Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Online Machine Learning Algorithms Review and Comparison in Healthcare

Currently, the healthcare industry uses Big Data for essential patient care information. Electronic Health Records (EHR) store massive data and are continuously updated with information such as laboratory results, medication, and clinical events. There are various methods by which healthcare data is generated and collected, including databases, healthcare websites, mobile applications, wearable technologies, and sensors. The continuous flow of data will improve healthcare service, medical diagnostic research and, ultimately, patient care. Thus, it is important to implement advanced data analysis techniques to obtain more precise prediction results.Machine Learning (ML) has acquired an important place in Big Healthcare Data (BHD). ML has the capability to run predictive analysis, detect patterns or red flags, and connect dots to enhance personalized treatment plans. Because predictive models have dependent and independent variables, ML algorithms perform mathematical calculations to find the best suitable mathematical equations to predict dependent variables using a given set of independent variables. These model performances depend on datasets and response, or dependent, variable types such as binary or multi-class, supervised or unsupervised.The current research analyzed incremental, or streaming or online, algorithm performance with offline or batch learning (these terms are used interchangeably) using performance measures such as accuracy, model complexity, and time consumption. Batch learning algorithms are provided with the specific dataset, which always constrains the size of the dataset depending on memory consumption. In the case of incremental algorithms, data arrive sequentially, which is determined by hyperparameter optimization such as chunk size, tree split, or hoeffding bond. The model complexity of an incremental learning algorithm is based on a number of parameters, which in turn determine memory consumption

Terrain Accessibility Prediction for a New Multi axle Armoured Wheeled Vehicle

Better terrain accessibility of military vehicle makes it possible to project force at desired points in a theatre of operation. The factors responsible for terrain accessibility are slope, obstacles and soil. Torque requirement for meeting vehicle speed and gradient requirement is understood and can be analytically arrived at. It can be met by appropriate choice of engine and transmission using. There is dearth of information as well as a common metric in quantification of terrain accessibility especially soft soil trafficability. Approach adopted in this study is that of characterisation of vehicle in terms of mobility characteristic and mobility limit parameters and comparing them with vehicle in-service worldwide. Simple empirical relation has been preferred over complex analytical approach for mobility prediction and gradient climbing capability in sand has been predicted and compared with other vehicles. parametric study for tyre sizes vis-a-vis mobility parameters have been obtained and results have been presented for chosen vehicle configuration. Second part of this study is obstacle crossing capability study for standard set of obstacles. Vehicle model has been built in multi-body environment and parameters of significance viz., wheel displacement to verify correctness of model and acceleration at CG for ride comfort and ground reactions for evaluation of dynamic loads on axles have been obtained. Vehicle drivetrain configuration to achieve desired terrain accessibility in terms of soft-soil trafficability and obstacle crossing has been demonstrated

Wishbone Structure for Front Independent Suspension of a Military Truck

Wishbone structure for double wishbone front-independent Suspension for a military truck application is presented. At present, the vehicle is equipped with rigid axle with leaf springs. There are two aspects that dictate the design of wishbone structure, viz. the path of relative motion between the constituents of the suspension system and the forces transmitted between them. Also, enhancement of mobility was made possible by maintaining the live axle in the system. A double wishbone, double coil spring with twin damper configuration was employed for this application. MBD Analysis was carried out using MSC ADAMS. A double wishboneindependent suspension has been designed for the front axle and has been successfully integrated with the vehicle.Defence Science Journal, 2010, 60(2), pp.178-183, DOI:http://dx.doi.org/10.14429/dsj.60.33

QUASI-STEADY AND TRANSIENT STUDY OF FLOW-BOILING PHENOMENA IN A MICROCHANNEL

Ph.DDOCTOR OF PHILOSOPH

Investigation on Semi-active Suspension System for Multi-axle Armoured Vehicle using Co-simulation

The objective of the study is to evaluate the performance of various semi-active suspension control strategies for 8x8 multi-axle armoured vehicles in terms of comparative analysis of ride quality and mobility parameters during negotiation of typical military obstacles. Since the cost, complexity and time precludes realisation of actual system, co-simulation technique has been effectively implemented for this investigation. Co-simulation combines advanced virtual prototyping and control technology which offers a novel approach to investigate the dynamics of such complex system. The simulations for the integrated control system along with multi body model of the vehicle are carried out for the control strategies, viz. continuous sky hook control, cascade loop control and cascade loop with ride control and compared with passive suspension system. The vehicle with 8x8 configuration is run on the real world obstacle profiles, viz. step, trench, trapezoidal bump and corrugated road and the effect of control strategies on ride comfort, wheel displacement and ground reaction is presented. It is observed that cascade loop with ride control in semi-active mode offers better vehicle ride comfort while crossing the said obstacles. The improved performance parameters are achieved through stabilisation of heave, pitch and roll motions of the vehicle through outer loop and isolation of vehicle level uneven disturbances through the fuzzy logic controller employed in inner loop