An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.     

Dynamic output feedback control of a flexible air-breathing hypersonic vehicle via T–S fuzzy approach

By utilising Takagi–Sugeno (T–S) fuzzy set approach, this paper addresses the robust H_∞ dynamic output feedback control for the non-linear longitudinal model of flexible air-breathing hypersonic vehicles (FAHVs). The flight control of FAHVs is highly challenging due to the unique dynamic characteristics, and the intricate couplings between the engine and fight dynamics and external disturbance. Because of the dynamics’ enormous complexity, currently, only the longitudinal dynamics models of FAHVs have been used for controller design. In this work, T–S fuzzy modelling technique is utilised to approach the non-linear dynamics of FAHVs, then a fuzzy model is developed for the output tracking problem of FAHVs. The fuzzy model contains parameter uncertainties and disturbance, which can approach the non-linear dynamics of FAHVs more exactly. The flexible models of FAHVs are difficult to measure because of the complex dynamics and the strong couplings, thus a full-order dynamic output feedback controller is designed for the fuzzy model. A robust H_∞ controller is designed for the obtained closed-loop system. By utilising the Lyapunov functional approach, sufficient solvability conditions for such controllers are established in terms of linear matrix inequalities. Finally, the effectiveness of the proposed T–S fuzzy dynamic output feedback control method is demonstrated by numerical simulations.     

An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.     

Coupling-and-decoupling: A hierarchical model for occlusion-free object detection

Handling occlusion is a very challenging problem in object detection. This paper presents a method of learning a hierarchical model for X-to-X occlusion-free object detection (e.g., car-to-car and person-to-person occlusions in our experiments). The proposed method is motivated by an intuitive coupling-and-decoupling strategy. In the learning stage, the pair of occluding X?s (e.g., car pairs or person pairs) is represented directly and jointly by a hierarchical And–Or directed acyclic graph (AOG) which accounts for the statistically significant co-occurrence (i.e., coupling). The structure and the parameters of the AOG are learned using the latent structural SVM (LSSVM) framework. In detection, a dynamic programming (DP) algorithm is utilized to find the best parse trees for all sliding windows with detection scores being greater than the learned threshold. Then, the two single X?s are decoupled from the declared detections of X-to-X occluding pairs together with some non-maximum suppression (NMS) post-processing. In experiments, our method is tested on both a roadside-car dataset collected by ourselves (which will be released with this paper) and two public person datasets, the MPII-2Person dataset and the TUD-Crossing dataset. Our method is compared with state-of-the-art deformable part-based methods, and obtains comparable or better detection performance.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

Learning Natural Colors for Image Recoloring

We present a data‐driven method for automatically recoloring a photo to enhance its appearance or change a viewer's emotional response to it. A compact representation called a RegionNet summarizes color and geometric features of image regions, and geometric relationships between them. Correlations between color property distributions and geometric features of regions are learned from a database of well‐colored photos. A probabilistic factor graph model is used to summarize distributions of color properties and generate an overall probability distribution for color suggestions. Given a new input image, we can generate multiple recolored results which unlike previous automatic results, are both natural and artistic, and compatible with their spatial arrangements.     

Structure Aware Visual Cryptography

Visual cryptography is an encryption technique that hides a secret image by distributing it between some shared images made up of seemingly random black‐and‐white pixels. Extended visual cryptography (EVC) goes further in that the shared images instead represent meaningful binary pictures. The original approach to EVC suffered from low contrast, so later papers considered how to improve the visual quality of the results by enhancing contrast of the shared images. This work further improves the appearance of the shared images by preserving edge structures within them using a framework of dithering followed by a detail recovery operation. We are also careful to suppress noise in smooth areas.     

超声波对钢水凝固组织的影响

利用超声波对Q235钢和不锈钢凝固过程进行处理,实验结果显示:凝固过程中通入超声波能够明显地影响钢水的凝固过程,可得到细小的等轴晶铸态组织;实验发现超声波对不同的钢种作用不完全相同,对Q235钢超声波功率大于150 W,凝固组织的细化效果较好,而对不锈钢则需大于300 W本研究对超声波在钢铁凝固过程中细化晶粒、改善凝固组织上的应用有一定指导意义.     

自抗扰控制器及其在聚丙烯反应釜过程控制中的应用

介绍一种新型实用控制器--自抗扰控制器及其在聚丙烯反应釜过程控制中的成功应用.自抗扰控制器是一个不依赖于被控对象模型,而依靠过程误差来消除误差的非线性控制器.在聚丙烯反应釜过程控制中升温过程是控制的难点.自抗扰控制器解决了在升温过程中反应釜夹套的加热水和冷却水的自动切换,从而成功地实现了升温过程的自动控制.     

有机酸对产丙二醇菌生长和生产的影响

为提高克雷伯氏肺炎杆菌厌氧发酵生产1,3-丙二醇的能力,在发酵过程中添加有机酸(OA),考察其对菌体生长、1,3-丙二醇及其他副产物合成的影响。实验表明:添加OA可促进菌体生长,加入质量浓度为0.2~0.5g/L的OA,菌体生长的菌液在590 nm处吸光度(OD)值比参照值高出30%;加入过量的OA(>1.0 g/L),则菌体生长受到抑制,OD值比参照值低37%;添加OA可促进单位细胞合成1,3-丙二醇,加入质量浓度为0.2~0.5g/L的OA,单位菌体的1,3-丙二醇质量浓度比参照高3.8%~17.5%;添加OA能提高甘油转化为1,3-丙二醇的转化速率,加入0.2~0.5 g/L的OA,甘油转化为1,3-丙二醇的最大转化速率达0.81 h-1。在15 L罐上批式发酵48 h,1,3-丙二醇最终质量浓度达42.9 g/L,甘油转化为1,3-丙二醇的平均转化率达64%。