EDEN is the Engine for DEfinitive Notations. It is the primary software tool of the Empirical Modelling research group. We build models with it, using a variety of definitive notations that it implements.
The main characteristic to be aware of in these tools is that BE is primarily rule-based (using an embedded rule engine), whereas BW and iProcess are orchestration / flow engines. In BE we can use a state diagram to indicate a sequence of states which may define what process / rules apply, but this is really just another way of specifying a particular type of rules (i.e. state transition rules).
The main advantages to specifying behavior as declarative rules are:
Handling complex, event-driven behavior and choreography
Iterative development, rule-by-rule
The main advantages of flow diagrams and BPMN-type models are:
Ease of understanding (especially for simpler process routes)
Process paths are pre-determined and therefore deemed guaranteeable.
In combination these tools provide many of the IT capabilities required in an organization. For example, a business automation task uses BW to consolidate information from multiple existing sources, with human business processes for tasks such as process exceptions managed by iProcess. BE is used to consolidate (complex) events from systems to provide business information, or feed into or drive both BW and iProcess, and also monitors end-to-end system and case performance.
Transport in Porous Media publishes original research on the physical and chemical aspects of transport of extensive quantities such as mass of a fluid phase, mass of a component of a phase, momentum and energy, in single and multiphase flow in a (possibly deformable) porous medium domain. These are presented in the context of chemical, civil, agricultural, petroleum and mechanical engineering.
Transport phenomena, understood from the microscopic scale upward, form the basis for deterministic and stochastic models that describe them. The models are adaptable to describe flow and contaminant transport in aquifers; oil and gas movement in petroleum reservoirs; solvent drives and enhanced oil recovery; heat and mass transport in packed bed reactors in chemical engineering, in geothermal reservoirs and in building materials; spread of pollutants from radioactive waste repositories; filtration processes, and biomedical studies of fluid and chemical transport in lungs and other organs.
We propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small turbulence. It is particularly useful for motion detection from outdoor videos with low quality. It can also effectively delineate moving objects in both static and dynamic background. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.
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It’s super awesome to see a lot of libraries starting to adopt flow to add type-safetiness to their code… BUT… what a lot of people forget is that npm packages usually ship ES5 code without any type…