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Due to energy constraints and people's increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them. [ABSTRACT FROM AUTHOR]

This paper studies the fuel supply offset of diesel engines based on the crankshaft segment signal. Engine nonuniformity refers to the crankshaft torque fluctuation caused by cylinder-to-cylinder differences caused by misfiring or differences in the fuel supply or air supply. Fuel injection offset can reduce the nonuniformity between cylinders to realize high-efficiency and low pollution combustion. Based on crankshaft segment signal characteristics, an individual cylinder fuel offset control (FOC) strategy was built. The high-pressure common-rail diesel engine operating nonuniform control strategy was put forward. Based on crankshaft segment signal characteristics at different operating conditions, the reasonable operating condition of detecting the engine individual cylinder nonuniform degree was put forward. The open-loop and closed-loop control mode based on the condition was set up. The proportional-integral (PI) control algorithm is proposed to quantify engine individual cylinder nonuniform degree, and the fuel amount offset value was obtained. According to the principle of FOC, based on the automotive electronics development ASCET software platform, the FOC strategy module of the electronic control unit (ECU) was designed, and the simulation experiment was carried out. Research shows that for Z cylinder engine, just the first Z/2 harmonic components below fire frequency can fully reflect the state of the engine's nonuniform operation. The control target to individual cylinder FOC is zero for the synthetic waveform amplitude of the first Z/2 harmonic components. Compared with the traditional quantization method, the fuel offset information extracted from the crankshaft segment signal has stronger anti-interference and more accurate parameters. FOC algorithm can accurately reflect the engine's operating nonuniformity. The control of the nonuniformity is reasonable. The offset fuel amount calculated by FOC is very consistent with the fuel supply state of each cylinder set by the experiment, which meets the requirement of accurate fuel injection control of the diesel engine. [ABSTRACT FROM AUTHOR]

Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

How to operate an unmanned aerial vehicle (UAV) safely and efficiently in an interactive environment is challenging. A large amount of research has been devoted to improve the intelligence of a UAV while performing a mission, where finding an optimal maneuver decision-making policy of the UAV has become one of the key issues when we attempt to enable the UAV autonomy. In this paper, we propose a maneuver decision-making algorithm based on deep reinforcement learning, which generates efficient maneuvers for a UAV agent to execute the airdrop mission autonomously in an interactive environment. Particularly, the training set of the learning algorithm by the Prioritized Experience Replay is constructed, that can accelerate the convergence speed of decision network training in the algorithm. It is shown that a desirable and effective maneuver decision-making policy can be found by extensive experimental results. [ABSTRACT FROM AUTHOR]