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The utilization of surrogate models to approximate complex systems has recently gained increased popularity. Because of their capability to deal with black-box problems and lower computational requirements, surrogates were successfully utilized by researchers in various engineering and scientific fields. An efficient use of surrogates can bring considerable savings in computational resources and time. Since literature on surrogate modelling encompasses a large variety of approaches, the appropriate choice of a surrogate remains a challenging task. This review discusses significant publications where surrogate modelling for finite element method-based computations was utilized. We familiarize the reader with the subject, explain the function of surrogate modelling, sampling and model validation procedures, and give a description of the different surrogate types. We then discuss main categories where surrogate models are used: prediction, sensitivity analysis, uncertainty quantification, and surrogate-assisted optimization, and give detailed account of recent advances and applications. We review the most widely used and recently developed software tools that are used to apply the discussed techniques with ease. Based on a literature review of 180 papers related to surrogate modelling, we discuss major research trends, gaps, and practical recommendations. As the utilization of surrogate models grows in popularity, this review can function as a guide that makes surrogate modelling more accessible.

The fundamental criteria for industrial manipulator applications are vibration free and smooth motion with minimum time. This paper investigates the trajectory tracking and vibration control of rotary flexible joint manipulator with parametric uncertainties. Firstly, the dynamic modeling via Euler Lagrange equation for a single link flexible joint manipulator is discussed. Secondly, for the execution of smooth motion between two points, bounded and continuous jerk trajectory is developed and implemented. In addition, the prospective strategy uses the concatenation of fifth-order polynomials to provide a smooth trajectory between two-way points. In the planned algorithm, user can independently define the position, velocity, acceleration and jerk values at both initial and final positions. The feature of user-defined parameters gives the versatility to the suggested algorithm for generating trajectories for diverse applications of robotic manipulators. Moreover, the planned scheme is easy to implement and computationally efficient. In the last, the performance of the presented scheme is examined by comparison with cubic splines and a linear segment with parabolic blends (LSPB) techniques. Generated trajectories were evaluated successfully by carrying multiple experiments on QUANSER’s flexible joint manipulator.

Federated learning ( FL ) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning ( BCFL ) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL . Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL . Finally, we summarize the industrial application scenarios of BCFL .

This research presents a practical study of active disturbance rejection control law (ADRC) for the control of robotic manipulators in the presence of uncertainties. The control objective of the proposed control law is to track the trajectory accurately and rejects the disturbance caused by robotic manipulators. First, Euler Lagrange’s equations are used to model the dynamic behavior of a rotary flexible joint manipulator system (RFJMS). Secondly, the ADRC is designed for the rejection of lump disturbances (modeling uncertainties, nonlinearities, and external disturbances) of the manipulator; to estimate the lump disturbances a fifth-order extended state observer is constructed. Furthermore, a state error feedback control law with disturbance compensation is designed, which needs only a few control parameters to be adjusted. Furthermore, we demonstrate the robustness and effectiveness of the proposed control law by comparing the experimental results with those of LQR and PID. Experimental results show that high precision position control as well as vibration suppression can be achieved with the proposed controller, which is superior to LQR and PID controllers. In spite of uncertainties and nonlinearity of the flexible joint, QUANSER's RFJMS exhibits excellent tracking behavior and disturbance rejection.

Human activity recognition aims to determine actions performed by a human in an image or video. Examples of human activity include standing, running, sitting, sleeping, etc . These activities may involve intricate motion patterns and undesired events such as falling. This paper proposes a novel deep convolutional long short-term memory (ConvLSTM) network for skeletal-based activity recognition and fall detection. The proposed ConvLSTM network is a sequential fusion of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected layers. The acquisition system applies human detection and pose estimation to pre-calculate skeleton coordinates from the image/video sequence. The ConvLSTM model uses the raw skeleton coordinates along with their characteristic geometrical and kinematic features to construct the novel guided features. The geometrical and kinematic features are built upon raw skeleton coordinates using relative joint position values, differences between joints, spherical joint angles between selected joints, and their angular velocities. The novel spatiotemporal-guided features are obtained using a trained multi-player CNN-LSTM combination. Classification head including fully connected layers is subsequently applied. The proposed model has been evaluated on the KinectHAR dataset having 130,000 samples with 81 attribute values, collected with the help of a Kinect (v2) sensor. Experimental results are compared against the performance of isolated CNNs and LSTM networks. Proposed ConvLSTM have achieved an accuracy of 98.89% that is better than CNNs and LSTMs having an accuracy of 93.89 and 92.75%, respectively. The proposed system has been tested in realtime and is found to be independent of the pose, facing of the camera, individuals, clothing, etc . The code and dataset will be made publicly available.

Autonomous vehicles require accurate, and fast decision-making perception systems to know the driving environment. The 2D object detection is critical in allowing the perception system to know the environment. However, 2D object detection lacks depth information, which are crucial for understanding the driving environment. Therefore, 3D object detection is essential for the perception system of autonomous vehicles to predict the location of objects and understand the driving environment. The 3D object detection also faces challenges because of scale changes, and occlusions. Therefore in this study, a novel object detection method is presented that fuses the complementary information of 2D and 3D object detection to accurately detect objects in autonomous vehicles. Firstly, the aim is to project the 3D-LiDAR data into image space. Secondly, the regional proposal network (RPN) to produce a region of interest (ROI) is utilised. The ROI pooling network is used to map the ROI into ResNet50 feature extractor to get a feature map of fixed size. To accurately predict the dimensions of all the objects, we fuse the features of the 3D-LiDAR with the regional features obtained from camera images. The fused features from 3D-LiDAR and camera images are employed as input to the faster-region based convolution neural network (Faster-RCNN) network for the detection of objects. The assessment results on the KITTI object detection dataset reveal that the method can accurately predict car, van, truck, pedestrian and cyclist with an average precision of 94.59%, 82.50%, 79.60%, 85.31%, 86.33%, respectively, which is better than most of the previous methods. Moreover, the average processing time of the proposed method is only 70 ms which meets the real-time demand of autonomous vehicles. Additionally, the proposed model runs at 15.8 frames per second (FPS), which is faster than state-of-the-art fusion methods for 3D-LiDAR and camera.

The important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment.

Crop protection is the prime hindrance to food security. Plant diseases destroy the overall quality and quantity of agricultural products. Grape is an important fruit and a major source of vitamin C nutrients. The automatic decision-making system plays a paramount role in agricultural informatics. This paper aims to detect the diseases in grape leaves using convolutional capsule networks. The capsule network is a promising neural network in deep learning. This network uses a group of neurons as capsules and effectively represents spatial information of features. The novelty of the proposed work relies on the addition of convolutional layers before the primary caps layer, which indirectly decreases the number of capsules and speeds up the dynamic routing process. The proposed method has experimented with augmented and non-augmented datasets. It effectively detects the diseases of grape leaves with an accuracy of 99.12%. The method's performance is compared with state-of-the-art deep learning methods and produces reliable results.

The sugarcane industry is technologically pioneering in the area of food production. On the other side, this industry produces a huge amount of by-products. Proper handling of these by-products has remained a challenge. An efficient multi-echelon Sugarcane Supply Chain Network (SSCN) is designed and proposed in this paper to handle the by-products produced by the sugarcane industry that can be utilized further with little modification. It helps to reduce the overall working cost of the network. Usually, the supply chain problems are complex in nature, and complexity further increases with increasing problem instances. Metaheuristics techniques are, in general, applied to handle such NP-hard problems. This work proposes three hybrid metaheuristics algorithms, namely H-GASA, a hybrid of Genetic Algorithm with Simulated Annealing, H-KASA, a hybrid of Keshtel Algorithm with Simulated Annealing, and H-RDASA, a hybrid of Red Deer Algorithm with Simulated Annealing to handle the complexity of the problem. The algorithms’ performance is probed using the Taguchi experiments, and the best combinations of parameters are identified. This hybrid algorithms’ efficacy is compared with their basic version of the algorithms, i.e. GA, KA, RDA, and SA using different criteria. A set of test problems is generated to ensure the capability of the presented model. The obtained results suggest that H-KASA significantly outperforms in small-sized problems, while the H-RDASA significantly outperforms in medium- and large-sized problem instances. In addition, the sensitivity analysis confirms that by adopting this proposed multi-echelon SSCN, decision-makers can achieve a significant cost reduction of 8.3% in terms of the total cost.

To strengthen and improve the ideological and political education of college students in the new era, the team of counselors is the backbone and plays a crucial role. With the development of higher education in China, the number of college students is constantly increasing; The development of the Internet has, to a certain extent, made the living environment of students more complex and their ideological concepts diversified, which has brought new difficulties and challenges to the work of college counselors. Under the new situation, strengthening the research on the professional ability of college counselors is of great significance for the construction of the team of college counselors. The college counselors’ ability maturity evaluation is a multiple-attribute decision-making (MADM). Recently, the TODIM technique and VIKOR technique has been conducted to cope with MADM issues. The triangular fuzzy neutrosophic sets (TFNSs) are conducted as a useful technique for characterizing fuzzy decision information during the college counselors’ ability maturity evaluation. In this paper, the triangular fuzzy neutrosophic TODIM-VIKOR (TFN-TODIM-VIKOR) technique is put forward to solve the MADM under TFNSs. Finally, a numerical example for college counselors’ ability maturity evaluation is employed to validate the proposed TFN-TODIM-VIKOR technique. The main contribution of this paper is conducted: (1) the TODIM and VIKOR technique was extended to TFNSs; (2) Entropy technique is employed to manage the weight values under TFNSs. (3) the TFN-TODIM-VIKOR technique is founded to manage the MADM under TFNSs; (4) Algorithm analysis for college counselors’ ability maturity evaluation and comparison analysis are constructed based on one numerical example to verify the effectiveness of the TFN-TODIM-VIKOR technique.