刺形感器 的英文怎麼說
中文拼音 [cìxínggǎnqì]
刺形感器
英文
sensillum chaeticum-
Are realistic in terms of length, weight, balance, hilt, blade shape and etc. that enable the user to enjoy the same feeling of holding the real weapon. moreover, they can simulate the slippery effect of the contact of metal blades and have enough stiffness to deflect and parry incoming blows. in addition, one can spar with the same degree of force that was used in cutting through or thrusting through the targets with real weapons, thus suitable in training historical accurate weaponry techniques
像真是指長度重量平衡手柄刃形狀等都像真兵器一樣，令使用者拿在手上時能與拿著真兵器無異，而且在兵刃相交時亦能模擬出金屬磨擦的順滑感覺，也有足夠硬度使出擋撥等技巧，更能以真兵器斬或刺透目標的力度進行對打，適用於訓練史實的兵器技術。Firstly, the paper, combining the characteristic of synchronous pulse bursts and inhibition with the modified pcnn model, presents a way of finding the foveation points in the images adaptively and effectively, and simulates the human vision system. secondly, pcnn is extended to pcnns, based on the properties of information couple and transmission, an algorithm that is used to fuse images of the same target got by several sensors to an image is presented to simulate the human vision system. thirdly, combining the properties of synchronous pulse bursts, capture, and transmission and competition of waves, the paper presents two ways of classification, one is an algorithm based on the properties of neuron to capture and inhibit to classify the data taking on any complex unlinear distribution robustly, the other is based on the restricted distance and modified of the former to remove the influence of inferior samples in classification ; fin ally, based on the accumulative difference pictures, and the forming and transmission of pcnn wave, selecting and controlling the direction of autowave by connecting the neighbouring neurons selectively, the paper presents a way to simulate the tracks of moving object and detect the moving direction
首先結合pcnn的同步脈沖發放和側抑制特性，提出了基於改進型pcnn的圖像凹點檢測演算法，該演算法是一種自適應而有效的圖像凹點檢測方法，並且較好地模擬了人類視覺系統；然後，結合信息傳遞和信息耦合特性，將pcnn擴展成pcnns （ pcnn網路群） ，提出了一種基於pcnns的圖像融合演算法，能夠將多個傳感器獲取的同一目標的圖像信息融合到一幅圖像中，有效模擬了人類視覺系統；另外，結合pcnn的同步脈沖發放特性、捕獲特性和波的傳播競爭特性，開拓地將pcnn用於模式分類中，提出了基於耦合神經元點火捕獲抑制特性的分類方法和改進的約束距離下的pcnn分類方法，前者可實現對樣本空間中任意復雜分佈訓練樣本的穩健非線性分類，而後者能夠消除訓練樣本中刺點對分類的影響；最後，結合累積差分圖像思想、 pcnn波的形成與傳播特性，通過各神經元之間連接取向來選擇與控制自動波的流向，將pcnn用於運動視覺分析中的運動軌跡模擬及運動方向檢測。
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