菌酶协同改善花生粕的饲用品质

花生粕是重要的蛋白饲料原料，但由于其氨基酸不平衡，特别是精氨酸与赖氨酸比例严重失衡（精氨酸与赖氨酸含量比值在3~4，理想的精氨酸与赖氨酸含量比值为1.0），限制了其在动物养殖中的应用。研究了复合酶预处理结合乳酸菌发酵花生粕对其品质的改善。结果表明：经菌酶协同处理后，花生粕粗蛋白质含量由46.4%提高至506%，大分子蛋白明显降解为小分子蛋白，酸溶蛋白质含量由2.3%提高至17.8%，多肽含量由1.6%提高至15.7%，蛋氨酸和赖氨酸含量分别提高了77.1%和42.0%，精氨酸降解率为18.7%，精氨酸与赖氨酸含量比值从3.7降低至2.1，总酸含量由06%提高到4.7%，其中乳酸含量由0.64 mg/g提高至14.63 mg/g。菌酶协同处理后的花生粕抗氧化性明显增强，其中每克菌酶协同处理后的花生粕对羟自由基的清除能力与171.6 mg VC相当，比花生粕（与47.6 mg VC相当）提高了2.6倍。     

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。     

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.     

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.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

Flame characteristics of premixed H2-air mixtures explosion venting in a spherical container through a duct

The explosion venting duct can effectively reduce the hazard degree of a gas explosion and conduct the venting energy to the safe area. To investigate the flame quantitative propagation law of explosion venting with a duct, the effects of hydrogen fraction and explosion venting duct length on jet flame propagation characteristics of premixed H₂-air mixtures were analyzed through experiment and simulation. The experiment results under initial conditions of room temperature and 1 atm show that when hydrogen fraction was high enough, part of the unburned hydrogen was mixed with air again to reach an ignitable concentration, resulting in the secondary combustion was easier produced and the duration of the secondary flame increased. With the increase of venting duct length, the flame front distance and propagation velocity increased. Meanwhile, the spatial distribution of pressure field and temperature field, and the propagation process and mechanism of the flame venting with a duct were analyzed using FLUENT software. The variation of the pressure wave and the pressure reflection oscillation law in the explosion venting duct was captured. Therefore, in the industrial explosion venting design with a duct, the hazard caused by the coupling of venting pressure and venting flame under different fractions should be considered comprehensively.     

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.