The Construction and Performance of a Novel Intergroup Complementary Code

 On the basis of the analyses for intergroup complementary (IGC) code and zero correlation zone complementary code, a novel IGC code has been proposed to adapt M-ary orthogonal code spreading spectrum system or quasi-synchronous CDMA system. The definition and construction methods of the new IGC codes are presented and an applied example is given in this paper. Theoretical research and simulation results show that the main advantages of the novel IGC code are as following: The code sets of the novel IGC code is more than IGC code under the same code length. The zero correlation zone length is longer than the intergroup IGC code, but shorter than the intergroup IGC code. Under the same code length, the auto-correlation performance of the novel IGC code is better than that of the IGC code, and both are of similar cross-correlation performance

Research on Sensor Network Spectrum Detection Technology based on Cognitive Radio Network

With the bursting development of computer science and the hardware technology, Internet of Things and wireless sensor networks has been popularly studied in the community of engineering. Under the environment of Internet of Things, we carry out theoretical analysis and numerical simulation on the sensor network spectrum detection technology based on cognitive radio network. As a means of information and intelligence, information service system is an important research hotspot in the field of Internet of things. Wireless sensor network is composed of a large number of micro sensor nodes, which have the function of information collection, data processing, and wireless communication, characterized by the integration of wireless self-organization. However, most of the methodologies proposed by the other institutes are suffering form the high complexity while with the high time-consuming when processing information. Therefore, this study is to assess the economic feasibility of using the optimized multipath protocol availability and the increased bandwidth and several mobile operators through the use of cost-benefit analysis, single path selection model is to develop more path agreement to achieve better performance. To test the robustness, we compare our method with the other state-of-the-art approach in the simulation section and proves the effectiveness of our methodology. The experimental result reflected that our approach could achieve higher accuracy with low time-consuming when dealing with complex sources of information

Unsupervised Feature Selection Algorithm via Local Structure Learning and Kernel Function

In order to reduce dimensionality of high-dimensional data, a series of feature selection algorithms have been proposed. But these algorithms have the following disadvantages: (1) they do not fully consider the nonlinear relationship between data features (2) they do not consider the similarity between data features. To solve the above two problems, we propose an unsupervised feature selection algorithm based on local structure learning and kernel function. First, through the kernel function, we map each feature of the data to the kernel space, so that the nonlinear relationship of the data features can be fully exploited. Secondly, we apply the theory of local structure learning to the features of data, so that the similarity of data features is considered. Then we added a low rank constraint to consider the global information of the data. Finally, we add sparse learning to make feature selection. The experimental results show that the proposed algorithm has better results than the comparison methods

Prediction of Gas Consumption in Transportation Based on Grey System Theory

Prediction of gasoline consumption in transportation accurately has important reference value for the scientific planning and decision making on the energy needs and environmental protection. For the lack of historical data of gasoline consumption in transportation, a grey gas consumption prediction model is built based on grey system modeling and the analysis of degree of grey incidence and residual. Contrastive analysis of specific value of the variance and small error probability of a case study with accuracy grades indicated that the gray gas consumption prediction model is fitting precisely and reliable

Discriminant WSRC for Large-Scale Plant Species Recognition

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted

Recent progress in ammonia fuel cells and their potential applications

Conventional technologies are largely powered by fossil fuel exploitation and have ultimately led to extensive environmental concerns. Hydrogen is an excellent carbon-free energy carrier, but its storage and long-distance transportation remain big challenges. Ammonia, however, is a promising indirect hydrogen storage medium that has well-established storage and transportation links to make it an accessible fuel source. Moreover, the notion of ‘green ammonia’ synthesised from renewable energy sources is an emerging topic that may open significant markets and provide a pathway to decarbonise a variety of applications reliant on fossil fuels. Herein, a comparative study based on the chosen design, working principles, advantages and disadvantages of direct ammonia fuel cells is summarised. This work aims to review the most recent advances in ammonia fuel cells and demonstrates how close this technology type is to integration with future applications. At present, several challenges such as material selection, NOx formation, CO2 tolerance, limited power densities and long term stability must still be overcome and are also addressed within the contents of this review

Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm

Perovskite oxide LaCr0.25Fe0.25Co0.5O3-δ as an efficient non-noble cathode for direct ammonia fuel cells

LaCoO3-δ (LCO) based perovskites including LaCr0.5Co0.5O3-δ (LCCO), LaFe0.5Co0.5O3-δ (LFCO) and LaCr0.25Fe0.25Co0.5O3-δ (LCFCO) were prepared and their oxygen reduction reaction (ORR) activity was studied. The presence of both Cr and Fe played vital roles in enhancing activity, affecting structural and electronic properties respectively. Cr doping was associated with surface oxygen vacancy formation whilst Fe doping enhanced the onset and half-wave potential. Furthermore, the effects of calcinating temperatures were explored and LCFCO fired at 700 oC (LCFCO-700) gave superior activity. Remarkably, when LCFCO-700 was employed in a direct ammonia fuel cell (DAFC), an OCV of 0.72 V and a maximum current density of ∼320 mAcm-2 was achieved when using air as the oxidant. Under similar conditions, DAFCs with LaCr0.25Fe0.25Co0.5O3-δ/C cathode, show comparable performance to the DAFC using Pt/C cathode, indicating that perovskite oxides, such as LaCr0.25Fe0.25Co0.5O3-δ, are promising non-noble cathodes for DAFCs

Optimization of a perovskite oxide-based cathode catalyst layer on performance of direct ammonia fuel cells

To maximize fuel cell performance, transport pathways for electrons, ions, and reactants should be connected well. This demands a well-constructed microstructure in the catalyst layer (CL). Herein we design and optimize a cathode CL for a direct ammonia fuel cell (DAFC) using a perovskite oxide as the catalyst to reduce reliance on platinum group metals (PGMs). The effects of tailoring carbon, ionomer, and polytetrafluoroethylene (PTFE) content in cathode CLs (CCLs) were explored, and several DAFCs were tested. Using the same catalyst and operating conditions, the lowest maximum current density and peak power density obtained were 85.3 mA cm–2 and 5.92 mW cm–2, respectively, which substantially increased to 317 mA cm–2 and 30.1 mW cm–2 through proper carbon, ionomer, and PTFE optimization, illustrating the importance of an effective three-phase interface. The findings reveal that despite employment of an active catalyst for oxygen reduction at the cathode site, the true performance of the catalyst cannot be reflected unless it is supported by proper design of the CCL. The study also reveals that by optimizing the CCL, similar performances to those of Pt/C-based CCLs in literature can be obtained at a cost reduction