A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

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.     

英东油田成藏条件及勘探开发关键技术

英东油田是柴达木盆地单个油藏储量规模最大、丰度最高、物性最好、开发效益最佳的整装油气田。通过对英东油田的地质再认识，对成藏条件及技术难题进行了攻关研究。研究结果表明：英雄岭地区位于柴西地区茫崖生烃凹陷，油源条件丰富，新近系油砂山组以宽缓的三角洲前缘-滨浅湖沉积为主，砂、泥岩纵向交替出现，砂体分布广泛且物性较好，泥岩为主要盖层，与古近系优质烃源岩形成一套完整的下生上储式生-储-盖组合；英东地区的大型滑脱断层沟通了古近系优质烃源岩和中、浅层构造圈闭，使早期聚集的油气藏发生调整，同时后期形成的油气持续运聚，深、浅层断裂形成"接力式"输导，在浅层晚期形成的构造圈闭中聚集成藏；中、浅层断层具备良好的侧向封堵性能，有利于油气的保存。针对英东地区复杂的地形地貌和油藏特征，形成了复杂山地三维地震采集处理解释一体化技术，为落实油气勘探目标奠定了基础。针对英东油田含油气井段长、流体识别难度大等问题，建立了长井段多油气水系统油藏开发模式、多条件约束复杂断块地质建模技术。通过明确英东油田的成藏机理，攻克工程复杂关键技术，为实现英东油田的高效开发和增储上产提供了重要的地质理论支撑和技术保障。     

When is it reasonable to implement the discontinuous sliding‐mode controllers instead of the continuous ones? Frequency domain criteria

Professor Utkin proposed an example showing that the amplitude of chattering caused by the presence of parasitic dynamics (stable actuators) in some systems governed by the First‐Order Sliding‐Mode Controller is lower than that produced by the Super‐Twisting Algorithm. This example served to motivate this paper reconsidering the problem of comparison of chattering in systems with stable actuators, and driven by Discontinuous Sliding‐Mode Controllers (DSMCs) and Continuous Sliding‐Mode Controllers (CSMCs). Comparison of chattering produced by DSMC and CSMC taking into account their amplitudes, frequencies, and average power (AP) needed to maintain the system into real‐sliding modes, allowing to conclude the following: (i) for systems with slow actuators, the amplitude of oscillations and AP produced by DSMC be smaller than those caused by CSMC; (ii) for bounded disturbances with fixed Lipschitz constant, there exist sufficiently fast actuators for which the amplitude of oscillations and AP produced by CSMC be smaller than those caused by DSMC.     

Evaluation of air blast parameters in block cave mining using particle flow code

Air blast, a sudden mass movement of air, can occur in underground mining system where caving develops an extensive mass of unsupported rock spanning a large void. Air blast can result in injury to mine personnel, damage to equipment or disrupts mine operation. Evaluation of air blast parameters is, therefore, an essential part to develop strategies to mitigate the hazard. The properties of a muckpile or a caved zone are significant factors affecting the magnitude of air blast in particular on the undercut and extraction levels. This research investigates the effect of muckpile properties on air flow using the numerical code, PFC2D. The critical parameters such as thickness, block size and porosity (swell factor) of the muckpile have been studied to quantify how much they could change the magnitude of air pressures and velocities while the air flows through the muckpile. It was found that the porosity of the muckpile is the most effective parameter on the magnitude of air blast and by designing a thick layer of blasted rock with low porosity in the caved zone, the intensity of the air blast can be significantly reduced. The findings of this study can be used to design air blast plugs or bulkheads in order to isolate any potential air blast from the active workings, or to quantify the minimum thickness of the muckpile above extraction levels to manage air blast hazards.     

Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing

An intelligent manufacturing system is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. This kind of intelligent system is called a human–cyber–physical system (HCPS). In terms of technology, HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing. It can be concluded that the essence of intelligent manufacturing is to design, construct, and apply HCPSs in various cases and at different levels. With advances in information technology, intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing, and is evolving toward new-generation intelligent manufacturing (NGIM). NGIM is characterized by the in-depth integration of new-generation artificial intelligence (AI) technology (i.e., enabling technology) with advanced manufacturing technology (i.e., root technology); it is the core driving force of the new industrial revolution. In this study, the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs, and the implications, characteristics, technical frame, and key technologies of HCPSs for NGIM are then discussed in depth. Finally, an outlook of the major challenges of HCPSs for NGIM is proposed.     

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.     

Physicochemical Characterization of Polymer-Stabilized Coacervate Protocells

The bottom-up construction of cell mimics has produced a range of membrane-bound protocells that have been endowed with functionality and biochemical processes reminiscent of living systems. The contents of these compartments, however, experience semidilute conditions, whereas macromolecules in the cytosol exist in protein-rich, crowded environments that affect their physicochemical properties, such as diffusion and catalytic activity. Recently, complex coacervates have emerged as attractive protocellular models because their condensed interiors would be expected to mimic this crowding better. Here we explore some relevant physicochemical properties of a recently developed polymer-stabilized coacervate system, such as the diffusion of macromolecules in the condensed coacervate phase, relative to in dilute solutions, the buffering capacity of the core, the molecular organization of the polymer membrane, the permeability characteristics of this membrane towards a wide range of compounds, and the behavior of a simple enzymatic reaction. In addition, either the coacervate charge or the cargo charge is engineered to allow the selective loading of protein cargo into the coacervate protocells. Our in-depth characterization has revealed that these polymer-stabilized coacervate protocells have many desirable properties, thus making them attractive candidates for the investigation of biochemical processes in stable, controlled, tunable, and increasingly cell-like environments.     

域名管理体系与注册保护初探

域名具有重要的品牌价值,研究域名管理体系有利于开展域名注册与保护。现行域名注册规则容易导致域名与字号、商标、姓名等传统权利的冲突。互联网域种类繁多、层级结构复杂,企业应当建立域名注册评估机制、抢注侵权监测机制,加强域名注册与维权保护。