Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

Identification of relationship between sunspots and natural runoff in the Yellow River based on discrete wavelet analysis

Annual natural runoff is an important index of a river, which may be affected by solar activities. In this study, 304 years of annual natural runoff at the Sanmenxia station located in the Yellow River and the sunspot relative number are decomposed with the application of a Complex Morlet. According to the results of real part, modulus and second power of modulus, the annual runoff series at the Sanmenxia station has an obvious periodic oscillation on 90–100, 50–80, 35–50, 15–35, about 10, and less than 10-year scales. Also, there are obvious periodic variability with 60–90 years, 30–50 years and about 10 years. There are two centers of energy: one is about 1840–1850 on 7–11-year scale and the other is about 1825–1925 on 60–70-year scale. From the wavelet variance, 3, 26, 46, 68 year periods are detected within a 100-year scale, and the 68-year period is the most significant. Similar analyses are conducted for the sunspot relative number within the same period 1700–2003. The sunspot series shows 11- and 60-year period variation, as well as eight energy centers. Then, the correlation analyses for 11- and 60-year serial scales are computed. From a long-term period (1700–2003) view, there is no notable correlation between the natural runoff and the sunspot relative number; however, it is evident that the correlations exist within a short-term period. The results also indicate that the relationships between solar activities and the natural runoff in the Yellow River are complicated.     

Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil

Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility with the petroleum-based diesel fuel (PBDF). Therefore, in this study, the prediction of the engine performance and exhaust emissions is carried out for five different neural networks to define how the inputs affect the outputs using the biodiesel blends produced from waste frying palm oil. PBDF, B100, and biodiesel blends with PBDF, which are 50% (B50), 20% (B20) and 5% (B5), were used to measure the engine performance and exhaust emissions for different engine speeds at full load conditions. Using the artificial neural network (ANN) model, the performance and exhaust emissions of a diesel engine have been predicted for biodiesel blends. According to the results, the fifth network is sufficient for all the outputs. In the fifth network, fuel properties, engine speed, and environmental conditions are taken as the input parameters, while the values of flow rates, maximum injection pressure, emissions, engine load, maximum cylinder gas pressure, and thermal efficiency are used as the output parameters. For all the networks, the learning algorithm called back-propagation was applied for a single hidden layer. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been used for the variants of the algorithm, and the formulations for outputs obtained from the weights are given in this study. The fifth network has produced R² values of 0.99, and the mean % errors are smaller than five except for some emissions. Higher mean errors are obtained for the emissions such as CO, NO_x and UHC. The complexity of the burning process and the measurement errors in the experimental study can cause higher mean errors.     

基于有监督直接局部保持投影的人脸识别

提出一种用于图像识别的有监督直接局部保持投影算法,该算法结合样本类别信息,通过同时对角化的方法求解局部保持投影问题,避免矩阵的奇异性。在ORL人脸库上的测试结果表明,该算法的识别率高于PCA, PCA+LPP等方法。     

Leveling up: Are non-gamers and women disadvantaged in a virtual world classroom?

Today's young gamers are tomorrow's students who expect more immersion from their online learning experiences. Teachers and administrators, however, must ask are some students at a disadvantage in such a class and does gender play a role? We examine the degree to which gaming experience and gender influence sense of presence in a virtual world learning classroom. Feelings of presence are key to involvement in virtual worlds. Participants, 348 undergraduates, interacted within a custom designed virtual world classroom focusing business negotiation skills. Results reveal that while gaming experience gives a slight advantage, lack of previous software ownership and gender do not put learners at a disadvantage. This finding shows that non-gamers and females can equally participate in a virtual world classroom, relieving concerns of adoption.     

鄂尔多斯盆地东南部长7油层组沉积相研究

为了明确鄂尔多斯盆地东南部长7油层组沉积体系类型和沉积相展布特征,总结其沉积演化规律,以沉积学的基本原理和方法为指导,综合运用地球化学、盆地分析等理论和方法,通过野外露头、钻井、测井等资料的综合分析,认为鄂尔多斯盆地东南部长7油层组主要发育三角洲和湖泊沉积体系,可进一步划分为3类亚相,其中以三角洲前缘和深湖2种亚相为主．     

突发重金属水污染事故环境风险评价模型研究

为降低我国重金属突发水污染事故发生频率、减轻事故危害,针对目前突发水污染事故环境风险评价缺乏系统性研究的现状,构建了基于蒙特卡洛模拟法的突发水污染事故环境风险评价模型,提出了一套计算突发水污染事故环境风险的方法。该模型由突发水污染事故概率分析模型、重金属迁移转化分相模型、突发水污染事故损失评估模型耦合而成。应用该模型对佛山市的环境风险进行了评价,结果表明:计算时间段为固定值时,重大风险值和特大风险值之和为8%;计算时间段为区间值时,重大风险值和特大风险值之和为21%,说明该地区的环境风险已经十分严峻。     

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.     

基于城市污水好氧颗粒污泥脱氮除磷系统种群多样性研究

采用16S rDNA克隆文库方法对城市污水脱氮除磷好氧颗粒污泥的细菌种群进行了多样性研究.从16SrDNA克隆文库中随机挑选110个克隆子测序并进行了BLAST比对.结果表明:好氧颗粒污泥序批式反应器(GSBR)系统中细菌群落具有高度多样性,包含14个类群,优势细菌类群为Proteobacteria类群(变形菌类群),占85.18%;细菌类群优势顺序为β-Proteobacteria、α-Proteobacteria、γ-Proteobacteria、uncultured Bacteroidetes、Candidatedivision TM7、δ-proteobacterium、Firmicutes、Planctomycetacia、Actinobacteria、Sphingobacteria、Flavobacteria、Cytophagia、Uncultured bacterium和Uncultured anaerobic bacterium.初步分析了不同细菌类群在脱氮除磷系统中的作用,其中,Proteobacteria纲中部分为聚磷菌,Acidovorax sp.、Planctomycetacia、Cytophagia、Flavobacteria对氮的脱除均有一定作用.     

Reactively sputtered N-doped titanium oxide films as visible-light photocatalyst

Nitrogen-doped titanium oxide (TiO_xN_y) films were prepared by reactive magnetron sputtering of a titanium metal target in gas mixtures of argon, oxygen and nitrogen. Two types of nitrogen species are formed in the films following the fraction of N₂ (F_N2) in the reactive atmosphere. One is substitutional nitrogen in anatase titania phase and the other is nitrogen in TiN phase. In a large range of F_N2 from 0 to 0.57, TiO_xN_y films in anatase structure with about 1.0–1.4 at.% substitutional nitrogen are produced and the films exhibit red shifts to ∼ 500 nm from the absorption edge of ∼ 380 nm of undoped TiO₂. The nitrogen is readily doped in the films by energetic nitrogen ions in the plasma and the films exhibited photocatalytic properties under visible light. When excess nitrogen is supplied as the F_N2 above 0.75, the resulting film contains 20.8 at.% of nitrogen with formation of TiN that makes the film opaque and destroys the photocatalytic activity largely.