Simulation Outcomes Research Articles

Integrating spatial and channel attention mechanisms with domain knowledge in convolutional neural networks for friction coefficient prediction

AbstractThe pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction mechanisms and deep neural networks has not been fully studied. This study employed spatial‐channel attention mechanisms to integrate frictional domain knowledge and convolutional neural networks (CNNs) that predict the friction coefficient as the output. The models’ inputs include 3D texture data, corresponding finite element (FE) simulation outcomes, and 2D wavelet decomposition outcomes. An additional spatial attention (ASA) mechanism guided the CNNs to focus on the tire–road contact region, using tire–road contact stress from FE simulation as domain knowledge. Multi‐scale channel attention (MSCA) mechanisms enabled the CNNs to learn the channel weights of 2D‐wavelet‐based multi‐scale inputs, thereby assessing the contribution of different texture scales to tire–road friction. A multi‐attention and feature fusion mechanism was designed, and the performances of various combinations were compared. The results showed that the fusion of ASA and MSCA achieved the best performance, with a regression R2 of 0.8470, which was a 20.25% improvement over the baseline model.

Modeling for underground logistics system in the Korean metropolitan area

This study evaluated the development and validation of an integrated operational model for the Underground Logistics System (ULS) in South Korea’s metropolitan area, aiming to address challenges in urban logistics and freight transportation by highlighting the potential of innovative logistics systems that utilize underground spaces. This study used conceptual modeling to define the core concepts of ULS and explored the system architecture, including cargo handling, transportation, operations and control systems, as well as the roles of cargo crews and train drivers. The ULS operational scenarios were verified through model simulation, incorporating both logical and temporal analyses. The simulation outcomes affirm the model’s logical coherence and precision, emphasizing ULS’s pivotal role in boosting logistics efficiency. Thus, ULS systems in Korea offer prospects for elevating national competitiveness and spurring urban growth, underscoring the merits of ULS in navigating contemporary urban challenges and championing sustainability.

The Deformation of a Liquid Metal Droplet Under Continuous Acceleration in a Variable Cross-Section Groove

This paper constructs a numerical simulation model for the deformation of droplets in a variable cross-section groove of a liquid droplet MEMS switch under different directions, amplitudes, frequencies, and waveforms of acceleration. The numerical simulation utilizes the level set method to monitor the deformation surface boundary of the metal droplets. The simulation outcomes manifest that when the negative impact acceleration on the X-axis is 12.9 m/s2, the negative impact acceleration on the Y-axis is 90 m/s2, the negative impact acceleration on the Z-axis is 34.5 m/s2, and the metal droplet interfaces with the metal electrode. The droplet deformation under the effect of a sine wave acceleration signal in the X and Y directions is lower than that under impact acceleration, while in the Z direction, the deformation is higher than that under impact acceleration. The deformation of metal droplets under square wave acceleration is more pronounced than that under sinusoidal wave acceleration. The deformation escalates with the augmentation in square wave amplitude and dwindles with the reduction in square wave acceleration frequency. Furthermore, there exists a phase difference between the deformation curve of the metal droplet and the continuous acceleration signal curve, and the phase difference is dependent of the material properties of the metal droplet. This work elucidates the deformation of the liquid-metal droplets under continuous acceleration and furnishes the foundation for the continuous operation design of MEMS droplet switches.

Immersive learning in medical education: analyzing behavioral insights to shape the future of VR-based courses

BackgroundThe emergence of virtual reality (VR) for medical education enables a range of new teaching opportunities. Skills and competences can be trained that cannot be demonstrated in any other way due to physical or ethical limitations. Immersion and presence may play an important role for learning in this context. This study investigates whether this VR-based, immersive software is an effective tool for assessing medical learning objectives by comparing behavioral outcomes in VR and actor-based simulations, and examines how these behaviors relate to immersion levels and their impact on learning success.MethodsTo evaluate the effectiveness of the new teaching method, objective behavioral outcomes were identified as part of a dermatological learning unit and VR as a method was compared with actor-based simulation training. In addition, subjective questionnaires were collected to compare the levels of immersion in both concepts.ResultsIt was shown that primary learning objectives can be addressed well in VR. However, secondary learning objectives that fall into the field of basic skills seem to be delivered better in the actor-based training than in VR. This appears to be an effect of weaker immersion measured in VR training.ConclusionsIt can be said that the implementation of basic skills training depends largely on the level of immersion in the teaching method used. While primary learning subjectives can be trained and assessed well, at present, it does not appear to be fully possible to train secondary skills with the technical status quo in VR. However, the observation of secondary learning objectives can serve as an indicator for the assessment of immersion in the future.

Fusion Unbiased Pseudo-Linear Kalman Filter-Based Bearings-Only Target Tracking

In the realm of bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) has garnered significant interest, due to its low computational demands and robust stability. However, the interrelation between the measurement matrix and noise introduces bias into the PLKF’s target state estimation. To address this issue, we introduce a fusion unbiased PLKF (FUBKF) algorithm. This algorithm initiates with a global pseudo-linear treatment of the measurement equation, subsequently isolating the noise within the measurement matrix. By employing the unscented Kalman filter (UKF), the algorithm achieves precise estimation of the measurement matrix, thereby mitigating the estimation error stemming from the correlation between the measurement matrix and noise. Simulation outcomes demonstrate that the proposed algorithm substantially enhances tracking accuracy and sustains high stability in both 2D and 3D bearings-only target tracking scenarios, encompassing both non-maneuvering and maneuvering conditions.

The optimization of phase change heat transfer model for direct contact condensation heat transfer simulation based on Bayesian inversion method

Direct contact condensation heat transfer between steam and water is widely applied across various industries. The utilization of phase change models significantly influences the precision of numerical simulation outcomes in this domain. This study presents numerical simulations of direct contact condensation between steam and liquid films generated by nozzles. Employing Bayesian inversion method, this research conducts optimizations on the Lee and Schrage models and examines the impact of varying treatments of interface area density. Additionally, it proposes enhancements to these methodologies. The findings of this study are expected to foster a more profound comprehension of phase change models and their role in numerical simulations.

Magnetic–Thermal Coupling-Based Study on the Temperature Characteristics of Flameproof Cable Boxes for Coal Mines at Various Failure Modes

Temperature monitoring is the main indicator to ensure the safe operation of the coal mine explosion-proof box. In this study, a three-way flameproof cable box was considered for coal mines. A three-dimensional transient temperature field simulation model was constructed for magnetic–thermal coupling and subsequently used to conduct temperature field simulation for the failure of the flameproof cable box for coal mines. An experimental platform was developed to test the temperature characteristics of failure flameproof cable boxes for coal mines and obtain temperature curves at various failure currents. Using the experimental results of temperature characteristics and temperature field simulation outcomes, we analyzed the time responses and spatial distribution characteristics of the temperature rise of flameproof cable boxes for coal mines. The error between simulation and experimental results does not exceed 1 K, and the upper limit of time to ensure safe operation under different failure modes is determined. Thus, the acquired temperature distribution characteristics in a failure mode can be used as a reference for the design, inspection, and status warning of flameproof cable boxes for coal mines.

Adaptive Time-Varying Formation Maneuvering Control for Multi-Robot Systems in Complex Obstacle-Rich Environments

To tackle the challenge of time-varying formation control for underactuated robots under model parameter uncertainties and environmental disturbances, this study proposes an affine formation control approach enhanced by an Extended State Observer. Initially, using affine positioning theory and polynomial interpolation, guidelines for selecting leader vehicles and trajectory planning methods are established, whereby the trajectory of follower vehicles is uniquely determined through the stress matrix. To address the cumulative disturbances arising from model uncertainties and environmental factors impacting the formation, an Extended State Observer with optimized parameters is introduced. Furthermore, a distributed affine formation control method with disturbance rejection is designed specifically for underactuated robots with “nonlinear, strongly coupled” dynamics, ensuring that the formation system can track the target configuration within bounded error margins. Finally, theoretical analysis and simulation outcomes ultimately confirm the efficacy of the control approach in achieving resilient formation tracking.

Optimizing the Performance of DC Electric Smart Gate Motor using Ziegler Nichols Tuning Methods

One of the primary functions of the DC motor is exemplified in its utilization for the automated operation of irrigation gates, which involves a variety of DC motor models. The DC motor chosen was influenced by the particular operational requirements of the irrigation gate. The ability to support the massive weight of the gate as well as the extreme pressure produced by river water currents are among these requirements. The gate motor utilized functions at a sluggish pace of 20 revolutions per minute. The approach employed involves tuning utilizing the Ziegler-Nichols (ZN) method. The (Proportional-Integral-Derivative) PID controller is designed to precisely manage a DC motor's position, improving the motor's overall performance. The parameters were designed to be Kp = 17, Ti = 1, and Td = 0.1. The Simulation derived from the analysis of MATLAB step response data are side by side with algorithm utilized to certain the dynamic response of the closed-loop configuration. Essential Specifications such as rise time and settling time, both measured in seconds, will be integrated into the simulation outcomes. The utilization of the (ZN) based algorithm for adjusting the gain constants of the PID control system tends to yield superior outcomes in comparison to that under a unity feedback control system .The rising time was 12.6, but when performance improved, it dropped to 0.713. This improvement is also applicable to the settling time, which decreased from 22.4 to 1.13. The holistic evaluation demonstrates that the (ZN) method yields superior performance in contrast to a system employing unity feedback control.

Design and development of knee rehabilitation robot

This research presents a comprehensive design and analysis of a knee rehabilitation platform aimed at aiding individuals with knee dysfunction. Dysfunction in the knee joint can lead to an imbalance in gait and posture during activities of daily living (ADLs) such as standing, walking, and running. This study focuses on developing a 2-degree-of-freedom (2-DoF) knee rehabilitation device capable of mimicking linear and angular movements. A slider mechanism-based knee rehabilitation device is developed and simulated alongside various other mechanisms. The proposed mechanism achieves 32.5° of flexion for a linear movement of 0.45 m within 6 seconds, outperforming other mechanisms. To validate simulation results, a 3D-printed model is fabricated, and experimental studies are conducted under no-load conditions, showing close alignment with simulation outcomes with a deviation of ±5%. The device’s key features include portability, compliance, compactness, and enhanced stiffness. Future research will involve conducting pilot studies to further evaluate the practical efficacy and potential enhancements of the proposed knee rehabilitation platform.

Prebriefing in simulation from the nursing student perspective: A qualitative descriptive study

BackgroundPrebriefing is the phase of simulation before the scenario to assist learners to meet learning objectives. The purpose of this study is to explore nursing students’ experiences with prebriefing and their descriptions of factors in prebriefing they feel facilitate or hinder learning. MethodsA qualitative descriptive study design using focus groups was guided by Experiential Learning Theory. Participants were purposively sampled from a Midwestern university in the US. Content analysis was used to analyze data. ResultsTwenty-five participants across three focus groups identified four main categories: preparation should give a clear picture of the simulation, get in the right mindset in briefing, there's a disconnect between preparation and briefing, and outcomes of prebriefing. Subcategories further delineated these findings. ConclusionsFindings underscore the necessity for integrated preparation and briefing phases in prebriefing, emphasizing clarity, relevance, and timing of information delivery. These insights inform the development of tailored prebriefing strategies to enhance educational outcomes in healthcare simulations.

Framework for abnormal event detection and tracking based on effective sparse factorization strategy

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.

Using of Deep Learning in Beamforming Antenna Array

Digital beamforming (DBF) is a crucial technology for large antenna arrays, offering precise control over beam steering. This research introduces a novel method to enhance millimeter wave transmission by incorporating DBF with long-term memory (LSTM) based deep learning Our system utilizes digital signal processing and LSTM networks to optimize beamforming parameters instead of relying on traditional analog beamforming. The objective is to achieve high spectral efficiency. The methodology is executed in the programming language MATLAB and the obtained simulation outcomes validate a substantial enhancement in the metrics that evaluate performance, thereby demonstrating the potential of combining digital beamforming (DBF) with Long Short-Term Memory (LSTM) for forthcoming communication systems. Furthermore, the inclusion of LSTM in the process of digital beamforming presents a comprehensive comprehension of the proposed approach, which imparts valuable insights for advanced communication technologies. To substantiate the efficacy of the technique, several illustrative examples are employed to steer the beam pattern in the desired direction.

Mitigating blackhole attacks in wireless body area network

<p>In this paper, we aimed to develop a trusted secured routing Ad-hoc on-demand distance vector (AODV) protocol to fight against blackhole attacks within the wireless body area network (WBAN). The trusted secure routing protocol incorporates a routing strategy based on trust value to detect malicious nodes based on their trust value, a routing technique based on node residual energy to select the node with the highest residual energy during the communication process, and a hybrid cryptography algorithm that merges the Affine cipher with the modified RSA cipher algorithm to secure communication against malevolent biomedical sensor attacks. Simulation outcomes demonstrate that the suggested protocol outperforms the traditional AODV routing protocol in all evaluation metrics, including data rate, energy consumption, and packet delivery ratio. Its main strength is that it considers several factors, like illegitimate medical sensor detection, efficient network energy use, and secure data transmission, unlike similar secured routing protocols. Furthermore, the hybrid cipher algorithm improves the effectiveness and increases the security level of sensitive data compared to traditional cipher algorithms such as the Affine cipher and the RSA cipher.</p>

Synergistic dielectric regulation strategy of one-dimensional MoO2/Mo2C/C heterogeneous nanowires for electromagnetic wave absorption

Dielectric-dielectric nanocomposites with excellent synergistic effects are considered as a prospective avenue for the development of high-performance microwave absorbers. In this work, one-dimensional MoO2/Mo2C/C heterogeneous nanowires were synthesized via a straightforward co-precipitation and in situ pyrolysis process. The components were modulated by adjusting the reduction temperatures to achieve tunable electromagnetic parameters thereby optimizing the dielectric loss. The results showed that the optimized MoO2/Mo2C/C composite exhibited a minimum reflection loss of -50.7 dB at an ultra-thin thickness of 1.8 mm and an effective absorption bandwidth of 6 GHz at 2.3 mm when the calcination temperature was 700 °C. Based on the studies of electromagnetic parameters and radar cross section simulation outcomes, both the interwoven one-dimensional structure and synergistic effects between the components have endowed the material with good impedance matching and introduced various loss mechanisms such as conductivity loss, multiple polarization relaxation, and multiple reflection/scattering. This work presented a viable strategy for the preparation of one-dimensional MoO2-based dielectric microwave absorption materials.

The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China

With the intensification of climate change and human activities, the impacts of land use shifts on hydrological processes are becoming more pronounced, especially in regions with complex geographic, geological, and climatic conditions such as the Northeast Black Soil Region, China. This study quantitatively examines the variations in various land use types from 1980 to 2020 by means of a land use transfer matrix, and it incorporates the multi-year average runoff value to mitigate the interference of short-term climate fluctuations on the runoff trend, thereby enhancing the representativeness and stability of the simulation outcomes. The SWAT (Soil and Water Assessment Tool) model is employed to simulate land use alterations in different periods. The findings indicate that the area of farmland increased by 5.34% and the area of grassland decreased by 5.36% over 40 years. The areas of forest land and wetland have fluctuated significantly due to policy interventions and population growth. This study discovers that LUCC has resulted in a marginal increase in annual water yield. For instance, the water yield of paddy fields in 2020 amounts to 92.26 mm/year, which is 0.52–9.42% higher than the historical scenario and exhibits a notable upward trend in summer. Spatial analysis discloses regional disparities, with substantial changes in the hydrological behavior of northern watersheds (such as the Huma River) and southeastern regions (such as the Toudao River). The augmentation of wetland and forest coverage has effectively mitigated peak runoff, especially during extreme rainfall events. Wetlands have manifested strong water regulation capabilities and alleviated the impact of floods. This study quantitatively discloses the complex response pattern of LUCC to runoff by introducing a multi-scale analysis approach, which furnishes a scientific basis for flood risk assessment, land use optimization, and water resource management, and demonstrates the potential for extensive application in other countries and regions with similar climatic and topographic conditions.

An efficient convolutional neural network for adversarial training against adversarial attack

Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like fast gradient descent (FGS), recursive-FGSM (I-FGS), Deep-Fool, and Carlini and Wagner (CW) techniques are utilized that generate adversarial input by means of the CNN acknowledged to the attacker. In the experimentation process, the MNIST dataset comprising 60K and 10K training and testing grey-scale images are utilized. In the experimental section, the adversarial training model reduces the attack accuracy rate (ASR) by an average of 29.2% for different malicious inputs, when preserving the accuracy of 98.9% concerning actual images in the MNIST database. The simulation outcomes show the preeminence of the model against adversarial attacks.

SEPIC Based BLDC Motor Control for Electric Vehicles

Abstract: This paper presents a BLDC motor that can be utilized in electric vehicle applications where speed control is necessary. It is driven by a single-ended primary inductive converter (SEPIC). The inverter that feeds the motor uses PWM-based switching; however, a SEPIC converter in the DC link can be used to adjust the voltage level. Electronic commutation gating pulses are provided to the inverter input and the DC input. It is effective for electric vehicles since it uses a converter that switches to manage voltage. The SEPIC is lightweight and suitable for use with small electric cars. This paper describes using a SEPIC converter to drive a BLDC motor. The BLDC motor's closed-loop speed control is modelled using MATLAB /SIMULINK, and the simulation's outcomes are presented.

Investigation of Multiple LED Array Failure on DCO-FBMC Modulation for Indoor Visible Light Communication

Visible light communication (VLC) is a high-speed and power-efficient optical wireless communication method that has gained momentum for data transmission in indoor environments. Indoor VLC systems often use multiple LED arrays to increase the uniformity of brightness and enhance the received energy and the four LED array model is the most commonly used. To suppress the impact of intersymbol interference (ISI) and achieve higher data rates, multicarrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) have been introduced in VLC systems. The FBMC technique is a promising candidate for the 5G communication system and it has recently been applied to VLC systems. FBMC has several advantages: higher spectrum efficiency, narrower out-of-band radiations and asynchronous transmission. This study investigates the performance of an FBMC-based VLC system when one or more lamps in the multiple LED arrays experience failure. The VLC- FBMC model was implemented using a 4-QAM and 16-QAM format with three various LED array configurations while assuming a line of sight (LOS) model. The LED array model was evaluated based on its ability to achieve good bit error rate (BER) performance, communication quality and high bit rate and meet the required signal-to-noise ratio (SNR). The proposed FBMC model simulation's outcome indicates that the system is still able to transmit data reliably, even in the event of a failure of up to 50% of the lamps. The results indicate that the 2- and 4-lamp models yielded good BER performance, achieving a value of 3.6 × 10−5 and 5.4 × 10−5 respectively. Furthermore, the simulation demonstrated that the optical FBMC-based VLC system attained a high bit rate of 73.2 Mbit/s in the 4-QAM format using the 2-lamp model and a bit rate of 29.3 Mbit/s in 16-QAM format using the 4-lamp model, while maintaining acceptable BER performance.

Delay-dependent observer based control for uncertain fuzzy-chaotic systems with deception attacks and faulty actuators

In this study, the robust fault-tolerant control problem for T-S fuzzy chaotic systems is studied with the use of a proportional integral observer. Particularly, to accurately consider the real-world scenario, the chaotic system model incorporates the parameter uncertainties, input delay and external disruptions. Furthermore, a fuzzy-based observer is put forward based on analyzed system output with the intent of estimating the states of undertaken chaotic systems. Herein, the system output is susceptible to randomly occurring deception attacks and adheres to the Bernoulli distribution. Precisely, the consideration of deception attacks in the output channel allows for more secure state estimation in a networked setting. Subsequently, we develop a proportional integral observer-based fault-tolerant control that allows for the attainment of goals despite faults and delays in the input channel. Moreover, by setting up the Lyapunov-Krasovskii functional and blending it with Wirtinger's integral inequalities, we put together adequate conditions that ensure the asymptotic stability of the closed-loop systems by establishing conditions in the form of linear matrix inequalities. In the follow-up, based on the established matrix inequalities, the exact procedure for computing the gain matrices is outlined. As a last step, we put forward numerical simulation outcomes that exhibit the viability of the theoretical findings.