Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing, including patient privacy, data de-identification, regulation, intellectual property, and data storage. These difficulties have lessened the impracticality of central data storage. In this survey, we will look at 24 research publications that concentrate on machine learning approaches linked to privacy preservation techniques for multi-institutional data, highlighting the multiple shortcomings of the existing methodologies. Researching different approaches will be made simpler in this case based on a number of factors, such as performance measures, year of publication and journals, achievements of the strategies in numerical assessments, and other factors. A technique analysis that considers the benefits and drawbacks of the strategies is additionally provided. The article also looks at some potential areas for future research as well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative evaluation of the approaches offers a thorough justification for the research’s purpose.

Design and Implementation of a High-Speed Multi-Channel Data Acquisition System

A data acquisition system （DAS） to implement high-speed, real-time and multi-channel data acquisition and store is presented. The control of the system is implemented by the combination of complex programable logic device （CPLD） and digital signal processing （DSP）, the bulk buffer of the system is implemented by the combination of CPLD, DSP, and synchronous dynamic random access memory （SDRAM）, and the data transfer is implemented by the combination of DSP, first in first out （FIFO）, universal serial bus （USB） and USB hub. The system could not only work independently in single-channel mode, but also implement high-speed real-time multi-channel data acquisition system （MCDAS） by the combination of multiple single-channels. The sampling rate and data storage capacity of each channel could reach up to 100 million sampiing per second and 256 MB respectively.

New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

Towards a Comprehensive Security Framework of Cloud Data Storage Based on Multi Agent System Architecture

The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many different locations and interconnected by high speed networks. CDS, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper a comprehensive security framework based on Multi-Agent System (MAS) architecture for CDS to facilitate confidentiality, correctness assurance, availability and integrity of users' data in the cloud is proposed. Our security framework consists of two main layers as agent layer and CDS layer. Our propose MAS architecture includes main five types of agents: Cloud Service Provider Agent (CSPA), Cloud Data Confidentiality Agent (CDConA), Cloud Data Correctness Agent (CDCorA), Cloud Data Availability Agent (CDAA) and Cloud Data Integrity Agent (CDIA). In order to verify our proposed security framework based on MAS architecture, pilot study is conducted using a questionnaire survey. Rasch Methodology is used to analyze the pilot data. Item reliability is found to be poor and a few respondents and items are identified as misfits with distorted measurements. As a result, some problematic questions are revised and some predictably easy questions are excluded from the questionnaire. A prototype of the system is implemented using Java. To simulate the agents, oracle database packages and triggers are used to implement agent functions and oracle jobs are utilized to create agents.

A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement

For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.

ANALYSIS OF MULTI-TONE INTERFERENCE SUPPKESSION IN HF SERIAL DATA TRANSMISSION SYSTEM

The multi-tone interference suppression in HF serial data transmission systemsis analyzed.Analytic expression for the tap weights and minimum mean square errors of theadaptive equalizer in HF serial systems are obtained.The rate of convergence of equalizer indicatethat the equalizer in HF serial system can not only track the rapid variation of HF channel butalso suppress the multi-tone interferences perfectly.

Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

Research on Product Data Management Based on Multi-agent System

PDM (product data management) is one kind of techniques based on software and database, which integrates information and process related to products. But it is not enough to perform the complication of PDM in enterprises. Then the mechanism to harmonize all kinds of information and process is needed. The paper introduces a novel approach to implement the intelligent monitor of PDM based on MAS (multi agent system). It carries out the management of information and process by MC (monitor center). The paper first puts forward the architecture of the whole system, then defines the structure of MC and its interoperation mode.

Consensus for second-order multi-agent systems with position sampled data

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated.The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Asymptotic bounded consensus tracking of double-integrator multi-agent systems with bounded-jerk target based on sampled-data without velocity measurements

This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to partial agents based on sampled-data without velocity measurements. A sampled-data consensus tracking protocol（CTP） without velocity measurements is proposed to guarantee that double-integrator MASs track an AUABJT available to only partial agents.The eigenvalue analysis method together with the augmented matrix method is used to obtain the necessary and sufficient conditions for ABCT. A numerical example is provided to illustrate the effectiveness of theoretical results.

A multi-source data fusion modeling method for debris flow prevention engineering

The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.

Application of data fusion on multi-function earth drill

taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control dependence, the detecting method of the earth drill’s working state is introduced. Multi sensor data fusion is done with the aid of BP neural network in Matlab. The data to be interfused are pre processed and the program of simulation and “point checking” is given.

Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.

融合多源数据的湖北省城市洪涝灾害韧性综合评估

【目的】洪涝灾害是湖北省主要自然灾害之一,为了评估湖北省城市洪涝灾害韧性水平,以加快韧性城市建设、助力推进省域协同发展,【方法】从洪涝灾害的暴露度、敏感性、适应力和恢复力4个维度,构建湖北省城市洪涝灾害韧性评价指标体系,采用G1法(序关系法)和熵权法对评价指标进行赋权,收集气象、遥感影像、基础地理、社会经济等多源数据,应用GIS空间分析技术,对湖北省城市洪涝灾害韧性进行评估。【结果】结果显示:湖北省城市洪涝灾害韧性整体处于中等韧性水平。其中,高韧性区域主要分布在神农架林区、宜昌市和武汉市,较高和中等韧性水平区域大多位于荆门市及其以西的鄂西地区,其余地区洪涝灾害韧性水平较低,总体呈西强东弱的分布态势。【结论】融合多源数据的洪涝灾害韧性评估方法能准确高效地评估研究区洪涝灾害韧性水平,研究成果可为湖北省制定区域洪涝灾害管理及韧性城市规划提供参考。

An efficient spatio-temporal data structure for multi version management of engineering drawings

In the engineering database system, multiple versions of a design including engineering drawings should be managed efficiently. The paper proposes an efficient spatial data structure, that is an expansion of the R tree and HR tree, for version management of engineering drawings. A novel mechanism to manage the difference between drawings is introduced to the HR tree to eliminate redundant duplications and to reduce the amount of storage required for the data structure. Data management mechanism and structural properties of our data structure called the MVR + tree are described.

道路环境与驾驶行为数据同步采集系统研发

高精度、可移植、多维度的在途车辆及其周边驾驶环境数据在L3级及以上自动驾驶汽车场景库测试、不良驾驶行为谱构建以及在途实时交通管控等领域有至关重要的作用,由单一传感器构成的数据采集系统在精度、维度上都受到了较大限制,导致其可靠度较低;为实现多类数据传感器协同一体化,研发了一款包含激光雷达、车载运动测量与组合定位、车载踏板与方向盘位移传感器和雷视道路多维感知一体机的多模块道路环境数据与驾驶行为同步采集系统,支持多项交通数据采集任务的并行处理,融合了用户的真实驾驶行为,并基于该系统设计了依维柯某车型的数据实车采集平台;该系统由上位机与下位机两个子系统构成,集成了多模块数据采集、储存、传输与标定等功能,且对各个模块完成了时间同步处理,适用于两客一危场景,并设计了易操作的上位机用户交互界面,对多模块数据实现实时可视化,直观查看各时段数据的变化规律,具有较高的应用价值。