The PASCAL Visual Object Classes Challenge

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

The increase in energy demand and reduction in resources for conventional energy production along with various environmental impacts, promote the use of renewable energy for electricity generation and other energy-need applications around the world. Wind power has emerged as the biggest renewable energy source in the world, whose potential, when employed properly, serves to provide the best power output. In order to achieve self-sustenance in energy supply and to match the critical needs of impoverished and developing regions, wind power has proven to be the best solution. However, wind power is intermittent and unstable in nature and hence creates lot of grid integration and power fluctuation issues, which ultimately disturb the stability of the grid. In such cases, energy storage technologies are highly essential and researchers turned their attention to find efficient ways of storing energy to achieve maximum utilization. The use of batteries to store wind energy is very expensive and not practical for wind applications. Compressed Air Energy Storage (CAES) is found to be a viable solution to store energy generated from wind and other renewable energy systems. A detailed review on various aspects of a CAES system has been made and presented in this paper which includes the thermodynamic analysis, modeling and simulation analysis, experimental investigation, various control strategies, some case studies and economic evaluation with the role of energy storage towards smart grid and poly-generation.

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A review on compressed air energy storage – A pathway for smart grid and polygeneration

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

https://www.mdpi.com/2313-433X/6/8/73/pdf

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression

Due to the complexity issue of the hand gesture recognition feature extraction, for example the variation of the light and background. In this paper, the convolution neural network is applied to the recognition of gestures, and the characteristics of convolution neural network are used to avoid the feature extraction process, reduce the number of parameters needs to be trained, and finally achieve the purpose of unsupervised learning. Error back propagation algorithm, is loaded into the convolution neural network algorithm, modify the threshold and weights of neural network to reduce the error of the model. In the classifier, the support vector machine that is added to optimize the classification function of the convolution neural network to improve the validity and robustness of the whole model.

Hand gesture recognition based on convolution neural network

Human computer interaction plays an increasingly important role in our life. People need more intelligent, concise and efficient human-computer interaction. It is of great significance to optimize the process of human-computer interaction by using appropriate calculation methods. In order to eliminate the interference data of thumb recognition based on sEMG signal in the process of human-computer interaction, simplify the data processing, and improve the working efficiency of general equipment of sEMG signal. In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed. The redundancy of five groups of action signals is divided into 16 levels and visualized. By comparing the results of thumb motion recognition when different redundant channels are removed, the optimal channel combination in the process of thumb motion recognition is obtained. Finally, two kinds of classifiers suitable for sEMG signal field are selected, and the classification results are compared, and the best method of thumb motion pattern recognition is obtained.

Intelligent human computer interaction based on non redundant EMG signal

In the field of human-computer interaction, vision-based gesture recognition methods are widely studied. However, its recognition effect depends to a large extent on the performance of the recognition algorithm. The skeletonization algorithm and convolutional neural network (CNN) for the recognition algorithm reduce the impact of shooting angle and environment on recognition effect, and improve the accuracy of gesture recognition in complex environments. According to the influence of the shooting angle on the same gesture recognition, the skeletonization algorithm is optimized based on the layer-by-layer stripping concept, so that the key node information in the hand skeleton diagram is extracted. The gesture direction is determined by the spatial coordinate axis of the hand. Based on this, gesture segmentation is implemented to overcome the influence of the environment on the recognition effect. In order to further improve the accuracy of gesture recognition, the ASK gesture database is used to train the convolutional neural network model. The experimental results show that compared with SVM method, dictionary learning + sparse representation, CNN method and other methods, the recognition rate reaches 96.01%.

Gesture recognition based on skeletonization algorithm and CNN with ASL database

Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model

A convenient and effective binocular vision system is set up Gesture information can be accurately extract from the complex environment with the system The template calibration method is used to calibrate the binocular camera and the parameters of the camera are accurately obtained In the phase of stereo matching, the BM algorithm is used to quickly and accurately match the images of the left and right cameras to get the parallax of the measured gesture Combined with triangulation principle, resulting in a more dense depth map Finally, the depth information is remapped to the original color image to realize three-dimensional reconstruction and three-dimensional cloud image generation According to the cloud image information, it can be judged that the binocular vision system can effectively segment the gesture from the complex background

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Jianyi Kong

Papers

Hand gesture recognition based on convolution neural network

Intelligent human computer interaction based on non redundant EMG signal

Gesture recognition based on skeletonization algorithm and CNN with ASL database

Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model

Gesture recognition based on binocular vision