基于数据库和RS-485总线的传感器智能化识别技术研究

为满足测试系统构建效率的需求,构建了基于Rs-485总线的钟邑数据采集系统。利用单片机对传统传感器智能化进行改造,在LabvIEW环境下开发的数据采集系统访问设备数据库,实现数据采集系统对传感器的自动识别,并在船舶模型试验测试系统中进行了验证。     

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.     

DYLEMA: Using walking robots for landmine detection and location

Detection and removal of antipersonnel landmines is an important worldwide concern. A huge number of landmines has been deployed over the last twenty years, and demining will take several more decades, even if no more mines were deployed in future. An adequate mine-clearance rate can only be achieved by using new technologies such as improved sensors, efficient manipulators and mobile robots. This paper presents some basic ideas on the configuration of a mobile system for detecting and locating antipersonnel landmines efficiently and effectively. The paper describes the main features of the overall system, which consists of a sensor head that can detect certain landmine types, a manipulator to move the sensor head over large areas, a locating system based on a global-positioning system, a remote supervisor computer and a legged robot used as the subsystems’ carrier. The whole system has been configured to work in a semi-autonomous mode with a view also to robot mobility and energy efficiency.     

Hybrid deliberative/reactive control of a scanning system for landmine detection

Antipersonnel mines infest fields all over the world. According to recent estimates, landmines are killing and maiming more than 2000 innocent civilians per month. The problem of landmine detection and removal requires the cooperation of a number of engineering fields, which in turn poses a need for new technologies, such as improved sensors, efficient manipulators and mobile robots. This paper describes the configuration and control architecture of a scanning manipulator for detecting antipersonnel landmines. The scanning system is part of a demining system based on a walking robot that acts as the carrier for the scanning manipulator. Broadly speaking, the scanning system consists of a sensor head that can detect certain kinds of landmines and, to move the sensor head over large areas, a manipulator that has been appropriately sensorized to scan irregular terrains in the presence of obstacles. The proposed control architecture is of the hybrid deliberative/reactive type: A deliberative controller defines a sweep trajectory that furnishes complete coverage of the infested area, while two reactive controllers are involved in on-line adaptation to the environment. Experiments show good performance of the whole system.     

基于专家系统的汽车衡故障传感器判别

传感器是自动化设备的核心部件,传感器故障检测显得尤为重要。针对目前汽车衡维护、检修的困难,为有效准确判断故障传感器,提出了以径向基函数神经网络（RBFNN）预估值的初始化数据库专家系统判别方法。经现场测试准确率达到96%以上,从而有效简单地判定传感器好坏和识别故障传感器的位置。     

基于NB-IoT的土壤墒情监测系统设计与应用

为满足数字农业中对土壤墒情动态监测的应用需求,该文提出了基于窄带物联网NB-IoT技术的土壤墒情远程监测系统,并实现了工程应用。该系统可以通过监测网站远程监测传感器安装地的土壤温度、含水量及电导率等墒情信息。系统以采集传输控制器为核心,实现土壤传感器的数据采集及其与网络云平台、数据库、监测网站之间的远程传输。应用证明,该系统数据传输准确、稳定,应用效果良好。     

Structural environmental informatization using sensor network with heterogeneous sensors

In the present paper, a configuration of a sensor network system for monitoring the usage patterns of a house is proposed. The sensor network system has many active type radio-frequency identifications (RFIDs) as a network node. The RFID has advantages of low cost and low power consumption. Also the RFID can connect various sensors for measuring environment information, since having I/O port as external interface. Environment information is stored on a database through the RFID’s wireless radio communication function. The sensor types in this system are variety from slow changing information like temperature and humidity data to event notification like a human detection data. The paper will be described a acquisition procedure for various characteristic sensor data on the same sensor network. Also, the installation locations of the sensors are critical for highly accurate detection of the environmental conditions. Finally, based on the sensor data, capability of analysis of the usage pattern for a house will be discussed.     

CIRCE: Correcting Imprecise Readings and Compressing Excrescent points for querying common patterns in uncertain sensor streams

Continuous sensor stream data are often recorded as a series of discrete points in a database from which knowledge can be retrieved through queries. Two classes of uncertainties inevitably happen in sensor streams that we present as follows. The first is Uncertainty due to Discrete Sampling (DS Uncertainty); even if every discrete point is correct, the discrete sensor stream is uncertain – that is, it is not exactly like the continuous stream – since some critical points are missing due to the limited capabilities of the sensing equipment and the database server. The second is Uncertainty due to Sampling Error (SE Uncertainty); sensor readings for the same situation cannot be repeated exactly when we record them at different times or use different sensors since different sampling errors exist. These two uncertainties reduce the efficiency and accuracy of querying common patterns. However, already known algorithms generally only resolve SE Uncertainty. In this paper, we propose a novel method of Correcting Imprecise Readings and Compressing Excrescent (CIRCE) points. Particularly, to resolve DS Uncertainty, a novel CIRCE core algorithm is developed in the CIRCE method to correct the missing critical points while compressing the original sensor streams. The experimental study based on various sizes of sensor stream datasets validates that the CIRCE core algorithm is more efficient and more accurate than a counterpart algorithm to compress sensor streams. We also resolve the SE Uncertainty problem in the CIRCE method. The application for querying longest common route patterns validates the effectiveness of our CIRCE method.     

Motion planning for a landmine-detection robot

This article describes a landmine-detection system that contains a landmine-detection mobile robot and a following mobile robot. In this system, the landmine-detection robot goes ahead, and uses a landmine detector and a GPS module to find a landmine, records the coordinates of its location, and transmits these coordinates to the following mobile robot via a wireless RF interface. The following robot can record the location and orientation of the landmine-detection robot and all the landmines in the region. The following robot moves close to the landmine, and programs a path to avoid obstacles and landmines automatically. The driving system of the landmine-detection mobile robot uses a microprocessor dsPIC 30F4011 as the core, and controls two DC servomotors to program the motion path. The user interface of the landmine-detection robot and the following robot uses Borland C₊₊ Builder language to receive the location data. In the experimental results, the landmine-detection robot records the location of landmines using a GPS module, and transmits the locations to the following robot via a wireless RF interface. The following robot avoids the landmines, and improves the safety of people or materials being carried through the landmine area.     

A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce confidence levels on a grid covering the test-bed. A confidence level expresses a confidence or belief in a landmine detection on a certain position. The grid with confidence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster–Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of different sensors, we provide an analysis of the different methods by viewing them as discriminant functions in the sensor confidence space. The results of experiments on real sensor data are evaluated with the leave-one-out method.     

Estimation of landmine characteristics in sandy desert using neural networks

Many places in the world are heavily contaminated with landmines, which cause that many resources are not utilized. This makes landmine detection and removal challenges for research. To guarantee reliable landmine sensing system, deep analysis and many test cases are required. The proposed concept is based on application of 1 kPa external constant pressure (lower than the landmine activation pressure) to the sand surface. The resultant contact pressure distribution is dependent on the imbedded object characteristics (type and depth). Then neural networks (NN) are trained to find the inverse solution of the sand–landmine problem. In other words, when the contact pressure is known, NN can estimate the imbedded object type and depth. In this work, using finite element modeling, the existence of landmines in sand is modeled and analyzed. The resultant contact pressure distribution for five objects (1—anti-tank, 2—anti-personnel, 3—can with diameter and height of 200 mm, 4—spherical rock with 200 mm diameter, and 5—sand without any object) in sand at different depths is used in training NN. Three NN are developed to estimate the landmine characteristics. The first one is perceptron type which classifies the introduced objects in sand. The other two feed-forward NN (FFNN) are developed to estimate the depth of two landmine types. The NN detection rates of anti-tank and anti-personnel landmines are 100 and 67 % in training, and 95 and 70 % in validation, respectively. As test cases, the detection rates of the NN in case of landmine inclination angles (0°–30°) are studied. The results show same detection rates as those at no inclination. A random noise 10 % of the average signal does not affect NN detection rates, which are the same as 95 and 70 % as in validation for anti-tank and anti-personnel, respectively, while with 20 % noise detection rates are decreases to 90 and 50 % for anti-tank and anti-personnel, respectively.

     

基于数据库的属性约简模型的快速求核算法

对于基于数据库系统的属性约简模型,给出相应的简化差别矩阵和相应核的定义,并证明该核与基于数据库系统的属性约简模型的核是等价的。在此基础上设计了一个新的求核算法,其时间复杂度和空间复杂度分别为max{O（|C||U/C|²）,O（|C||U|）}和O（|U|）。     

A review of satellite and airborne sensors for remote sensing based detection of minefields and landmines

This paper focuses on the use of space and airborne sensors that can be applied to detect landmines and minefields. First the landmine and minefield problem is addressed and examples of the use of remote sensing images are presented that could provide valuable information for the mine action process and assist in conventional minefield and landmine detection methods. This is followed by an overview on relevant (declassified) aspects related to strategic overhead detection techniques developed by the military/intelligence community as well as those of civilian space and airborne remote sensing programmes. The airborne sensing techniques describe the state of the art of sensors such as optical (film, multi- and hyperspectral sensors), thermal infrared as well as microwave sensors and their suitability--limitations for remote sensing based minefield and landmine detection purposes.     

An Information Roadmap Method for Robotic Sensor Path Planning

A new probabilistic roadmap method is presented for planning the path of a robotic sensor deployed in order to classify multiple fixed targets located in an obstacle-populated workspace. Existing roadmap methods have been successful at planning a robot path for the purpose of moving from an initial to a final configuration in a workspace by a minimum distance. But they are not directly applicable to robots whose primary objective is to gather target information with an on-board sensor. In this paper, a novel information roadmap method is developed in which obstacles, targets, sensor’s platform and field-of-view are represented as closed and bounded subsets of an Euclidean workspace. The information roadmap is sampled from a normalized information theoretic function that favors samples with a high expected value of information in configuration space. The method is applied to a landmine classification problem to plan the path of a robotic ground-penetrating radar, based on prior remote measurements and other geospatial data. Experiments show that paths obtained from the information roadmap exhibit a classification efficiency several times higher than that of existing search strategies. Also, the information roadmap can be used to deploy non-overpass capable robots that must avoid targets as well as obstacles.     

道路养护车系统

系统以S3C2410处理器为核心,扩展外围集成电路,以Windows CE为操作平台,采用ADOCE3.1与SQL Server 2.0相结合的方法设计破损道路养护标准数据库,由超声波传感器采集具体的路面信息,通过IIC接口传输至嵌入式系统平台,系统操作平台根据道路养护数据库标准给出具体的修补方案。     

基于小波包的去噪方法在地雷识别中的应用

作为一种新的探雷方法,探地雷达在探雷领域得到了广泛的应用。由于雷达回波信号包含有很多杂散信号,目标信号很容易被强噪声信号所淹没,为了提高雷达回波信号的信噪比,有利于地雷的识别和检测,提出了一种基于信息熵的最优基选择与小波包阈值去噪相结合的方法对回波信号进行杂波抑制,采用该方法对DeTeC的数据进行分析的实验结果表明,该方法在有效地去除了直达波的同时,抑制了背景噪声,分离出目标信号。     

基于物联网技术的矿井安全监控系统设计

针对传统矿井安全监控系统技术单一等缺点,设计了一种基于物联网技术的矿井安全监控系统。系统通过Zig Bee技术组网,利用温湿度传感器、瓦斯传感器和一氧化碳传感器对矿井环境参数进行检测,视频监控设备对矿井作业进行监控,然后将数据传到ARM服务器,服务器将接收到的数据进行解析并存入数据库中,并通过3G网络接入互联网,客户端计算机和智能手机通过互联网分别以B/S/S模式和C/S模式访问ARM服务器的数据。实验结果表明:系统稳定性好、可靠性强,为矿井安全监控提供了比较新的可靠的组合技术方案。     

传感器网络数据库并行计算设计与应用

本文深入了解了国内外无线传感器网络领域目前的发展现状和发展动态,并对现有的无线传感器网络操作系统进行了大量的钻研,为研究无线传感器网络数据库打下了夯实的理论基础。论文从现有的无线传感器网络数据库分析入手,并着重设计和实现了基于TaraxOS无线传感器网络操作系统的数据库TaraxDB。然后基于对无线传感器网络数据库的分析,设计和实现了无线传感器网络数据库TaraxDB,设计实现了客户端查询的接收、解析和表示,数据库客户端与传感器网络之间发送查询和接收结果,数据库客户端与用户之间接收查询和提交结果。