Aerodynamic Parameter Estimation of a Symmetric Projectile Using Adaptive Chaotic Mutation Particle Swarm Optimization

This article details a new optimizing algorithm called Adaptive Chaotic Mutation Particle Swarm Optimization (ACM-PSO). The new algorithm is used to perform aerodynamic parameter estimation on a spinning symmetric projectile. The main creative ideas of this new algorithm are as follows. First, a self-adaptive weight function is used so that the inertial weight can be adjusted dynamically by itself. Second, the initialized particle is generated by chaos theory. Last, a method that can be used to judge whether the algorithm has fallen into a local optimum is established. The common testing function is used to test the new algorithm, and the result shows that, compared with the basic particle swarm optimization (PSO) algorithm, it is more likely to have a quick convergence and high accuracy and precision, leading to extensive application. Simulated ballistic data are used as testing data, and the data are subjected to the new algorithm to identify the aerodynamic parameters of a spinning symmetric projectile. The result shows that the algorithm proposed in this paper can effectively identify the aerodynamic parameters with high precision and a quick convergence velocity and is therefore suitable for use in actual engineering

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. Therefore, we generalize the concept of ISAC inspired by human synesthesia to establish a unified framework of intelligent multi-modal sensing-communication integration and provide a comprehensive review under such a framework in this paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such intelligent integration and details its paradigm for the first time. We commence by justifying the necessity of the new paradigm. Subsequently, we offer a definition of SoM and zoom into the detailed paradigm, which is summarized as three operation modes. To facilitate SoM research, we overview the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then, we introduce the mapping relationships between multi-modal sensing and communications. Afterward, we cover the technological review on SoM-enhance-based and SoM-concert-based applications. To corroborate the superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design. Finally, we propose some potential directions to inspire future research efforts.Comment: This paper has been accepted by IEEE Communications Surveys & Tutorial

Analytical Prediction Model of Stability Boundary for Guided Projectiles

Aiming at the problem of projectile stability under the action of control force, this paper studied the analytical prediction model of stability boundary for guided projectiles. By linearizing the longitudinal axial angular velocity of body axis coordinate, the angular motion equation under the body axis coordinate is established. Through linearizing the roll angle between the body axis coordinate and non-rolling coordinate, a five-order equation of angular motion is proposed under the non-rolling coordinate. Using the stability theory of linear system, the analytical prediction model of stability boundary under the body axis coordinate and non-rolling coordinate are obtained. Simulations of the two models under various working conditions are conducted. Results indicate that the proposed model under the body axis coordinate can be applied in the rising arc segment and the falling arc segment, whereas the control force orientation is limited. The model under the non-rolling coordinate is not limited by the control force orientation, and the prediction accuracy is good. The drawback of this model is that it can only be used for the falling arc section, and its accuracy will be harmfully affected by excessive control force. It is suggested that the two models should be applied comprehensively in practical engineering

A Block Iteration with Parallelization Method for the Greedy Selection in Radial Basis Functions Based Mesh Deformation

Greedy algorithm is one of the important point selection methods in the radial basis function based mesh deformation. However, in large-scale mesh, the conventional greedy selection will generate expensive time consumption and result in performance penalties. To accelerate the computational procedure of the point selection, a block iteration with parallelization method is proposed in this paper. By the block iteration method, the computational complexities of three steps in the greedy selection are all reduced from O ( n 3 ) to O ( n 2 ) . In addition, the parallelization of two steps in the greedy selection separates boundary points into sub-cores, efficiently accelerating the procedure. Specifically, three typical models of three-dimensional undulating fish, ONERA M6 wing and three-dimensional Super-cavitating Hydrofoil are taken as the test cases to validate the proposed method and the results show that it improves 17.41 times performance compared to the conventional method

Insight of a Phase Compatible Surface Coating for Long-Durable Li-Rich Layered Oxide Cathode

Li-rich layered oxides (LLOs) can deliver almost double the capacity of conventional electrode materials such as LiCoO2 and LiMn2O4; however, voltage fade and capacity degradation are major obstacles to the practical implementation of LLOs in high-energy lithium-ion batteries. Herein, hexagonal La0.8Sr0.2MnO3−y (LSM) is used as a protective and phase-compatible surface layer to stabilize the Li-rich layered Li1.2Ni0.13Co0.13Mn0.54O2 (LM) cathode material. The LSM is MnOMbonded at the LSM/LM interface and functions by preventing the migration of metal ions in the LM associated with capacity degradation as well as enhancing the electrical transfer and ionic conductivity at the interface. The LSM-coated LM delivers an enhanced reversible capacity of 202 mAh g−1at 1 C (260 mA g−1) with excellent cycling stability and rate capability (94% capacity retention after 200 cycles and 144 mAh g−1 at 5 C). This work demonstrates that interfacial bonding between coating and bulk material is a successful strategy for the modification of LLO electrodes for the next-generation of high-energy Li-ion batteries

Experience of Online Learning from COVID-19: Preparing for the Future of Digital Transformation in Education

COVID-19 has affected traditional instructional activities. Home-based isolation and restrictive movement measures have forced most learning activities to move from an offline to an online environment. Multiple studies have also demonstrated that teaching with virtual tools during the COVID-19 pandemic is always ineffective. This study examines the different characteristics and challenges that virtual tools brought to online education in the pre-pandemic and pandemic era, with the aim of providing experience of how virtual tools supported purely online learning during a health crisis. By searching keywords in public databases and review publications, this study tries to summarize the major topics related to the research theme. These topics are the characteristics of learning supported by technologies in pre-pandemic and pandemic era, the challenges that education systems have faced during the COVID-19 pandemic. This study also compares the functions, advantages and limitations of typical virtual tools, which has rarely been done in previous studies. This study tries to present the features of virtual tools that support online learning and the challenges regarding real-life risk scenarios, and tries to provide educational institutions with a distinct perspective for efficient teaching and learning in future potential health crises

Insight of a Phase Compatible Surface Coating for Long-Durable Li-Rich Layered Oxide Cathode

Li-rich layered oxides (LLOs) can deliver almost double the capacity of conventional electrode materials such as LiCoO2 and LiMn2O4; however, voltage fade and capacity degradation are major obstacles to the practical implementation of LLOs in high-energy lithium-ion batteries. Herein, hexagonal La0.8Sr0.2MnO3−y (LSM) is used as a protective and phase-compatible surface layer to stabilize the Li-rich layered Li1.2Ni0.13Co0.13Mn0.54O2 (LM) cathode material. The LSM is MnOMbonded at the LSM/LM interface and functions by preventing the migration of metal ions in the LM associated with capacity degradation as well as enhancing the electrical transfer and ionic conductivity at the interface. The LSM-coated LM delivers an enhanced reversible capacity of 202 mAh g−1at 1 C (260 mA g−1) with excellent cycling stability and rate capability (94% capacity retention after 200 cycles and 144 mAh g−1 at 5 C). This work demonstrates that interfacial bonding between coating and bulk material is a successful strategy for the modification of LLO electrodes for the next-generation of high-energy Li-ion batteries

SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting

Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able to handle only low-resolution inputs, typically less than 1 K. With the improvement of Internet transmission capacity and mobile device cameras, the resolution of image and video sources available to users via the cloud or locally is increasing. For high-resolution images, the common inpainting methods simply upsample the inpainted result of the shrinked image to yield a blurry result. In recent years, there is an urgent need to reconstruct the missing high-frequency information in high-resolution images and generate sharp texture details. Hence, we propose a general deep learning framework for high-resolution image inpainting, which first hallucinates a semantically continuous blurred result using low-resolution inpainting and suppresses computational overhead. Then the sharp high-frequency details with original resolution are reconstructed using super-resolution refinement. Experimentally, our method achieves inspiring inpainting quality on 2K and 4K resolution images, ahead of the state-of-the-art high-resolution inpainting technique. This framework is expected to be popularized for high-resolution image editing tasks on personal computers and mobile devices in the future