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Fatigue and creep are the key factors for the failure of polymethyl methacrylate (PMMA) in the engineering structure, so a great of quantity attention is focused on the life prediction under the creep and fatigue conditions. This paper aims to mainly summarize the traditional life assessment method (S–N curve), life assessment method based on crazing density and life assessment method based on transmittance. S–N curve and classical creep curve are introduced on the traditional life assessment method; the variation of the craze density with the logarithm of cyclic numbers is given in different fatigue load. A linear relationship is obtained, and a higher stress leads to a higher slope, suggesting a faster growth of craze. Furthermore, a craze density model is purposed to describe this relationship; the variation of craze density with the time at different creep load is given. The craze density has two obvious stages. At the first stage, craze density ranged from approximately 0.02 to 0.17, and a linear relationship is obtained. In the following stage, a nonlinear relationship appears till specimen rupture, a new creep life model is proposed to depict two stages. The relationship between transmission and time under creep load is shown. With increasing of time, the transmittance shows a nonlinear decrease. Through polynomial nonlinear fitting, a relationship between the transmittance and residual life can be obtained. To provide reference for the life assessment of transparent materials, the paper compares three life assessment methods of PMMA.

This paper uses the traditional life assessment method (S–N curve), life assessment method based on crazing density, life assessment method based on transmittance.

The variation of the craze density with the logarithm of cyclic numbers is given in different fatigue loads. A linear relationship is obtained, and a higher stress leads to a higher slope, suggesting a faster growth of craze. Furthermore, a craze density model is proposed to describe this relationship, and the variation of craze density with the time at different creep loads is given. The craze density has two obvious stages. The relationship between transmission and time under creep load is shown. With increasing of time, the transmittance shows a nonlinear decrease. Through polynomial nonlinear fitting, a relationship between the transmittance and residual life can be obtained.

Fatigue and creep are the key factors for the failure of PMMA in the engineering structure, so a great of quantity attention is focused on the life prediction under the conditions of creep and fatigue. This paper mainly summarizes traditional life assessment method (S–N curve), life assessment method based on crazing density and life assessment method based on transmittance.

The density method is one of the powerful topology optimization methods of magnetic devices. The density method has the advantage that it has a high degree of freedom of shape expression which results in a high-performance design. On the other hand, it has also the drawback that unsuitable shapes for actually manufacturing are likely to be generated, e.g. checkerboards or grayscale. The purpose of this paper is to develop a method that enables topology optimization suitable for fabrication while taking advantage of the density method.

This study proposes a novel topology optimization method that combines convolutional neural network (CNN) as an effective smoothing filter with the density method and apply the method to the shield design with magnetic nonlinearity.

This study demonstrated some numerical examples verifying that the proposed method enables to efficiently obtain a smooth and easy-to-manufacture shield shape with high shielding ability. A network architecture suitable as smoothing filter was also exemplified.

In the field of magnetic field analysis, very few studies have verified the usefulness of smoothing by using CNN in the topology optimization of magnetic devices. This paper develops a novel topology optimization method that skillfully combines CNN with the nonlinear magnetic field analysis and also clarifies a suitable network architecture that makes it possible to obtain a target device shape that is easy to manufacture while minimizing the objective function value.

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample.

To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed.

The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices.

With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

This is an attempt to better bridge the gap between the mathematical and the engineering/physical aspects of the topic. The authors trace the different sources of non-convexification in the context of topology optimization problems starting from domain discretization, passing through penalization for discreteness and effects of filtering methods, and end with a note on continuation methods.

Starting from the global optimum of the compliance minimization problem, the authors employ analytical tools to investigate how intermediate density penalization affects the convexity of the problem, the potential penalization-like effects of various filtering techniques, how continuation methods can be used to approach the global optimum and how the initial guess has some weight in determining the final optimum.

The non-convexification effects of the penalization of intermediate density elements simply overshadows any other type of non-convexification introduced into the problem, mainly due to its severity and locality. Continuation methods are strongly recommended to overcome the problem of local minima, albeit its step and convergence criteria are left to the user depending on the type of application.

In this article, the authors present a comprehensive treatment of the sources of non-convexity in density-based topology optimization problems, with a focus on linear elastic compliance minimization. The authors put special emphasis on the potential penalization-like effects of various filtering techniques through a detailed mathematical treatment.

Traffic density is one of the most important parameters to consider in the traffic operation field. Owing to limited data sources, traditional methods cannot extract traffic density directly. In the vehicular ad hoc network (VANET) environment, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction technologies create better conditions for collecting the whole time-space and refined traffic data, which provides a new approach to solving this problem.

On that basis, a real-time traffic density extraction method has been proposed, including lane density, segment density and network density. Meanwhile, using SUMO and OMNet++ as traffic simulator and network simulator, respectively, the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed.

Based on the simulation platform, a simulated intersection in Shanghai was developed to investigate the adaptability of the model.

Most research studies use separate simulation methods, importing trace data obtained by using from the simulation software to the communication simulation software. In this paper, the tight coupling simulation method is applied. Using real-time data and history data, the research focuses on the establishment and validation of the traffic density extraction model.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

This paper aims to extend the hybrid atomistic-continuum multiscale method developed by Vu et al. (2016) to study the gas flow problems in long microchannels involving density variations.

The simulation domain is decomposed into three regions: the bulk where the continuous Navier–Stokes and energy equations are solved, the neighbourhood of the wall simulated by molecular dynamics and the overlap region which connects the macroscopic variables (density, velocity and temperature) between the two former regions. For the simulation of long micro/nanochannels, a strategy with multiple molecular blocks all along the fluid/solid interface is adopted to capture accurately the macroscopic velocity and temperature variations.

The validity of the hybrid method is shown by comparisons with a simplified analytical model in the molecular region. Applications to compressible and condensation problems are also presented, and the results are discussed.

The hybrid method proposed in this paper allows cost-effective computer simulations of large-scale problems with an accurate modelling of the transfers at small scales (velocity slip, temperature jump, thin condensation films, etc.).

The purpose of this paper is to compare qualitatively two methods for coordinating traffic lights: a static optimization “green wave” method and an adaptive self‐organizing method.

Statistical results were obtained from implementing a recently proposed model of city traffic based on elementary cellular automata in a computer simulation.

The self‐organizing method delivers considerable improvements over the green‐wave method. Seven dynamical regimes and six phase transitions are identified and analyzed for the self‐organizing method.

The paper shows that traffic light coordination can be improved in cities by using self‐organizing methods.

This improvement can have a noticeable effect on the quality of life of citizens.

Understanding how self‐organization obtains adaptive solutions for complex problems can contribute to building more efficient systems.

Collision avoidance is considered as a crucial issue in mobile robotic navigation to guarantee the safety of robots as well as working surroundings, especially for humans. Therefore, the position and velocity of obstacles appearing in the working space of the self-driving mobile robot should be observed to help the robot predict the collision and choose traversable directions. This paper aims to propose a new approach for obstacle tracking, dubbed MoDeT.

First, all long lines, such as walls, are extracted from the 2D-laser scan and considered as static obstacles (or mapped obstacles). Second, a density-based procedure is implemented to cluster nonwall obstacles. These clusters are then geometrically fitted as ellipses. Finally, the combination of Kalman filter and global nearest-neighbor (GNN) method is used to track obstacles’ position and velocity.

The proposed method (MoDeT) is experimentally verified by using an autonomous mobile robot (AMR) named AMR SR300. The MoDeT is found to provide better performance in comparison with previous methods for self-driving mobile robots.

The robot can only see a part of the object, depending on the light detection and ranging scan view. As a consequence, geometrical features of the obstacle are sometimes changed, especially when the robot is moving fast.

This proposed method is to serve the navigation and path planning for the AMR.

(a) Proposing an extended weighted line extractor, (b) proposing a density-based obstacle detection and (c) implementing a combination of methods [in (a) and (b) constant acceleration Kalman and GNN] to obtain obstacles’ properties.

The Ni-based superalloy IN-738 LC is known to be susceptible to porosity and different types of cracking during the build-up process and, thus, challenging to manufacture using selective laser melting (SLM). Determining a feasible set of operating parameters for SLM of nickel-based superalloys involves new approach to experimental design based on the Doehlert method that assists in determining an optimal (feasible) set of operating parameters for SLM of IN-738 LC powder alloy.

The SLM parameters are evaluated in terms of their effectiveness in obtaining the microstructure with a porosity content of <0.5 per cent and without micro-cracking. The experimental approach is exemplified with the Doehlert matrix response variable, relative density, by comparing Archimedes method with microstructural assessments of pores and cracks from image analysis. The effect of heat treatment (HT) and hot isostatic pressing (HIP) on the microstructure of the SLMed IN-738 LC powder alloy has been examined and the consequential tensile response characterised.

By using optimised process parameters (low heat input, medium scanning speed and small hatching distance) which provides medium energy density, samples of IN-738 LC with a macroscopic porosity <0.5 per cent and free of micro-cracks can be manufactured by SLM. The results indicate that HIP of SLMed material did not lead to a noticeable effect on mechanical properties compared to HT of SLMed material suggesting that the level of both porosity and crack density might be already below the detection limit for the mere heat-treated material.

SLM processing parameters (power, scan speed, hatching distance) for IN-738 LC were successfully optimised after only 14 experiments using Doehlert design. Two independent methods, Archimedes method and image analysis, were used in this study to assess relative density of SLM-produced samples with sets of processing parameters showing coherency in prediction with predicted response by Doehlert design.