Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing th...
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Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.
In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the develop...
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In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp
Developing force control mechanisms employing electromagnetics is on the rise in active control applications for flexible mechanical systems like marine engines and shipboard machinery. Electromagnetic control devices...
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Developing force control mechanisms employing electromagnetics is on the rise in active control applications for flexible mechanical systems like marine engines and shipboard machinery. Electromagnetic control devices offer superior performance indicators compared to traditional mechanical force actuators in terms of longevity, energy efficiency, maintenance requirements, rapid control response, and high operating speeds. This article investigates the use of magnetic actuation and switching power electronics in addressing the stabilization and tracking control challenges encountered in the dynamics of a mechanical system with a single degree of freedom, comprising mass, spring, and damper elements. Particularly, a linear mechanical oscillator is nonlinearly coupled with an electromagnet and its associated driving circuit via the magnetic field. The electromagnetically actuated mechanical system exhibits characteristics of a differentially flat nonlinear system. A control strategy is suggested for the purpose of tracking reference position trajectories using output feedback linearization. The synthetic linearized control signal is subsequently guided to a DC-DC buck converter, able to regulate the system’s input voltage in a wide range of operation, by switching the duty cycle. The converter is described using a precise electrical model of the system, accounting for parasitic resistances in the inductor, capacitor, and switches. An averaged state space approach is utilized to create a mathematical nonlinear model for the converter which is then linearized by employing the Exact Feedback Linearization technique. By applying optimal control theory, the controller's coefficients are fine-tuned for optimal performance. To assess the proposed method's performance, the dynamics of the compensated mechatronic system is simulated using MATLAB/Simulink. The simulation results demonstrate that the proposed control scheme choice for active control of vibrating mechanical system
The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated usin...
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The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a Distributed Economic Model Predictive control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.
Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively lear...
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In this paper, a novel control strategy based on model predictive control is proposed for tracking the trajectory of unmanned aerial vehicles (UAV). First, the dynamic model of an unmanned aerial vehicle UAV is establ...
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Learning from Demonstration (LfD) has emerged as a crucial method for robots to acquire new skills. However, when given suboptimal task trajectory demonstrations with shape characteristics reflecting human preferences...
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The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the app...
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ISBN:
(数字)9798350372748
ISBN:
(纸本)9798350372755
The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches.
Formaldehyde is an important carcinogen commonly found *** indoor sources have been intensively *** study on outdoor formaldehyde concentration,which is potentially an important source to indoors,remains *** study att...
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Formaldehyde is an important carcinogen commonly found *** indoor sources have been intensively *** study on outdoor formaldehyde concentration,which is potentially an important source to indoors,remains *** study attempts to characterize temporal and spatial distribution of the atmospheric formalde-hyde concentration in Chinese *** variation of ambient formaldehyde was examined in 6 cities and peak hours were identified between 12:00 pm and 3:00 ***,outdoor formaldehyde concentrations were measured in the peak hours in 30 cities during the summer months of Jul.-Aug.,*** formaldehyde concentrations in the peak hours fell into a range of 0.005-0.087 mg/m^(3)(median value is 0.027 mg/m^(3)),87.7%of which have exceeded the chronic reference exposure criteria of 0.009 mg/m^(3) set by Office of Environmental Health Hazard *** risk analysis suggests that exposure to ambient formaldehyde could cause a median carcinogenic risk of 1.9×10^(−5)(3.17×10^(−6)-6.13×10^(−5)),higher than threshold limit of 10−*** correlation analysis of the 30 cities shows that ambient summertime atmospheric formaldehyde concentrations of the city are positively correlated with its Gross Domestic Product(r=0.48).We also found that the outdoor formaldehyde concentrations in urban areas(median:0.017 mg/m^(3))is slightly higher than those in suburban areas(median:0.013 mg/m^(3)).Results here prove that outdoor formaldehyde is ubiquitous in Chinese cities and reduces effectiveness of ventilation in dilution indoor *** it would underestimate air cleaner capacity needed by a factor of about *** should be accounted for in health analysis and air quality engineeringcontrol design of built environment in the future.
Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diag...
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