A study on parking supply optimization in central business districts considering the two way interaction between car traveling and parking

This paper proposes a bilevel programming model to optimize the parking supply in a central business district (CBD) considering the interaction between car traveling and car parking. The upper model (UM) determines the number and locations of parking lots in a CBD with the objective of minimizing the average impedance of all car trips. The lower model (LM) is a modal split and assignment combination model for calculating the traffic flow under various parking supply schemes. In addition to the bilevel model, a gravity model (GM) is proposed to calculate the car trips that are induced by the added parking lots. The interaction between car traveling and parking can be simulated by the feedbacks between the UM and LM. A case study is performed with real data from Dalian City. The results show that there is a negative correlation between parking supply increments and the average traveling impedance when the number of parking spaces is lower than the optimal value; however, the average traveling impedance will start to increase with the increase in parking supply when the number of parking spaces is higher than the optimal value

Novel resists for next generation lithography

With progress in the semiconductor industry, transistor density on a single computer chip has increased dramatically. This has resulted in a continuous shrinkage of the minimum feature size printed through microlithography technology. Resist, as the pattern recording medium of such printing, has been extensively studied to achieve higher resolution, higher sensitivity and lower line edge roughness. For decades this has been realized through chemical amplification. With the feature size continuously shrinking and the energy of exposure source therefore exceeding the resist ionization threshold, the performance of conventional chemically amplified resists is approaching the limits. Novel high-performance chemically amplified resists or non-chemically amplified resists are urgently needed to meet the requirement of next generation lithography. In this work a negative tone chemically amplified resist system based on a novel method to control the catalytic chain reaction is presented. The method to control the catalytic chain reaction is demonstrated using two model polymer resists. This method is then applied to a fullerene-based molecular resist system and a combination of good industrial compatibility, high resolution and good sensitivity has been achieved in this resist. Through a chromatographic separation, another chemically amplified molecular resist was also developed with further improved performance. An alternative route to sensitivity improvement other than chemical amplification is then introduced and a family of fullerene-based metal containing materials is presented. Lithographic performance is compared between the fullerene-metal resists and their control materials without metal. Using an aberration corrected scanning transmission electron microscope, the distribution of metal in the resist film and its behavior during the lithography process is evaluated and discussed

Study of L0-norm constraint normalized subband adaptive filtering algorithm

Limited by fixed step-size and sparsity penalty factor, the conventional sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms suffer from trade-off requirements of high filtering accurateness and quicker convergence behavior. To deal with this problem, this paper proposes variable step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs) for sparse system identification. We first analyze mean-square-deviation (MSD) statistics behavior of the L0-NSAF algorithm innovatively in according to a novel recursion form and arrive at corresponding expressions for the cases that background noise variance is available and unavailable, where correlation degree of system input is indicated by scaling parameter r. Based on derivations, we develop an effective variable step-size scheme through minimizing the upper bounds of the MSD under some reasonable assumptions and lemma. To realize performance improvement, an effective reset strategy is incorporated into presented algorithms to tackle with non-stationary situations. Finally, numerical simulations corroborate that the proposed algorithms achieve better performance in terms of estimation accurateness and tracking capability in comparison with existing related algorithms in sparse system identification and adaptive echo cancellation circumstances.Comment: 15 pages,15 figure

Intention-Aware Decision-Making for Mixed Intersection Scenarios

This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner that it can understand pedestrians in urban traffic and can react accordingly to their intentions. That way, a human-like response to the actions of the pedestrian is ensured, leading to a higher acceptance of AVs. The core notion of this paper is that the intention prediction of the pedestrian to cross the street and decision-making are divided into two subsystems. On the one hand, the intention detection is a data-driven, black-box model. Thus, it can model the complex behavior of the pedestrians. On the other hand, the decision-making is a white-box model to ensure traceability and to enable a rapid verification and validation of AVs. This white-box decision-making provides human-like behavior and a guaranteed prevention of deadlocks. An additional benefit is that the proposed decision-making requires low computational resources only enabling real world usage. The automated parameterization uses a particle swarm optimization and compares two different models of the pedestrian: The social force model and the Markov decision process model. Consequently, a rapid design of the decision-making is possible and different pedestrian behaviors can be taken into account. The results reinforce the applicability of the proposed intention-aware decision-making