Direction-of-arrival estimation based on direct data domain (D3) method

A direction-of-arrival （DOA） estimation algorithm based on direct data domain （D3） approach is presented. This method can accuracy estimate DOA using one snapshot modified data, called the temporal and spatial two-dimensional vector reconstruction （TSR） method. The key idea is to apply the D3 approach which can extract the signal of given frequency but null out other frequency signals in temporal domain. Then the spatial vector reconstruction processing is used to estimate the angle of the spatial coherent signal source based on extract signal data. Compared with the common temporal and spatial processing approach, the TSR method has a lower computational load, higher real-time performance, robustness and angular accuracy of DOA. The proposed algorithm can be directly applied to the phased array radar of coherent pulses. Simulation results demonstrate the performance of the proposed technique.

Direct data domain approach to space-time adaptive processing

In non-homogeneous environment, traditional space-time adaptive processing doesn＇t effectively suppress interference and detect target, because the secondary data don＇ t exactly reflect the statistical characteristic of the range cell under test. A ravel methodology utilizing the direct data domain approach to space-time adaptive processing （ STAP ） in airbome radar non-homogeneous environments is presented. The deterministic least squares adaptive signal processing technique operates on a ＂snapshot-by-snapshot＂ basis to dethrone the adaptive adaptive weights for nulling interferences and estimating signal of interest （SOI）. Furthermore, this approach eliminates the requirement for estimating the covariance through the data of neighboring range cell, which eliminates calculating the inverse of covariance, and can be implemented to operate in real-time. Simulation results illustrate the efficiency of interference suppression in non-homogeneous environment.

A ROBUST PHASE-ONLY DIRECT DATA DOMAIN ALGORITHM BASED ON GENERALIZED RAYLEIGH QUOTIENT OPTIMIZATION USING HYBRID GENETIC ALGORITHM

A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.

Incorporating Domain Knowledge into Data Mining Process:An Ontology Based Framework

With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.

Cost-Aware Multi-Domain Virtual Data Center Embedding

Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.

A Model-free Approach to Fault Detection of Continuous-time Systems Based on Time Domain Data

In this paper, a model-free approach is presented to design an observer-based fault detection system of linear continuoustime systems based on input and output data in the time domain. The core of the approach is to directly identify parameters of the observer-based residual generator based on a numerically reliable data equation obtained by filtering and sampling the input and output signals.

Domain-oriented data-driven data mining:a new understanding for data mining

Recent advances in computing,communications,digital storage technologies,and high-throughput data-acquisition technologies,make it possible to gather and store incredible volumes of data.It creates unprecedented opportunities for large-scale knowledge discovery from database.Data mining is an emerging area of computational intelligence that offers new theories,techniques,and tools for processing large volumes of data,such as data analysis,decision making,etc.There are many researchers working on designing efficient data mining techniques,methods,and algorithms.Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods,while little to basic issues of data mining.In this paper,we will propose a new understanding for data mining,that is,domain-oriented data-driven data mining(3DM)model.Some data-driven data mining algorithms developed in our Lab are also presented to show its validity.

Domain-Oriented Data-Driven Data Mining Based on Rough Sets

Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.

基于知识标注平台的水利枢纽工程知识图谱构建及应用

大量水利异构数据的产生,为领域知识图谱的构建及应用提供了场景,但也导致了水利知识图谱构建过程的差异。针对现有水利知识图谱构建流程复杂的问题,提出了一套有效的基于知识标注平台的水利知识图谱构建流程。以小浪底水利枢纽工程知识的智能应用为例,使用该枢纽的工程数据,应用提出的流程在水利领域构建水利枢纽工程知识图谱(Water Conservancy Hub Project Knowledge Graph,WCHP-KG)。首先以小浪底水利枢纽工程为中心,依据行业术语标准和现有词汇表,制定了概念分类和关系描述体系,形成了WCHP-KG的模式层。通过BiLSTM-CRF和序列标注模型,在水利专家的指导下,使用知识标注平台对非结构化文本进行了半自动标注和人工校对,实现了知识融合,进而构建了WCHP-KG的数据层。结果表明WCHP-KG涵盖了43种水利实体以及110种实体关系。经过实践验证,构建的WCHP-KG为小浪底水利枢纽工程的相关应用提供了有力的结构化知识基础,为工程决策和管理提供了可靠的参考依据,进而证明了所提构建流程的有效性。未来将进一步扩展WCHP-KG和完善水利知识图谱的构建流程,以适应更多的应用场景和领域需求。

跨域数据管理

随着数据成为新的生产要素和数字中国顶层战略的推进,跨域数据共享和流通对于实现数据要素价值最大化变得至关重要。国家通过布局全国一体化大数据中心体系、启动“东数西算”工程等一系列举措,为数据要素的跨域应用提供了基础设施。然而,传统的数据管理局限于单一域内,无法满足跨域场景下的数据管理需求。跨域数据管理面临通信层面的跨空间域挑战、数据建模层面的异构模型融合问题,以及数据访问层面的跨信任域挑战。从跨空间域、跨管辖域和跨信任域3个视角出发,探讨了跨域数据管理的内涵、研究挑战及关键技术,并展望了其未来发展趋势。

Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.

Incremental Learning Based on Data Translation and Knowledge Distillation

Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.

基于AIS数据的船舶风险领域模型

为构建船舶风险领域及分析其特征,基于船舶自动识别系统(Automatic Identification System,AIS)数据提出一种具有风险级别的船舶领域模型。首先,根据预处理后的AIS数据,获取他船相对于本船的位置。然后,采用椭圆领域边界对船舶相对位置数据进行筛选,同时获取到代表风险级别的临界点,并使用最小二乘法对其进行拟合,从而得到船舶风险领域。最后,利用老铁山水道中149 m、190 m、229 m、300 m船舶的AIS数据对所提方法进行验证,并分析船舶风险领域的特征。结果表明,该方法可以较好地反映船舶的风险级别;在同一风险级别时,不同尺度船舶间的风险领域长、短半轴与船长之比差异较小;风险级别为1的船舶领域边界接近于圆形;他船在本船周围分布的密集程度不同。本研究所提模型对航行安全保障、航行风险研究有一定的参考意义。

数据产权法律构造论

在数字经济环境中,数据已成为一种新型生产要素,需要更好地挖掘和开发其经济价值。数据不同于传统有体物以及知识产权客体的特征和属性使其在法律制度构建和运行上也具有特殊性。数据产权制度是数据基础制度的核心内容,其应涵盖保护数据主体合法权益、协调和平衡数据利益关系、促进数据有序流动和分享、促进数据要素利益公平分配等内容。数据产权的法律构造旨在建立数据资源持有权、数据加工使用权和数据产品经营权的数据动态流转和价值实现机制,最终服务于数字经济发展目标。

用户恶意跨域数据安全加密共享算法仿真

在跨域数据共享场景下,涉及到多个组织或多个域的用户参与,为了保证数据安全,避免隐私信息泄露,提出了支持恶意用户追踪的跨域数据安全共享算法。将基于密文策略的属性基加密方法与白盒追踪算法和黑盒追踪算法结合。设计恶意用户追踪机制,实现对恶意用户的有效追踪。将对称加密算法和非对称加密算法相结合,生成公钥、私钥和密钥,对数据展开加解密处理,实现跨域数据安全共享。实验结果表明,所提算法的数据安全共享效果较高,在不同实验环境下均能够保持较低的内存,且信息熵较高,能够确保数据的安全性。

基于集成式张量域自适应的运动想象脑电分类

实际应用中脑电信号一直面临采集成本高、用户间差异大等问题,制约着基于脑电信号的运动想象领域的发展。针对跨受试者运动想象脑电信号识别任务,本研究提出了一种基于集成式张量域自适应的迁移学习方法。首先采用改进的欧氏空间对齐方法对多维脑电数据进行协方差对齐,消除原始数据的边缘分布偏移;其次提出基于张量子空间的改进联合概率分布方法,求得不同类别的映射子空间并实现未知标签的目标域识别分类。分别在7人200个样本和9人144个样本的BCI数据集上进行了实验,平均准确率达到82.18%和76.45%,证明了该方法在跨域分类识别上具有很好的性能。同时对于该方法各环节的效果也进行了可视化验证,展示了该集成式方法在跨域问题上的效果。

基于组合时域特征提取和Stacking集成学习的燃煤锅炉NO_(x)排放浓度预测

为提高火电厂锅炉出口NO_(x)排放浓度的预测精度,提出一种考虑组合时域特征的Stacking集成学习模型。首先,为挖掘数据深层信息,采用时序分析、完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise analysis,CEEMDAN)和统计学计算数据标准差、偏度等特征的方法进行组合时域特征提取以构建重构数据;其次,考虑到重构数据中存在的冗余变量对模型的精度有所影响,利用遗传算法(genetic algorithm,GA)对重构数据进行特征降维;最后,为充分发挥各个模型的优势以提高模型的预测精度,构建以极限学习机(extreme learning machines,ELM)、深度神经网络(deep neural networks,DNN)、多层感知器(multilayer perceptron,MLP)、极限梯度提升算法(extreme gradient boosting,XGBoost)为基模型和以回声状态网络(echo state network,ESN)为元模型的Stacking集成学习NOx排放浓度预测模型。实验结果表明:该预测模型在不同数据集下都有着不错的预测效果,预测误差均小于2%,能够对锅炉NOx排放浓度实现精准预测。

基于三维重建的物体空间数据合成增广方法

深度学习目标检测算法需大量训练数据支撑,数据增广是一种自动生成训练样本的有效手段。为在原始样本稀缺时提供大量高质量训练样本,提出一种基于三维重建的物体空间数据增广合成方法,克服现有数据增广要么需要复杂手工建模、要么样本视角多样性不足的问题。首先用少量多视角图像重建目标三维模型,再通过多域随机化图像煊染得到不同环境下任意视角图像,然后将染图与随机背景融合并自动生成标注信息,得到海量多样化合成样本。构建5种训练数据集并在典型目标检测算法上对上述增广方法开展实验验证,结果表明合成样本对原始稀缺样本数据集有显著增强作用,可大幅提高目标检出概率和目标检测定位精度,而且对一般数据集也具有很好的适用性。

基于数据-模型混合驱动的电力系统机电暂态快速仿真方法

数据驱动建模方法改变了发电机传统的建模范式,导致传统的机电暂态时域仿真方法无法直接应用于新范式下的电力系统。为此,该文提出一种基于数据-模型混合驱动的机电暂态时域仿真(data and physics driven time domain simulation,DPD-TDS)算法。算法中发电机状态变量与节点注入电流通过数据驱动模型推理计算,并通过网络方程完成节点电压计算,两者交替求解完成仿真。算法提出一种混合驱动范式下的网络代数方程组预处理方法,用以改善仿真的收敛性;算法设计一种中央处理器单元-神经网络处理器单元(central processing unit-neural network processing unit,CPU-NPU)异构计算框架以加速仿真,CPU进行机理模型的微分代数方程求解;NPU作协处理器完成数据驱动模型的前向推理。最后在IEEE-39和Polish-2383系统中将部分或全部发电机替换为数据驱动模型进行验证,仿真结果表明,所提出的仿真算法收敛性好,计算速度快,结果准确。