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1.
Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.  相似文献   
2.
In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).  相似文献   
3.
机制砂的空隙率是衡量混凝土性能的重要指标。空隙率的在线检测能够提升混凝土性能。现有的测量方法无法对机制砂空隙率进行在线检测。因此,提出一种通过动态图像法建立软测量模型,进而实现空隙率在线检测的方法。首先,采用基于动态图像法原理构建的机制砂形态测量平台来采集机制砂图像。然后,计算机制砂的关键形态参数,选择合适的软测量模型算法。最后,构建并比较不同软测量模型的预测性能。对比结果显示,随机森林模型的准确率最高,预测值和试验值最大误差为0.6%。相较于传统方法,该方法可在机制砂生产线中在线检测空隙率,有效提升混凝土性能。  相似文献   
4.
Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.  相似文献   
5.
Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy. Therefore, we aimed to develop a novel integrated approach to increase the accuracy of detection. This approach utilized both convolutional neural networks and signal processing techniques. In this proposed method, the biological electro retinogram (ERG) sensor network (BSN) and deep convolution neural network (DCNN) were developed to detect and classify DR. In the BSN system, electrodes were used to record ERG signal, which was pre-processed to be noise-free. Processing was performed in the frequency domain by the application of fast Fourier transform (FFT) and mel frequency cepstral coefficients (MFCCs) were extracted. Artificial neural network (ANN) classifier was used to classify the signals of eyes with DR and normal eye. Additionally, fundus images were captured using a fundus camera, and these were used as the input for DCNN-based analysis. The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images, non-proliferative DR (NPDR) or early-stage DR images, and proliferative DR (PDR) or advanced-stage DR images. Furthermore, it classified NPDR according to microaneurysms, hemorrhages, cotton wool spots, and exudates, and the presence of new blood vessels indicated PDR. The accuracy, sensitivity, and specificity of the ANN classifier were found to be 94%, 95%, and 93%, respectively. Both the accuracy rate and sensitivity rate of the DCNN classifier was 96.5% for the images acquired from various hospitals as well as databases. A comparison between the accuracy rates of BSN and DCNN approaches showed that DCNN with fundus images decreased the error rate to 4%.  相似文献   
6.
Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. For demonstrating the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.  相似文献   
7.
Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost, and the invulnerability of the network is effectively enhanced.  相似文献   
8.
Injector is the critical element in the Liquid Rocket Engine (LRE), to ensure proper mixing of propellants (fuel and oxidizer) in the thrust chamber for achieving the optimum thrust. LRE injector is calibrated in order to deliver required flow rates of propellants by sizing the orifices through simple injector water calibration (IWC) techniques. In LRE-IWC process, a huge 6” turbine flow meter (TFM) is employed for the flow-rate measurement. In order to achieve and maintain the required accuracy and precision in the LRE-IWC process, periodical calibration of TFM is mandatory. It involves tremendous time, cost and human effort. Soft sensors can provide an economical and effective alternative solution for TFM flow-rate measurement. The objective of the proposed work is to develop and implement a recurrent neural network based soft sensor (RNN-SS) for TFM flow-rate measurement. In the LRE-IWC process, experimental flow trials were carried out for different flow patterns, and the necessary measurement data were generated for the soft sensor design. The designed RNN-SS was trained by tuning various hyper parameters to replace the TFM, using three related measurement parameters acquired during the experimental trials. The precise TFM flow-rate estimation was achieved by the designed RNN-SS, with a worst-case mean absolute percentage error (MAPE) of 1.91% for the experimental flow patterns considered, with good repeat-ability. The proposed RNN-SS model for TFM flow-rate estimation gives a MAPE of 0.58%, for the required flow-pattern which is well suited for practical use.  相似文献   
9.
沈月  闻明  李思勰  刘继松  吕刚 《贵金属》2022,43(2):17-24
采用浆料涂覆烧结法制备铂电极,对比了基材处理方式(磁控溅射/喷砂)、铂黑(Ptb)和氧化铂(PtO2)粒径差异对电极形貌、附着力、方阻及电催化性能的影响。结果表明,基材采用磁控溅射法制备的涂层表面结构优于喷砂法的涂层,使其剥离强度均略高于喷砂处理的样品;针对于粉末粒度,需控制在一定范围内(即Ptb(350 nm)和PtO2(350 nm)),其制备的涂层表面易形成蜂窝状或絮状的微连接结构,可显著降低方阻,提高其附着力。对结构和附着力较好的Ptb/Pt电极和PtO2/Pt电极进行CV曲线分析,PtO2/Pt电极的电催化性能优于Ptb/Pt电极。  相似文献   
10.
含水量是衡量原油质量的重要指标,在原油的生产和储运过程中油水混合液分界面不断变化,因此全过程都要用到高精度传感器对其进行界面检测。基于电容边缘效应设计了一种侵入式平面电容传感器,其主要结构由基板和平面电极阵列两部分组成。运用有限元软件建立8电极阵列传感器模型,对不同电极工作时的电场分布进行研究,分析了平面电容传感器的检测灵敏度和成像精度。并且,研究了电极宽度、长度和相邻间距对传感器灵敏场分布的影响。对介电分布进行图像重建,使设计的平面电容阵列传感器可以检测3个分界面的高度,且经过尺寸参数优化,提高了传感器的成像精度。实验证明,运用平面电容阵列检测油水界面的方法具有可行性和有效性。  相似文献   
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