Abstract: For large-scale automated, water quality monitoring, some physical or chemical parameters are unable to be measured directly due to financial or environmental limitations. As an example, the excess nitrogen run-off can cause severe ecological damage to ecosystems. However, the cost of high accuracy measurement of nitrogen is prohibitive, and one can only measure nitrogen in creeks and rivers at selected locations. If nitrate concentrations are related to some other, more readily measured water parameters, it may be possible to use these parameters (“surrogates”) to estimate nitrogen concentrations. Though one can estimate water quality parameters based on some different, but simultaneously monitored parameters, most surrogate models lack the consideration of spatial variation among monitoring stations. Those models are usually developed based on water quality data from a single station and applied to target stations in different locations for estimating water quality properties. In this case, the different weather, geophysical or biological conditions may reduce the effectiveness of the surrogate model’s performance because the surrogate relationship may not be strong between the source and target stations. We propose a deep surrogate model (DSM) for indirect nitrogen measurement in large-scale water quality monitoring networks. The DSM applies a stacked denoising autoencoder to extract the features of the water quality surrogates. This strategy allows one to utilize all the sensory data across the monitoring network, which can significantly extend the size of training data. For data-driven modeling, large amounts of training data collected from various monitoring stations can substantially improve the generalization of the DSM. Furthermore, instead of only learning the regression relationship between water quality surrogates and the nitrogen concentration in the source stations, the DSM is designed to gain the sensor data distribution differences between the source and target stations by calculating the Kullback-Leibler divergence. In this approach, the training of DSM can be guided by acknowledging the information from the target station. Therefore, the performance of the DSM approached will be significantly higher than source station-based approaches. It is because of that the surrogate relationship learned by the DSM includes the diversity among monitoring stations. We evaluate the DSM by using real-world time series data from a wireless water quality monitoring network in Australia. Compared to models based on Support Vector Machine and Artificial Neural Network, the DSM achieves up to 49.0\% and 42.4\% improvements regarding the RMSE and MAE respectively. Hence, the DSM is an attractive strategy for generating the estimated nitrogen concentration for large-scale environmental monitoring projects.

Abstract: Six degrees of freedom coupled simulation is presented for a tilt-rotor plane represented by V-22 Osprey. The Moving Computational Domain (MCD) method is used to compute a flow field around aircraft and the movement of the body with high accuracy. This method enables to move a plane through space without restriction of computational ranges. Therefore it is different from computation of such the flows by using conventional methods that calculate a flow field around a static body placing it in a uniform flow like a wind tunnel. To calculate with high accuracy, no simplification for simulating propeller was used. Fluid flows are created only by moving boundaries of an object. A tilt-rotor plane has a hovering function like a helicopter by turning ax-es of rotor toward the sky during takeoff or landing. On the other hand in flight, it behaves as a reciprocating aircraft by turning axes of rotor forward. To per-form such two flight modes in the simulation, multi-axis sliding mesh approach was proposed which is a computational technique to enable us to deal with multiple axes of different direction. Moreover, using in combination with the MCD method, the approach has been able to be applied to the simulation which has more complicated motions of boundaries.

Abstract: The introduction of AVs will have far-reaching effects on road traffic in cities and on highways. The implementation of AHS, possibly with a dedicated lane only for AVs, is believed to be a requirement to maximise the benefit from the advantages of AVs. We study the ramifications of an increasing percentage of AVs on the whole traffic system with and without the introduction of a dedicated highway AV lane. We conduct a macroscopic simulation of the city of Singapore under user equilibrium conditions with realistic traffic demand. We present findings regarding average travel time, throughput, road usage, and lane-access control. Our results show a reduction of average travel time as a result of increasing the portion of AVs in the system. We show that the introduction of an AV lane is not beneficial in terms of average commute time. Furthermore a notable shift of travel demand away from the highways towards major and small roads is noticed in early stages of AV penetration of the system. Finally, our findings show that after a certain threshold percentage of AVs the differences between AV and no AV lane scenarios become negligible.

Abstract: A key metric in the design of interior layouts of public transport vehicles is the dwell time required to allow passengers to board and alight. Real-world experimentation using physical vehicle mock-ups and involving human participants can be performed to compare dwell times among vehicle designs. However, the associated costs limit such experiments to small numbers of trials. In this paper, we propose an agent-based simulation model of the behavior of passengers during boarding and alighting. High-level strategical behavior is modeled according to the Recognition-Primed Decision paradigm, while the low-level collision-avoidance behavior relies on an extended Social Force Model tailored to our scenario. To enable successful navigation within the confined space of the vehicle, we propose a mechanism to emulate passenger turning while avoiding complex geometric computations. We validate our model against real-world experiments from the literature, demonstrating deviations of less than 11%. In a case study, we evaluate the boarding and alighting times required by three autonomous vehicle interior layouts proposed by industrial designers.

Abstract: Multi-Label user profile is widely used and have made great contributions in the field of recommendation systems, personalized searches, etc. Current researches on multi-label user profile either ignore the associations among labels or only consider the explicit associations among them, which are not sufficient to take full advantage of the internal associations. In this paper, a new insight is presented to mine the internal correlation among implicit association labels. To take advantage of this insight, a multi-label propagation method with implicit associations (MLP-IA) is proposed to get user profile. A probability matrix is first designed to rec-ord the implicit associations and then combine the multi-label propagation method with this probability matrix to get more accurate user profile. Finally, this method proves to be convergent and faster than traditional label propagation algorithm. Experiments on six real-world datasets in Weibo show that, compared with state-of-the-art methods, our approach can accelerate the convergence and its perfor-mance is significantly better than the previous ones.