Answers from top 7 papers
Open access•Journal Article•DOI
13 Dec 2013-Ethics & Global Politics
|Exemplified by the covert drone, this is power with formidable reach.|
01 Aug 2020
|The results indicate that enough power can be harvested from the powerline to recharge the drone batteries.|
17 May 2016
|In this study, the feasibility of the WPT charging system applied to a demonstrative drone has been proved.|
|It is suitable for application in the drone system.|
Open access•Proceedings Article•DOI
01 Dec 2016
|However, considering the tradeoff between spectral efficiency and energy efficiency of the drone, we show that 10.5\% spectral efficiency gain can be obtained without negatively affecting energy consumption of the drone.|
|Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs.|
15 Apr 2018
|We find that the drone-to-ground path loss differences are frequency dependent and closely related to drone altitude.|
How can drones be used to detect electricity theft?4 answersDrones can be used to detect electricity theft through various methods. One approach is the use of data analytics techniques such as Maximum Information Coefficient (MIC) and clustering by fast search and find of density peaks (CFSFDP). MIC can find correlations between non-technical loss (NTL) and electricity behavior, enabling the detection of thefts that appear normal in shapes. CFSFDP, on the other hand, can identify abnormal users among thousands of load profiles, making it suitable for detecting thefts with arbitrary shapes. By combining the advantages of these techniques, a framework can be created for accurate detection of various types of electricity thefts. Another approach involves using drones to recharge their batteries on power lines through inductive coupling. This allows drones to have extended flying time, enabling them to perform time-consuming and complex tasks related to electricity theft detection.
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How to measure algorithm "power consumption"?
5 answersPower consumption of algorithms can be measured using various methods. One approach is to estimate energy consumption based on the type of algorithm being used. Researchers in computer architecture have published extensive works on approaches to estimate energy consumption in machine learning scenarios. Another method is to measure the power consumption of the target being measured by determining the wire direct current equivalent resistor, obtaining power input and output voltages, and calculating the wire current and actual voltage. Additionally, deep learning models can be optimized for power consumption by finding the optimal set of hyper-parameters using methods such as genetic algorithms. By optimizing the hyper-parameters, deep learning models can achieve better performance in predicting power consumption.How can the pursuit-evasion game be modeled as a multi-agent reinforcement learning problem in UAVs?
5 answersThe pursuit-evasion game can be modeled as a multi-agent reinforcement learning problem in UAVs by using a variant of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method within a Multi-Agent Reinforcement Learning (MARL) framework. This approach addresses scenarios with multiple evasive targets in non-stationary and unknown environments with random obstacles. The pursuers in the game have heterogeneous roles, allowing for enhanced exploratory actions and exploitation of previously identified targets. The algorithm also incorporates a Voronoi-based rewarding policy to facilitate exploration for possible targets. The proposed method has been implemented, tested, and validated in both simulation environments and real-world multi-robot systems comprising of UAVs. The results demonstrate that the multi-agent pursuit team can learn highly efficient coordinated control policies for target tracking and exploration, even in complex environments with multiple fast evasive targets.How can drone collectives be used to improve the efficiency of drone operations?
3 answersDrone collectives can improve the efficiency of drone operations in several ways. Firstly, by utilizing swarm-based methods, drones can perform 4D path planning, which includes generating multiple flight paths for each drone to increase the acceptance rate of flight requests. This allows for more efficient use of airspace and reduces conflicts between drones. Secondly, collaborative execution models based on multi-agent reinforcement learning (MARL) can enable drones to learn and plan trajectories based on their current status and environment, leading to more efficient mission execution. Additionally, blockchain technology can be used as a communication tool within drone swarms, improving connectivity and energy efficiency during collaborative tasks. By leveraging these approaches, drone collectives can optimize time, reduce energy consumption, and enhance the overall efficiency of drone operations.What are the most effective ways to design products that save energy?
5 answersDesigning products that save energy can be achieved through various strategies. One effective approach is to optimize energy requirements by improving device efficiency and balancing the resources required for the Internet of Things (IoT) infrastructure, while reducing environmental costs. Another important aspect is to incorporate energy considerations within the design phase of a product, as this phase determines the majority of a product's environmental impact. Additionally, the use of advanced high-efficiency and energy-saving equipment, as well as scientific selection of control and management systems, can maximize energy savings in buildings. Furthermore, product designers can influence users towards energy-saving behaviors by visually representing energy use and providing engaging and aesthetic experiences with eco-feedback. By considering these strategies, designers can play a crucial role in addressing the environmental challenges associated with excessive energy consumption.What is the type of smart facilities management in building?
5 answersSmart facilities management in buildings encompasses various technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Digital Twin (DT), artificial intelligence (AI), and blockchain. These technologies aim to support facilities management and improve decision-making. One approach is the "Software Defined Building 2.0," which enables customization and reprogramming of the IT infrastructure in buildings, allowing cooperation among Cyber-Physical Systems (CPSs). Another aspect of smart facilities management is the use of smart technologies to make facility management more efficient, such as mobile apps and smart technologies for real estate management. Additionally, ambient technologies, including beacons and sensors, are used to create data-driven environments in smart offices, contributing to energy efficiency, optimized space utilization, and enhanced workplace experience. In the context of sports facilities, there is a focus on operation management and optimization, with an emphasis on sustainability and energy efficiency.What are the latest techniques in task scheduling in fog computing?
5 answersTask scheduling techniques in fog computing have been a focus of recent research. Various approaches have been proposed to address the challenges of completing tasks within the allotted time and processing the increased amount of data in fog computing environments. These techniques include using nonlinear mathematical programming for work scheduling, utilizing fuzzy logic to split up work between cloud and fog layers, formulating the problem as a mathematical model and introducing a new algorithm based on wavefront cellular learning automata improved by genetic algorithm (WCLA + GA), and developing a task priority dynamic implementation (TPDI) algorithm based on priority levels in the fog layer. Additionally, a hybrid meta-heuristic optimization algorithm (HMOA) that combines modified particle swarm optimization (MPSO) and deterministic spanning tree (SPT) has been proposed for energy-efficient task scheduling. These techniques aim to improve response time, energy consumption, resource utilization, and overall performance in fog computing environments.How to decompose weather-dependent electricity consumption of households?
5 answersWeather-dependent electricity consumption of households can be decomposed using various methods proposed in the literature. One approach is to develop separate models for daily maximum (peak) and minimum (idle) consumption. Another method involves disaggregating total household electricity use into different load categories/parameters such as base load, activity load, heating season gradient, cooling season gradient, and lowest external temperature at which air-conditioning is used. A two-stage model can also be used, where the first stage divides consumption into weather- and illumination-related and residual consumption, and the second stage disaggregates residual consumption into consumptions by different groups of appliances. Additionally, an extended version of cooling degree days (ECDD) has been proposed to better characterize the relationship between warm-season electricity consumption and weather conditions, taking into account temperature, residual temperature, and specific humidity effects. These methods provide valuable insights for understanding and managing weather-dependent electricity consumption in households.To what extent does technological development contribute to climate change In asia?
3 answersTechnological development plays a significant role in contributing to climate change in Asia. The region's agriculture sector, particularly rice production, is a major source of greenhouse gas (GHG) emissions, including methane and carbon dioxide, due to the increased use of synthetic fertilizers and energy for irrigation. Additionally, the use of polluting energy sources and the lack of green technologies worsen global warming and climate damage. However, there is potential to mitigate these emissions through the adoption of climate-smart agriculture, energy efficiency measures, and sustainable consumption practices. The integration of policies at multiple levels and the implementation of regulatory and incentive mechanisms are crucial for achieving the climate goals outlined in the Paris Agreement and the United Nations' Sustainable Development Goals.What is the history of etiquette and protocol in Jordan?
5 answersEtiquette and protocol in Jordan have a rich history that is influenced by the country's culture and traditions. Jordan is a conservative Muslim state with a Sunni Muslim majority, but it also maintains good relations with citizens of other faiths. The values that Jordanian women are expected to embody reflect the multiple and sometimes contradictory values of their families, nations, and faiths. The history of Jordan covers various periods, including the pre-state Transjordan era, the making of the modern state, the road to independence, and the years of prosperity. While the abstracts provided do not specifically mention the history of etiquette and protocol in Jordan, they provide insights into the country's culture, religion, and social customs, which are likely to have influenced the development of etiquette and protocol practices in the country.What is the state-of-the-art in drone autonomous navigation systems?
5 answersThe state-of-the-art in drone autonomous navigation systems involves the use of lightweight and drift-free vision-IMU-GNSS multisensor fusion strategies. These strategies utilize front-facing cameras to create visual geometric constraints and generate 3D environmental maps, while GNSS receivers provide continuous signals for navigation. Additionally, the deployment of deep Neural Networks (NN) and artificial intelligence enables applications such as obstacle avoidance and steering tasks. To optimize the deployment of NN models on tiny drones, efficient end-to-end optimization methods have been proposed, including cloud-IoT implementations and onboard processing. These methods achieve high accuracy, reduced computation complexity, and energy efficiency, even on small IoT devices. Furthermore, for specific applications such as powerline inspection, autonomous flight operations are facilitated through abstract representations of identified and tracked cables, along with trajectory planning routines.How can drone autonomous navigation systems be improved?
4 answersDrone autonomous navigation systems can be improved by using deep neural networks (DNN) for visual perception and reinforcement learning-based neural schedulers. These systems can dynamically adjust task execution location, input resolution, and image compression ratio to achieve low inference latency, high prediction accuracy, and long flight distances. Additionally, edge computing can assist in offloading computation tasks from drones to external servers, reducing network latency. The optimization objective for these systems is to enhance the quality of navigation. Experimental results have shown that these adaptive solutions can reduce end-to-end latency by 28.06% and extend flight distances by up to 27.28% compared to non-adaptive solutions.