China has the largest number of the elderly among all ageing countries. However, little attempt has been made to accommodate the growing need for the elderly. This research aims to provide an insight into this phenomenon, focusing on issues relating to the built environment. Following the current trend of ‘ageing-in-place’, it intends to provide a critical view on the re-design and retrofits of existing communities, using projects located in Suzhou as examples, where the elderly (i.e. age over 60) has accounted for more than 24% of the local population by 2014. Based on literature review and case studies worldwide, a series of issues have been summarized as benchmark criteria for age-friendly communities. Then local communities (e.g. Zhuhui New Village, Living Bank Community, Olive Bay Community, etc.) are studied through such lens – by comparing the target communities against the benchmark criteria, it is found that several important issues (including necessary facilities and services) have not been well addressed in the existing communities, though they tend to be taken into account in the design processes more often from a longitudinal perspective. Onsite semi-structured interviews and focus groups have also been conducted to explore the relative importance of these issues in the given context, in line with the changing needs (e.g. physical and psychological needs) of different families over time. Some early findings (e.g. evidence on age-friendly communities) will be incorporated into the redesign/retrofit guidance for existing communities and thereby inform the transformation of local neighbourhoods towards lifetime standards.
The optimization of distribution routing is a key problem of logistics systems optimization, and can be considered as a traveling salesman problem (TSP). In this paper, the optimization of distribution routing is transformed into TSP, and dissimilar order crossover operator is proposed to improve the genetic algorithm (GA) to solve TSP. The improved crossover operator is based on natural representation, which makes the offspring inherit the excellent substrings of its parent and speeds up the convergence of GA. Simultaneously, steady state reproduction without duplicates is adopted to avoid prematurity. It is proved that the improved GA provides prominent performance for both TSP and asymmetric traveling salesman problem (ATSP), which will facilitate the optimization of distribution routing.
We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.
Deep neural networks have achieved a great success in solving many machine learning and computer vision problems. The main contribution of this paper is to develop a deep network based on Tucker tensor decomposition, and analyze its expressive power. It is shown that the expressiveness of Tucker network is more powerful than that of shallow network. In general, it is required to use an exponential number of nodes in a shallow network in order to represent a Tucker network. Experimental results are also given to compare the performance of the proposed Tucker network with hierarchical tensor network and shallow network, and demonstrate the usefulness of Tucker network in image classification problems.
With dual connectivity, a mobile user can be served by a macro base station (MBS) and a pico base station (PBS) simultaneously. In this paper, we address the problem of optimizing user-PBS association and power allocation in the uplink such that the network can serve the users' demand at the minimum cost, where the PBSs are subject to backhaul capacity limitations and minimum rate requirements of users. We show that this non-convex problem can be formulated as a signomial geometric programming (SGP) whose solution can be found by solving a series of geometric programming (GP) problems. Simulation results are provided to demonstrate traffic offloading trend to PBSs for different cost and backhaul capacity settings, confirming the effectiveness of the proposed iterative algorithm. They also show that the output of the proposed algorithm closely matches the global optimal solution with affordable complexity.
Abstract Optimal dispatch is one of the key technologies to realize the efficient and economical operation of the thermal power system in thermal power plants. In order to reduce the energy consumption of thermal power system in thermal power plants, ensure the optimal dispatching effect and improve the efficiency of optimal dispatching, this paper introduces deep reinforcement learning to design a new optimal dispatching method for thermal power system in thermal power plants. The thermal power system structure of thermal power plant is analyzed, and the models of boiler, steam turbine and temperature and pressure reducer are established. The optimal scheduling problem of steam turbine and boiler thermal system is studied. By setting the objective function and determining the constraint function, the relevant optimal scheduling model is constructed. The SAC algorithm in deep reinforcement learning is used to solve the model to achieve the important goal of optimal scheduling. The experimental results show that the total fuel consumption of the proposed method is small, and the proposed method has a better optimal scheduling effect of thermal power system in thermal power plants, and can effectively improve the optimal scheduling efficiency.
In erasure broadcast channels, network coding has been demonstrated to be an efficient way to satisfy each user's demand. However, the erasure broadcast channel model does not fully characterize the information available in a "lost" packet, and therefore any retransmission schemes designed based on the erasure broadcast channel model cannot make use of that information. In this paper, we characterize the quality of erroneous packets by Signal-to-Noise Ratio (SNR) and then design a network coding retransmission scheme with the knowledge of the SNRs of the erroneous packets, so that a user can immediately decode two source packets upon reception of a useful retransmission packet. We demonstrate that our proposed scheme, namely Quality-Aware Instantly Decodable Network Coding (QAIDNC), can increase the transmission efficiency significantly compared to the existing Instantly Decodable Network Coding (IDNC) and Random Linear Network Coding (RLNC).
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