scispace - formally typeset
Search or ask a question

Answers from top 10 papers

More filters
Papers (10)Insight
Comparing to the conventional energy conservation or harvesting approaches, wireless charging can replenish energy in a more controllable manner and does not require accurate location of or physical alignment to sensor nodes.
In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters.
The advantages of the multiple primary coil topology increase the feasibility of charging multiple wireless portable devices simultaneously.
Dynamic wireless charging holds promise to partially or completely eliminate the overnight charging through a compact network of dynamic chargers installed on the roads that would keep the vehicle batteries charged at all times, consequently reducing the range anxiety and increasing the reliability of EVs.
The Inductive Power Transfer represents a viable solution of wireless battery charging for all users of electric mobility.
When the loss in wireless charging is non-negligible, we propose to exploit detachable battery pack (DBP) and propose a DBP-PSB algorithm to avoid energy loss.
Wireless charging should not, however, affect instrumentation located inside the AUV.
Wireless charging technology is a novel electric energe replenish mode, and have many attractive properties according to traditional contact charging.
Wireless charging offers a safe and reliable method for autonomous power transfer between a charging station and a vehicle.
The results provide a theoretical and practical support for design of a wireless charging system.

See what other people are reading

What are teh current challenges of ADAS for low end vehicles?
5 answers
Current challenges of Advanced Driver Assistance Systems (ADAS) for low-end vehicles include the limited prevalence of ADAS in such vehicles due to cost constraints. Additionally, there is a gap in providing proper training to drivers, leading to overreliance on ADAS features and potential safety issues. Moreover, the development and testing of autonomous ground vehicles (AGVs) for effective operation in off-road environments and areas with limited infrastructure pose significant challenges. To address these challenges, there is a need for cost-effective solutions that can make ADAS features accessible to low-end vehicles, along with comprehensive driver training programs to ensure safe and effective use of these systems. Integrating essential ADAS features into low-cost platforms like smartphones can potentially bridge the gap and enhance safety in low-end vehicles.
What is the definition of management dillemma?
5 answers
A management dilemma, as discussed in various contexts, refers to a situation where decision-makers face conflicting choices or challenges in managing a particular issue. In the context of delirium management, non-pharmacological interventions play a crucial role in prevention and treatment, emphasizing strategies like reorientation, cognitive stimulation, early physiotherapy, and effective communication. In the realm of authoritarian leadership, the successor's dilemma arises when predecessors appoint successors who may pose a threat due to their impatience and power, leading to potential removal from power. Additionally, in the management of dilated cardiomyopathy, especially in resource-limited settings, tailoring management strategies to available resources is essential to maintain overall prognosis despite limitations in diagnostic capabilities and treatment options.
What applications are envisioned for this latency-sensitive federated learning archi- tecture?
5 answers
The latency-sensitive federated learning architectures, such as the proposed Vision-Aided Federated Wireless Networks (VFWN) and Delay-Aware Federated Learning (DFL), are envisioned for applications requiring real-time machine learning capabilities at the wireless edge. These architectures are particularly suitable for scenarios like self-driving vehicles, intelligent transportation systems, and industrial automation, where instantaneous learning, adaptation, and decision-making are crucial. By leveraging multi-sensor data and advanced deep learning techniques, these architectures aim to predict beam blockages in wireless networks, optimize model training efficiency, reduce communication costs, and minimize latency for time-sensitive applications. Overall, the applications envisioned for these latency-sensitive federated learning architectures span across various domains requiring rapid and accurate decision-making capabilities in dynamic environments.
What are the most commonly used data aggregation techniques in R?
5 answers
In R, common data aggregation techniques include collapsing multiple variables into representative summaries, averaging aggregation methods such as arithmetic mean, median, geometric mean, and harmonic mean, and iterative filtering algorithms for energy-efficient data aggregation in sensor networks. Additionally, R offers functions for power means, weighted averages like the Borda count, and handling non-independent inputs with OWS functions and the Choquet integral. Furthermore, data aggregation methods in R are also applied in medical image analysis, particularly in diagnosing and treating heart conditions using semantic analysis of coronary vessels from computed tomography scans. These diverse techniques cater to various fields, from genomic analysis to sensor networks and medical imaging, showcasing the versatility of data aggregation in R.
What are the implications of having an optimum cluster size for data analysis and decision-making?
4 answers
Having an optimal cluster size in data analysis is crucial for effective decision-making. Determining the right number of clusters enhances the performance of subsequent processes applied to the data, aiding in pattern recognition and insight extraction. Various clustering algorithms rely on fixed cluster numbers, making the identification of the optimal size essential. The choice of cluster size impacts the quality of structures formed, affecting the accuracy of classification algorithms in multi-class problems. Optimal clustering models can minimize energy consumption in hybrid networks, maximizing the network's lifetime before recharging. In financial domains, like investment analysis, the Davies–Bouldin index helps find the optimum number of clusters for further analysis and decision-making.
What are the results produced from the topic "Optimization of the operational state’s routing for mobile wireless sensor networks"?
5 answers
The research on optimizing the operational state's routing for mobile wireless sensor networks has yielded significant results. Various algorithms and protocols have been proposed to enhance energy efficiency, network performance, and data transmission in such networks. For instance, the proposed Optimum Deterministic Data Gathering Sub-Path Finding (ODDGSPF) algorithm aims to optimize data collection routes and reduce energy consumption. Additionally, the Adaptive fuzzy optimized routing based on Maximum Energy support routing protocol using the SSAMR algorithm has shown improved network performance and minimized delivery time for data transmission. Moreover, the Hybrid Optimization-based Efficient Routing Protocol (HOERP) utilizes grey wolf optimization and particle swarm optimization to minimize energy consumption in mobile wireless sensor networks, demonstrating superior energy efficiency compared to existing routing protocols.
What is the working principle of piezoelectric nanogenerator?
4 answers
The working principle of a piezoelectric nanogenerator involves converting mechanical energy, such as ambient vibrations, into electrical energy. This process is crucial for harvesting energy to power electronic devices and sensors, especially in wearable and self-powered systems. To enhance the power generation performance of polymer-based piezoelectric nanogenerators, incorporating graphene oxide (GO) into the material can significantly improve the induced charge transfer and piezoelectric response, leading to better power generation capabilities. Additionally, the design of piezoelectric nanogenerators, such as a multilayered structure with specific materials like zinc oxide and aluminum electrodes, plays a vital role in optimizing their electromechanical behavior and maximizing output power.
How to setup and read strain gauge?
5 answers
To set up and read a strain gauge, various methods and materials can be utilized based on different applications. One approach involves using a wireless strain sensor powered by a light source, enabling continuous operation and dynamic interrogation wirelessly. Another method employs graphene-based strain gauges, offering high sensitivity and reproducibility in small strain fields, with potential for self-sensing structures. Additionally, all-elastomer strain measurement devices with high gauge factors and mechanical compliance similar to human skin can be used for soft material strain quantification, such as on the human body. Furthermore, micro-mechanical strain sensors utilizing aligned carbon black particles in a polymer matrix demonstrate high sensitivity with reversible resistivity changes upon stretching. These diverse approaches showcase the versatility and advancements in strain gauge technology for various applications.
What are the current research gaps in the development and implementation of smart greenhouses for sustainable agriculture?
4 answers
Current research gaps in the development and implementation of smart greenhouses for sustainable agriculture include the lack of standardization in data collection equipment and greenhouse specifications. Additionally, challenges persist in disease monitoring, requiring advanced technologies like convolutional neural networks for accurate plant health classification. There is a need for more efficient and sustainable food production systems, with traditional farming methods proving energy-intensive and sub-optimal. Moreover, while AI technologies show promise in optimizing crop yields and resource use efficiency, critical challenges such as disparities in technology access and commercialization, energy consumption, and accuracy-computational speed tradeoffs remain unresolved. Addressing these gaps is crucial for advancing smart greenhouse technologies towards enhanced sustainability in agriculture.
What are the types of electric transformer in power systems?
5 answers
Electric transformers in power systems can be categorized into various types based on their functionality and design. These types include large scale power transformers crucial for interconnections, transformers used for voltage and current level modifications without changing frequency, single-stage AC-AC converters for direct conversion, transformers essential for AC power distribution systems with the ability to adjust voltages for long-range transmission, and variable frequency transformers (VFT) designed to control power flow and dampen inter-area oscillations in interconnected power systems. Each type serves a specific purpose in power systems, ranging from basic voltage transformation to advanced control mechanisms for enhancing system stability and efficiency.
What are the tools and pillars of PREVENTIVE Maintenance strategy?
5 answers
Preventive maintenance strategies encompass various tools and pillars to ensure efficient equipment upkeep. These include management tools like Lean Manufacturing, Gantt Chart, and Maintenance Performance Indicators, as well as Total Productive Maintenance (TPM) with four pillars, Electrical Predictive Maintenance, and Maintenance Management Program (MMP). Additionally, a preventive maintenance system integrates facility state information collection, facility information input, preventive maintenance processing, and preventive maintenance information output units. Furthermore, a preventive maintenance system for machine tools incorporates vibration detection, sound detection, servo motor current value detection, abnormality detection, and detection starting/finishing command setting parts. Proactive maintenance strategies for Wireless Sensor Networks involve analytical models for energy dissipation estimation, mobile robots for sensor replacement, and different scheduling approaches like Heuristic Centralized Proactive Maintenance Strategy (HCPMS) and Market-based Distributed Proactive Maintenance Strategy (MDPMS).