AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features

Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. We believe that AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency.Comment: ICPR 202

Mooring line fatigue damage evaluation for floating marine energy converters: Field measurements and prediction

publication-status: Publishedtypes: ArticleThe vision of large-scale commercial arrays of floating marine energy converters (MECs) necessitates the robust, yet cost-effective engineering of devices. Given the continuous environmental loading, fatigue has been iden- tified as one of the key engineering challenges. In particular the mooring sys- tem which warrants the station-keeping of such devices is subject to highly cyclic, non-linear load conditions, mainly induced by the incident waves. To ensure the integrity of the mooring system the lifecycle fatigue spec- trum must be predicted in order to compare the expected fatigue damage against the design limits. The fatigue design of components is commonly as- sessed through numerical modelling of representative load cases. However, for new applications such as floating marine energy converters numerical models are often scantily validated. This paper describes an approach where load measurements from large- scale field trials at the South West Mooring Testing Facility (SWMTF) are used to calculate and predict the fatigue damage. The described procedure employs a Rainflow cycle analysis in conjunction with the Palmgren-Miner rule to estimate the accumulated damage for the deployment periods and individual sea states. This approach allows an accurate fatigue assessment and prediction of mooring lines at a design stage, where field trial load measurements and wave climate information of potential installation sites are available. The mooring design can thus be optimised regarding its fatigue life and costly safety factors can be reduced. The proposed method also assists in monitoring and assessing the fatigue life during deployment periods

Corporate sustainability reporting index and baseline data for the cruise industry

Sustainability policies and corporate reports demonstrate the impacts cruise companies acknowledge as their responsibility, and the actions put in place to address them. This paper develops a corporate social responsibility index based on the Global Reporting Initiative, with industry specific additions including labor and human rights, health and safety, and environmental and economic aspects. Companies disclose more management than performance data, which is typical of early stages of development. Companies disclosing less information focus on soft indicators which are easy to mimic and demonstrate posturing. Items disclosed tend to be marginal to the core of the business, have a positive economic impact or pre-empt sector regulation. Reports echo the voice of the corporations and not the demands of stakeholders. Institutional isomorphism has not influenced a homogenization in reporting, with only the largest firms reporting at this stage

Energy-water-food nexus in the Spanish greenhouse tomato production

The nexus energy–water–food of the tomato greenhouse production in the Almeria region (Spain) has been studied following a Process Systems Analysis Method connecting the ecosystem services to the market demands with a holistic view based on Life Cycle Assessment. The management of the agri-food subsystem, the industrial subsystem and the urban subsystem plays an important role in the nexus of the E–W–F system, where transport and information technologies connect the three subsystems to the global markets. The local case study of the tomato production in Almeria (Spain) has been developed as an example of the food production under cropland restrictions, semiarid land. After study of the economic and social sustainability in time, the evolution of the ecosystem services supply is the main restriction of the system, where after the land use change in the region, water and energy supply play the mean role with a trade-off between the water quality degradation and the economic cost of the energy for water desalination. Water footprint, Carbon footprint and Chemicals footprint are useful indicators to the environmental sustainability assessment of local alternatives in the E–W–F system under study. As it is shown in the conclusions, the holistic view based on the process analysis method and the life cycle assessment methodology and indicators is an useful tool for decision support

Turkey's Long-Term Electricity Consumption Forecast

1336-1341Demand forecasting is essential primarily for planning. Although it is crucial in many sectors and issues, it has particular importance for electricity. Therefore, the issue of electricity consumption forecasting has recently become a prevalent topic. In light of the above, this study aimed to develop an appropriate model to estimate the long-term electricity consumption of Turkey. The study consists of three steps. In the first step, eight models were developed to separately investigate the effects of eight input variables frequently used in electricity consumption forecasting studies in the literature. In the second step of the study, two models consisting of input variables with high impact in the first step were developed, and the trained performances of the developed models were calculated by using the regression analysis. In the final step, the combined effect of eight variables on electricity consumption forecasting was investigated using regression analysis. It can be conclude that the model in the third step showed significant results, and the model performance was good. Finally, Turkey's electricity consumption forecast for the years 2020–2030 was performed using the model in the third step

Long Memory Properties in Return and Volatility: An Application of the Impact of Arab Spring in Turkey Financial Market

Abstract: The Arab Spring which began on 17 December 2010 with the civil rebellions, revolutionary wave of demonstrations and protests in the Tunisia, Egypt, Libya, Yemen, Bahrain and Syria. The Arab Spring not only created a domino effect between Arabic countries but also it reflected a significant influence on the financial markets all over the world. The objective of this study is to analyze the impact of the Arab Spring in Turkey Financial Market in consideration of long memory. Long memory can be defined as the persistence of the unexpected shocks on the underlying has long lasting effects. Modeling long memory in stock returns and volatility has also attracted great deal of attention from finance literature recently. Existence of long memory is determined both for the returns and volatility of the time series by using different methods. Existence of long memory can be tested by Rescaled Range Statistics (R/S), Geweke and Porter-Hudak (GPH) Model and Gaussian Semi Parametric (GSP) Method. In consequence of these tests, if the stock returns have long memory affect then respectively Fractionally Integrated Autoregressive Moving Average Model (ARFIMA) and the Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH) model are used to detect the long memory in respectively return and volatility. In this study, the impact of the Arab Spring is investigated by modeled the long memory in Istanbul Stock Exchange using ISE 30 index prices in betwee