基于特征贡献率的机械故障分类方法

为提高往复压缩机、航空发动机等复杂机械故障分类的准确率，依据特征参数对不同故障的敏感度存在差异的特性，提出一种狄利克雷过程混合模型(Dirichlet process mixture model,简称DPMM)与贝叶斯推断贡献(Bayesian inference contribution,简称BIC)相结合的分析方法。采用DPMM方法自学习机械振动信号高维特征的统计分布模型，并依据BIC理论计算得到各特征参数对模型的贡献率，通过对比观测数据与各类故障数据特征贡献率间的差异实现故障分类。试验结果表明，该方法的平均分类准确率比基于高斯混合模型(Gaussian mixture model,简称GMM)的故障诊断方法的平均分类准确率提高19.29%，比基于Relief算法的故障诊断方法的平均分类准确率提高32.71%，且该方法的时效性高，泛化性能强，能够更有效地进行复杂机械故障分类。     

Stability and thermophysical properties of fly ash nanofluid for heat transfer applications

Improving the performance of heat transfer fluids is altogether significant. The best approach for improving the thermal conductivity is the addition of nanoparticles to the base fluid. In the present study, specific heat, dynamic viscosity, and thermal conductivity of water-based Indian coal fly ash stable nanofluid for 0.1% to 0.5% volume concentration in the temperature range of 30 to 60°C has been investigated. To evaluate an average particle diameter of 11.5 nm, the fly ash nanoparticles were characterized with scanning electron microscopy and dynamic light scattering. Using zeta potential, the stability of nanofluid in the presence of surfactant Triton X-100 was tested. Thermal conductivity and viscosity of fly ash nanofluid increased, while specific heat decreased as volume concentration increased. The effect of temperature on the fly ash nanofluid was directly proportional to its thermal conductivity and specific heat and inversely proportional to viscosity.     

Automatic recommendation to appropriate log levels

A log statement is one of the key tactics for a developer to record and monitor important run-time behaviors of our system in a development phase and a maintenance phase. It composes of a message for stating log contents, and a log level (eg, debug or warn) to denote the severity of a message and controlling its visibility at run time. In spite of its usefulness, a developer does not tend to deeply consider which log level is appropriate in writing source code, which causes the system to be unmaintainable. To address this issue, this paper proposes an automatic approach to validating the appropriateness of the log level in consideration of the semantic and syntactic features and recommending a proper alternative log level. We first build the semantic feature vector to quantify the semantic similarity among application log messages using the word vector space, and the syntactic feature vector to capture the application context that surrounds the log statement. Based on the feature vectors and machine learning techniques, the log level is automatically validated, and an alternative log level is recommended if the log level is invalid. For the evaluation, we collected 22 open-source projects from three application domains, and obtained the 77% of precision and 75% of recall in validating the log levels. Also, our approach showed 6% higher accuracy than that of the developer group who has 7 to 8 years of work experience, and 72% of the developers accepted our recommendation.     

煤基高能量密度燃料的合成及表征

高能量密度燃料是为新型高性能飞行器提供动力保障的关键,其合成及应用研究具有重要的前瞻性和重大战略意义。煤炭是我国的主体能源和重要原料,通过煤直接转化获取的煤基油,充分保留了煤中特有的环状分子化学结构,具有良好的热安定性和较高的能量密度,被认为是高超音速飞行器的优选燃料。以煤直接液化工艺生产的煤液化石脑油馏分为起始原料,通过富集轻质芳烃、化学合成、催化加氢稳定和产物分离提纯等方法制备煤基高能量密度燃料,并对其产物进行分子结构表征和性能评价。结果表明,煤直接液化生产的石脑油馏分是一种优异的催化重整原料,经催化重整富集轻质芳烃后,其轻质芳烃质量分数高达71.05%。Diels-Alder化学合成主产物是由多个封闭环平面组成且具有空间立体构型的二环或三环烃类物质,质量分数为46.18%,因分子内存在较大的张力能,结构紧凑,其拥有更大的密度和体积热值。煤基高能量密度燃料的密度和体积热值分别为0.8990 g/cm3与38.06 MJ/L,均大大超过现行的国内石油基喷气燃料(RP-3和RP-6)、煤基大比重喷气燃料、美国和俄罗斯军用标准。与单一纯物质合成高能量密度燃料(JP-10和T-10)比较,其密度与体积热值偏小。究其原因主要是轻质芳烃的富集度仅为71.05%,需进一步提高其轻质芳烃质量分数。另外,制备的煤基高能量密度燃料种类复杂,其主产物质量分数仅46.18%,下一步可重点调控合成产物的分子构型和纯化分离。     

YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

The effect of the nutri-score label on consumer’s attitudes,taste perception and purchase intention: An experimental pilot study

A large number of studies has emerged on the environmental impacts of diets, with most studies concluding that a diet rich in plant-based foods, that are low in salt, sugar, and fat, and score high in nutritional values, confers both improved health and environmental benefits. Currently, new interventions are being implemented to improve people’s eating behavior, because most people overconsume unhealthy snacks, containing high proportions of salt, sugar and fat. The purpose of the current pilot study was to investigate the effects of the Nutri-Score label on three different snack bars on consumers’ attitudes, taste perception, and purchase intention towards these food products. An experimental study was conducted with a between subject design (food packaging with Nutri-Score label versus without) among 192 participants (M_age 31.7 years, SD 14.3 years; 63% female). Results showed that there were no effects found for the effect of the Nutri- Score label on consumers’ attitudes, taste perception and purchase intention. Bayesian analyses support the conclusion that the null hypothesis is accepted. These findings show that integrating the Nutri-Score label on food packages did not modify cognitive responses of consumers towards these food products. Changing consumption behaviors is challenging and more empirical and theoretical understanding is needed.     

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset.     

空间曲线的特征识别与高质量非均匀采样

为避免传统均匀采样方法因忽视曲线重要特征而生成不理想的采样结果,获得给定数量且由特征点和辅助点组成的采样点序列,提出基于特征识别的高质量空间曲线非均匀采样方法.首先使用抛物线插值法得到曲线上所有曲率极大值点和挠率极大值点的近似位置,经筛选后产生特征点,以更好地抓住空间曲线的轮廓特征.然后定义基于弧长、曲率和挠率加权组合的特征函数,并以此自适应地选取曲线上的辅助点.与3种主流采样方法比较的实验结果表明,该方法能够获得更高质量的采样结果且具有更好的实用性,从而进一步改善空间曲线的B样条拟合效果.     

基于SVD和SSA-VMD降噪的轴承故障特征提取

针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。