaneously emit heat to outer space through atmospheric transparent window, presenting great potential in building energy conservation. However, realizing stable and on-demand control of the cooling efficiency for PRC materials is still challenging. Herein, an electro-controlled polymer-dispersed liquid crystal (PDLC) smart window showing PRC property is designed and prepared by adding mid-infrared emitting reactive monomers into the conventional PDLC matrix. It is found that not only the electro-optical properties but also the PRC efficiency of PRC PDLC film are tunable by regulating the content of the mid-infrared emitting components, film thickness, and micromorphology. This advanced PRC PDLC material achieves a near/sub-ambient temperature when the solar irradiance is below 400 W m⁻² and can dynamically manage daytime cooling efficiency. Importantly, its PRC efficiency is capable of being tuned in an on-demand and ultrafast millisecond-scale way, whose controllable transparency enables multistage heat regulation. This study is hoped to provide new inspiration in the preparation of advanced optical devices and energy-efficient equipment.     

Targeting orthotopic gliomas with renal-clearable luminescent gold nanoparticles

A major clinical translational challenge in nanomedicine is the potential of toxicity associated with the uptake and long-term retention of non-degradable nanoparticles (NPs) in major organs.The development of inorganic NPs that undergo renal clearance could potentially resolve this significant biosafety concern.However,it remains unclear whether inorganic NPs that can be excreted by the kidneys remain capable of targeting tumors with poor permeability.Glioblastoma multiforme,the most malignant orthotopic brain tumor,presents a unique challenge for NP delivery because of the blood-brain barrier and robust blood-tumor barrier of reactive microglia and macroglia in the tumor microenvironment.Herein,we used an orthotopic murine glioma model to investigate the passive targeting of glutathione-coated gold nanoparticles (AuNPs) of 3 nm in diameter that undergo renal clearance and 18-nm AuNPs that fail to undergo renal clearance.Remarkably, we report that 3-nm AuNPs were able to target intracranial tumor tissues with higher efficiency (2.3x relative to surrounding non-tumor normal brain tissues) and greater specificity (3.0x)than did the larger AuNPs.Pharmacokinetics studies suggested that the higher glioma targeting ability of the 3-nm AuNPs may be attributed to the longer retention time in circulation.The total accumulation of the 3-nm AuNPs in major organs was significantly less (8.4x) than that of the 18-nm AuNPs.Microscopic imaging of blood vessels and renal-clearable AuNPs showed extravasation of NPs from the leaky blood-tumor barrier into the tumor interstitium.Taken together,our results suggest that the 3-nm AuNPs,characterized by enhanced permeability and retention,are able to target brain tumors and undergo renal clearance.