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18 条记录 (耗时 0.024 秒)

    Carbon fiber arrow and continuously winding method thereof
    1.
    发明授权
    Carbon fiber arrow and continuously winding method thereof 失效
    碳纤维箭头及其连续卷绕方法

    公开(公告)号：US06251036B1

    公开(公告)日：2001-06-26

    申请号：US09516576

    申请日：2000-03-01

    申请人： Xiang Wu ,  Qingzhu Mao ,  Wenhao Chen ,  Dahai Sun ,  Minyong Liu ,  Weifang Chen

    发明人： Xiang Wu ,  Qingzhu Mao ,  Wenhao Chen ,  Dahai Sun ,  Minyong Liu ,  Weifang Chen

    IPC分类号： F42B604

    CPC分类号： F42B6/04

    摘要： A carbon fiber arrow shaft comprises a hollow tube, a reinforced carbon fiber, and a resinoid binder. The hollow tube has a length between 500-900 mm, an inner diameter between 2-10 mm, and a tube wall thickness between 0.3-2 mm. The reinforced carbon fiber and the resinoid binder form a resin-impregnated carbon fiber. The carbon fiber arrow shaft is formed by directly and continuously winding the resin-impregnated carbon fiber around the hollow tube. The carbon fiber arrow shaft has a head section (L1), a middle section (L2) and a tail section (L3). A winding angle of the carbon fiber at the head section (L1) is changed from a larger winding angle to a smaller winding angle, a winding angle at the middle section (L2) stays constant, and a winding angle at the tail section (L3) is changed from a smaller winding angle to a larger winding angle. The lengths of the head section (L1) and the tail section (L3) are between 25-250 mm. The initial winding angle at the head section (L1) is between 30-90 degrees and is gradually decreased to between 2-30 degrees at the boundary of the head section (L1) and the middle section (L2). The winding angle at the middle section (L2) stays constant, and the winding angle at the tail section (L3) is gradually increased from between 2-30 degrees at the boundary of the middle section (L2) and the tail section to between 30-90 degrees at the end of the tail section (L3). The peripherally located winding angles at both the head section (L1) and the tail section (L3) are at least two times the winding angle at the middle section (L2). The shaft provides uniformity of wall thickness and eliminates overlapping joints or surface protrusions.

    摘要翻译： 碳纤维箭头轴包括中空管，增强碳纤维和树脂粘合剂。 中空管的长度为500-900mm，内径在2-10mm之间，管壁厚在0.3-2mm之间。 增强碳纤维和树脂粘合剂形成树脂浸渍的碳纤维。 碳纤维箭头轴通过将树脂浸渍的碳纤维直接且连续地缠绕在中空管周围而形成。 碳纤维箭头轴具有头部（L1），中间部（L2）和尾部（L3）。 在头部（L1）处的碳纤维的卷绕角度从较大的卷绕角度变为较小的卷绕角度，中间部分（L2）处的卷绕角度保持恒定，并且在尾部（L3 ）从较小的卷绕角度变化到较大的卷绕角度。 头部（L1）和尾部（L3）的长度在25-250mm之间。 头部（L1）的初始卷绕角度在30-90度之间，并且在头部（L1）和中间部分（L2）的边界处逐渐减小到2-30度之间。 中间部分（L2）处的卷绕角度保持恒定，并且尾部（L3）处的卷绕角度从中间部分（L2）和尾部部分的边界处的2-30度之间逐渐增加到30 -90度在尾部（L3）的末端。 在头部部分（L1）和尾部部分（L3）处的外围定位的卷绕角度至少是中间部分（L2）处的卷绕角度的两倍。 轴提供壁厚均匀性，消除重叠的接头或表面突起。

    Developmental Network Two, Its Optimality, and Emergent Turing Machines
    2.
    发明申请
    Developmental Network Two, Its Optimality, and Emergent Turing Machines 审中-公开

    公开(公告)号：US20190392321A1

    公开(公告)日：2019-12-26

    申请号：US16265212

    申请日：2019-02-01

    申请人： Juyang Weng ,  Zejia Zheng ,  Xiang Wu

    发明人： Juyang Weng ,  Zejia Zheng ,  Xiang Wu

    IPC分类号： G06N3/08

    摘要： This invention includes a new type of neural network that is able to automatically and incrementally generate an internal hierarchy without a need to handcraft a static hierarchy of network areas and a static number of levels and the static number of neurons in each network area or level. This capability is achieved by enabling each neuron to have its own dynamic inhibitory zone using neuron-specific inhibitory connections.

    Starch-oil sizing for glass fibers
    4.
    发明授权
    Starch-oil sizing for glass fibers 失效
    用于玻璃纤维的淀粉 - 油上浆

    公开(公告)号：US5393335A

    公开(公告)日：1995-02-28

    申请号：US52323

    申请日：1993-04-23

    申请人： Garry D. Puckett ,  Ernest L. Lawton ,  Xiang Wu

    发明人： Garry D. Puckett ,  Ernest L. Lawton ,  Xiang Wu

    IPC分类号： C03C25/32 ,  C08L3/00 ,  D06M15/11 ,  C09D103/02

    CPC分类号： D06M15/11 ,  C03C25/321 ,  C08L3/00 ,  Y10T428/2962 ,  Y10T428/31612

    摘要： An aqueous starch-oil sizing composition is provided which produces improved processibility in woven and non-woven applications. The sizing has low viscosity starch, a lubricant which is a mixture of oil and wax where the wax is present in an amount of at least twice the oil, cationic lubricants, and a humectant. The size also includes an organo-functional silane coupling agent such as gamma-glycidoxypropyltrimethoxy silane which may be hydrolyzed. Emulsifiers, defoamers, and biocides may be present. Strands of glass fiber which have been treated with this size have generally shown a reduced tendency to shed the size from the strands, a reduction in the buildup of broken filaments or "fuzz" on processing equipment and reduced strand breakage.

    摘要翻译： 提供了一种水性淀粉 - 油上胶组合物，其在织造和非织造应用中产生改进的加工性能。 上浆具有低粘度淀粉，润滑剂是油和蜡的混合物，其中蜡的存在量至少为油的两倍，阳离子润滑剂和湿润剂。 该尺寸还包括可水解的有机官能的硅烷偶联剂，例如γ-环氧丙氧基丙基三甲氧基硅烷。 可能存在乳化剂，消泡剂和杀生物剂。 已经用这种尺寸处理的玻璃纤维束通常显示出从股线上减小尺寸的趋势，减少在加工设备上破裂的丝或“绒毛”的积聚和减少的线断裂。

    IMAGING AND FORMING METHOD USING PROJECTION OPERATION AND BACK PROJECTION METHOD
    5.
    发明申请
    IMAGING AND FORMING METHOD USING PROJECTION OPERATION AND BACK PROJECTION METHOD 审中-公开

    公开(公告)号：US20180153205A1

    公开(公告)日：2018-06-07

    申请号：US15570749

    申请日：2016-04-23

    申请人： Xiang Wu

    发明人： Xiang Wu

    IPC分类号： A23P10/00 ,  B33Y50/00 ,  A23L15/00

    CPC分类号： A23P10/00 ,  A23L15/00 ,  B29C64/00 ,  B29C64/386 ,  B29C67/00 ,  B33Y10/00 ,  B33Y50/00 ,  B33Y50/02 ,  G03B21/00 ,  H04N1/40

    摘要： A method of the present invention comprises: manufacturing a two-dimensional or three-dimensional real image using a projection operation and back projection method, and also comprises: completing two-dimensional display, three-dimensional display, two-dimensional printing and three-dimensional printing using the real image. The present invention belongs to the field of flat-panel display, the field of 3D stereographic display, the technical field of printing, the field of rapid prototyping, the field of additive manufacturing, and the field of 3D printing. The method is similar to a projection data collection and back projection reconstruction method in the computed tomography (CT) technology. The achievement of the computed tomography (CT) technology is the projection data collection of real objects and reconstruction of digitized tomographic images, so as to convert the real objects into virtual data. Projection data collection is replaced with projection operation, the digitized back projection reconstruction method is replaced with the real back projection method, so as to convert virtual data into real objects or real images. The adopted projection rays comprise light, electromagnetic waves, high-energy rays, particle flows, sound waves, shock waves, currents or chemical waves.

    Net chain driving structure
    7.
    发明授权
    Net chain driving structure 有权

    公开(公告)号：US11618630B2

    公开(公告)日：2023-04-04

    申请号：US17497986

    申请日：2021-10-11

    申请人： Xiang Wu

    发明人： Xiang Wu

    IPC分类号： B65G17/08 ,  B65G15/48 ,  B65G17/40 ,  B65G23/06

    摘要： A net chain driving structure includes a conveying net chain. The conveying net chain includes a plurality of chain links successively inserted in a conveying direction, a plurality of lugs spaced apart from each other are arranged on two sides of the chain links, an end portion of the lug is an arcuate face, a meshing groove is disposed on the chain link, the meshing groove is spliced by an open slot provided on a back side of the chain link and the lugs on the adjacent chain links, a gear in transmission meshing with the meshing groove is disposed on each end of the conveying net chain, a tangent plane tangential to the arcuate face is disposed on the arcuate face of the lug, and transmission contact toothed surfaces of the tangent plane and the gear are attached to each other in parallel during meshing transmission.

    Robot that Concurrently Learns Recognition and Synthesis while Developing a Motor
    8.
    发明申请
    Robot that Concurrently Learns Recognition and Synthesis while Developing a Motor 有权

    公开(公告)号：US20230034287A1

    公开(公告)日：2023-02-02

    申请号：US17379344

    申请日：2021-07-19

    申请人： Juyang Weng ,  Xiang Wu

    发明人： Juyang Weng ,  Xiang Wu

    IPC分类号： G06N3/00 ,  G06N3/04 ,  G06N3/08

    摘要： Traditionally, learning speech synthesis and speech recognition were investigated as two separate tasks. This separation hinders incremental development for concurrent synthesis and recognition, where partially-learned synthesis and partially-learned recognition must help each other throughout lifelong learning. This invention is a paradigm shift—we treat synthesis and recognition as two intertwined aspects of a lifelong learning robot. Furthermore, in contrast to existing recognition or synthesis systems, babies do not need their mothers to directly supervise their vocal tracts at every moment during the learning. We argue that self-generated non-symbolic states/actions at fine-grained time level help such a learner as necessary temporal contexts. Here, we approach a new and challenging problem—how to enable an autonomous learning system to develop an artificial motor for generating temporally-dense (e.g., frame-wise) actions on the fly without human handcrafting a set of symbolic states. Here the artificial motor corresponds to a combination of a multiplicity of robotic effectors, including, but not limited to, speaking, singing, dancing, riding a bike, swimming, and driving a car. The self-generated states/actions are Muscles-like, High-dimensional, Temporally-dense and Globally-smooth (MHTG), so that these states/actions are directly attended for concurrent synthesis and recognition for each time frame. Human teachers are relieved from supervising learner's motor ends. The Candid Covariance-free Incremental (CCI) Principal Component Analysis (PCA) is applied to develop such an artificial speaking motor where PCA features drive the motor. Since each life must develop normally, each Developmental Network-2 (DN-2) reaches the same network (maximum likelihood, ML) regardless of randomly initialized weights, where ML is not just for a function approximator but rather an emergent Turing Machine. The machine-synthesized sounds are evaluated by both the neural network and humans with recognition experiments. Our experimental results showed learning-to-synthesize and learning-to-recognize-through-synthesis for phonemes. This invention corresponds to a key step toward our goal to close a great gap toward fully autonomous machine learning directly from the physical world.