Anti-collision systems in tunneling to improve effectiveness and safety in a system-quality approach: A review of the state of the art

Tunnelling and underground construction operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. This can lead to collisions between vehicles or between vehicles and pedestrians or structural elements, causing accidents and fatalities. To improve the OS&H conditions, it is important to investigate the possible introduction of innovative techniques and technologies to reduce the occurrences and consequences of shared spaces (spaces used by both vehicles and pedestrians). For this reason, research was conducted to investigate the possible use of different technologies of anti-collision systems in tunnelling operations. First, to achieve this goal, an extensive review of the literature was carried out in accordance with the PRISMA statement to select the current techniques and technologies used by general anti-collision systems in civil and mining construction sites. Then, the operating principles, the relative advantages and disadvantages, combinations, and costs were examined for each of these. Eight types of systems and many examples of applications of anti-collision systems in underground environments were identified as a result of the analysis of the literature. Generally, it was noted that the anti-collision techniques available have found limited application in the excavation sites of underground civil works up to the present day, though the improvement in terms of safety and efficiency would be considerable

Feasibility of reduced gravity experiments involving quiescent, uniform particle cloud combustion

The study of combustible particle clouds is of fundamental scientific interest as well as a practical concern. The principal scientific interests are the characteristic combustion properties, especially flame structure, propagation rates, stability limits, and the effects of stoichiometry, particle type, transport phenomena, and nonadiabatic processes on these properties. The feasibility tests for the particle cloud combustion experiment (PCCE) were performed in reduced gravity in the following stages: (1) fuel particles were mixed into cloud form inside a flammability tube; (2) when the concentration of particles in the cloud was sufficiently uniform, the particle motion was allowed to decay toward quiescence; (3) an igniter was energized which both opened one end of the tube and ignited the suspended particle cloud; and (4) the flame proceeded down the tube length, with its position and characteristic features being photographed by high-speed cameras. Gravitational settling and buoyancy effects were minimized because of the reduced gravity enviroment in the NASA Lewis drop towers and aircraft. Feasibility was shown as quasi-steady flame propagation which was observed for fuel-rich mixtures. Of greatest scientific interest is the finding that for near-stoichiometric mixtures, a new mode of flame propagation was observed, now called a chattering flame. These flames did not propagate steadily through the tube. Chattering modes of flame propagation are not expected to display extinction limits that are the same as those for acoustically undisturbed, uniform, quiescent clouds. A low concentration of fuel particles, uniformly distributed in a volume, may not be flammable but may be made flammable, as was observed, through induced segregation processes. A theory was developed which showed that chattering flame propagation was controlled by radiation from combustion products which heated the successive discrete laminae sufficiently to cause autoignition

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Optimizing Techniques and Cramer-Rao Bound for Passive Source Location Estimation

This work is motivated by the problem of locating potential unstable areas in underground potash mines with better accuracy more consistently while introducing minimum extra computational load. It is important for both efficient mine design and safe mining activities, since these unstable areas may experience local, low-intensity earthquakes in the vicinity of an underground mine. The object of this thesis is to present localization algorithms that can deliver the most consistent and accurate estimation results for the application of interest. As the first step towards the goal, three most representative source localization algorithms given in the literature are studied and compared. A one-step energy based grid search (EGS) algorithm is selected to address the needs of the application of interest. The next step is the development of closed-form Cram´er-Rao bound (CRB) expressions. The mathematical derivation presented in this work deals with continuous signals using the Karhunen-Lo`eve (K-L) expansion, which makes the derivation applicable to non-stationary Gaussian noise problems. Explicit closed-form CRB expressions are presented only for stationary Gaussian noise cases using the spectrum representation of the signal and noise though. Using the CRB comparisons, two approaches are proposed to further improve the EGS algorithm. The first approach utilizes the corresponding analytic expression of the error estimation variance (EEV) given in [1] to derive an amplitude weight expression, optimal in terms of minimizing this EEV, for the case of additive Gaussian noise with a common spectrum interpretation across all the sensors. An alternate noniterative amplitude weighting scheme is proposed based on the optimal amplitude weight expression. It achieves the same performance with less calculation compared with the traditional iterative approach. The second approach tries to optimize the EGS algorithm in the frequency domain. An analytic frequency weighted EEV expression is derived using spectrum representation and the stochastic process theory. Based on this EEV expression, an integral equation is established and solved using the calculus of variations technique. The solution corresponds to a filter transfer function that is optimal in the sense that it minimizes this analytic frequency domain EEV. When various parts of the frequency domain EEV expression are ignored during the minimization procedure using Cauchy-Schwarz inequality, several different filter transfer functions result. All of them turn out to be well known classical filters that have been developed in the literature and used to deal with source localization problems. This demonstrates that in terms of minimizing the analytic EEV, they are all suboptimal, not optimal. Monte Carlo simulation is performed and shows that both amplitude and frequency weighting bring obvious improvement over the unweighted EGS estimator

Internet of things for disaster management: state-of-the-art and prospects

Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster's occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems. © 2013 IEEE

Разработка имитационной модели движения горно-выработочной машины

An importance of solving the scientific problem of identifying deviations of a mining machine in the extraction of potash ore and other ores is determined. The impossibility of direct application of both existing positioning systems inside buildings and underground positioning systems offered on the market for this purposes is shown. That is why difficult conditions during the mining and high vibration. It is proposed to determine the deviation of the mining machine by indications of the distance to the mine wall sensors installed on the sides. The neural network could be used as an identification subsystem in the future. For the learning, a model is needed for simulate data from sensors according with a predetermined deviation. Indications of the sensors is imitated by a simple geometrical way inside the pixel on the monitor screen trailed by a segment of the cutting edge of the mining machine. A simulation model of a two-dimensional underground movement of a mining machine is created. It allows to set deviations of different types and to simulate the indications of distance sensors at the same time. These calculations are based on determining the point of rotation of the mining machine during a small deviation from a straight course of movement. Further, the next position of the machine and the pixels painted by the cutting edge during the movement are determined by an explicit method. The number of pixels between the sensor and the non-shaded area in the direction perpendicular to the axis of the mining machine is evaluated through the scale into the distance to the bottom wall. The error of sensors with a predetermined spread and its statistical distribution is also simulated. The possibility of qualitative identification of evasion by the indications of four sensors and possibility of using the model for learning a neural network are shown.Определена важность решения научной задачи идентификации уклонений горно-выработочной машины при добыче калийной руды и других руд. Показана невозможность непосредственного применения для этого как существующих систем позиционирования внутри зданий, так и предлагаемых на рынке систем подземного позиционирования. Причиной являются сложные условия в ходе выработки и высокая вибрация. Предложено определять уклонение горно-выработочной машины по показаниям установленных на бортах датчиков расстояния до стенки забоя. В качестве идентифицирующей подсистемы в дальнейшем будет использоваться нейронная сеть. Для ее обучения необходима модель, позволяющая имитировать данные с датчиков при наперед заданном уклонении. Предложено определять показания датчиков простым геометрическим способом путем трассировки внутри пиксельного следа, оставляемого на экране монитора отрезком режущей кромки горно-выработочной машины. Создана имитационная модель двумерного подземного движения горно-выработочной машины, позволяющая задавать уклонения разных видов и имитировать показания датчиков расстояния при этом. Расчеты базируются на определении точки вращения горно-выработочной машины в ходе малого уклонения от прямолинейного курса движения. Далее явным методом определяется следующее положение машины и пиксели, закрашиваемые отрезком режущей кромки при перемещении. Количество пикселей между датчиком и не закрашенной областью в направлении, перпендикулярном оси горно-выработочной машины, переводится через масштаб в расстояние до стенки забоя. При этом имитируется также погрешность датчиков с заданным наперед разбросом и его статистическим распределением. Показана возможность качественной идентификации уклонения по показаниям четырех датчиков, а также возможность использования модели для обучения нейронной сети

Underground Mining Monitoring and Communication Systems based on ZigBee and GIS

ZigBee as a wireless sensor network (WSN) was developed for underground mine monitoring and communication systems. The radio wave attenuations between ZigBee nodes were investigated to measure underground communication distances. Various sensor node arrangements of ZigBee topologies were evaluated. A system integration of a WSN-assisted GIS for underground mining monitoring and communication from a surface office was proposed. The controllable and uncontrollable parameters of underground environments were assessed to establish a reliable ZigBee network