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To address the problem of optimal allocation of water resources in water shortage areas, a reservoir and a pumping station water resource optimal scheduling model under the condition of insufficient irrigation is proposed. The model takes the maximum relative yield of crops as the objective function, the amount of water supplied, water spilled and water replenished as decision variables, the total amount of water supplied by the system, the water right of the pumping station and the operation criteria as constraints, and uses a dynamic programming method to solve the model. The optimal water supply and water spill process of the irrigation reservoir and the optimal water replenishment process of a pumping station during the entire growth period of dry crops were obtained. Moreover, under the conditions of 50 and 75% probability of exceedance, the optimized relative yields of crops increased by 17.1% and 19.6%, respectively. The results show that ensuring the optimal operation of joint water source projects can achieve the optimal allocation of limited water resources, and improve the relative yield of crops in irrigation areas, which has important guiding significance for the planning and management of water resources in similar irrigation areas. HIGHLIGHTS An optimal water allocation model for the joint operation of a reservoir and pump station in arid areas is established, considering regional water rights.; An operation rule for a reservoir is integrated into the recursive process of the dynamic programming to solve the model.; The penalty function method is introduced into the dynamic programming to deal with the constraint of water rights.

At present, meta-heuristic algorithms are the most popular methods for the optimization of the operations of reservoirs. In order to avoid inappropriate solutions, i.e. spills occurring when the reservoir is not full, a modified method is proposed that can adjust the trajectories of the particles, using the particle swarm algorithm, according to the operation rule of the reservoir. The method was tested in a case study, and was compared to two commonly used methods for generating particle position vectors. These included the direct method, which uses water supply and water spills as the iteration variables, and the indirect method, which uses water storages (water levels) as the iteration variables. The results showed that the three methods could achieve similar solutions at the 75% probability of exceedance. There was no difference in the convergence speeds or the final objective function values of the three models. However, at the 50% probability of exceedance, the modified method produced results that followed the operation rule of the reservoir, whereas the other two methods could lead to inappropriate water spills. This new method may provide a reference for other meta-heuristic algorithms in models of the optimal operation of reservoirs.

Environmental sound recognition (ESR) refers to the recognition of all sounds other than the human voice or musical sounds. Typical ESR methods utilize spectral information and variation within it with respect to time. However, in the case of transient sounds, spectral information is insufficient because only an average quantity of a given signal within a time period can be recognized. In this study, the waveform of sound signals and their spectrum were analyzed visually to extract temporal characteristics of the sound more directly. Based on the observations, features such as the initial rise time, duration, and smoothness of the sound signal; the distribution and smoothness of the spectrum; the clarity of the sustaining sound components; and the number and interval of collisions in chattering were proposed. Experimental feature values were obtained for eight transient environmental sounds, and the distributions of the values were evaluated. A recognition experiment was conducted on 11 transient sounds. The Mel-frequency cepstral coefficient (MFCC) was selected as reference. A support vector machine was adopted as the classification algorithm. The recognition rates obtained from the MFCC were below 50% for five of the 11 sounds, and the overall recognition rate was 69%. In contrast, the recognition rates obtained using the proposed features were above 50% for all sounds, and the overall rate was 86%.

Double-reservoir-and-double-pumping-station systems are commonly used for irrigation water supply in hilly regions of southern China. An optimization model for this water supply system is proposed to minimize water shortage. The model features few coupling constraints, including available water in the system and pumping volume limited by regional water rights. Dynamic programming was adopted to solve the subsystem and aggregation models. The results with the model and that with the standard operation policy were compared; the total water shortage was reduced by 87.7%, total water replenishment from outside was reduced by 2.2%, and total water spill was reduced by 60.6% for a system in Nanjing, China. The method may provide a reference for optimal operation of water supply systems comprising reservoirs and pumping stations.

Autonomous urban driving navigation is still an open problem and has ample room for improvement in unknown complex environments and terrible weather conditions. In this paper, we propose a two-stage framework, called IPP-RL, to handle these problems. IPP means an Imitation learning method fusing visual information with the additional steering angle calculated by Pure-Pursuit (PP) method, and RL means using Reinforcement Learning for further training. In our IPP model, the visual information captured by camera can be compensated by the calculated steering angle, thus it could perform well under bad weather conditions. However, imitation learning performance is limited by the driving data severely. Thus we use a reinforcement learning method-Deep Deterministic Policy Gradient (DDPG)-in the second stage training, which shares the learned weights from pretrained IPP model. In this way, our IPP-RL can lower the dependency of imitation learning on demonstration data and solve the problem of low exploration efficiency caused by randomly initialized weights in reinforcement learning. Moreover, we design a more reasonable reward function and use the n-step return to update the critic-network in DDPG. Our experiments on CARLA driving benchmark demonstrate that our IPP-RL is robust to lousy weather conditions and shows remarkable generalization capability in unknown environments on navigation task.

The temperature, vibration, and noise in air-conditioned Chinese vessel cabins in wintertime were investigated in this study. Field measurements were used to appraise the thermal sensation vote (TSV) of crew members and passengers in specific cabin environments. The survey data was summarized and analyzed to determine the neutral temperature, preferred temperature, thermal preference, and thermal acceptance rate. The determined neutral and preferred temperatures of 20.9 and 21.5 °C indicated that the ship occupants preferred a temperature higher than the neutral temperature in winter. The acceptable operative temperature range was determined to be 18.54–23.29 °C. Although most passengers preferred the neutral environment or a slightly warmer one, the acceptable air temperature was appropriately lower. A fitting analysis was conducted on the TSV and predicted mean vote (PMV) using different levels of subject activity. The thermal neutral temperature predicted by the PMV was higher than that predicted by the TSV; however, a modification of the PMV brought the former closer to the latter. The vibration vote indicated that most of the subjects considered the ship's vibration level to be acceptable. Although the noise level in the passenger cabins was higher than the recommendation, only 12% of the subjects considered it a little troublesome. Conversely, the noise evaluation in the dining room was not particularly impressive, with 6% of the subjects considering it very troublesome, and24% considering it a little troublesome.

Making use of the PID controller to make up the defeat of the geometric controller like Pure Pursuit is a very effective way to improve the effects of the Pure Pursuit (PP). However, existed methods didn't explain the internal principle of the fusion. This paper will try to give a detailed analysis about the fusion mechanism. Firstly, we will discuss the error caused by PP, and then we will separately analyze how the PI controller correct the error. Based on the above discussion, we conclude that the weight parameters are very important to the performance of the fusion controller. Thereby, we proposed a Genetic Algorithm (GA) based parameter tuning method to improve the accuracy of the fusion method. Finally, the experiments on CARLA simulator prove the effects of GA based PP_PI (PP fusion with PI) method in urban driving scenario.

Based on a `shortcut-to-adiabaticity' (STA) scheme, we theoretically design and experimentally realize a set of high-fidelity single-qubit quantum gates in a superconducting Xmon qubit system. Through a precise microwave control, the qubit is driven to follow a fast `adiabatic' trajectory with the assistance of a counter-diabatic field and the correction of derivative removal by adiabatic gates. The experimental measurements of quantum process tomography and interleaved randomized benchmarking show that the process fidelities of our STA quantum gates are higher than 94.9% and the gate fidelities are higher than 99.8%, very close to the state-of-art gate fidelity of 99.9%. An alternate of high-fidelity quantum gates is successfully achieved under the STA protocol., 18 pages, 6 figures