Size effect on heat transfer in micro gas sensors

Gas gap is usually used as an important thermal insulation in micro gas sensors to reduce the heating power. The heat transport through the gap consists of two parts, heat conduction by air and thermal radiation between surfaces. It is usually regarded that thermal radiation through the gap is negligible compared with conductive heat transfer by air. This work investigates the heat transport by thermal radiation and heat conduction through a broad size range of gas gaps from one nanometer to dozens of micrometers. The result shows that thermal radiation is the major way of heat transfer when the gap is less than 20 nm, which will result in unexpected high energy consumption in the process of minimization. The equivalent thermal conductivity of thermal radiation is computed and a partition map is depicted to demonstrate the relative importance of radiation and conduction on different gap scales under dissimilar surface temperatures. A practical gas sensor heated by a micro hotplate (MHP) is thermally analyzed. The calculation shows that extra energy consumption comes forth as the gap distance reduces to several tens of nanometers.     

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult.By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system.We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.     

DCOM技术在分布式刑侦信息系统中的应用

DCOM(Distributed Component Object Model)是微软的组件对象模型 (COM)在分布计算和网络环境下的扩展 ,网络上的不同进程可以通过 DCOM协议进行相互通讯。结合刑侦信息系统的开发 ,论述了基于DCOM技术 ,在 MIDAS(Multi tiered distributed Application System Suiee)体系结构下构建分布式应用系统的方法 ,并给出了关键的实现步骤。该系统已在公安专用网中投入实际运行     

基于特征对齐和关键点辅助激励的退化热成像图目标检测

在热成像图目标检测中,存在图像的纹理单一、目标边界模糊等退化现象,这造成目标定位困难、目标与预定义锚点框无法精准匹配等问题.因此,文中提出基于特征对齐和关键点辅助激励的退化热成像图目标检测算法.引入可见光图分支,计算2个分支指定层的特征差异,提升热成像域与可见光域之间的相似度.为了丰富网络高层中的目标细节信息,修改特征图级联和检测尺度.部署包含关键点辅助激励的无锚点检测器,较好地定位目标并学习预定义锚点框覆盖较差的实例.在2个数据集上的对比实验表明,文中算法可准确定位热成像目标,有效提升退化热成像图目标检测精度.     

Evaluation of thermal and evaporative resistances in cricket helmets using a sweating manikin

The main objective of this study is to establish an approach for measuring the dry and evaporative heat dissipation cricket helmets. A range of cricket helmets has been tested using a sweating manikin within a controlled climatic chamber. The thermal manikin experiments were conducted in two stages, namely the (i) dry test and (ii) wet test. The ambient air temperature for the dry tests was controlled to ∼23 °C, and the mean skin temperatures averaged ∼35 °C. The thermal insulation value measured for the manikin with helmet ensemble ranged from 1.0 to 1.2 clo. The results showed that among the five cricket helmets, the Masuri helmet offered slightly more thermal insulation while the Elite helmet offered the least. However, under the dry laboratory conditions and with minimal air movement (air velocity = 0.08 ± 0.01 ms⁻¹), small differences exist between the thermal resistance values for the tested helmets. The wet tests were conducted in an isothermal condition, with an ambient and skin mean temperatures averaged ∼35 °C, the evaporative resistance, R_et, varied between 36 and 60 m² Pa W⁻¹. These large variations in evaporative heat dissipation values are due to the presence of a thick layer of comfort lining in certain helmet designs. This finding suggests that the type and design of padding may influence the rate of evaporative heat dissipation from the head and face; hence the type of material and thickness of the padding is critical for the effectiveness of evaporative heat loss and comfort of the wearer. Issues for further investigations in field trials are discussed.     

主轴系统仿蜂巢冷却结构流固耦合热设计研究

主轴系统的热误差是影响数控机床加工精度的重要因素。受自然界蜂巢分形结构的启发,设计了一种基于蜂巢仿生流道的主轴系统冷却结构,并建立了蜂巢仿生流道冷却结构模型。在数值传热学相关理论基础上,通过Fluent有限元软件对蜂巢仿生流道(单孔和多孔流道截面结构)和螺旋形流道冷却结构方案进行仿真计算,流固耦合分析结果显示两种前者流道结构比后者的流动特性较好,散热效果更好。在同等边界条件和冷却液体积相同情况下,多孔蜂巢形流道截面结构比单孔蜂巢形流道截面结构的散热效果更理想、温差更小。研究结果可以为主轴系统的冷却结构乃至电子元器件等微系统结构热设计提供参考。     

基于复杂对象分解的相似性计算方法

针对目前常用的相似性度量方法难以满足复杂对象的相似性计算要求,提出了一种基于复杂对象结构分解的分层相似性度量方法。根据属性间的关系紧密程度将复杂对象结构迭代分解,直至基于分解后的简单对象结构的对象可以使用传统相似性度量方法计算对象间的相似性。分解过程可以得到树型结构的对象结构划分。在此基础上,利用树型的对象结构从叶节点向根方向对对象逐层进行相似性度量,最终得到复杂对象的相似性综合度量。结合某大型电信运营商的套餐数据进行应用分析,证明该方法具有较好的性能。     

The significant other: the value of jewellery in the conception,design and experience of body focused digital devices

In this paper, we demonstrate how craft practice in contemporary jewellery opens up conceptions of ‘digital jewellery’ to possibilities beyond merely embedding pre-existing behaviours of digital systems in objects, which follow shallow interpretations of jewellery. We argue that a design approach that understands jewellery only in terms of location on the body is likely to lead to a world of ‘gadgets’, rather than anything that deserves the moniker ‘jewellery’. In contrast, by adopting a craft approach, we demonstrate that the space of digital jewellery can include objects where the digital functionality is integrated as one facet of an object that can be personally meaningful for the holder or wearer.     

Learning object identification rules for information integration

When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects’ shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects. This manual process is time consuming and error-prone. In our approach. Active Atlas learns to tailor mapping rules, through limited user input, to a specific application domain. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.     

Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability

We describe a method of learning generative models of objects from a set of images of the object under different, and unknown, illumination. Such a model allows us to approximate the objects' appearance under a range of lighting conditions. This work is closely related to photometric stereo with unknown light sources and, in particular, to the use of Singular Value Decomposition (SVD) to estimate shape and albedo from multiple images up to a linear transformation (Hayakawa, 1994). Firstly we analyze and extend the SVD approach to this problem. We demonstrate that it applies to objects for which the dominant imaging effects are Lambertian reflectance with a distant light source and a background ambient term. To determine that this is a reasonable approximation we calculate the eigenvectors of the SVD on a set of real objects, under varying lighting conditions, and demonstrate that the first few eigenvectors account for most of the data in agreement with our predictions. We then analyze the linear ambiguities in the SVD approach and demonstrate that previous methods proposed to resolve them (Hayakawa, 1994) are only valid under certain conditions. We discuss alternative possibilities and, in particular, demonstrate that knowledge of the object class is sufficient to resolve this problem. Secondly, we describe the use of surface consistency for putting constraints on the possible solutions. We prove that this constraint reduces the ambiguities to a subspace called the generalized bas relief ambiguity (GBR) which is inherent in the Lambertian reflectance function (and which can be shown to exist even if attached and cast shadows are present (Belhumeur et al., 1997)). We demonstrate the use of surface consistency to solve for the shape and albedo up to a GBR and describe, and implement, a variety of additional assumptions to resolve the GBR. Thirdly, we demonstrate an iterative algorithm that can detect and remove some attached shadows from the objects thereby increasing the accuracy of the reconstructed shape and albedo.     

DISCOV: A Framework for Discovering Objects in Video

This paper presents a probabilistic framework for discovering objects in video. The video can switch between different shots, the unknown objects can leave or enter the scene at multiple times, and the background can be cluttered. The framework consists of an appearance model and a motion model. The appearance model exploits the consistency of object parts in appearance across frames. We use maximally stable extremal regions as observations in the model and hence provide robustness to object variations in scale, lighting and viewpoint. The appearance model provides location and scale estimates of the unknown objects through a compact probabilistic representation. The compact representation contains knowledge of the scene at the object level, thus allowing us to augment it with motion information using a motion model. This framework can be applied to a wide range of different videos and object types, and provides a basis for higher level video content analysis tasks. We present applications of video object discovery to video content analysis problems such as video segmentation and threading, and demonstrate superior performance to methods that exploit global image statistics and frequent itemset data mining techniques.     

基于热力系统动力学的冷凝器动态仿真建模

冷凝器中进行的是一个融合了传热、传质、流动的复杂热力过程,仿真分析是其动态特性研究的有效手段。传统上对冷凝器进行仿真建模是基于“热静力学”的观点和方法,其揭示的内容与深度都是不够的。为深入理解动态特性,基于热力系统“热动力学”的观点和方法,视对象各参数之间是定量耦合的。采用一组常微分方程,建立起能够有效反映冷凝器工作过程和动态特性的仿真模型。仿真模型能够给出以往的仿真模型所不能揭示的系统参数之间的耦合关系。模型形式简洁,便于数值计算。     

Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum

In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects. Further adapting a successful manipulation skill to new objects with different shapes and physical properties is a similarly challenging problem. In this work, we show that natural and robust in-hand manipulation of simple objects in a dynamic simulation can be learned from a high quality motion capture example via deep reinforcement learning with careful designs of the imitation learning problem. We apply our approach on both single-handed and two-handed dexterous manipulations of diverse object shapes and motions. We then demonstrate further adaptation of the example motion to a more complex shape through curriculum learning on intermediate shapes morphed between the source and target object. While a naive curriculum of progressive morphs often falls short, we propose a simple greedy curriculum search algorithm that can successfully apply to a range of objects such as a teapot, bunny, bottle, train, and elephant.     

Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction

This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we introduce a fixed viewpoint approach to dense surface reconstruction of transparent objects based on refraction of light. We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the object shape nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a relatively complex shape and structure, with unknown and inhomogeneous refractive index. We also show that for thin transparent objects, our proposed acquisition setup can be further simplified by adopting a single refraction approximation. Experimental results on both synthetic and real data demonstrate the feasibility and accuracy of our proposed approach.     

Efficient pose estimation using view-based object representations

We present an efficient method for estimating the pose of a three-dimensional object. Its implementation is embedded in a computer vision system which is motivated by and based on cognitive principles concerning the visual perception of three-dimensional objects. Viewpoint-invariant object recognition has been subject to controversial discussions for a long time. An important point of discussion is the nature of internal object representations. Behavioral studies with primates, which are summarized in this article, support the model of view-based object representations. We designed our computer vision system according to these findings and demonstrate that very precise estimations of the poses of real-world objects are possible even if only a small number of sample views of an object is available. The system can be used for a variety of applications.     

3D Object Recognition in Cluttered Environments by Segment-Based Stereo Vision

We propose a new method for 3D object recognition which uses segment-based stereo vision. An object is identified in a cluttered environment and its position and orientation (6 dof) are determined accurately enabling a robot to pick up the object and manipulate it. The object can be of any shape (planar figures, polyhedra, free-form objects) and partially occluded by other objects. Segment-based stereo vision is employed for 3D sensing. Both CAD-based and sensor-based object modeling subsystems are available. Matching is performed by calculating candidates for the object position and orientation using local features, verifying each candidate, and improving the accuracy of the position and orientation by an iteration method. Several experimental results are presented to demonstrate the usefulness of the proposed method.     

Distant Collision Response in Rigid Body Simulations

We use a finite element model to predict the vibration response of objects in a rigid body simulation, such that rigid objects are augmented to provide a plausible elastic collision response between distant objects due to vibration. We start with a generalized eigenvalue decomposition of the elastic model to precompute a response to an impact at any point on an elastic object with fixed boundary conditions. Then, given a collision between objects, we generate an approximate response impulse to distribute to other objects already in contact with the colliding bodies. This can lead to distant impacts causing an object to slip, or a delicate stack of objects to fall. We also use a geodesic distance based spatial attenuation approximation for travelling waves in objects to respond to an impact at one contact with an impulse at other locations. This response ultimately allows a long distance relationship between contacts, both across a single object being struck, but also traversing the contact graph of a larger collection of objects. We qualitatively validate our approach with a ground truth simulation, and demonstrate a number of scenarios where a long distance relationship between contacts is valuable.     

CISS: An efficient object clustering framework for DHT-based peer-to-peer applications

In most DHT-based peer-to-peer systems, objects are totally declustered since such systems use a hash function to distribute objects evenly. However, such an object de-clustering can result in significant inefficiencies in advanced access operations such as multi-dimensional range queries, continuous updates, etc, which are common in many emerging peer-to-peer applications. In this paper, we propose CISS (Cooperative Information Sharing System), a framework that supports efficient object clustering for DHT-based peer-to-peer applications. CISS uses a Locality Preserving Function (LPF) instead of a hash function, thereby achieving a high level of clustering without requiring any changes to existing DHT implementations. To maximize the benefit of object clustering, CISS provides efficient routing protocols for multi-dimensional range queries and continuous updates. Furthermore, our cluster-preserving load balancing schemes distribute loads without hotspots while preserving the object clustering property. We demonstrate the performance benefits of CISS through extensive simulation.     

采用多齿肋片的电机控制器散热器设计分析

电机控制器作为纯电动汽车的控制核心,其散热性能直接影响到驱动系统的可靠性.以某MOS多管并联的电机控制器样机为研究对象,将原设计的光滑肋片替换为多齿肋片结构,以MOS管芯片温升和肋片散热器重置为评价指标,对原散热器结构进行改进设计,并进行CFD数值分析.结果表明,相对于原设计的光滑肋片,多齿肋片结构增加了对流散热的表面...