Active Control of Non-stationary Response of Vehicles with Nonlinear Suspensions

Active control of non-stationary response of a single degree of freedom vehicle model with nonlinear passive suspension elements is considered in this paper. The method of equivalent linearization is used to derive the equivalent linear model and the optimal control laws are obtained by using stochastic optimal control theory based on full state information. Velocity squared quadratic damping and hysteresis type of stiffness nonlinearities are considered. The effect of the nonlinearities on the active system performance is studied. The performance of active suspensions with nonlinear passive elements is found to be superior to the corresponding passive suspension systems.     

Automated detection of lane-blocking freeway incidents using artificial neural networks

A major source of urban freeway delay in the U.S. is non-recurring congestion caused by incidents. The automated detection of incidents is an important function of a freeway traffic management center. A number of incident detection algorithms, using inductive loop data as input, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These algorithms have shown varying degrees of success in their detection performance. In this paper, we present a new incident detection technique based on artificial neural networks (ANNs). Three types of neural network models, namely the multi-layer feedforward (MLF), the self-organizing feature map (SOFM) and adaptive resonance theory 2 (ART2), were developed to classify traffic surveillance data obtained from loop detectors, with the objective of using the classified output to detect lane-blocking freeway incidents. The models were developed with simulation data from a study site and tested with both simulation and field data at the same site. The MLF was found to have the highest potential, among the three ANNs, to achieve a better incident detection performance. The MLF was also tested with limited field data collected from three other freeway locations to explore its transferability. Our results and analyzes with data from the study site as well as the three test sites have shown that the MLF consistently detected most of the lane-blocking incidents and typically gave a false alarm rate lower than the California, McMaster and Minnesota algorithms currently in use.     

Chaotic Motion of Wheels

Trolleys have wheels which can choose the direction of their rolling. Studying the motion of a wheel like this, we can often find periodic motions (“shimmy”) or even chaotic ones. It has also been experienced that the chaotic motions sometimes disappear quite unexpectedly. A strongly simplified model of these systems is analysed in the paper by means of the methods of bifurcation theory. Analytical and numerical results are shown to characterize the system, including simulation results. Similar behaviour can be found in more complicated systems as well, like the trailers or the nose-gears of aeroplanes. The development of the so-called transient chaotic motion is explained in these systems.     

On the structure of weekly activity/travel patterns

Understanding the process of activity scheduling is a critical pre-requisite to an understanding of changes in travel behavior. To examine this process, a computerized survey instrument was developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes an investigation of the structure of activity/travel patterns based on data collected from a pilot study of the instrument. The term “structure” refers to the sequence by which various activities enter one’s daily activity scheduling process. Results of the empirical analyses show that activities of shorter duration were more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Additionally, analysis of travel patterns reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

Integrating Naval Aviation Maintenance Training with Sailors' Proficiency Qualification and Its Correlation with Improved Weapon System Readiness

A core role for human systems integration is providing support of weapon systems operational effectiveness with optimal human performance (HP). When applied to the readiness of an aviation squadron, this focus on enhanced operational HP translates into ensuring that highly trained and proficient maintenance technicians are resident in these warfighting units. A key objective of the Naval Aviation Enterprise (NAE) Total Force Readiness program is to bridge Core Capabilities to Missions. This is accomplished through the linking of Navy Mission Essential Task Lists (NMETLs) to Skill Sets and Performance Standards for its future workforce. These goals can be accomplished from Initial Development to Deployment of a weapons system through its operational and support phases of the Department of Defense acquisition process using a mission/task-centric approach to Manpower, Personnel, and Training (MPT) requirements, with NMETLs as the operational thread. This paper will describe a life-cycle MPT analytic environment with its task-level links to systems and human engineering disciplines. A systematic approach to accessing and training a proficient maintenance workforce is presented, focusing on task-based maintenance training solutions for optimal unit readiness and mission capabilities. An innovative analytic framework from various skills-based manpower analysis studies will be cited as a means to transform system and unit MPT performance for Total Force Readiness. Enhanced by a Qualified and Proficient Technician methodology, a capability (missions) and competency (proficient) future workforce with skills-efficient maintainers will be best suited to achieving optimal readiness of weapons systems within the NAE.     

Forecasting front displacements with a satellite based ocean forecasting (SOFT) system

Relatively long term time series of satellite data are nowadays available. These spatio–temporal time series of satellite observations can be employed to build empirical models, called satellite based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems predicting the sea surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly focussed on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatio–temporal variability is more complex and propagating structures are frequently present. In this case, traditional SOFT systems based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction systems. Instead, SOFT systems based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases.In this work we study and compare the performance of an EOF and CEOF based SOFT systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The SOFT system was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from both SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.     

Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.     

Technical Note: Force Control in Electrohydraulic Active Suspensions Revisited

The inner loop control for the actuator force can be quite simply compensated by means of a correction term to the desired force signal at the input. The correction requires only a measurement of the actuator piston velocity and is employed to cancel the loop disturbance caused by the oil flow to the actuator. Although an additional time constant is introduced by the integration in the inner loop force control, the overall performance is nevertheless quite close to that of an ideal optimal system.     

How much disruption to activities could fuel shortages cause? – The British fuel crisis of September 2000

In September of 2000 the UK experienced a blockade of oil refineries in response to rising fuel prices. These protests resulted in severe fuel supply disruptions that intensified over the course of about one week. During the peak of the crisis, travel activity by car was curtailed. This paper analyzes survey data collected about two months after the crisis utilizing the recent memory of respondents as to how they would expect this sort of disruption to affect their participation in daily activities. Specifically, we focused on a variety of non-discretionary and discretionary activities and examined what factors are associated with respondents expecting disruption to those activities. Statistical models were developed to analyze how demographic factors, commute mode selection, vehicle characteristics, and various other factors can explain how individuals expect disruption to their activities. Results suggest that the majority of individuals do not expect major disruptions, although for more car-dependent individuals, disruption was expected to be substantial, especially for work-related trips. These results have implications for the potential success and benefits of an integrated transport policy.