Lightweight Strategy Research Articles

A critical review of structural supercapacitors and outlook on future research challenges

Structural composites and electrochemical energy storage underpin electrification of transportation, but advances in electric vehicles are shackled by parasitic battery mass. The emergence of structural power composites, multifunctional materials that simultaneously carry structural loads whilst storing electrical energy, promises dramatic improvements in effective performance Here, we assess the literature on structural supercapacitors, not only providing a comprehensive and critical review of the constituent (i.e., structural electrode, structural electrolyte and structural separator) developments, but also considering manufacture, characterisation, scale-up, modelling and design/demonstration. We provide a rigorous analysis of the multifunctional performance data reported in the literature, providing the reader with a detailed comparison between the different structural supercapacitor developments. We conclude with insights into the future research and adoption challenges for structural supercapacitors. There are several significant hurdles which must be addressed to mature this technology. These include development of a processable structural electrolyte; optimisation of current collection to facilitate device scale-up; identification of load-transmitting encapsulation solutions; standard protocols for characterisation and ranking of structural supercapacitors and; predictive multiphysics models for structural supercapacitors. Through addressing such issues, these emerging multifunctional materials will deliver a novel lightweighting strategy that can contribute to managing the ongoing climate crisis.

Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene

Railway intrusion detection is important for the safety of the railway operation. However, how to detect intruding events in the scene of unmanned aerial vehicle (UAV) surveillance is a challenging task, especially when there are multiple railway regions or unknown types of intruding objects. To tackle the above problems, a railway intrusion detection method is proposed for the scene of UAV surveillance. It can detect intruding events accurately without pre-setting the intruding object type by detecting the normal railway region and the abnormal railway region with intruding objects rather than recognizing the intruding objects directly. The proposed network consists of a novel Fused-convolutional long short-term memory (Fused-ConvLSTM) module for efficiently propagating spatial and temporal information; several attention modules for adaptively enhancing the feature information, such as the improved feature fusion layer; several lightweight strategies for reducing the computation complexity, such as lightweight feature extraction and width multiplier. Experiments are performed on the real aerial videos obtained by a micro UAV. The experimental results demonstrate that the proposed method can accurately detect railway intrusion events in aerial video, and effectively deal with the case of multiple railway regions and unknown intruding object types. The experimental results also demonstrate that the proposed network can achieve better performance than other object detection networks.

L 2 BEC 2 : Local Lightweight Bidirectional Encoding and Channel Attention Cascade for Video Frame Interpolation

Video frame interpolation (VFI) is of great importance for many video applications, yet it is still challenging even in the era of deep learning. Some existing VFI models directly exploit existing lightweight network frameworks, thus making synthesized in-between frames blurry and creating artifacts due to imprecise motion representation. The other existing VFI models typically depend on heavy model architectures with a large number of parameters, preventing them from being deployed on small terminals. To address these issues, we propose a local lightweight VFI network ( L 2 BEC 2 ) that leverages bidirectional encoding structure with channel attention cascade. Specifically, we improve visual quality by introducing a forward and backward encoding structure with channel attention cascade to better characterize motion information. Furthermore, we introduce a local lightweight strategy into the state-of-the-art Adaptive Collaboration of Flows (AdaCoF) model to simplify its model parameters. Compared with the original AdaCoF model, the proposed L 2 BEC 2 obtains performance gain at the cost of only one-third of the number of parameters and performs favorably against the state-of-the-art works on public datasets. Our source code is available at https://github.com/Pumpkin123709/LBEC.git .

A Lightweight Hybrid Convolutional Neural Network for Hyperspectral Image Classification

Recent studies have demonstrated the potential of hybrid convolutional models that combine 3D and 2D convolutional neural networks (CNNs) for hyperspectral image (HSI) classification. However, these models do not fully utilize the benefits of hybrid convolution due to inefficient connections between the two types of CNNs. Moreover, most CNNs, including hybrid models, require a significant number of parameters and computational resources for accurate classification, which increases the need for labeled samples and computational cost. Although the common lightweight strategies like depthwise separable convolution (DSC) can reduce parameters and computation compared to normal convolution (NC), they often compromise accuracy. To address these challenges, we propose a lightweight hybrid convolutional neural network (Lite-HCNet) for HSI classification with minimal model parameters and computational effort. Firstly, we design a novel channel attention module (NCAM) and combine it with a convolutional kernel decomposition (CKD) strategy to propose a lightweight and efficient DSC (LE-DSC) deployed in Lite-HCNet. The LE-DSC not only reduces the DSC volume further but also enhances its performance. Secondly, a lightweight and efficient hybrid convolutional layer (LE-HCL) is designed in Lite-HCNet to explore the efficient connection structure between 3D CNNs and 2D CNNs. Experiments show that the Lite-HCNet reduces the required computational cost and practical deployment difficulty while offering advanced performance with a small number of training samples. Furthermore, abundant ablation experiments confirm the superior performance of the designed LE-DSC.

Experimental analysis on wave dynamics of pressure oscillating tube

The 600 mm double-opening pressure oscillation tube as the core component of industrial-scale gas wave refrigerator (GWR) and its visualization investigation platform are designed and setup in this paper. The quantitative description of the density gradient field in the gas wave oscillation tube is obtained by high speed multi-schlieren splicing technology which is firstly proposed in this paper, and its accuracy is validated with two-dimensional Euler equation under different pressure ratios and rotating speeds.The experimental results show that the shockwave Mach number increases with the increasing of pressure ratio or rotating speed.When the pressure ratio increases from 1.5 to 3, the intensity of both shockwave S1 and 1st reflected expansion wave E1 increase significantly and strengthening the expansion process after HP nozzle closed.When the rotating speed is increased from 800 rpm to 2400 rpm, the movement path of the 2nd expansion waves E2 as nozzle closing gradually bend towards the LT port, which slows down the movement speed of the 1st reflected expansion wave E1 and increases the residence time of the 2nd expansion wave E2, which benefits the refrigeration. The higher pressure ratio and rotation speed mean the shorter distance of shockwave to the HP nozzle, which is an important guidance to lightweight strategy of GWR and extend the application into compact construction requirements.

Research on Intelligent Video Detection of Small Targets Based on Deep Learning Intelligent Algorithm.

Compared with the traditional object detection algorithm, the object detection algorithm based on deep learning has stronger robustness to complex scenarios, which is the hot direction of current research. According to the process characteristics of the object detection algorithm based on deep learning, it is divided into two-stage object detection algorithm and single-stage object detection algorithm, focusing on the problems solved by some classical algorithms and their advantages and disadvantages. In view of the problem of object detection, especially small object detection, the commonly used data sets and performance evaluation indicators are summarized; the characteristics, advantages, and detection difficulties of various common data sets are compared; the challenges faced by commonly used object detection methods and small object detection are systematically summarized; the latest work of small object detection methods based on deep learning is sorted out; and the small object detection methods based on multiscale and small object detection methods based on super-resolution are introduced. At the same time, the lightweight strategy for target detection methods and the performance of some lightweight models are introduced; the characteristics, advantages, and limitations of various methods are summarized; and the future development direction of small object detection methods based on deep learning is prospected.

Finding an elite feature for (D)DoS fast detection—Mixed methods research

Distributed Denial of Service (DDoS) attacks are a persistent security issue in the cyber world due to their diverse character. Emerging technologies such as the Internet of Things and Software Defined Networking leverage lightweight strategies for the early detection of DDoS attacks. This study proposes mathematical modeling to extract the best single feature for fast DDoS attack detection since feature selection is important in developing a lightweight detection model. The primary goal of this article is to demonstrate the importance of the proposed single feature for DDoS attack detection through the use of a mixed methods approach. The qualitative analysis is performed by pinpointing the reasons for various DDoS attacks in order to derive the best single feature, and the quantitative analysis of the derived feature delivers superior results in both the proposed framework evaluation and comparative analysis. All observations are statistically proven by the analysis of variance tests.

Implementing the Lightweight mCrypton Algorithm in Hardware using FPGA

The use of lightweight cryptography might be beneficial for low-power devices such as RFID tags, nodes in the Internet of Things, and nodes in wireless sensor network nodes. When developing a lightweight strategy, it is essential to take into consideration a cryptographic algorithm’s data storage requirements, processing speed, throughput, and energy consumption. Taking everything into consideration, the findings of this research provide a contemporary foundation for the effective lightweight cryptographic technology known as mCrypton. The restoration not only reduces the overall size of the common rooms but also boosts the efficiency of those spaces. FPGA boards manufactured by Xilinx, namely the Spartan 3, were used in the construction of the recommended design by ISE. The results of the simulation show that the proposed architecture is capable of functioning at a frequency of 302 MHz, accomplishing a amount of 646 Mbps, also accomplishing an efficacy of 1.7 Mbps/slice while using a entire of 0.089 W of power. Because of this, the proposed layout achieves superior results than those of its contemporaries in terms of the amount of space used, the energy consumption, the performance, and the throughput.

A Fast View Synthesis Implementation Method for Light Field Applications

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

Current Trends in Automotive Lightweighting Strategies and Materials.

The automotive lightweighting trends, being driven by sustainability, cost, and performance, that create the enormous demand for lightweight materials and design concepts, are assessed as a part of the circular economy solutions in modern mobility and transportation. The current strategies that aim beyond the basic weight reduction and cover also the structural efficiency as well as the economic and environmental impact are explained with an essence of guidelines for materials selection with an eco-friendly approach, substitution rules, and a paradigm of the multi-material design. Particular attention is paid to the metallic alloys sector and progress in global R&D activities that cover the “lightweight steel”, conventional aluminum, and magnesium alloys, together with well-established technologies of components manufacturing and future-oriented solutions, and with both adjusting to a transition from internal combustion engines to electric vehicles. Moreover, opportunities and challenges that the lightweighting creates are discussed with strategies of achieving its goals through structural engineering, including the metal-matrix composites, laminates, sandwich structures, and bionic-inspired archetypes. The profound role of the aerospace and car-racing industries is emphasized as the key drivers of lightweighting in mainstream automotive vehicles.

Guided pattern mining for API misuse detection by change-based code analysis

Lack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

MGSeg: Multiple Granularity-Based Real-Time Semantic Segmentation Network.

Recent works on semantic segmentation witness significant performance improvement by utilizing global contextual information. In this paper, an efficient multi-granularity based semantic segmentation network (MGSeg) is proposed for real-time semantic segmentation, by modeling the latent relevance between multi-scale geometric details and high-level semantics for fine granularity segmentation. In particular, a light-weight backbone ResNet-18 is first adopted to produce the hierarchical features. Hybrid Attention Feature Aggregation (HAFA) is designed to filter the noisy spatial details of features, acquire the scale-invariance representation, and alleviate the gradient vanishing problem of the early-stage feature learning. After aggregating the learned features, Fine Granularity Refinement (FGR) module is employed to explicitly model the relationship between the multi-level features and categories, generating proper weights for fusion. More importantly, to meet the real-time processing, a series of light-weight strategies and simplified structures are applied to accelerate the efficiency, including light-weight backbone, channel compression, narrow neck structure, and so on. Extensive experiments conducted on benchmark datasets Cityscapes and CamVid demonstrate that the proposed method achieves the state-of-the-art performance, 77.8%@50fps and 72.7%@127fps on Cityscapes and CamVid datasets, respectively, having the capability for real-time applications.

Lightweight Design of Palletizing Manipulator Based on Structural Performance Influence Analysis

In order to better coordinate the structural performance with lightweight goal, multi-stage lightweight strategy based on structural performance influence analysis is proposed. The framework of knowledge-guided lightweight design with multiple behavior routes was constructed firstly. Some behavior routes, such as initial structural design, lightweight optimization combined with structural performance influence analysis and knowledge-guided lightweight optimization, were designed in this section. Secondly, the decomposition of decision task and the multi-stage lightweight decision based on structural performance influence knowledge were discussed. Finally, the palletizing manipulator with parallelogram leveling mechanism was taken as an example to apply proposed strategy. Comparing with the initial optimization model in the first stage, the total weight of the components after multi-stage lightweight has been reduced by 23.37%, but the maximum stress of total manipulator only increased by 10.3Mpa, which shows the significant effect of lightweight.

Zn/Cu regulated critical strain and serrated flow behavior in Al–Mg alloys

Next-generation high performance aluminum alloys are required for lightweight strategies and advanced automotive applications. A current challenge for 5xxx series alloys is their poor strength and dreadful surface appearance caused by serrated yielding behavior during plastic deformation. Here we show that the serrated yielding behavior can be eliminated in Zn/Cu modified novel alloys, while simultaneously achieving above 100 MPa improvement of the strength at a pre-aging temper. The formation of GP zones of the precursor of T-Mg32(AlZnCu)49 phase during pre-aging treatment and the depletion of vacancies are mainly attributed to the elimination of dynamic strain aging. The critical strain shows a transformation from normal to inverse behavior with an increase of Zn content in the high-strength novel alloys. Such inverse behavior is because of the impediment of GP zones and solute atoms to the dislocations at lower strain rates. These results provide insight into the serrated yielding behavior of the aluminum alloys.

A Lightweight Caching Decision Strategy Based on Node Edge-Degree for Information Centric Networking

In-network caching as a core function in ICN can greatly improve the content distribution efficiency and reduce redundant network traffic. It is a challenge to design caching strategies in ICN to improve the performance of in-network caching. Traditional lightweight strategies are inefficient due to problems such as edge caching pollution and slow diffusion speed. Current caching strategy with popularity prediction does not fully consider the content capacity of ICN network. They usually have huge computation overhead and cannot meet line-speed requirement. This paper focuses on the research of lightweight on-path caching. Our caching strategy is proposed according to the edge-degree of routers along the forwarding path. This strategy can quickly form a hierarchical content placement with little overhead. A large number of experiments are conducted to examine the performance of our strategy in different topologies and workloads. The simulation result reveals that our caching strategy can reduce the average download latency by 10.2% and increase the cache hit ratio by 9.6% at most compared with other lightweight strategies.

Lightweight materials equal lightweight greenhouse gas emissions?: A historical analysis of greenhouse gases of vehicle material substitution

Abstract The impacts of greenhouse gases (GHG) to vehicles through improvements in fuel efficiency via material substitutions and their historical trends were analyzed to assess if lightweighting actually provides its expected benefits. Data for the weight and fuel efficiency of American and Japanese cars over the past 30 years was used for trend analysis. Also, the impacts of GHG in substituting steel for high strength steel, aluminum, carbon fiber reinforced polymer, and magnesium were analyzed. This study shows that while the amount of low-density materials in a vehicle has increased, the weight itself has increased because of the “rebound of design” reflecting the consumer demands. In lightweighting strategy, the material substitution of steel for high strength steel is the most promising option to reduce GHG emissions. Based on recent historical trends, this analysis shows that the GHG payback miles have increased and will continue to increase unless the reduction in GHG emissions occur at higher rates than that of fuel efficiency improvement from steel-based vehicles. This suggests that the competition over steel will become severer in the future for other materials with low densities. To reverse the result, it is necessary to reduce GHG intensities of materials at higher rates than that of improvement of fuel efficiency.

Mechanical Performance of Additively Manufactured Fiber-Reinforced Functionally Graded Lattices

Latticing has become a common design practice in additive manufacturing (AM) and represents a key lightweighting strategy to date. Functional graded lattices (FGLs) have recently gained immense traction in the AM community, offering a unique way of tailoring the structural performance. This paper constitutes the first ever investigation on the combination of graded strut- and surface-based lattices with fiber-reinforced AM to further increase the performance-to-weight ratio. The energy absorption behavior of cubic lattice specimens composed of body-centered cubic and Schwarz-P unit cells with different severities of grading but the same mass, considered for uniaxial compression testing and printed by fused deposition modelling of short fiber-reinforced nylon, were investigated. The results elucidate that grading affects the energy absorption capability and deformation behavior of these lattice types differently. These findings can provide engineers with valuable insight into the properties of FGLs, aiding targeted rather than expertise-driven utilization of lattices in design for AM.

Dry and lubricated friction behaviour of thermal spray low carbon steel coatings: Effect of oxidational wear

The development of thermal spray steel coatings for Al-Si engine blocks with wear resistant cylinder bores constitutes an important aspect of automotive lightweighting strategy. In this work tribological behaviour of low carbon steel cylinder bore coatings produced by a plasma transferred wire arc (PTWA) method were studied using samples sliding against CrN coated top compression ring samples. The focus of the work was to study the role of oxidational wear on the friction behaviour of spray coatings consisting of α-Fe splats delineated by thin FeO layers. In-situ Raman spectroscopy was employed to observe the oxide transformations that occurred during dry and lubricated sliding tests conducted using a reciprocating tribometer. Lubricated sliding tests were performed within a speed range of 0.02–0.92 m/s covering the boundary and mixed lubrication conditions using a Polyol ester, POE, base oil with and without zinc dialkyldithiophosphate (ZDDP) addition. The use of ZDDP containing POE led to the generation of an amorphous carbon tribolayer incorporating ZnS and FePO4 nano-crystals that inhibited sliding-induced Fe2O3 debris formation, and consequently resulted in a reduced wear rate, but the steady-state coefficient of friction (COF) was higher during the boundary lubricated sliding. According to the Stribeck curves generated, the prevention of oxidational wear by the formation of oil residue layer delayed the transition from the boundary lubrication to the mixed lubrication regime.

Advanced High-Strength Steel and Carbon Fiber Reinforced Polymer Composite Body in White for Passenger Cars: Environmental Performance and Sustainable Return on Investment under Different Propulsion Modes

Vehicle lightweighting strategies must deliver sustainable returns to customers and society. This work evaluates the sustainable return on investment (SROI) of lightweighted advanced high strength ...

Biopen–Fusing password choice and biometric interaction at presentation level

The paper presents experiments with the home-made, low-cost prototype of a sensor-equipped pen for handwriting-based biometric authentication. The pen allows to capture the dynamics of user writing on normal paper, while producing a kind of password (passphrase) chosen in advance. The use of a word of any length instead of the user's signature makes the approach more robust to spoofing, since there is no repetitive pattern to steal. Moreover, if the template gets violated, this is much less harmful than signature catch. The entailed sensors are a pair of accelerometer and gyroscope and a pressure sensor. The aim is a natural yet precise interaction, that allows recognizing the user by the signals recorded while producing a specific word chosen during enrollment and possibly changed later. The pen can be exploited in a number of applications requiring user recognition, yet relieving from the need to learn complex procedures, and to undergo critical capture operations. The approach fuses the use of a kind of password, though not necessarily complex as those requested by traditional approaches, and biometric recognition. The novelty with respect to most proposals in literature is the combination of three elements at once: the matching of any handwritten text instead of user signature, the on-line capture of seven sensor signals to recognize handwriting dynamics (three from accelerometer, three from gyroscope and one from pressure sensor), and the use of normal paper instead of a digitizing tablet. Presented experiments test two different recognition techniques, implemented by two modules that can be alternatively plugged into the system. An SVM-based verification module entails to extract the most relevant features from writing dynamics, and to acquire a sufficient amount of enrolling data (30 samples per user) to train an SVM for each user. A pure Dynamic Time Warping (DTW) verification module does not require such training, and is tested using either a gallery with the same number of templates per user as those used for SVM training, or with a gallery containing a much lower number of templates per user (namely 5). Obtained results encourage further investigation of lightweight strategies for written password dynamics recognition.