Automated Discovery of Network Cameras in Heterogeneous Web Pages

Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks. Network cameras offer real-time visual data that can be used for studying traffic patterns, emergency response, security, and other applications. Although many sources of Network Camera data are available, collecting the data remains difficult due to variations in programming interface and website structures. Previous solutions rely on manually parsing the target website, taking many hours to complete. We create a general and automated solution for aggregating Network Camera data spread across thousands of uniquely structured web pages. We analyze heterogeneous web page structures and identify common characteristics among 73 sample Network Camera websites (each website has multiple web pages). These characteristics are then used to build an automated camera discovery module that crawls and aggregates Network Camera data. Our system successfully extracts 57,364 Network Cameras from 237,257 unique web pages.

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such ‘free’ model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process – the genetic algorithm (GA) – has become especially popular. As applied to modeling, GAs represent a Darwinian process – an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as ‘parents’ who pass their parameters onto a new generation. At the Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to ‘score’ the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions.

Strengthening Military Defense Resources to Non-Military in Facing Nuclear Emergencies to Support National Defense

Indonesia has the potential for a nuclear emergency, so it is necessary to prepare resources to deal with nuclear emergencies to minimize losses. A nuclear emergency caused by a nuclear reactor accident is a non-military defense sector as the main component supported by other elements of the nation's power. Nuclear Biology and Chemical Company of the Indonesian Armed Forces Army (Kizinubika) is another element of the nation's power that provides reinforcement in non-military defense in the face of nuclear emergencies. The purpose of this study is to strengthen the Kizinubika resources for the Nuclear Energy Supervisory Agency (Bapeten) and the Directorate for the Management of Nuclear Facilities at the National Research and Innovation Agency (DPFKN-BRIN) in dealing with nuclear emergencies in order to support national defense. This type of research is qualitative by using literature study, observation, and interview methods. Internal resource criteria are determined based on the Resources Based View (RBV) theory. The results of the study in the form of recommended resources in strengthening the Kizinubika against Bapeten and DPFKN-BRIN in the form of; (1) The use of the Kizinubika facility as a joint training facility and infrastructure; (2) The use of special equipment Kizinubika in support of nuclear emergency response; (3) Kizinubika's strategic location close to DPFKN-BRIN supports speed in emergency response; (4) Use of Kizinubika's Human Resources through joint training in increasing the quantity and quality of training; and (5) Kizinubika's internal organizational relations support the task of dealing with nuclear emergencies.