The Impact of Roof Pitch and Ceiling Insulation on Cooling Load of Naturally-Ventilated Attics

A 2D unsteady computational fluid dynamics (CFD) model is employed to simulate buoyancy-driven turbulent ventilation in attics with different pitch values and ceiling insulation levels under summer conditions. The impacts of roof pitch and ceiling insulation on the cooling load of gable-roof residential buildings are investigated based on the simulation of turbulent air flow and natural convection heat transfer in attic spaces with roof pitches from 3/12 to 18/12 combined with ceiling insulation levels from R-1.2 to R-40. The modeling results show that the air flows in the attics are steady and exhibit a general streamline pattern that is qualitatively insensitive to the investigated variations of roof pitch and ceiling insulation. Furthermore, it is predicted that the ceiling insulation plays a control role on the attic cooling load and that an increase of roof pitch from 3/12 to 8/12 results in a decrease in the cooling load by around 9% in the investigated cases. The results suggest that the increase of roof pitch alone, without changing other design parameters, has limited impact on attics cooling load and airflow pattern. The research results also suggest both the predicted ventilating mass flow rate and attic cooling load can be satisfactorily correlated by simple relationships in terms of appropriately defined Rayleigh and Nusselt numbers

Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).Comment: 6 pages, 3 figures, This paper has been accepted for publication in IEEE Global Communications Conference (GLOBECOM) 202

Temperature-dependent exciton-related transition energies mediated by carrier concentrations in unintentionally Al-doped ZnO films

The authors reported on a carrier-concentration mediation of exciton-related radiative transition energies in Al-doped ZnO films utilizing temperature-dependent (TD) photoluminescence and TD Hall-effect characterizations. The transition energies of free and donor bound excitons consistently change with the measured TD carrier concentrations. Such a carrier-concentration mediation effect can be well described from the view of heavy-doping-induced free-carrier screening and band gap renormalization effects. This study gives an important development to the currently known optical properties of ZnO materials.This research is supported by the State Key Program for Basic Research of China under Grant No. 2011CB302003, National Natural Science Foundation of China (Nos. 61025020, 60990312, and 61274058), Basic Research Program of Jiangsu Province (BK2011437), and the Priority Academic Program Development of Jiangsu Higher Education Institutions

Thermal pretreatment of sapphire substrates prior to ZnO buffer layer growth

The properties of ZnO buffer layers grown via metal-organic chemical vapor deposition (MOCVD) on sapphire substrates after various thermal pretreatments are systematically investigated. High-temperature pretreatments lead to significant modifications of the sapphire surface, which result in enhanced growth nucleation and a consequent improvement of the surface morphology and quality of the ZnO layers. The evolution of the surface morphology as seen by atomic force microscopy indicates an obvious growth mode transition from three-dimensional to quasi-two-dimensional as the pretreatment temperature increases. A minimum surface roughness is obtained when the pretreatment temperature reaches 1150 °C, implying that a high-temperature pretreatment at 1150 °C or above may lead to a conversion of the surface polarity from O-face to Zn-face, similar to processes in GaN material growth via MOCVD. By analyzing the evolution of the film properties as a function of pretreatment temperature, the optimal condition has been determined to be at 1150 °C. This study indicates that a high-temperature pretreatment is crucial to grow high-quality ZnO on sapphire substrates by MOCVD.This research was supported by the State Key Program for Basic Research of China under Grant No. 2011CB302003, National Natural Science Foundation of China (Nos. 61025020, 60990312, and 61274058), Basic Research Program of Jiangsu Province (BK2011437), and the Priority Academic Program Development of Jiangsu Higher Education Institutions

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.Comment: 15 pages, 10 figures, 2 algorithm

Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless Networks

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP's signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes' transmission scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP's beamforming vector and the IRS's phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users' transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.Comment: 31 pages, 6 figures, 2 tables, 3 algorithm

Cognitive and Action Sequence Prediction using Deductive Reasoning

Early in the process of the development of an aircraft cockpit, although the designers always introduce a set of operational procedures with the expectation that all pilots would follow, it is very difficult to guarantee that the flight crew will do exactly they are expected to do. The deviation of the pilots’ operation from the intended procedures may lead to an unsafe situation, and could also be an indication to the inherent reason for the biases in the pilots’ cognitive process. It became very obvious that a tool that could help to predict a comprehensive set of possible operations that the pilots would operate the aircraft will be very useful both in the flight deck design process and pilot training practices. This paper presents the development of the researches in the “Cognitive and Action Sequence Prediction using Deductive Creation Theory (CASEPREDICT)”. Unlike any human-made system which the response of the system can be predicted to certain degree of accuracy, a human-in-theloop system is always associated with a great deal of uncertainty issues which comes from the cognitive process of human operators

Perpendicular in-plane negative magnetoresistance in ZrTe5

The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we report the observation of in-plane NMR in the topological material ZrTe5 when the in-plane magnetic field is both parallel and perpendicular to the current direction, revealing an unusual case of quantum transport beyond the chiral anomaly. We find that a general theoretical model, which considers the combined effect of Berry curvature and orbital moment, can quantitatively explain this in-plane NMR. Our results provide new insights into the understanding of in-plane NMR in topological materials