ASSESSMENT OF POTENTIAL MARINE CURRENT ENERGY IN THE STRAITS OF THE LESSER SUNDA ISLANDS

The Lesser Sunda Islands extend from Bali to Timor and consist of two geologically distinct parts formed by a subduction system of oceanic crust along the Java-Timor Trench. The northern part which includes Bali, Lombok, Sumbawa, Flores, Wetar, Pantar and Alor, is volcanic in origin; whilst the southern part is non-volcanic, encompassing the islands of Sumba, Timor and Rote. The straits along the Lesser Sunda Islands are formed as a result of very complex geological processes and tectonics in this area. These straits are the most important cross-sections in the southern part of the Indonesian Throughflow (ITF), functioning as outlets for the mass flows of seawater from the Pacific Ocean to the Indian Ocean through the Flores and the Savu Seas. In these straits, relatively high current speeds are occurred, not only caused by the ITF but also due to its geometry, the influence of tidal flow, and monsoonal currents.Site study and ocean current measurement were conducted by using an echosounder, a pair of Acoustic Doppler Current Profilers (ADCP), and other supporting equipment. In general, the average of most ocean current speeds is less than 1.5 m/s with a duration flow of 8 -12 hours a day, and the maximum speed reaches up to 3 m/s. The tidal types in almost all the straits are mixed semidiurnal tides, in which two high waters and two low waters occur twice a day, with the high and low tides differ in height.The Lesser Sunda Straits were selected as the potential sites for ocean current power plant because their current speeds are relatively high and their characteristics are more predictable compared with other straits from other regions. Based on the results of bathymetry survey and current characteristics from the deployed ADCP at a fixed (stationary) location on the seabed, the best location for the current power turbines is at the depth of 15-30 m where the seabed gently sloping.

Selecting Time Series Length to Moderate the Impact of Nonstationarity in Extreme Rainfall Analyses

Observed and projected increases in the frequency of extreme rainfall complicate the extreme value analyses of precipitation that are used to guide engineering design specifications, because conventional methods assume stationarity. Uncertainty in the magnitude of the trend in future years precludes directly accounting for the trend in these analyses. While previous extreme value analyses have sought to use as long a record as possible, it is shown using stochastically generated time series that this practice exacerbates the potential error introduced by long-term trends. For extreme precipitation series characterized by a trend in the location parameter exceeding approximately 0.005% yr−1, limiting the record length to fewer than 70 years is recommended. The use of longer time periods results in partial-duration series that are significantly different from their stationary counterparts and a greater percentage of rainfall extremes that exceed the 90% confidence interval corresponding to a stationary distribution. The effect is most pronounced on the shortest (i.e., 2 yr) recurrence intervals and generally becomes undetectable for recurrence intervals of more than 25 years. The analyses also indicate that the practice of including stations with records of limited length that end several decades prior to the present should be avoided. Distributions having a stationary location parameter but trended scale parameter do not exhibit this behavior.

Where Have You Been? Inferring Career Trajectory from Academic Social Network

A person’s career trajectory is composed of her/his past work or educational affiliations (institutions) at different points of times. Knowing people’s, especially scholars’, career trajectories can help the government make more scientific strategies to allocate resources and attract talent and help companies make smart recruiting plans. It could also support individuals find appropriate co-researchers or job opportunities. The paper focuses on inferring career trajectories in the academic social network. For about 1/3 of authors not having any affiliations in the dataset, we need to infer the missings at various years. Traditional affiliation/location inferring methods focus on inferring a stationary location (one and only) for a person. Nevertheless, people won’t stay at a place all their lives. We propose a Space-Time Factor Graph Model (STFGM) incorporating spatial and temporal correlations to fulfill the challenging and new task of inferring temporal locations. Experiments show our approach significantly outperforms baselines. At last, as case study, we develop several applications based on our approach which demonstrate the effectiveness further.

Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance

Ionospheric data assimilation is a powerful approach to reconstruct the 3-D distribution of the ionospheric electron density from various types of observations. We present a data assimilation model for the ionosphere, based on the Gauss–Markov Kalman filter with the International Reference Ionosphere (IRI) as the background model, to assimilate two different types of slant total electron content (TEC) observations from ground-based GPS and space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for the background model error and observational error play important roles in data assimilation. The objective of this study is to investigate impacts of stationary (location-independent) and non-stationary (location-dependent) classes of the background model error covariance on the quality of assimilation analyses. Location-dependent correlations are modeled using empirical orthogonal functions computed from an ensemble of the IRI outputs, while location-independent correlations are modeled using a Gaussian function. Observing system simulation experiments suggest that assimilation of slant TEC data facilitated by the location-dependent background model error covariance yields considerably higher quality assimilation analyses. Results from assimilation of real ground-based GPS and F3/C radio occultation observations over the continental United States are presented as TEC and electron density profiles. Validation with the Millstone Hill incoherent scatter radar data and comparison with the Abel inversion results are also presented. Our new ionospheric data assimilation model that employs the location-dependent background model error covariance outperforms the earlier assimilation model with the location-independent background model error covariance, and can reconstruct the 3-D ionospheric electron density distribution satisfactorily from both ground- and space-based GPS observations.

Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance

Ionospheric data assimilation is a powerful approach to reconstruct the 3-D distribution of the ionospheric electron density from various types of observations. We present a data assimilation model for the ionosphere, based on the Gauss–Markov Kalman filter with the International Reference Ionosphere (IRI) as the background model, to assimilate two different types of total electron content (TEC) observations from ground-based GPS and space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for the background model error and observational error play important roles in data assimilation. The objective of this study is to investigate impacts of stationary (location-independent) and non-stationary (location-dependent) classes of the background model error covariance on the quality of assimilation analyses. Location-dependent correlations are modeled using empirical orthogonal functions computed from an ensemble of the IRI outputs, while location-independent correlations are modeled using a Gaussian function. Observing System Simulation Experiments suggest that assimilation of TEC data facilitated by the location-dependent background model error covariance yields considerably higher quality assimilation analyses. Results from assimilation of real ground-based GPS and F3/C radio occultation observations over the continental United States are presented as TEC and electron density profiles. Validation with the Millstone Hill incoherent scatter radar data and comparison with the Abel inversion results are also presented. Our new ionospheric data assimilation model that employs the location-dependent background model error covariance outperforms the earlier assimilation model with the location-independent background model error covariance, and can reconstruct the 3-D ionospheric electron density distribution satisfactorily from both ground- and space-based GPS observations.

A Quick Extraction Algorithm Using Window-Scanning for Moving Vehicles

Aiming at the particularity of traffic monitoring video sequences and the regularity of vehicle movement, a quick extraction algorithm using window-scanning for moving vehicles in traffic monitoring videos is proposed in this paper. This algorithm uses hypothesis testing to higher order statistics of frame differences to achieve the rough separation of moving vehicles and background. Then obtain the length of the vehicle and extract the vertical coordinates of the initial point of moving vehicle by setting a static window with a stationary location, combining with the velocity of the vehicle and the moving pixel distribution probability in the window. And obtain the width of the vehicle the horizontal coordinates of the initial point of moving vehicle by setting a dynamic window, combining with the distribution probability of moving pixels in the window. Finally this algorithm achieved the quick extraction of vehicles with the window obtained by length and width, combining with the coordinates of the initial point of moving vehicle. Experiments show the feasibility of the algorithm that the time and space efficiency is relatively high.

APPLICATION OF COMPUTER SIMULATION TO CONSTRUCTION OF INCREMENTAL LAUNCHING BRIDGES

Construction of bridges is associated with uncertainties that rise due to unavailability of resources, equipment breakdown and/or working environment. Bridge construction techniques can be grouped into six main categories: 1) cast‐in‐situ on false work, 2) cantilever carriage, 3) stepping formwork, 4) launching girder, 5) pre‐cast balanced cantilever, and 6) incremental launching. The latter technique is characterised by minimising the use of falsework. Further, the fabrication and casting of bridge segments are executed at a stationary location, named casting yard (which includes several facilities), deck form, concrete mixing unit, and pumping system. This paper presents a special purpose simulation model to capture the uncertainty associated with bridge construction. The model accounts for the interaction between the different involved resources in construction of bridges using incremental launching technique. The paper describes two methods (single form and multiple forms) of execution used for the segments fabrication. The proposed simulation model utilises STROBOSCOPE as a simulation engine and is coded by Visual Basic 6.0. An actual case study is presented to illustrate the capabilities of the developed model and validate its performance.