Studying local liquid velocity in liquid–solid packed bed using the newly developed X-ray DIR technique

Combination of X-ray Digital Industrial Radiography (DIR) and Particle Tracking Velocimetry (PTV) techniques for local liquid velocity measurement (V_LL) has been newly developed and successfully applied for trickle bed reactor (TBR). The technique was validated against newly developed fiber optical probe technique. This work attempts to highlight the applicability of this newly developed technique on a liquid–solid packed bed reactor. In this work, liquid was represented by water and solids were represented by EPS beads. The EPS beads were chosen because of its low density property. Three superficial liquid velocities (V_SL) were applied to the system. The experiment was replicated four times. The digital industrial radiography (DIR) consists of a complementary metal oxide semiconductor (CMOS) digital detector and X-ray source. Results of this work suggest that the technique has been successfully applied and comparable with previous work that has been done in the literature. It also suggests that there will be a maximum measurable interstitial liquid velocity when it travel inside the packed bed. The measured V_LL can have a maximum range that is between 4 and 4.7 times that of its V_SL. For V_SL=0.42±±2%, the V_LL-Max is in between 1.7 cm/s and 1.9 cm/s, V_SL=0.84±±2%, the V_LL-Max is in between 3.6 cm/s and 4.0 cm/s, and for V_SL=1.11±±2%, the V_LL-Max is in between 4.3 cm/s and 4.8 cm/s.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Epos – an instrument for the assessment of the ethical position in software development

Digital technology becomes more powerful, intelligent, pervasive and ubiquitous. Ethical aspects of this development have not yet drawn the appropriate attention of researchers and engineers. This paper presents an instrument that aims at measuring the individual ethical position with regard to the design and development of computer software. The development of the Epos tool was based on two data collections. The data of the first survey (n₁ = 147 participants) were used to select items and to determine the factorial structure of the questionnaire. Results show that the Epos instrument reliably assesses peoples’ ethical opinion with respect to five central components: (1) regulation, (2) data privacy, (3) domain specific knowledge, (4) societal responsibility and (5) company responsibility. In the second survey, we determined the stability of the instruments factor structure by assessing a sample of n₂?=?196 participants. A confirmatory factor analysis (CFA) supported the initial factor structure. Next steps and further implications are discussed regarding the final version of the questionnaire.     

高效液相色谱法测定多环芳烃的出峰顺序探讨

目的探讨水﹑气﹑土壤中多环芳烃检测标准(HJ 478-2009﹑HJ 647-2013、HJ 784-2016)的正确出峰时间和顺序。方法用高效液相色谱来对苊烯、芴、苊、?、苯并(a)蒽进行定性分析,并与3个标准中的出峰顺序进行比较。结果苊烯、苊、芴、苯并(a)蒽、?的出峰时间分别为6.450、7.923、8.233、17.760、18.740min,与标准HJ478-2009﹑HJ647-2013的出峰顺序存在差异。结论在使用标准HJ478-2009﹑HJ647-2013、HJ 784-2016同时测定16种多环芳烃时,多环芳烃的出峰顺序及时间应以HJ 784-2016为准。     

Liquid Marble Patchwork on Super-Repellent Surface

Liquid marble (LM) is a droplet that is wrapped by hydrophobic solid particles, which behave as a non-wetting soft solid. Based on these properties, LM can be applied in fluidics and soft device applications. A wide variety of functional particles have been synthesized to form functional LMs. However, the formation of multifunctional LMs by integrating several types of functional particles is challenging. Here, a general strategy for the flexible patterning of functional particles on droplet surfaces in a patchwork-like design is reported. It is shown that LMs can switch their macroscopic behavior between a stable and active state on super-repellent surfaces in situ by jamming/unjamming the surface particles. Active LMs hydrostatically coalesce to form a self-sorted particle pattern on the droplet surface. With the support of LM handling robotics, on-demand cyclic activation–manipulation–coalescence–stabilization protocols by LMs with different sizes and particle types result in the reliable design of multi-faced LMs. Based on this concept, a single bi-functional LM is designed from two mono-functional LMs as an advanced droplet carrier.     

[Zr0.73(Cu0.59Ni0.41)0.27]87Al13合金过冷液相区热塑性成形研究

为探究[Zr_(0.73)(Cu_(0.59)Ni_(0.41))_(0.27)]_(87)Al_(13)非晶合金的热塑性成形性能以及绘制其对应的热加工图谱,用Gleeble3500型热模拟压缩实验机对该非晶合金进行不同参数下的热模拟压缩实验。结果表明,合金在压缩过程中变形行为由牛顿流变演变为非牛顿流变;同时,过高或过低的热加工温度均能导致合金晶化;进一步对数据分析得到该合金在不同热塑性成形参数下的功率耗散图与流变失稳图,并绘制出相应的热加工谱图,谱图分析结果表明,该合金在温度为420与430℃、应变速率为10~(-3) s~(-1)时具有较高功率耗散系数且没有失稳区域,因此,合金可选的热塑性加工参数为温度420～430℃,应变速率10~(-3) s~(-1)。     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.     

液相色谱法快速检测染色梅鱼中日落黄、柠檬黄的含量

目的建立一种快速、低成本检测染色梅鱼中日落黄、柠檬黄的方法。方法将染色梅鱼剥皮,鱼皮经70%甲醇氨水提取液提取,经EclipseXDB-C_(18)色谱柱分离,以甲醇和乙酸铵水溶液为流动相,进行梯度洗脱,流速为1.0 mL/min,柱温35℃,利用紫外检测器检测日落黄、柠檬黄的含量。结果日落黄、柠檬黄在1.25、2.5、12.5、25 mg/kg添加水平的加标回收率为80%～90%,相对标准偏差小于10%(n=6),柠檬黄检出限为0.05 mg/kg,日落黄检出限为0.025 mg/kg。结论该方法快速、准确、成本低,适合染色梅鱼中日落黄、柠檬黄的快速检测。     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.