A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Co?N graphene encapsulated cobalt catalyst for H2O2 decomposition under acidic conditions

Hydrogen peroxide (H₂O₂) has been listed as one of the 100 most important chemicals in the world. However, huge amount of residual H₂O₂ is hard to timely decomposed into O₂ and H₂O under acidic condition, easily resulting in explosion hazard. Here, we reported a core–shell structure catalyst, that is graphene with Co N structure encapsulated Co nanoparticles. Co N graphene shell serves as the active site for the H₂O₂ decomposition, and Co core further enhance this decomposition. Benefiting from it, the H₂O₂ decomposition were close to 100% after 6 cycles without pH adjustment, which increased 6 orders of magnitude compared with no catalyst. At the same time, the O₂ generation reached 99.67% in 2 h with little metal leaching, and ·OH has been greatly inhibited to only 0.08%. This work can cleanly remove H₂O₂ with little deep oxidation and protect the process of H₂O₂ utilization to achieve a safer world.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

基于概要数据结构的全网络持续流检测方法

持续流是隐蔽的网络攻击过程中显现的一种重要特征，它不产生大量流量且在较长周期内有规律地发生，给传统的检测方法带来极大挑战。针对网络攻击的隐蔽性、单监测点的重负荷和信息有限的问题，提出全网络持续流检测方法。首先，设计一种概要数据结构，并将其部署在每个监测点；其次，当网络流到达监测点时，提取流的概要信息并更新概要数据结构的一位；然后，在测量周期结束时，主监测点将来自其他监测点的概要信息进行综合；最后，提出流持续性的近似估计，通过一些简单计算为每个流构建一个位向量，利用概率统计方法估计流持续性，使用修正后的持续性估计检测持续流。通过真实的网络流量进行实验，结果表明，与长持续时间流检测算法（TLF）相比，所提方法的准确性提高了50%，误报率和漏报率分别降低了22%和20%，说明全网络持续流检测方法能够有效监测高速网络流量。     

Two placebo-controlled crossover studies in healthy subjects to evaluate gastric acid neutralization by an alginate–antacid formulation (Gaviscon Double Action)

Objective: To investigate the intragastric acid neutralization activity of a combined alginate-antacid formulation.

Significance: Published studies have investigated the reflux-suppressing alginate component of Gaviscon Double Action (Gaviscon DA; RB, UK) but intragastric acid neutralization activity of the antacid component has not been evaluated in vivo.

Methods: Intragastric pH monitoring, using a custom-made 10-electrode catheter, was evaluated in a two-part exploratory study in healthy subjects; Part I (n?=?6) tested suitability of the catheter using antacid tablets (Rennie; Bayer, Germany); Part II (n?=?12) evaluated gastric acid neutralization activity of Gaviscon DA liquid (20?ml) versus placebo in fasted subjects using a randomized, open-label, crossover design. The primary endpoint was the percentage of time that intragastric pH ≥4 was measured during 30?min post-treatment. A confirmatory study of identical design was subsequently conducted (n?=?20).

Results: Monitoring pH using the multielectrode catheter was a viable approach, directly detecting changes in intragastric pH following a single dose of antacid tablets. In the exploratory study, the percentage of time that pH ≥4 during 30?minutes post-treatment was 46.8% with Gaviscon DA liquid versus 4.7% with placebo (p?=?0.0004). These findings were supported by the confirmatory study, where pH ≥4 was recorded 50.8% of the time with Gaviscon DA versus 3.5% with placebo (p?=?0.0051). In this study, Gaviscon DA was safe and well tolerated.

Conclusions: These studies demonstrate the effective acid neutralizing capacity of Gaviscon DA versus placebo in healthy, fasted subjects. This adds to the evidence base for the combination of alginates and antacids.     

硫酸体系中锰钽铌矿加压浸出研究

针对单一硫酸体系锰钽铌矿加压浸出下钽铌浸出率均不到10%的问题,在硫酸体系引入助浸剂,考察氟化铵、双氧水、反应压强、硫酸浓度、反应温度、氟化铵矿比对锰钽铌矿加压浸出的影响,采用XRD对浸渣进行物相表征。实验结果表明,在锰钽铌矿加压浸出中,引入助浸剂氟化铵,温度200℃、硫酸浓度50%及氟化铵/矿比为0.8∶1的条件下钽浸出率超过93%,铌浸出率超过96%。引入氟化铵对硫酸体系下锰钽铌矿的加压浸出效果的提升十分显著。     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

超高效液相色谱法测定清咽糖中6种绿原酸类化合物的含量

目的建立超高效液相色谱法同时测定清咽糖中新绿原酸、绿原酸、隐绿原酸、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸6种绿原酸类化合物的含量。方法采用Agilent Eclipse Plus C18 RRHD色谱柱(2.1 mm×150 mm, 1.8μm),以乙腈、0.4%磷酸水溶液为流动相,梯度洗脱,流速0.8 mL/min,检测波长327 nm,柱温35℃。结果 6种成分在相应的线性范围内呈良好的线性关系。精密度、重复性、稳定性实验中6种成分峰面积的相对标准偏差均小于3.0%,平均加样回收率为93.77%～104.37%,相对标准偏差为0.28%～1.86%。结论该方法具有良好的专属性和重现性,加样回收率实验符合要求,结果准确、稳定,适用于同时测定清咽糖中新绿原酸、绿原酸、隐绿原酸、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸的含量。     

基于深度学习的四旋翼无人机单目视觉避障方法

针对无人机避障问题，提出一种基于深度学习的四旋翼无人机单目视觉避障方法。首先通过目标检测框选出目标在图像中的位置，并通过计算目标选框上下边距的长度，以此来估量出障碍物到无人机之间的距离；然后通过协同计算机判断是否执行避障动作；最后使用基于Pixhawk搭建的飞行实验平台进行实验。实验结果表明，该方法可用于无人机低速飞行条件下避障。该方法所用到的传感器只有一块单目摄像头，而且相对于传统的主动式传感器避障方法，所占用无人机的体积大幅减小。该方法鲁棒性较好，能够准确识别不同姿态的人，实现对人避障。