Comparison of two scaling approaches for the development of biomechanical multi-body human models

Dimensional scaling approaches are widely used to develop multi-body human models in injury biomechanics research. Given the limited experimental data for any particular anthropometry, a validated model can be scaled to different sizes to reflect the biological variance of population and used to characterize the human response. This paper compares two scaling approaches at the whole-body level: one is the conventional mass-based scaling approach which assumes geometric similarity; the other is the structure-based approach which assumes additional structural similarity by using idealized mechanical models to account for the specific anatomy and expected loading conditions. Given the use of exterior body dimensions and a uniform Young’s modulus, the two approaches showed close values of the scaling factors for most body regions, with 1.5 % difference on force scaling factors and 13.5 % difference on moment scaling factors, on average. One exception was on the thoracic modeling, with 19.3 % difference on the scaling factor of the deflection. Two 6-year-old child models were generated from a baseline adult model as application example and were evaluated using recent biomechanical data from cadaveric pediatric experiments. The scaled models predicted similar impact responses of the thorax and lower extremity, which were within the experimental corridors; and suggested further consideration of age-specific structural change of the pelvis. Towards improved scaling methods to develop biofidelic human models, this comparative analysis suggests further investigation on interior anatomical geometry and detailed biological material properties associated with the demographic range of the population.     

Feature generation based on relation learning and image partition for occluded person re-identification

In order to solve the challenging tasks of person re-identification(Re-ID) in occluded scenarios, we propose a novel approach which divides local units by forming high-level semantic information of pedestrians and generates features of occluded parts. The approach uses CNN and pose estimation to extract the feature map and key points, and a graph convolutional network to learn the relation of key points. Specifically, we design a Generating Local Part (GLP) module to divide the feature map into different units. Based on different occluded conditions, the partition mode of GLP has high flexibility and variability. The features of the non-occluded parts are clustered into an intermediate node, and then the spatially correlated features of the occluded parts are generated according to the de-clustering operation. We conduct experiments on both the occluded and the holistic datasets to demonstrate its effectiveness.     

IPC-Net: Incomplete point cloud classification network based on data augmentation and similarity measurement

Existing point cloud classification researches are usually conducted on datasets with complete structure and clear semantics. However, in real point cloud scenes, the occlusion and truncation may destroy the completeness of objects affecting the classification performance. To solve this problem, we propose an incomplete point cloud classification network (IPC-Net) with data augmentation and similarity measurement. The proposed network learns the feature representation of incomplete point clouds and the semantic differences compared to the complete ones for classification. Specifically, IPC-Net adopts a random erasing-based data augmentation to deal with incomplete point clouds. IPC-Net also introduces an auxiliary loss function weighted by attention scores to measure the similarity between the incomplete and the complete point clouds. Extensive experiments verify that IPC-Net has the ability to classify incomplete point clouds and significantly improves the robustness of point cloud classification under different completeness.     

Performance analysis for hierarchical resource allocation in multiplexed mobile packet data networks

The general packet radio service (GPRS) can be thought of as an overlay network above a Global Systems for Mobile (GSM) network. In cooperation with the GSM system, GPRS supports both voice and packet data services. Obviously, combining voice and data traffic implies the nature of contending with finite radio resources. The paper focuses on voice/data integrated GPRS downlink traffic and proposes two hierarchical resource allocation strategies by considering time slots and radio blocks simultaneously with two different TDMA frame configurations. The radio block based resource allocation can characterize the multiplexing scheme of the practical GPRS operation. The performance of the proposed strategies were evaluated analytically by multidimensional Markov chain and verified through intensive computer simulations. Based on both the analytical and simulation results, it showed that the proposed allocation strategies can offer high utilization of transmission resource, as well as low blocking probability to GPRS users. The results of the study provide not only a practical thinking for implementation but also a guideline for analysis.     

汽车后扭力梁结构的模态分析

为揭示某汽车后扭力梁在实际道路激励工况下容易出现振动疲劳的机制,需要对结构固有振动特性进行分析。为了得到后扭力梁结构更为准确的固有振动特性,对模态试验中的悬挂位置、激励位置和测点位置的布置方式进行重点分析,并结合结构的实际情况进行优化。采用优化后的最佳模态试验法识别后扭力梁结构在自由-自由工况下的模态参数,得到结构固有频率和振型特征。通过对比可知：后扭力梁结构前10阶自由模态频率吻合很好,误差控制在6%内。这表明采用优化后的模态试验方案能够更为准确地识别结构的模态频率,得到结构固有振动特性。     

一种基于折反射相机的客车全景环视系统

针对大客车体积大、盲区多,全景环视技术在客车上的应用较少且不成熟这一现状,开展大客车的全景环视技术研究,在嵌入式平台上实现了全景环视系统.同时,为解决大客车全景环视系统对标定设备和标定场地要求复杂、标定时间长的问题,提出了一种在折反射鱼眼相机模型基础上,加入标定棋盘平面与地面的映射,采用抬高的棋盘进行计算标定的方法.实验证明,该系统全景环视效果良好、标定过程简单快速、适合推广应用.     

Refractive index modulation in polymer film doped with diazo Meldrum’s acid

Diazo Meldrum’s acid undergoes a photoreaction induced by UV light and it is used as photosensitizer in photoresists. Upon photoreaction, a change in refractive index occurs, which makes this system interesting for volume holography. We report on the sublimation effect at room temperature and the effect of photoirradiation on the refractive index in thin films of CAB (Cellulose acetate butyrate) doped with different amount of diazo Meldrum’s acid. A net modulation of the refractive index of 0.01 is achieved with 40% of doping ratio together with a reduction of the film thickness.     

Feasibility of preparing additive manufactured porous stainless steel felts with mathematical micro pore structure as novel catalyst support for hydrogen production via methanol steam reforming

In this paper, an additive manufacturing prepared porous stainless steel felt (AM-PSSF) is proposed as a novel catalyst support for hydrogen production via methanol steam reforming (MSR). In the method, 316 L stainless steel powder with diameter of 15–63 μm is processed by the additive manufacturing technology of selective laser melting (SLM). To accomplish the preparation, the reforming chamber where the AM-PSSF is embedded is firstly divided into an all-hexahedron mesh. Then, the triply periodic minimal surface (TPMS) unit with mathematical form, high interconnectivity and large specific surface area is mapped into the hexahedrons based on shape function, forming the fully connected three-dimensional (3D) micro pore structure of the AM-PSSF. By correlating the mathematical parameter and the porosity of the TPMS unit, and taking into account the SLM process, the porosity of the AM-PSSF is well controlled. Based on the designed 3D pore structure model, the AM-PSSF is produced using standard SLM process. The application of the AM-PSSF as catalyst support for hydrogen production through MSR indicates that: 1) both the naked and catalyst-coated AM-PSSF have the characteristics of high porosity, large specific surface area and high connectivity; 2) the MSR hydrogen production performance of the AM-PSSF is better than that of the commercial stainless steel fiber sintered felt. The feasibility of AM-PSSF as catalyst support for MSR hydrogen production may pave a better way to balance different requirements for catalyst support, thanks to the excellent controllability provided by AM on both the external shape and the internal pore structure, and to the produced rough surface morphology that benefits the catalyst adhesion strength. In addition, catalyst support with pore structures that are more accommodated with the flow field and the reaction rate of MSR reaction may be prepared in future, since the entire catalyst support structure, from macro scale to micro scale, is under control.     

Wireless telemetry system for real-time estimation of ship air wakes with UAVs

This paper presents a wireless instrumentation system developed for real-time estimation of air turbulence patterns arising from the interaction of wind with any structure under consideration, which is an important study in the aerospace industry. In particular, this paper focuses on the application of the proposed system in a naval research problem for off-board measurement of ship air wake patterns using an instrumented radio controlled (RC) helicopter. We propose the use of an Inertial Measurement Unit (IMU) as a sensor to measure air wake in the form of induced vibrations on the helicopter while it maneuvers through regions of active air wake. The proposed system makes use of Back Propagation Neural Networks to compensate for the vibrational noise contributed by pilot inputs. The instrumentation system was integrated and tested on a modified training vessel in the Chesapeake Bay, which provided a wide range of wind conditions.