Opportunities and challenges for Chinese elderly care industry in smart environment based on occupants' needs and preferences

New developments in intelligent devices for assisting elderly people can provide elders with friendly, mutual, and personalized interactions. Since the intelligent devices should continually make an important contribution to the smart elderly care industry, smart services or policies for the elders are recently provided by a large number of government programs in China. At present, the smart elderly care industry in China has attracted numerous investors’ attention, but the smart elderly care industry in China is still at the beginning stage. Though there are great opportunities in the market, many challenges and limitations still need to be solved. This study analyzes 198 news reports about opportunities and challenges in the smart elderly care industry from six major Chinese portals. The analysis is mainly based on needs assessment for elderly people, service providers, and the Chinese government. It is concluded that smart elderly care services satisfy the elders’ mental wants and that needs for improving modernization services are the most frequently mentioned opportunities. Also, the frequently mentioned challenges behind opportunities are intelligent products not being able to solve the just-needed, user-consumption concept and the ability to pay, which is the most frequently mentioned challenge. The results of this study will enable stakeholders in the smart elderly care industry to clarify the opportunities and challenges faced by smart elderly care services in China’s development process and provide a theoretical basis for better decision making

Simultaneously enhancing adsorbed hydrogen and dinitrogen to enable efficient electrochemical NH3 synthesis on Sm(OH)3

The electrochemical N2 reduction reaction (ENRR), driven by renewable electricity and run under ambient conditions, offers a promising sustainable avenue for carbon-neutral NH3 production. Yet, to efficiently bind and activate the inert N2 remains challenge. Herein, effective and stable electrochemical NH3 synthesis on Sm(OH)3 via enhanced adsorption of hydrogen and dinitrogen by dual integration of sulfur dopants and oxygen vacancies (VO) is reported. The resulting S-doped lanthanide electrocatalyst attains both a good NH3 yield rate, exceeding 21 μgNH3 h−1 mgcat.−1, and an NH3 faradaic efficiency of over 29% at −0.3 V (vs reversible hydrogen electrode) in an H-type cell using a neutral electrolyte, figures of merit that are largely maintained after 2 days of consecutive polarization. Density functional theory calculations show that the adsorption energy barrier of N2 on S-Sm(OH)3(VO) is greatly lowered by the introduction of VO. In addition, the S sites improve the adsorption of hydrogen produced via the Volmer reaction, which is conducive to the formation of the *N–NH intermediate (i.e., the potential determining step, PDS) on adjacent Sm sites, and thereby significantly promotes the reaction kinetics of ENRR. The PDS free energy for the catalyst is comparable with the values at the peak of the ENRR volcano plots of leading transition metal catalyst surfaces

Application of genetic algorithms in the design of a solar array-exclusive standalone photovoltaic system

In this paper a methodology for optimum design of solar array and battery bank for a solar array-exclusive standalone photovoltaic system using energy balance concept is presented. Long-term data of solar radiation obtained from Malaysian Meteorological Services Department and typical load requirement of a residential house in Malaysia are used. The constraint of system cost function based on loss of power supply probability (LPSP) is implemented using Genetic Algorithms. For a given desired LPSP, the optimum arrangement of solar array and battery bank that gives the minimum system cost is then determined

Tensorized Projection for High-Dimensional Binary Embedding

Embedding high-dimensional visual features (d-dimensional) to binary codes (b-dimensional) has shown advantages in various vision tasks such as object recognition and image retrieval. Meanwhile, recent works have demonstrated that to fully utilize the representation power of high-dimensional features, it is critical to encode them into long binary codes rather than short ones, i.e., b ~ O(d). However, generating long binary codes involves large projection matrix and high-dimensional matrix-vector multiplication, thus is memory and computationally intensive. To tackle these problems, we propose Tensorized Projection (TP) to decompose the projection matrix using Tensor-Train (TT) format, which is a chain-like representation that allows to operate tensor in an efficient manner. As a result, TP can drastically reduce the computational complexity and memory cost. Moreover, by using the TT-format, TP can regulate the projection matrix against the risk of over-fitting, consequently, lead to better performance than using either dense projection matrix (like ITQ) or sparse projection matrix. Experimental comparisons with state-of-the-art methods over various visual tasks demonstrate both the efficiency and performance ad- vantages of our proposed TP, especially when generating high dimensional binary codes, e.g., when b ≥ d

Distributed Composite Quantization

Approximate nearest neighbor (ANN) search is a fundamental problem in computer vision, machine learning and information retrieval. Recently, quantization-based methods have drawn a lot of attention due to their superior accuracy and comparable efficiency compared with traditional hashing techniques. However, despite the prosperity of quantization techniques, they are all designed for the centralized setting, i.e., quantization is performed on the data on a single machine. This makes it difficult to scale these techniques to large-scale datasets. Built upon the Composite Quantization, we propose a novel quantization algorithm for data dis- tributed across different nodes of an arbitrary network. The proposed Distributed Composite Quantization (DCQ) decom-poses Composite Quantization into a set of decentralized sub-problems such that each node solves its own sub-problem on its local data, meanwhile is still able to attain consistent quantizers thanks to the consensus constraint. Since there is no exchange of training data across the nodes in the learning process, the communication cost of our method is low. Ex- tensive experiments on ANN search and image retrieval tasks validate that the proposed DCQ significantly improves Composite Quantization in both efficiency and scale, while still maintaining competitive accuracy

A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors

This paper proposes a new high-precision rotor position measurement (RPM) method for permanent magnet spherical motors (PMSMs). In the proposed method, a LED light spot generation module (LSGM) was installed at the top of the rotor shaft. In the LSGM, three LEDs were arranged in a straight line with different distances between them, which were formed as three optical feature points (OFPs). The images of the three OFPs acquired by a high-speed camera were used to calculate the rotor position of PMSMs in the world coordinate frame. An experimental platform was built to verify the effectiveness of the proposed RPM method