改质沥青生产常见事故处理

分析改质沥青生产中常见的过油不畅、中间槽液下泵损坏、改质釜搅拌器及电机损坏原因 ,提出改进措施 ,取得了良好的效果     

如何解决乙炔发生系统蝶阀泄漏乙炔气的问题

蝶阀装置在乙炔生产中具有安全密封作用,但加料操作过程易对其造成损伤,不仅影响生产,而且是造成乙炔气大量泄漏的主要原因。根据长期生产实践,分别从材质到操作等细节问题进行了分析,为避免蝶阀泄漏找到了比较妥善的解决办法,降低了蝶阀装置的维修率。     

煤炭综合利用在枣庄柴里矿的实践

山东枣矿集团柴里煤矿热电公司,依托矿区资源优势,通过开展煤炭综合利用,大力发展循环经济,壮大了非煤产业,逐步发展成为以煤矸石、煤泥、煤气发电为主,集中供热为辅,兼以生产水泥、建筑建材等为一体的综合利用企业,实现了清洁生产。     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

基于虚拟磁盘的携带型操作系统加密方案

针对移动硬盘的加密系统分区无法被正确辨识而导致误格式化的问题,应用文件系统过滤驱动技术,结合硬盘主引导程序及分区表机理,对现有的基于虚拟磁盘的移动硬盘加解密方案进行了改进,在加密分区包含可运行操作系统的情况下,增加了盘符自动隐藏及恢复的功能。以开源加密软件Truecrypt为例,利用安装了视窗操作系统的固态移动硬盘进行测试。结果表明,在不影响加密操作系统正常运行的同时,盘符自动隐藏及恢复功能避免了用户的误格式化操作,从而提高了移动硬盘加密系统分区的安全性和隐蔽性。     

Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching

Abstract. This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov‐switching parameters. A model selection procedure is proposed which is based on optimization of complexity‐penalized likelihood criteria. The efficacy of the procedure is evaluated by means of Monte Carlo experiments.     

基于贝叶斯网络的新媒体事件分类模型

为了对新媒体事件进行准确有效分类,提出一种将K-means算法和贝叶斯网络相结合的混合算法。该算法首先运用K-means算法将训练样本聚类,再根据聚类的结果,运用改进的贝叶斯网络对新媒体事件进行分类。其中,层次贝叶斯网络模型的构建,避免了贝叶斯网络在参数学习时陷入局部寻优;同时引入隐藏节点,更大程度满足了贝叶斯网络的条件独立假设。实验结果表明该算法效果明显。     

Jump Filter: A Dynamic Sketch for Big Data Governance

With the rapid development of information technology, the volume of data maintains exponential growth, and the value of data is hard to mine. This brings significant challenges to the efficient management and control of each link in the data life cycle, such as data collection, cleaning, storage, and sharing. Sketch uses a hash table/matrix/bit vector to track the core characteristics of data, such as frequency, cardinality, and membership. This mechanism makes the sketch itself metadata, which has been widely used in sharing, transmission, update, and other scenarios. The rapid flow characteristic of big data has spawned dynamic sketches. The existing dynamic sketches have the advantage of expanding or shrinking the capacity with the size of the data stream by dynamically maintaining a list of probabilistic data structures in a chain or tree structure. However, there are problems with the excessive space overhead and time overhead increasing with the increase in the dataset cardinality. This paper designs a dynamic sketch for big data governance on the basis of the advanced jump consistent hash. This method can simultaneously achieve the space overhead that grows linearly with the dataset cardinality and the constant time overhead of data processing and analysis, effectively supporting the demanding big data processing and analysis tasks for big data governance. The validity and efficiency of the proposed method are verified by the comparison with traditional methods on various synthetic and natural datasets.     

Cross-source Data Error Detection Approach Based on Federated Learning

With the emergence and accumulation of massive data, data governance has become an important manner to improve data quality and maximize data value. Specifically, data error detection is a crucial step to improve data quality, which has attracted wide attention from both industry and academia. At present, various detection methods tailored for a single data source have been proposed. However, in many real-world scenarios, data are not centrally stored or managed. Data from different sources but highly correlated can be employed to improve the accuracy of error detection. Unfortunately, due to privacy/security issues, cross-source data are often not allowed to be integrated centrally. To this end, this paper proposes FeLeDetect, a cross-source data error detection method based on federated learning, so as to improve the error detection accuracy by using cross-source data information on the premise of data privacy. First, a Graph-based Error Detection Model, namely GEDM, is presented to capture sufficient data features from each data source. On this basis, the paper then designs a federated co-training algorithm, namely FCTA, to collaboratively train GEDM by using different cross-source data without privacy leakage of data. Furthermore, the paper designs a series of optimization methods to reduce communication costs during federated learning and manual labeling efforts. Finally, extensive experiments on three real-world datasets demonstrate that (1) GEDM achieves an average improvement of 10.3% and 25.2% in terms of the $F1$ score in the local and centralized scenarios, respectively, outperforming all the five existing state-of-the-art methods for error detection; (2) the F1 score of the error detection by FeLeDetect is 23.2% on average higher than that by GEDM in the local scenario.