Efficient generation of hydrogen from water-splitting is an underpinning chemistry to realize the hydrogen economy. Low cost, transition metals such as nickel and iron-based oxides/hydroxides have ...been regarded as promising catalysts for the oxygen evolution reaction in alkaline media with overpotentials as low as ~200 mV to achieve 10 mA cm
, however, they are generally unsuitable for the hydrogen evolution reaction. Herein, we show a Janus nanoparticle catalyst with a nickel-iron oxide interface and multi-site functionality for a highly efficient hydrogen evolution reaction with a comparable performance to the benchmark platinum on carbon catalyst. Density functional theory calculations reveal that the hydrogen evolution reaction catalytic activity of the nanoparticle is induced by the strong electronic coupling effect between the iron oxide and the nickel at the interface. Remarkably, the catalyst also exhibits extraordinary oxygen evolution reaction activity, enabling an active and stable bi-functional catalyst for whole cell water-splitting with, to the best of our knowledge, the highest energy efficiency (83.7%) reported to date.
Reconfigurable Intelligent surface (RIS), which can induce phase shift of the received signals and then forward them by its reflecting elements, possesses high energy efficiency (EE). This letter ...studies the EE for the RIS-assisted multicast communication. A multiple-antenna base station (BS) delivers common messages to a group of mobile users (MUs), The RIS is placed to maintain the transmission. First, this letter analyzes the equivalent multicast channel and total power consumption to formulate an EE maximization problem. Then, this letter proposes the functional algorithms by fractional programming and alternating optimization to obtain a locally optimal solution for this problem. Lastly, we show asymptotic behaviors of the EE when the numbers of reflecting elements, antennas, and MUs go to infinity.
In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large ...amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.
•We present a high-dimensional data indexing scheme based on Voronoi Diagrams for spatial query processing in IoT.•The scheme aggregates multi-attribute sensor data in an energy- and time-efficient manner.•Hierarchical in-network storage is used to provide fast query responses.•Performance evaluation demonstrates effectiveness of our scheme compared to other data structures.
Emerging applications such as deep neural network demand high off-chip memory bandwidth. However, under stringent physical constraints of chip packages and system boards, it becomes very expensive to ...further increase the bandwidth of off-chip memory. Besides, transferring data across the memory hierarchy constitutes a large fraction of total energy consumption of systems, and the fraction has steadily increased with the stagnant technology scaling and poor data reuse characteristics of such emerging applications. To cost-effectively increase the bandwidth and energy efficiency, researchers began to reconsider the past processing-in-memory (PIM) architectures and advance them further, especially exploiting recent integration technologies such as 2.5D/3D stacking. Albeit the recent advances, no major memory manufacturer has developed even a proof-of-concept silicon yet, not to mention a product. This is because the past PIM architectures often require changes in host processors and/or application code which memory manufacturers cannot easily govern. In this paper, elegantly tackling the aforementioned challenges, we propose an innovative yet practical PIM architecture. To demonstrate its practicality and effectiveness at the system level, we implement it with a 20nm DRAM technology, integrate it with an unmodified commercial processor, develop the necessary software stack, and run existing applications without changing their source code. Our evaluation at the system level shows that our PIM improves the performance of memory-bound neural network kernels and applications by 11.2× and 3.5×, respectively. Atop the performance improvement, PIM also reduces the energy per bit transfer by 3.5×, and the overall energy efficiency of the system running the applications by 3.2×.
Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting ...capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment's dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.
Thanks to the strong ability against the inter-cell interference, cell-free network is considered as a promising technique to improve network capacity. However, further capacity improvement requires ...to deploy more base stations (BSs) with high cost and power consumption. To address this issue, inspired by the recently developed reconfigurable intelligent surface (RIS) technique, we propose the concept of RIS-aided cell-free network to improve the capacity with low cost and power consumption. The key idea is to replace some of the required BSs by low-cost and energy-efficient RISs. Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization framework to solve this challenging problem. In particular, we decouple this problem via fractional programming, and solve the subproblems alternatively. Note that most of the scenarios considered in existing works are special cases of the general scenario studied in this paper, and the proposed joint precoding framework can serve as a general solution to maximize the capacity in most existing RIS-aided scenarios. Finally, simulation results demonstrate that, compared with the conventional cell-free network, the network capacity under the proposed scheme can be improved significantly.
•Coordinated system-level power generation and demand side management is proposed.•A cost-effective and emission-aware power management system for ship is introduced.•Optimal utilization of energy ...storage and shore power supply systems is studied.•An interactive algorithm based on PSO and fuzzy rule-based approach is proposed.
The extensive exploitation of electric power in ships enables the development of more efficient and environmentally friendlier ships, as it allows for a more flexible ship power system operation and configuration. In this paper, an optimal power management method for ship electric power systems comprising integrated full electric propulsion, energy storage and shore power supply facility is proposed. The proposed optimization method is exploiting an interactive approach based on particle swarm optimization (PSO) method and a fuzzy mechanism to improve the computational efficiency of the algorithm. The proposed fuzzy-based particle swarm optimization (FPSO) algorithm aims at minimizing the operation cost, limiting the greenhouse gas (GHG) emissions and satisfying the technical and operational constraints of the ship.
•Thoroughly review and compare 83 papers on vessel fuel consumption prediction and optimization in recent 13 years.•Present features and sources of main datasets used in current ship fuel consumption ...prediction models.•Classify ship fuel consumption prediction models into white-box models (WBMs), black-box models (BBMs), and grey-box models (GBMs) combining WBMs and BBMs.•Analyze fuel consumption optimization models from the perspectives of decision variables and objective functions.•Review technical reports on ship fuel consumption prediction as a typical type of “grey literature”.•Propose and discuss research challenges and promising research questions on ship fuel management.
The shipping industry is associated with approximately three quarters of all world trade. In recent years, the sustainability of shipping has become a public concern, and various emissions control regulations to reduce pollutants and greenhouse gas (GHG) emissions from ships have been proposed and implemented globally. These regulations aim to drive the shipping industry in a low-carbon and low-pollutant direction by motivating it to switch to more efficient fuel types and reduce energy consumption. At the same time, the cyclical downturn of the world economy and high bunker prices make it necessary and urgent for the shipping industry to operate in a more cost-effective way while still satisfying global trade demand. As bunker fuel bunker (e.g., heavy fuel oil HFO, liquified natural gas LNG) consumption is the main source of emissions and bunker fuel costs account for a large proportion of operating costs, shipping companies are making unprecedented efforts to optimize ship energy efficiency. It is widely accepted that the key to improving the energy efficiency of ships is the development of accurate models to predict ship fuel consumption rates under different scenarios. In this study, ship fuel consumption prediction models presented in the literature (including the academic literature and technical reports as a typical type of “grey literature”) are reviewed and compared, and models that optimize ship operations based on fuel consumption prediction results are also presented and discussed. Current research challenges and promising research questions on ship performance monitoring and operational optimization are identified.
With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, ...UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.
Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving demand-side energy efficiency. At the same time, the thermodynamics of buildings and uncertainties ...regarding human activities make effective management challenging. While the concept of model-free reinforcement learning demonstrates various advantages over existing strategies, the literature relies heavily on value-based methods that can hardly handle complex HVAC systems. This paper conducts experiments to evaluate four actor-critic algorithms in a simulated data centre. The performance evaluation is based on their ability to maintain thermal stability while increasing energy efficiency and on their adaptability to weather dynamics. Because of the enormous significance of practical use, special attention is paid to data efficiency. Compared to the model-based controller implemented into EnergyPlus, all applied algorithms can reduce energy consumption by at least 10% by simultaneously keeping the hourly average temperature in the desired range. Robustness tests in terms of different reward functions and weather conditions verify these results. With increasing training, we also see a smaller trade-off between thermal stability and energy reduction. Thus, the Soft Actor Critic algorithm achieves a stable performance with ten times less data than on-policy methods. In this regard, we recommend using this algorithm in future experiments, due to both its interesting theoretical properties and its practical results.
•Comparison of four model-free actor-critic reinforcement learning algorithms in a data-centre simulation.•Each of the applied algorithms can learn the task and reduce energy consumption.•The conflict between thermal stability and energy savings is reduced during the training.•The algorithms are robust to changing reward functions and weather conditions.•Soft Actor Critic shows the highest data efficiency and is recommended for applications.
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