UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Fusion of low-cost and light-weight sensor system for mobile flexible manipulator

There is a need for non-industrial robots such as in homecare and eldercare. Light-weight mobile robots preferred as compared to conventional fixed based robots as the former is safe, portable, convenient and economical to implement. Sensor system for light-weight mobile flexible manipulator is studied in this research. A mobile flexible link manipulator (MFLM) contributes to high amount of vibrations at the tip, giving rise to inaccurate position estimations. In a control system, there inevitably exists a lag between the sensor feedback and the controller. Consequently, it contributed to instable control of the MFLM. Hence, there it is a need to predict the tip trajectory of the MFLM. Fusion of low cost sensors is studied to enhance prediction accuracy at the MFLM’s tip. A digital camera and an accelerometer are used predict tip of the MFLM. The main disadvantage of camera is the delayed feedback due to the slow data rate and long processing time, while accelerometer composes cumulative errors. Wheel encoder and webcam are used for position estimation of the mobile platform. The strengths and limitations of each sensor were compared. To solve the above problem, model based predictive sensor systems have been investigated for used on the mobile flexible link manipulator using the selected sensors. Mathematical models were being developed for modeling the reaction of the mobile platform and flexible manipulator when subjected to a series of input voltages and loads. The model-based Kalman filter fusion prediction algorithm was developed, which gave reasonability good predictions of the vibrations of the tip of flexible manipulator on the mobile platform. To facilitate evaluation of the novel predictive system, a mobile platform was fabricated, where the flexible manipulator and the sensors are mounted onto the platform. Straight path motions were performed for the experimental tests. The results showed that predictive algorithm with modelled input to the Extended Kalman filter have best prediction to the tip vibration of the MFLM

Single Hydrophone Underwater Localization Approach in Sallow Waters

Applications of underwater signal processing are essential for environmental monitoring. Remote monitoring and passive sound source localization in an underwater environment can provide great insight into geological studies, environmental changes and marine lives monitoring. While various methods are available for Localization, they mostly employ arrays of hydrophones, requiring synchronization or prior knowledge of the source signals, which can prove costly, complicated, and hard to maintain. Remote monitoring applications require very high-range passive localization methods; and, given the frequency-selective nature of ambient noise and other channel parameters, current localization methods have short-distance range estimation or high localization error for long distances. The modal analysis makes it possible to study and localize sounds propagated over long distances using one passive hydrophone without a need for prior knowledge of the source or synchronization. This dissertation presents four new stand-alone multi/single hydrophone localization algorithms based on modal dispersion analysis to localize impulsive sound sources in a noisy shallow water environment. The first algorithm is named as selective multi-modal pair (SMP), enables utilizing modals with any wavenumbers as opposed to previously proposed methods based on only on the modes with sequential wavenumbers. The algorithm extracts the dispersion curves of the received signal to be compared against the dispersion curves computed using a custom channel. then chooses the most effective modes (that result in the lowest localization error) , estimated the range of a sound source. The resulting estimated range is the range that makes the best match between the selected modal dispersion curves and the estimated dispersion curves. Numerical results, using both simulated and actual recorded sounds of whale and underwater explosion show that the proposed algorithm can localize underwater sounds with high accuracy when the signal-to-noise ratio varies from 28dB to 45dB. The second Localization algorithm is named selective weighted genetic algorithm (SW-GA). This algorithm employs two weighting functions based on geolocation information of the source and a selective scaling function for the selection of the most noise resistive modal pairs. The proposed weighted localization scaling and selection functions are designed to ensure convergence towards the correct range estimation. We analyzed and compared this algorithm using the same signals and SNR scenarios as before and shows a better2D localization performance and noise resistivity compared with previously proposed methods. The third and fourth algorithms, named Weighted Multi-Modal (WMM)and Multi-Modal (MM) employs all available modal pairs instead of just a few or a sequential selection. They compute a modal contribution matrix based on all available modal pairs with common frequencies. Furthermore, the weighted version of the algorithm employs the contribution matrix for assigning weights based on contribution/noise resistivity to all modal pairs. Employing all available modes results in an algorithm capable of localizing signals with higher frequencies and the weighting function increase accuracy in low SNR environments. The noise performance analysis of both the WMM algorithm; and the non-weighted version yields considerable improvements in localization of sound sources in the presence of high level of ambient noise over other algorithms used in this work

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

Limited Bandwidth Wireless Communication Strategies for Structural Control of Seismically Excited Shear Structures

Structural control is used to mitigate unwanted vibrations in structures when large excitations occur, such as high winds and earthquakes. To increase reliability and controllability in structural control applications, engineers are making use of semi-active control devices. Semi-active control gives engineers greater control authority over structural response versus passive controllers, but are less expensive and more reliable than active devices. However, the large numbers of actuators required for semi-active structural control networks introduce more cabling within control systems leading to increased cost. Researchers are exploring the use of wireless technology for structural control to cut down on the installation cost associated with cabling. However wireless communication latency (time delays in data transmissions) can be a barrier to full acceptance of wireless technology for structural control. As the number of sensors in a control network grows, it becomes increasingly difficult to transmit all sensor data during a single control step over the fixed wireless bandwidth. Because control force calculations rely on accurate state measurements or estimates, the use of strategic bandwidth allocation becomes more necessary to provide good control performance. The traditional method for speeding up the control step in larger wireless networks is to spatially decentralize the network into multiple subnetworks, sacrificing communication for speed. This dissertation seeks to provide an additional approach to address the issue of communication latency that may be an alternative, or even a supplement, to spatial decentralization of the control network. The proposed approach is to use temporal decentralization, or the decentralization of the control network over time, as opposed to space/location. Temporal decentralization is first presented with a means of selecting and evaluating different communication group sizes and wireless unit combinations for staggered temporal group communication that still provide highly accurate state estimates. It is found that, in staggered communication schemes, state estimation and control performance are affected by the network topology used at each time step with some sensor combinations providing more useful information than others. Sensor placement theory is used to form sensor groups that provide consistently high-quality output information to the network during each time step, but still utilize all sensors. If the demand for sensors to communicate data outweighs the available bandwidth, traditional temporal and spatial approaches are no longer feasible. This dissertation examines and validates a dynamic approach for bandwidth allocation relying on an extended, autonomous and controller-aware, carrier sense multiple access with collision detection (CSMA/CD) protocol. Stochastic parameters are derived to strategically alter back-off times in the CSMA/CD algorithm based on nodal observability and output estimation error. Inspired by data fusion approaches, this second study presents two different methods for neighborhood state estimation using a dynamic form of measurement-only fusion. To validate these wireless structural control approaches, a small-scale experimental semi-active structural control testbed is developed that captures the important attributes of a full-scale structure

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors