Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

Fire spread and growth on real‐scale four cushion mock‐ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two‐term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two‐term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

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.     

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.     

Key-dependent side-channel cube attack on CRAFT

CRAFT is a tweakable block cipher introduced in 2019 that aims to provide strong protection against differential fault analysis. In this paper, we show that CRAFT is vulnerable to side-channel cube attacks. We apply side-channel cube attacks to CRAFT with the Hamming weight leakage assumption. We found that the first half of the secret key can be recovered from the Hamming weight leakage after the first round. Next, using the recovered key bits, we continue our attack to recover the second half of the secret key. We show that the set of equations that are solvable varies depending on the value of the key bits. Our result shows that 99.90% of the key space can be fully recovered within a practical time.     

Environmental impact estimation of ceramic lightweight aggregates production starting from residues

Within a circular economy approach, this study investigates the environmental impact of lightweight aggregates (LWAs) produced starting from different mixes of different clays with brewery sludge and cattle bone flour ash (CBA), used as poring and fertilizing agents, respectively. The environmental impact was evaluated by means of release tests, insulation capacity, carbon footprint (CFP), and particulate matter emission during pellet firing. Release tests representative of LWAs realistic application showed very high release of phosphate and satisfactory release of potassium. The thermal insulation of the LWAs was tested by thermal imaging camera and resulted highly variable depending on the composition, with the mix containing CBA performing best. This latter composition leads also to the smallest CO₂ equivalent emission, due to the calorific power of CBA, allowing lower consumption of fossil fuels during the LWA production. Finally, total particulate emissions during the thermal treatment resulted similar in terms of mass for all mixes, while differences in terms of particle morphology and composition occurred. Samples containing residue resulted with a quite good release behavior, CFP, and insulation properties, but higher emission of particles, particularly when glass is added.     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

Design of energy‐efficient train speed profiles considering fixed‐block signaling system

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy‐efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy‐efficient driving considering fixed‐block signaling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy‐efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy‐efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy‐efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.