Spatial Variations in Particle Size and Magnetite Concentration on Cedar Beach:Implications for Grain-Sorting Processes,Western Lake Erie,Canada

This study examined spatial variations in the concentration,grain size and heavy mineral assemblages on Cedar Beach（Lake Erie,Canada）.Magnetic studies of heavy mineral-enriched,dark-reddish sands present on the beach showed that magnetite（～150μm） is the dominant magnetic mineral.Surficial magnetic susceptibility values defined three zones：a lakeward region close to the water line（Zone 1）,the upper swash zone（Zone 2） and the region landwards of the upper swash zone （Zone 3）.Zone 2 showed the highest bulk and mass susceptibility（κ,χ） and the highest mass percentage of smaller grain-size（250μm） fractions in the bulk sand sample.Susceptibility（i.e.κandχ） values decreased and grain size coarsened from Zone 2 lakewards（into Zone 1） and landwards （into Zone 3）,and correlated with the distribution of the heavy mineral assemblage,most probably reflecting preferential separation of large,less dense particles by waves and currents both along and across the beach.The eroded western section of Cedar Beach showed much higher concentrations of heavy minerals including magnetite,and finer sand grain sizes than the accreting eastern section, suggesting that magnetic techniques could be used as a rapid,cost-effective way of examining erosion along sensitive coastline areas.

Comparative analysis shows high level of lineage sorting in genomic regions with low recombination in the extended Picea likiangensis species complex

Genome-scale data,while promising for illuminating phylogenetic relationships,frequently pose a conundrum by yielding conflicting topologies and highly variable gene tree distributions(Pease et al.,2016).This complexity likely arises from the reticulate evolution observed in many taxa,where genetic information exchange occurs through diverse biological processes.

Multi-objective Optimization of Industrial Purified Terephthalic Acid Oxidation Process

Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.

Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.

Sorting and Processing of the Alzheimer's Disease Amyloid Precursor Protein Mediated by the AP-4 Complex

Proteolytic processing of the transmembrane amyloid precursor protein (APP) to aggregation-prone amyloid-β (Aβ) peptide underlies the development of Alzheimer’s disease.

一种改进的闪存数据库Sort-Merge-Join算法

在对传统的Sort-Merge-Join算法进一步研究的基础上,提出了一种改进的闪存数据库Sort-Merge-Join算法。该算法只对小关系进行外排序,避免了大关系的外排序,节省了大量时间,同时最小化了中间临时表,达到了少写闪存、减小擦除代价的目的。通过理论分析和与传统Sort-Merge-Join算法在闪存上的比较实验,证明了该算法的优越性。

Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

Knowledge Processing Method of Fault Diagnosis Expert Systems for Letter Sorting Equipment

Based on the analysis of fault diagnosis knowledge of letter sorting machine, this paper proposes a processing method by which the fault diagnosis knowledge is divided into exact knowledge, inadequate knowledge and fuzzy knowledge. Then their presenting and implementing form in fault diagnosis expert system is discussed and studied. It is proved that the expert system has good feasibility in the field of the diagnosis of letter sorting machine.

Tobacco Sorter和Tobacco Scan 6000烟草异物剔除系统的比较

对TobaccoSorter和TobaccoScan 60 0 0系列的烟草异物剔除系统进行了对比 ,从系统组成、烟叶单层化及稳定传送装置、图像获取方式及识别算法、异物的剔除方法、照明光源等方面进行了详细的比较和分析 ,由此看出 :随着相关技术的快速发展 ,应用线性CCD摄像机作为检测装置的异物剔除系统将具有越来越明显的优势。

k-Bitonic sort

Ak-bitonic sort which generalizes the bitonic sort is proposed. The theorem of the bitonic sort, which merges two monotonic sequences into one order sequence, is extended into the theorem ofk-bitonic sort. Thek-bitonic sort merges (K (=2k or 2k?1) monotonic sequences into one order sequence in $\left\lceil {log_2 K} \right\rceil \left\lceil {log_2 N} \right\rceil - \tfrac{{\left\lceil {log_2 K} \right\rceil (\left\lceil {log_2 K} \right\rceil - 1)}}{2}$ steps, where $k = \left\lceil {\tfrac{K}{2}} \right\rceil $ is an integer andk≥1. Thek-bitonic sort is the Batcher's bitonic sort whenk=1.

A Unified O(log N) and Optimal Sorting Vector Algorithm

A unilied vector sorting algorithm (VSA) is proposed, which sorts N arbitrary num-bers with clog. N-bits on an SIMD multi-processor system (SMMP) with processors and a composite interconnected network in time, where c is an arbitrary positive constant. When is an arbitrary small posi-tive constant and u = log2 N, it is an O(logN) algorithm and when it is an optimal algorithm,pT = O(N log N)); where u = 1, c = 1 and e = 0.5 (a constant).

Double-cycle weighted imputation method for wastewater treatment process data with multiple missing patterns

Due to sensor malfunctions and communication faults,multiple missing patterns frequently happen in wastewater treatment process(WWTP).Nevertheless,the existing missing data imputation works cannot stand multiple missing patterns because they have not sufficiently utilized of data information.In this article,a double-cycle weighted imputation(DCWI)method is proposed to deal with multiple missing patterns by maximizing the utilization of the available information in variables and instances.The proposed DCWI is comprised of two components:a double-cycle-based imputation sorting and a weighted K nearest neighbor-based imputation estimator.First,the double-cycle mechanism,associated with missing variable sorting and missing instance sorting,is applied to direct the missing values imputation.Second,the weighted K nearest neighbor-based imputation estimator is used to acquire the global similar instances and capture the volatility in the local region.The estimator preserves the original data characteristics as much as possible and enhances the imputation accuracy.Finally,experimental results on simulated and real WWTP datasets with non-stationarity and nonlinearity demonstrate that the proposed DCWI produces more accurate imputation results than comparison methods under different missing patterns and missing ratios.

Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It took about 20.25 s to detect an image with 3024
4032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

A machine vision-intelligent modelling based technique for in-line bell pepper sorting

The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and influence.According to standards and export needs,the bell pepper should be graded based on maturity levels and size to five classes.This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell peppers.Multilayer perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that purpose.TheMLP modelswere trained and evaluated through five-fold cross-validation method.The optimum MLP classifier was compared with a linear discriminant analysis(LDA)model.The results showed that the MLP outperforms the LDA model.The processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line application.Accordingly,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line phase.The performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,respectively.The total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.

层次分析法在耕地力评价中的应用及其基于Spreadsheet的实现

运用层次分析法实现耕地地力评价时,若系统层次较多且相互关系复杂时,用人工来计算,工作量大且难以保证结果的准确性。提出了在VB环境中利用Spreadsheet组件强大的数据处理功能及较高的用户参与程度来实现层次分析过程的解决方案,是提高应用者决策分析工作效率和准确性的一种可行方法。

Sloping-and-shaking——Multiway merging and sorting

Most traditional merging and merging-based sorting algorithms are based on 2 sorters or 2 comparators A new merging technique is developed, namely sloping-and-shaking multiway merging, and a corresponding mul-tiway sorting method based only on k-sorters is proposed The sloping-and-shaking merging algorithm merges k sorted lists into one, where k can be any prime number The merging process is not a series of recursive applications of 2-way morging It sorts the keys on the m × k plane in vertical and horizontal directions, then along sloping lines with various slope rates step by step Only k-sorters are needed in the merging or sorting process. The time needed to merge ksorted lists, with m of each, is ( k + [log2( m / k) ]) tk, and the time for sorting N keys is (1 + (p - 1) k + 1/2( p -1) (p - 2)[ log2k])tk, where p - logkN, and tk is the time to sort k keys. The proposed algorithms can be implemented either by hardwared sorting networks, or on general purpose parallel and vector machines The traditional odd-even merging can be viewed as a special case of the multiway merging proposed (when k is 2) While theoretically the proposed algorithms provide a new understanding of parallel merging and sorting processes, they may be used in prac-tice to construct sorting circuits fasler than 2-sorter based sorting methods.

考虑洪涝灾害风险的县域应急避难场所选址模型

为提升应急避难场所在灾害管理过程中的服务水平,从洪涝灾害的视角出发,选取致灾因子危险性、孕灾环境敏感性、承载体脆弱性、防灾减灾能力4个指标,采用层次分析法(AHP)-熵权法模型,识别出洪涝灾害风险的空间分布,并将其风险值作为权重引入最小化未覆盖风险,同时,结合最小化加权疏散距离、最小化可达性差异,从公平性、效率性、均衡性视角,构建多目标避难场所选址模型;采用改进的非支配排序遗传算法Ⅱ(NSGA-Ⅱ)求解模型,并以莆田市仙游县现有避难场所布局为例,验证模型。结果表明:对比现有的避难场所布局,优化后的布局方案中总疏散距离、未覆盖风险值及可达性差异性系数分别平均降低35.90%、61.47%、33.62%。

面向学科交叉融合的综合性实训教学装置设计与教学实践

针对工程训练实训教学装置功能单一、缺乏多学科交叉与整合能力等问题,以多种特征的物料识别与分拣为应用背景,在综合应用PLC控制技术、传感器技术、气动控制与机电传动控制技术的基础上,结合数字图像处理与卷积神经网络技术,基于树莓派与Jetson嵌入式计算平台,自主研制了一种物料特征识别与分拣综合实训教学装置。与现有同类实训装置相比,该自制实训装置具有体积小,功能多和成本低的优势。实践教学表明,将传统PLC控制与人工智能技术元素有机结合,以及将课内教学延伸至创客活动和学生第二课堂,能有效提升学生的工程认知水平和工程实践能力,体现多学科交叉、多技术融合的高等工程教育特征。

缘条类复合材料零件变形修正系统的开发

复合材料比强度、比刚度、可设计性高,目前已经在飞机制造上得到广泛应用,但受到回弹变形等因素的影响,零件几何精度较低,复合材料变形修正问题亟待解决。针对如何将复合材料变形修正工艺数模规范化、快速化构建等问题,本文提出了自动追踪、离散布点等辅助生成外形面的方法,提出了归并排序法、插值查找法等辅助生成内形面的算法,列举了部分构造工艺数模中用到的曲面处理方法,在CATIA环境下完成了复合材料零件变形修正系统的开发。经实例验证,修正后总体效率提升了734.5%,表明该方法能够高效、规范地构建工艺数模,提升工艺数模的生成效率。

长条形件杂货集装箱化的智慧分拣系统设计与管理研究

以具备标准化运输的长条形件杂货为研究对象,参考标准40英尺集装箱以及现有件杂货装卸设备的尺寸结构,设计从存储到装卸分拣的智能设备和一体化系统。通过数值计算、应用CAD构图和三维模型对智慧分拣系统进行结构分析,并配合作业流程标准化设计,提出可适用于长条形件杂货集装箱化的智慧分拣系统实现方案。仿真模拟的结果表明,长条形件杂货集装箱化后箱体立体化堆叠效果可将土地利用率提高1.6~2.4倍,构建的智慧分拣系统能够减少5倍的人力需求并提高近2.4倍的装卸效率。智慧分拣系统的设计打破人工作业能力限制,满足高峰生产作业时段对效率的需求,也为件杂货智慧港口的发展提供了参考方向。