In Situ Electrical Network Activation and Deactivation in Short Carbon Fiber Composites via 3D Printing

Prior studies on carbon-filler based, conductive polymer composites have mainly investigated how conductive filler morphology and concentration can tailor a material's electrical conductivity and overlooks the effects of filler alignment due to the difficulty to control and quickly quantify the filler alignment. Here, direct ink write 3D printing's unique ability is utilized to control carbon fiber alignment with a single process parameter, velocity ratio, to instantaneously activate or deactivate the electrical network in composites. Maximum electrical conductivity is achieved by randomly aligning carbon fibers that enhances the chance of direct fiber-to-fiber contact and, thus, activating the electrical network. However, aligning the fibers by increasing the velocity ratio disrupts the electrical network by minimizing fiber-to-fiber contact that resulted in a drastic decrease in electrical conductivity by as much as five orders of magnitude in both short and long carbon fiber composites. With this study, this study demonstrates that electrically conductive or insulative composites can be fabricated sequentially with a single ink. This novel ability to instantaneously control the electrical conductivity of carbon fiber reinforced composites allow to directly embed conductive pathways into designs to 3D print multifunctional composites that are capable of localized heating and self-sensing.     

Three Potential Elements of Developing Nerve Guidance Conduit for Peripheral Nerve Regeneration

Autograft replaced by a nerve guidance conduit (NGC) is challenging in peripheral nerve injury because current NGC is still limited by precise conductivity and excellent biocompatibility in vivo, which influences the peripheral nerve repair even for a long lesion gap repair. Several particular elements have the potential function for nerve conductivity acceleration based on the traditional three factors of neural tissue engineering. The review aims to address three questions: 1) What is the superior factor for nerve conduction in the application? 2) How can a more conductive regenerative scaffold be constructed in vivo? 3) What is the next step in nerve regeneration for NGC? The bibliometrics analysis of NGC-related references is adopted to acquire that the conductive material, manufacturing technology of neural scaffold, and electrical stimulation (ES) play essential roles in the acceleration of nerve conduction. This review visually analyses the research status and summarizes the main types of conductive materials, the manufacturing technologies of neural scaffolds, and the characteristics of ES. The viewpoints and outlook of developing NGC are also discussed in this review. The proposed three elements are expected to improve the nerve conduction of NGC in vivo and even address the dilemma of long-distance peripheral nerve injury.     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system’s energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.     

Empowering wave energy with control technology: Possibilities and pitfalls

With an increasing focus on climate action and energy security, an appropriate mix of renewable energy technologies is imperative. Despite having considerable global potential, wave energy has still not reached a state of maturity or economic competitiveness to have made an impact. Challenges include the high capital and operational costs associated with deployment in the harsh ocean environment, so it is imperative that the full energy harnessing capacity of wave energy devices, and arrays of devices in farms, is realised. To this end, control technology has an important role to play in maximising power capture, while ensuring that physical system constraints are respected, and control actions do not adversely affect device lifetime. Within the gamut of control technology, a variety of tools can be brought to bear on the wave energy control problem, including various control strategies (optimal, robust, nonlinear, etc.), data-based model identification, estimation, and forecasting. However, the wave energy problem displays a number of unique features which challenge the traditional application of these techniques, while also presenting a number of control ‘paradoxes’. This review articulates the important control-related characteristics of the wave energy control problem, provides a survey of currently applied control and control-related techniques, and gives some perspectives on the outstanding challenges and future possibilities. The emerging area of control co-design, which is especially relevant to the relatively immature area of wave energy system design, is also covered.     

振动抑制与能量俘获专刊序

工程系统中梁结构经常处于各种激励的作用下,因而梁结构在这种环境下不可避免地发生着各种各样的强迫振动.在梁结构发生振动的过程中,其自身会受到的温度、湿度、电磁场、裂纹等众多内外部因素的影响,而众多内外部因素就构成梁的多物理场耦合环境.在多场耦合环境下,Green函数法作为一种解析方法在研究梁的多场耦合振动问题方面具有优势,有利于讨论力、电、热、裂纹等因素作用下梁的振动特性和多场耦合特性.Green函数法相比于模态叠加法,优点在于能够得到完整且精度较高的解析解,具有收敛性好,运算快的特点.本文主要阐述Green函数在梁的强迫振动、热力耦合振动、力电耦合振动、裂纹梁振动等研究问题上取得了大量的理论和工程研究成果.本文以裂纹为内因,热、力、电为外因进行分类,阐述了在内外因影响下梁的强迫振动问题Green函数解的研究现状,从而让读者进一步系统性的了解Green函数法在振动领域中的广泛应用,以及了解该方法本身的特色和优势奠定基础.     

基于Conv1d-LSTM模型的能源分配预测

能源分配问题往往与其所在区域环境有关,能源分配的预测可以通过当地环境因素数据来推测之后对该区域的能源分配数值,最大程度上分配好能源. LSTM网络预测短期效果良好,但预测较长时期的数据会导致误差积累,速度慢且准确性差; Informer是近期新提出的能源预测算法模型,速度快但在该任务上预测能力不够.本文提出Conv1d-LSTM模型,预测结果优于上述两个模型,具有更低的平均绝对误差和均方根误差.     

含氢储能的多源联合发电系统调度研究

风电和光伏发电具有间歇性和随机性,为了降低在多源联合发电系统中的弃风弃光率,采用含氢储能系统和火电机组配合来平滑风电和光电机组出力。文中以系统运行成本最小和弃电惩罚成本最小为目标,以系统功率平衡、火电机组出力和爬坡、热备用、风电和光电出力及储能系统储氢罐容量、电解槽和燃料电池功率等为约束条件构建了多源联合发电系统日前调度模型。通过YALMIP工具箱对模型进行编程,并调用CPLEX对编写的程序进行求解。对含有风电、光电、火电机组以及储能系统的多源联合发电系统进行算例分析,通过对比有无储能系统的弃风弃光量和系统总运行成本,证明了含氢储能系统可以有效降低系统的弃风弃光率,并提高系统的经济性。     

Energy and load balancing routing protocol for IoT

Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.