Active noise control in a duct system based on a frequency-estimation algorithm and the FX-LMS algorithm

In this paper, an indirect feedback active noise control (ANC) scheme, based on the fundamental frequency-estimation method, is proposed for systems with multiple tonal noises. The engine noise consists of the harmonic components of the rotation of the crankshaft in the engine. When it is difficult to obtain reference signals, which are necessary for a feedforward ANC, conventional ANC algorithms do not work. In this paper, a new method is proposed to generate reference signals with estimated frequencies. The proposed algorithm consists of two parts: the frequency-estimation (FE) algorithm for the estimation of the fundamental frequency of the rotating machinery and then the conventional filtered-x LMS (FX-LMS) algorithm. In the second algorithm, the reference signal is generated using the fundamental frequency as estimated in the first algorithm. The FE algorithm uses a second-order adaptive notch filter, which is insensitive to impulse noise. In addition, the FE algorithm has good tracking capability and a lower variance of frequency-estimation error for a constant sinusoid signal and chirp signal. The performance of the proposed ANC method is verified through simulations and an experiment using a DSP board (DS-1104) inside a short duct.     

Optimizing the transportation of international container cargoes in Korea

This paper considers a multimodal transportation problem, which is the problem of determining the transportation flow, i.e. volume of container cargoes, and the transportation mode in each trade route, for the objective of minimizing the sum of shipping and inland transportation costs. The problem takes account of two restrictions: maximum cargo volumes capacitated at each seaport and maximum number of vehicles available at each transportation mode. To solve optimally the problem, this paper employs a mixed integer programming, which is an operations research technique. A case study is performed on the container cargo data in Korea and we draw several implications to improve efficiency in the transportation of international trade cargoes in Korea.     

Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models

Abstract

Online traffic flow modeling is of increasing importance due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper, the dynamic flow-density relation is identified based on the classification of traffic states and is quantified employing fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.     

A comparative study of aggregate and disaggregate gravity models using Seoul metropolitan subway trip data

Abstract

Existing origin constrained and doubly constrained gravity models have not been compared, theoretically or empirically, in terms of their forecasting power. Due to the newly advanced technology of intelligent transport systems, the expanded data presently available have made various models more comparable in terms of forecasting power. This paper uses archived automatic passenger counting (APC) data for urban rail in the Seoul metropolitan area. The APC data contains information about each trip's origin, destination, ticket type, fare, and distance on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate gravity modeling using these data. A Hyman aggregate gravity model is used as the aggregate model without the spatial effect. The disaggregate model adopts a multinomial logit as the destination choice model with the spatial effect. In general, while the formulation of aggregate and disaggregate gravity model models are similar, the calibration and parameter estimation methods of the two models are different. As a result, this empirical study demonstrates that the variation in goodness-of-fit and forecasting power largely depends on the estimation method and selected variables. The forecasting power of the disaggregate modeling approach outperforms that of the aggregate model. This paper further confirms that spatial arrangement plays important roles in gravity modeling.     

A structural equation model of activity participation and travel behavior using longitudinal data

Abstract

This paper presents a dynamic structural equation model (SEM) that explicitly addresses complicated causal relationships among socio-demographics, activity participation, and travel behavior. The model assumes that activity participation and travel patterns in the current year are affected by those in previous years. Using the longitudinal dataset collected from Puget sound transportation panel ‘wave 3’ and ‘wave 4,’ these assumptions are tested with suggested SEMs. Within each wave, the model is structured to have a three-level causal relationship that describes interactions among endogenous variables under time-budget constraints. The resulting coefficients representing the activity durations indicate that people tend to allocate their time according to the importance and the obligation of the activity level. Results from the dynamic SEM confirm the fact that people's current activity and travel behavior do have effects on those in the future. The resulting model also shows that activity participation and travel behavior in ‘wave 3’ are closely related to those in ‘wave 4.’ These explicit explanations of relationships among variables could provide important perspectives in the activity-based approach which becomes recognized as a better analytical tool for the transportation planning and policy making process.     

Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation

Although various theories have been adopted to develop reliable pedestrian walking models, a limited effort has been made to calibrate them rigorously based on individual trajectories. Most researchers have validated their models by comparing observed and estimated traffic flow parameters such as speed, density, and flow rate, or replaced the validation by visual confirmation of some well-known phenomena such as channelization and platooning. The present study adopted maximum likelihood estimation to calibrate a social-force model based on the observed walking trajectories of pedestrians. The model was assumed to be made up of five components (i.e., inertia, desired direction, leader–follower relationship, collision avoidance, and random error), and their corresponding coefficients represented relative sensitivity. The model also included coefficients for individual-specific characteristics and for a distance-decay relationship between a pedestrian and his/her leaders or colliders. The calibration results varied with the two density levels adopted in the present study. In the case of high density, significant coefficient estimates were found with respect to both the leader–follower relationship and collision avoidance. Collision avoidance did not affect the pedestrian’s walking behavior for the low-density case due to channelization. The distance limit was confirmed, within which a pedestrian is affected by neighbors. At the low-density level, by comparison with women, men were found to more actively follow leaders, and pedestrians walking in a party were found to be less sensitive to the motion of leaders at the high-density level.     

Relationship between phytoplankton bloom and wind stress in the sub-polar frontal area of the Japan/East Sea

The seasonal dynamics of phytoplankton blooms in the central Japan/East Sea (JES) show pronounced year-to-year variability based on Sea-viewing Wide Field-of-view Sensor (SeaWiFS; 19972003) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra (20002003) observations. Wind seems to strongly influence this variability. To study the relationship between wind and bloom initiation, we analyzed daily, remotely sensed wind stress data (Active Microwave Instrument–wind [AMI–wind], NASA Scatterometer [NSCAT], and Quick Scatterometer [QuickSCAT]: 19972003) and daily chlorophyll concentrations based on ocean color data (SeaWiFS and MODIS). The results agreed well with the hypotheses; in spring, blooms began 615 days after wind stress weakened. Fall blooms started 39 days after wind strengthened. We also simulated seasonal changes using a simple light–nutrient model using two values for the respiration ratio: 10% and 20%. The use of 20% seemed to reproduce the timing of the spring bloom quite well but underestimated the absolute level of chlorophyll concentration. On the other hand, using 10% produced a better estimation of the chlorophyll concentration but failed to match the timing. Neither of the model runs reproduced the timing of the fall bloom well.     

Performance characteristics of a supplementary stack-cooling system for fuel-cell vehicles using a carbon dioxide air-conditioning unit

In fuel-cell-powered vehicles, the fuel-cell system requires a thermal-management subsystem to dissipate heat released during the reaction of hydrogen with oxygen. When the stack generates power at a high rate, a large amount of heat is also generated. If cooling by the radiator is insufficient, a supplementary stack-cooling system is needed to maintain a safe operating temperature. In this study, the performance of a CO₂ air-conditioning unit for stack cooling was investigated under various conditions, and the relationship between cabin cooling and stack cooling was also studied. The coefficient of performance (COP) increased from 1.9 to 2.4, with an increase in cabin-air inlet flow rate from 0 to 8 m³/min. When the air-conditioning unit was turned off, the cooling capacity of the stack cooler was increased; correspondingly, as the cabin-cooling capacity was increased, that of the stack cooler decreased. With an increase in ambient-air inlet temperature from 38°C to 45°C, the COP decreased by 24%. Additionally, both the stack-cooling capacity and cabin-cooling capacity were decreased by about 12% and 16%, respectively, due to reduced heat transfer in the gas cooler as the ambient air inlet temperature was increased. It is expected that the experimental results can serve as a resource in designing a stack-cooling system using a CO₂ air-conditioning unit to enhance stack power generation and efficiency.     

Stochastic analysis of the variation in injury numbers of automobile frontal crash tests

Although automobile crash test data have a comparatively large variation because of the complexity of the tests, only a limited number of crash tests are usually conducted due to monetary and time limitations. Thus, it is necessary to control input variables that cause the variation in test data to obtain consistent crash test results and to correctly assess the safety performance of an automobile under development. In this study, a MADYMO model was validated deterministically to yield the head, chest, pelvis deceleration pulses of anthropomorphic test devices and the belt load pulses similar to those from actual tests, and it was also validated stochastically to yield means and standard deviations of the head and chest injury numbers, i.e., HIC ₁₅ and 3 msec clip similar to those from actual tests. A stochastic analysis was conducted using the validated MADYMO model to calculate the sensitivity of the standard deviations of the injury numbers to the standard deviations of influential input variables to determine the most influential input variable that makes the largest contribution to the variation in the injury numbers. Moreover, the Taguchi approach was used to determine the optimal values of the influential input variables to improve safety performance.     

Age and cross-cultural comparison of drivers’ cognitive workload and performance in simulated urban driving

Driving demands significant psychomotor attention and requires even more when drivers are engaged in secondary tasks that increase cognitive workload and divert attention. It is well established that age influences driving risk. Less is known about how culture impacts changes in attention. We conducted parallel driving simulations in the US and Korea to measure the extent to which age and culture influence dual-task performance. There were 135 participants divided into two groups: a younger group aged 20∼29, and an older group aged 60∼69. Whereas some differences by culture appeared in absolute control measures, the younger participants showed similar mean velocity and compensatory patterns associated with increased cognitive load in the urban setting; however, the results from the older samples were less similar.