一种新的思维模型:多节拍主从结构离散神经网络(英)

本文基于McCulloch和Pitts提出的二值神经元阈值模型，以Hopfield联想神经网络为核心，加上两个从网络，构造了一个由节拍各异的三个离散网络组成的神经网络系统．本文分析了该系统的稳定性，并定性讨论了其联想思维功能．     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT System

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.     

人为地质灾害和地质环境

本文论述了人为地质灾害的概念, 典型事例以及人为地质灾害与地质环境的相互关系。指出, 人为地质灾害是由于人类的工程经济活动而引起的地质灾害。人为地质灾害可导致地质环境恶化, 使环境质量下降；而地质环境恶化又可导致地质灾害的加重, 使灾害频度增大, 强度增高。     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

人造麝香的危害性及其残留检测方法研究进展

综述了近年来人造麝香的危害性及其残留检测方法的研究进展。对化妆品、水、大气、土壤和药物中人造麝香的前处理方法和检测方法进行了比较,同时对此领域的发展方向进行了展望(引用文献31篇)。     

Convergence analysis and performance of the extended artificial physics optimization algorithm

This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual’s best fitness history. Including the history improves EAPO’s search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual’s stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters {m_i, w, G}. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity.     

Assessment of the degradation of polyurethane foams after artificial and natural ageing by using pyrolysis-gas chromatography/mass spectrometry and headspace-solid phase microextraction-gas chromatography/mass spectrometry

Polyurethane foams are widely present in museum collections either as part of the artefacts, or as a material for their conservation. Unfortunately many of PU foam artefacts are in poor condition and often exhibit specific conservation issues. Their fast thermal and photochemical degradations have been the aim of previous researches. It is now accepted that hydrolysis predominates for polyester-based polyurethane PU(ES) whereas oxidation is the principal cause of degradation for polyether-based polyurethane PU(ET) variety. Only a few studies have been devoted to volatile organic compounds (VOCs) emitted by polyurethanes and, to our knowledge, none were performed on polyurethane foams by using headspace-solid phase microextraction (HS-SPME). The objective of the work described here is to assess the impact of some environmental factors (humidity, temperature and daylight) on the degradation of PU foams by evaluating their volatile fractions. We investigated morphological changes, polymerized fractions and volatile fractions of (i) one modern produced PU(ES) foam and one modern PU(ET) foam artificially aged in different conditions as well as (ii) four naturally aged foams collected from various daily life objects and selected for the representativeness of their analytical data. Characterization procedure used was based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) and non-invasive headspace solid-phase microextraction coupled with gas chromatography and mass spectrometry (HS-SPME-GC/MS). In this paper, the formation of alcohol and acid raw products for PU(ES) and glycol derivatives for PU(ET) during natural and artificial ageing is confirmed. These main products can be considered as degradation markers for PU foams. Results show that artificial and natural ageing provide similar analytical results, and confirm that the dominant degradation paths for PU(ES) and for PU(ET) are hydrolysis and photo-oxidation, respectively. Lastly, we highlight that non-invasive HS-SPME-GC/MS analysis allows to distinguish between PU(ES) and PU(ET) at any point of their degradations.