Size-controlled preparation of polyelectrolyte-protected gold nanoparticles by natural sunlight radiation

By radiating a linear polyethylenimine–HAuCl₄ aqueous solution with natural sunlight, we obtained polyelectrolyte-protected gold nanoparticles, and the size of such gold nanoparticles can be controlled by varying the molar ratio of polyelectrolyte to gold.     

Al–Si cast alloys under isothermal and thermomechanical fatigue conditions

This work presents a survey of the isothermal and anisothermal fatigue behavior of aluminum casting alloys obtained from different processes. Experimental results have shown that porosity, especially large and irregular pores, provides the main factor in decreasing fatigue properties of the tested alloys. In materials with similar porosity levels, other microstructural factors became relevant such as, matrix morphology and the amount of alloying elements. Fractographic analyses showed that fatigue cracks preferentially start to propagate in microcracks or interdendritic shrinkage usually located next to the surface. In most cases, propagation takes place in the eutectic phase, although in the thixoformed material, a transition from transgranular to intergranular mode was observed in the crack propagation mode.     

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.     

Dynamic surface control of a piezoelectric fuel injector during rate shaping

Fuel injection rate shaping is a strategy to improve fuel efficiency and reduce harmful emissions in diesel engines. Due to their fast response, piezoelectric fuel injectors are capable of rate shaping operation. This paper describes a model-based closed-loop controller of injection flow rate for a piezoelectric fuel injector. This within-an-engine-cycle control strategy utilizes a dynamic surface control scheme and shows an injection flow rate tracking capability. Practical issues with LabVIEW FPGA control implementation are also addressed. The performance of the controller is verified with simulation and experimental results at different rail pressures and desired injection rates. The experiments show a maximum error of total fuel per one injection event of 2.5%. A stability analysis is also included.     

无人机群目标搜索的主动感知方法

为提升蚁群搜索算法在规模大的栅格环境中对未知目标的搜索效率,提出基于蚁群算法的主动感知搜索框架。该框架通过应用历史环境信息来选择无人机的运动方式,并由无人机运动方式和感知域信息得到新的环境信息,从而实现无人机群的智能自动化搜索功能。新方法计算出一种具有探索偏好的未搜索概率,可使无人机搜索时偏向未搜索程度高的栅格,以此来提高算法的搜索能力。同时,以未搜索概率和信息素作为运动方式决策的依据来建立一种新的运动方式选择机制。该机制不仅考虑了目标可能出现的区域,又可兼顾未知区域,从而可实现无目标先验信息条件下的搜索过程。仿真结果表明,此算法在规模大的栅格环境中,与现有算法相比具有更高的搜索效率,并且得到的目标分布信息将更加全面。     

Real-time congestion prediction for urban arterials using adaptive data-driven methods

Multimedia Tools and Applications - Congestion prediction can support traffic departments to take effective traffic management measures, and it aids users to adopt smarter trip strategies,...     

A fixed clustering protocol based on random relay strategy for EHWSN

In Energy Harvesting Wireless Sensor Networks (EHWSN), the communication protocol will directly affect the final performance of the network, so it is necessary to study the communication protocol based on EHWSN. In this paper, for the low-cost fixed clustering problem, a fixed clustering protocol RRCEH is based on random relaying. Our proposed RRCEH abandons the inefficient inter-cluster communication method of the traditional fixed clustering protocol. To coordinate the data upload of the cluster head, RRCEH allocates different random relay vectors to each ring area of the network, and combines all the random relay vectors into a random relay matrix of RRCEH. In each communication round, the cluster head node randomly selects its relay target node to send data according to the probability distribution in the random relay vector in the area. For two different cluster head configuration scenarios, by optimizing the random relay matrix, RRCEH can effectively reduce the network's configuration requirements for cluster head energy harvesting capability, thus reducing the deployment cost of EHWSN.     

Hyperelastic,Robust, Fire-Safe Multifunctional MXene Aerogels with Unprecedented Electromagnetic Interference Shielding Efficiency

MXene aerogels have shown great potential for many important functional applications, in particular electromagnetic interference (EMI) shielding. However, it has been a grand challenge to create mechanically hyperelastic, air-stable, and durable MXene aerogels for enabling effective EMI protection at low concentrations due to the difficulties in achieving tailorable porous structures, excellent mechanical elasticity, and desired antioxidation capabilities of MXene in air. Here, a facile strategy for fabricating MXene composite aerogels by co-assembling MXene and cellulose nanofibers during freeze-drying followed by surface encapsulation with fire-retardant thermoplastic polyurethane (TPU) is reported. Because of the maximum utilization of pore structures of MXene, and conductive loss enhanced by multiple internal reflections, as-prepared aerogel with 3.14 wt% of MXene exhibits an exceptionally high EMI shielding effectiveness of 93.5 dB, and an ultra-high MXene utilization efficiency of 2977.71 dB g g⁻¹, tripling the values in previous works. Owing to the presence of multiple hydrogen bonding and the TPU elastomer, the aerogel exhibits a hyperelastic feature with additional strength, excellent stability, superior durability, and high fire safety. This study provides a facile strategy for creating multifunctional aerogels with great potential for applications in EMI protection, wearable devices, thermal management, pressure sensing, and intelligent fire monitoring.     

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.     

Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review

This paper presents a literature review on the different aspects of task allocation and assignment problems in human–robot collaboration (HRC) tasks in industrial assembly environments. In future advanced industrial environments, robots and humans are expected to share the same workspace and collaborate to efficiently achieve shared goals. Difficulty- and complexity-aware HRC assembly is necessary for human-centric manufacturing, which is a goal of Industry 5.0. Therefore, the objective of this study is to clarify the definitions of difficulty and complexity used to encourage effective collaboration between humans and robots to leverage the adaptability of humans and the autonomy of robots. To achieve this goal, a systematic review of the following relevant databases for computer science was performed: IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and ASME Digital Collection. The results extracted from 74 peer-reviewed research articles published until July 2022 were summarized and categorized into four taxonomies for 145 difficulty and complexity definitions from the perspectives of (1) definition-use objectives, (2) evaluation objectives, (3) evaluation factors, and (4) evaluation variables. Next, existing definitions were primarily classified according to the following two criteria to identify potential future studies on the formulation of new definitions for human-centric manufacturing: (1) agent specificity and (2) common aspects in manual and robotic assemblies.