Clustering adaptive canonical correlations for high-dimensional multi-modal data

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.     

不同降雨入渗条件下预应力锚索加固边坡稳定性分析

为了研究不同降雨条件下边坡安全系数及预应力锚索轴力变化规律,以省道S303沿线某边坡为研究对象,考虑土体饱和-非饱和渗流与非饱和强度理论,基于有限元分析,对不同降雨工况下的孔隙水压分布、锚索轴力及采用强度折减法计算的边坡安全系数变化规律进行了研究。结果表明: 降雨量一定时,降雨入渗范围随雨强增大而减小,暂态饱和区域随雨强增加而增大; 边坡在加固前后的安全系数均随降雨量增加而不断减小,采用预应力锚索加固能有效提高边坡安全系数,并减小降雨对边坡稳定性的影响; 随着降雨量增加,各锚索轴力均增大,当降雨量一定时,雨强越小,对应的锚索轴力提升幅度越大,靠近底部的锚索轴力对降雨最为敏感。     

超生雾化降尘研究及应用

为解决某矿破碎筛分车间粉尘污染问题，采用喷嘴雾化理论设计喷嘴雾化降尘实验，并根据实验结果在该矿破碎筛分车间应用了超声雾化降尘技术。实验及应用结果表明：超声雾化发生器的液体雾化装置，气体辅助雾化装置和雾束形成器的几何结构影响喷雾的雾化参数与流量特性，液体雾化器与气体辅助雾化装置影响液体初次破碎与雾化，雾束形成器影响雾滴的二次破碎与约束液束形态；超声雾化发生器的气液比与气液压力比存在幂函数关系，幂指数为-1.09；雾滴粒径越小，雾滴的降尘效率越高，当雾滴D50为23 μm时，雾滴的降尘效率在94%以上；在破碎筛分车间振动筛给料皮带和筛上皮带受料点布置超声雾化发生器，超声雾化发生器开启后，车间内粉尘浓度降低至586～10.54 mg/m3，降尘效率在71.38%以上。     

Synthesis of a N-doped mesoporous carbon as an efficient electrocatalyst for oxygen reduction

A facile and effective approach was developed for the preparation of mesoporous Fe-NC by pyrolyzing the mixture of FeCl₂, urea, (NH₄)₂MoO₇, phthalic anhydride and SBA-15, during which the in-situ formation of iron phthalocyanine is confirmed. The obtained catalyst exhibits high catalytic activity towards ORR, whose half-wave potential can be 53 mV more positive than that of commercial Pt/C catalyst. Besides, the catalyst also exhibits high selectivity of four electron path, along with excellent stability and methanol tolerance in alkaline media. Based on the characterization results, we suggest, the higher surface areas, highly porous structures induced by SBA-15 addition, as well as high graphitic N content should be the proper origins for its outstanding catalytic performance.     

白象山铁矿精细化成本管控体系的构建及应用

铁矿是一种重要的矿物资源，其价格主要由市场决定。因此，在价格一定的条件下，铁矿企业要想进一步提高经济效益，提高市场竞争力，主要通过降低成本的途径来实现。以白象山铁矿为例，为实现白象山铁矿原矿成本控制，主要从技术创新和生产管理出发，通过采用防治水技术优化、采用安全高效采矿工艺、分级支护、大数据精准充填、自动化技术攻关、生产管理降本、财务收支管理降本、技术管理降本、增加零星工程自营等多种方法来降低成本。精细化成本管控体系不仅为该矿创造了巨大的经济效益，而且可为其他类似条件的矿山提供借鉴。     

铁氧化物在流化床温度和CO气氛下的形态迁移及其对NO催化还原

利用小型固定床实验台实验研究了铁氧化物在典型流化床温度和CO还原性气氛下的形态迁移及其生成物对NO的催化还原作用，采用分级还原结合X射线衍射（XRD）表征分析，确定铁氧化物与CO和NO反应后生成物的价态及各种铁氧化物对NO的还原机制。结果表明，Fe₂O₃在实验条件下可依次被CO还原为Fe₃O₄、FeO和单质铁，反应过程中随着还原度的增加，还原速率逐级下降，从Fe₂O₃还原到Fe₃O₄的速率最高，FeO还原到Fe速率最低，在实验温度范围内，床温升高有利于提高Fe₂O₃到Fe₃O₄的还原速率和还原度。不同形态的铁氧化物对NO的催化还原特性不同，Fe₂O₃及其部分还原后生成的Fe₃O₄都不能直接与NO反应，Fe₂O₃对CO催化还原NO的效果很弱，而Fe₃O₄对CO还原NO的反应却有很强的催化作用，而进一步还原生成FeO与单质铁还可直接与NO反应。     

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Power losses cause the underutilization of distributed generation (DG) units in addition to the cost increasing in microgrid. Minimizing these losses has been focused in many papers. Using energy storage system (ESS) is a crucial solution for loss reduction. ESS can balance the power exchange in on-peak times where its location and size optimization can improve the microgrid efficiency and reduce the loss cost significantly. Moreover, to ensure the power quality by improving the voltage profile, capacitor bank can be installed optimally on some buses. Optimization of size and location of the capacitor bank can enhance the reactive power that is leading to power loss reduction. In other words, the capacitor bank is applied to compensate the total reactive power and consequently, the current is reduced that results in power loss reduction. In this article, the problem is defined as the optimum location and size of ESS and capacitor bank in the microgrid. Due to the complexity of the problem in many options for selecting the buses to implement these elements (ESS and capacitor bank), robust approach using the particle swarm optimization algorithm and general algebraic modeling system are applied for optimization process. In addition, the uncertainty of renewable DGs such as photovoltaic and wind turbine is modeled by probability density functions and Monte-Carlo is used for selecting more probable cases in optimization processes. The results show the loss cost reduction and improvement in voltage and power profile with less fluctuations and more stability.     

Mn3O4 nanosheets coated on carbon nanotubes as efficient electrocatalysts for oxygen reduction reaction

MnO-MnC_x coated carbon nanotubes (MnO/MnC_x/CNTs) nanocomposites were prepared by a one-pot deposition method. The coating consisted of MnO, Mn₅C₂, Mn₁₅C₄ and Mn₂₃C₆ was formed on the surface of CNTs by heating a mixture of Mn particles and CNTs at 600 °C for 40 min under vacuum. Then after heated MnO/MnC_x/CNTs in air at 350 °C for 2 h, MnO nanoparticles were partially converted to Mn₃O₄ nanosheets. Then Mn₃O₄-MnC_x coated carbon nanotubes (Mn₃O₄/MnC_x/CNTs) composed of interconnected nanosheets structure were successfully synthesized by a two-step method of one-pot deposition and heat post-treatment. The Mn₃O₄/MnC_x/CNTs showed better oxygen reduction reaction performance in alkaline condition than MnO/MnC_x/CNTs and pristine CNTs. Besides, the formed MnC_x (Mn₅C₂ and Mn₂₃C₆) by one-pot deposition method provided a strong interface bonding between Mn₃O₄ and CNTs, leading to improved stability of Mn₃O₄/MnC_x/CNTs as an electrode material.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.