基于K-means和SOM的水下传感器数据采集方法

随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题，其设计方法是通过自组织映射（Self-organizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点，利用聚合点和传感器的节点得到传感器通信半径内的数据采集点，最后通过SOM得到水下机器人（Autonomous underwater vehicle，AUV）到各个数据采集点采集数据的最优路径。经过实验验证，在水下1 200 m×1 750 m范围内布置52个传感器节点的情景下，数据采集点相比于传感器节点路径规划采用相同的采集顺序得到的路径优化了6.7%；对数据采集点重新进行自组织路径规划得到的路径比传感器结点路径的最优解提高了12.2%。增加传感器节点的数量，其结果也大致相同，因此采用该方法可以提高水下机器人采集数据的效率。     

Effect of heat treatment on the synthesis of hydroxyapatite from Indian clam seashell by hydrothermal method

Hydroxyapatite (HA) powder has been successfully synthesized from low-cost Indian clam seashells by using hydrothermal method. The mixture of tri-calcium phosphate [Ca₃(PO₄)₂], heat-treated ball-milled clam seashell, and demineralized water are heat-treated at several temperatures (700 °C, 800 °C, 900 °C, 1000 °C, and 1100 °C) for various time periods (1 h, 2 h, and 3 h) to perform the hydrothermal reactions. The phases and microstructure of the solid-state reaction products are analyzed through X-ray diffraction (XRD) method and field emission scanning electron microscopy (FESEM) respectively. The crystallite size of all the synthesized powders is calculated by using Scherrer's model. Mainly HA phase is obtained in all the different reaction products. However, these HAs are found to be non-stoichiometric in nature. As per the literature, non-stoichiometric HA is a more biologically active material compared to the stoichiometric one. Almost pure HA is formed with any selected reaction temperature applied for 2 h time duration. The crystallinity and Ca/P ratio of the synthesized pure HA are estimated by using standard model and energy-dispersive X-ray spectroscopy (EDS) analysis, respectively. The highest amount of near stoichiometric crystalline HA has been obtained at 900 °C of reaction temperature applied for 2 h time duration. With raising reaction temperature, the grain size of pure HA is found to be increased. Needle/rod shaped nano grains are noticed to form at lower reaction temperature whereas; beyond 1000 ^oC of temperature globular/spherical shaped grains are also observed to form. At 3 h reaction time agglomeration of grains is found to occur in all the synthesized powders.     

Incorporating microgrids coupling with utilization of flexible switching to enhance self-healing ability of electric distribution systems

Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.     

基于网络药理学和文献探讨异喹啉类生物碱抗肝癌研究进展

探索五种异喹啉类生物碱抗肝癌的作用机制。借助TCMSP、String、venny数据库与WebGestalt在线分析软件获取靶点并进行蛋白互作网络（PPI）、基因本体（GO）、基因相互作用（KEGG）功能富集分析，利用Cytoscape软件构建网络。同时查阅文献并进行归纳、整理和分析得到小檗碱型中的小檗碱、双苄基类生物碱的粉防己碱、苯菲啶喹啉类的血根碱、吗啡烷类中的青藤碱和吡咯菲里啶中的石蒜碱等几种常见的生物碱，根据结构作用机制阐述每一类代表性异喹啉类生物碱治疗肝癌的潜力及抗肝癌作用机制。五个活性成分共筛选得到52个作用靶标如p53（抑癌基因）、NCOA2（核受体共激活剂2）、IL-2（白介素2），主要涉及钙离子信号通路、雌激素受体信号通路和PI3K/Akt信号传导途径等通路。通过网络药理学和文献收集证实异喹啉类生物碱可能参与调控PI3K-Akt通路等，可以通过多靶点、多通路对肝癌产生治疗作用，为进一步研究异喹啉类生物碱抗肝癌的临床研究提供参考和依据。     

Low area field-programmable gate array implementation of PRESENT image encryption with key rotation and substitution

Lightweight ciphers are increasingly employed in cryptography because of the high demand for secure data transmission in wireless sensor network, embedded devices, and Internet of Things. The PRESENT algorithm as an ultra-lightweight block cipher provides better solution for secure hardware cryptography with low power consumption and minimum resource. This study generates the key using key rotation and substitution method, which contains key rotation, key switching, and binary-coded decimal-based key generation used in image encryption. The key rotation and substitution-based PRESENT architecture is proposed to increase security level for data stream and randomness in cipher through providing high resistance to attacks. Lookup table is used to design the key scheduling module, thus reducing the area of architecture. Field-programmable gate array (FPGA) performances are evaluated for the proposed and conventional methods. In Virtex 6 device, the proposed key rotation and substitution PRESENT architecture occupied 72 lookup tables, 65 flip flops, and 35 slices which are comparably less to the existing architecture.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

堆本体抗震试验动力相似关系推导与验证

为确保堆本体抗震试验中流体对流效应、脉冲效应和堆本体结构响应的准确性，需保证重力、流体与固体惯性力、结构弹性力和结构应变的相似性。本文从固体结构的振动方程、不可压牛顿流体的动力学方程、流固交界面的边界条件和环形柱体域内液体线性晃动的动力学公式出发，基于控制方程的量纲分析法，推导了考虑液体晃动效应的堆本体地震响应动力相似关系。基于上述相似关系建立了堆容器堆内构件和堆容器内自由液面流体域的缩尺模型，通过有限体积法分析堆容器堆内构件原型和缩尺模型中液体的晃动固有频率、晃动波高、压力以及液体晃动对堆容器支承裙的倾覆力矩。结果表明本文动力相似关系具有合理性和准确性，可用于堆本体缩尺模型的抗震试验研究。     

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.