The present invention relates to monitoring system and, more specifically, to a system for multiscale monitoring using coarse grained, multilayer flow information dynamics.
Monitoring systems are often employed to observe and monitor a wide variety of complex systems. While monitoring a single data source is seemingly simple, monitoring large-scale, heterogeneous data sources can be incredibly complicated and is subject to error. Some prior art (see the List of Incorporated Literature References, Literature Reference No. 4) was developed to provide a new capability to analyze multiple heterogeneous data sources with a multilayer information dynamic framework, whereas other prior art (see Literature References No. 2 and 5) considered only single data sources.
While attempts have been to make sense of large-scale, heterogeneous data, the prior art still lacks the ability to extend the multilayer information dynamic framework to model multiple scales (e.g. spatial scales), so that the limited computation resources can be efficiently utilized. Thus, a continuing need exists for a system for multiscale monitoring using coarse grained, multilayer flow information dynamics to allow for efficient resource allocation.
This disclosure provides a system for multiscale monitoring. In various aspects, the system comprises one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform several operations, such as receiving surveillance data of a scene having a plurality of zones, the surveillance data having an object flow tensor V indicating a number of objects flowing from one zone to another zone at time t and an object communication tensor C indicating a number of communications sending from one zone to another zone at time t; determining a cluster membership of the plurality of zones; determining dependency links between communications and flows; designating at least one cluster of one or more zones as a region of interest based on the dependency links; and controlling a device based on the region of interest.
In another aspect, determining a cluster membership of the plurality of zones further comprises operations of constructing an adjacency matrix A based on the object flow tensor V; symmetrizing the adjacency matrix A; solving nonnegative matrix factorization of the symmetrized adjacency matrix; and assigning cluster membership of the objects in each of the plurality of zones to generate the cluster membership.
In yet another aspect, determining dependency links between communications and flows further comprises operations of constructing a low-resolution flow tensor based on the cluster membership by merging vessel flows V within each cluster; determining flow transfer entropy; and identifying dependency links and dependent clusters by thresholding.
Additionally, designating at least one cluster of one or more zones as a region of interest based on the dependency links includes designating the dependent clusters as regions of interest.
In another aspect, controlling a device based on the region of interest further comprises causing an unmanned aerial vehicle to move to the region of interest.
Further, controlling a device based on the region of interest further comprises causing surveillance apparatus in a satellite to zoom into the region of interest.
Finally, the present invention also includes a computer program product and a computer implemented method. The computer program product includes computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors, such that upon execution of the instructions, the one or more processors perform the operations listed herein. Alternatively, the computer implemented method includes an act of causing a computer to execute such instructions and perform the resulting operations.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The objects, features and advantages of the present invention will be apparent from the following detailed descriptions of the various aspects of the invention in conjunction with reference to the following drawings, where:
The present invention relates to monitoring system and, more specifically, to a system for multiscale monitoring using coarse grained, multilayer flow information dynamics. The following description is presented to enable one of ordinary skill in the art to make and use the invention and to incorporate it in the context of particular applications. Various modifications, as well as a variety of uses in different applications will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to a wide range of aspects. Thus, the present invention is not intended to be limited to the aspects presented, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
In the following detailed description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without necessarily being limited to these specific details. In other instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present invention.
The reader's attention is directed to all papers and documents which are filed concurrently with this specification and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference. All the features disclosed in this specification, (including any accompanying claims, abstract, and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.
Furthermore, any element in a claim that does not explicitly state “means for” performing a specified function, or “step for” performing a specific function, is not to be interpreted as a “means” or “step” clause as specified in 35 U.S.C. Section 112, Paragraph 6. In particular, the use of “step of” or “act of” in the claims herein is not intended to invoke the provisions of 35 U.S.C. 112, Paragraph 6.
Before describing the invention in detail, first a list of cited references is provided. Next, a description of the various principal aspects of the present invention is provided. Subsequently, an introduction provides the reader with a general understanding of the present invention. Finally, specific details of various embodiment of the present invention are provided to give an understanding of the specific aspects.
The following references are cited throughout this application. For clarity and convenience, the references are listed herein as a central resource for the reader. The following references are hereby incorporated by reference as though fully set forth herein. The references are cited in the application by referring to the corresponding literature reference number, as follows:
Various embodiments of the invention include three “principal” aspects. The first is a system for multiscale monitoring. The system is typically in the form of a computer system operating software or in the form of a “hard-coded” instruction set. This system may be incorporated into a wide variety of devices that provide different functionalities. The second principal aspect is a method, typically in the form of software, operated using a data processing system (computer). The third principal aspect is a computer program product. The computer program product generally represents computer-readable instructions stored on a non-transitory computer-readable medium such as an optical storage device, e.g., a compact disc (CD) or digital versatile disc (DVD), or a magnetic storage device such as a floppy disk or magnetic tape. Other, non-limiting examples of computer-readable media include hard disks, read-only memory (ROM), and flash-type memories. These aspects will be described in more detail below.
A block diagram depicting an example of a system (i.e., computer system 100) of the present invention is provided in
The computer system 100 may include an address/data bus 102 that is configured to communicate information. Additionally, one or more data processing units, such as a processor 104 (or processors), are coupled with the address/data bus 102. The processor 104 is configured to process information and instructions. In an aspect, the processor 104 is a microprocessor. Alternatively, the processor 104 may be a different type of processor such as a parallel processor, application-specific integrated circuit (ASIC), programmable logic array (PLA), complex programmable logic device (CPLD), or a field programmable gate array (FPGA).
The computer system 100 is configured to utilize one or more data storage units. The computer system 100 may include a volatile memory unit 106 (e.g., random access memory (“RAM”), static RAM, dynamic RAM, etc.) coupled with the address/data bus 102, wherein a volatile memory unit 106 is configured to store information and instructions for the processor 104. The computer system 100 further may include a non-volatile memory unit 108 (e.g., read-only memory (“ROM”), programmable ROM (“PROM”), erasable programmable ROM (“EPROM”), electrically erasable programmable ROM “EEPROM”), flash memory, etc.) coupled with the address/data bus 102, wherein the non-volatile memory unit 108 is configured to store static information and instructions for the processor 104. Alternatively, the computer system 100 may execute instructions retrieved from an online data storage unit such as in “Cloud” computing. In an aspect, the computer system 100 also may include one or more interfaces, such as an interface 110, coupled with the address/data bus 102. The one or more interfaces are configured to enable the computer system 100 to interface with other electronic devices and computer systems. The communication interfaces implemented by the one or more interfaces may include wireline (e.g., serial cables, modems, network adaptors, etc.) and/or wireless (e.g., wireless modems, wireless network adaptors, etc.) communication technology.
In one aspect, the computer system 100 may include an input device 112 coupled with the address/data bus 102, wherein the input device 112 is configured to communicate information and command selections to the processor 100. In accordance with one aspect, the input device 112 is an alphanumeric input device, such as a keyboard, that may include alphanumeric and/or function keys. Alternatively, the input device 112 may be an input device other than an alphanumeric input device. In an aspect, the computer system 100 may include a cursor control device 114 coupled with the address/data bus 102, wherein the cursor control device 114 is configured to communicate user input information and/or command selections to the processor 100. In an aspect, the cursor control device 114 is implemented using a device such as a mouse, a track-ball, a track-pad, an optical tracking device, or a touch screen. The foregoing notwithstanding, in an aspect, the cursor control device 114 is directed and/or activated via input from the input device 112, such as in response to the use of special keys and key sequence commands associated with the input device 112. In an alternative aspect, the cursor control device 114 is configured to be directed or guided by voice commands.
In an aspect, the computer system 100 further may include one or more optional computer usable data storage devices, such as a storage device 116, coupled with the address/data bus 102. The storage device 116 is configured to store information and/or computer executable instructions. In one aspect, the storage device 116 is a storage device such as a magnetic or optical disk drive (e.g., hard disk drive (“HDD”), floppy diskette, compact disk read only memory (“CD-ROM”), digital versatile disk (“DVD”)). Pursuant to one aspect, a display device 118 is coupled with the address/data bus 102, wherein the display device 118 is configured to display video and/or graphics. In an aspect, the display device 118 may include a cathode ray tube (“CRT”), liquid crystal display (“LCD”), field emission display (“FED”), plasma display, or any other display device suitable for displaying video and/or graphic images and alphanumeric characters recognizable to a user.
The computer system 100 presented herein is an example computing environment in accordance with an aspect. However, the non-limiting example of the computer system 100 is not strictly limited to being a computer system. For example, an aspect provides that the computer system 100 represents a type of data processing analysis that may be used in accordance with various aspects described herein. Moreover, other computing systems may also be implemented. Indeed, the spirit and scope of the present technology is not limited to any single data processing environment. Thus, in an aspect, one or more operations of various aspects of the present technology are controlled or implemented using computer-executable instructions, such as program modules, being executed by a computer. In one implementation, such program modules include routines, programs, objects, components and/or data structures that are configured to perform particular tasks or implement particular abstract data types. In addition, an aspect provides that one or more aspects of the present technology are implemented by utilizing one or more distributed computing environments, such as where tasks are performed by remote processing devices that are linked through a communications network, or such as where various program modules are located in both local and remote computer-storage media including memory-storage devices.
An illustrative diagram of a computer program product (i.e., storage device) embodying the present invention is depicted in
This disclosure provides a unique multi-scale multilayer graph framework for information dynamics, which analyzes and monitors the relationships of different types of activities and dynamics. Based on a time series of different types of observables (or measurements), the multi-scale multilayer graph representation for information dynamics can be used to detect and infer their dependencies that cannot be directly observed (or measured). The multiple spatial scale formulation of this framework allows the construction of the multilayer graph to adapt to the activities and dynamics to reduce measurement requirements while maintaining the analysis performance. A key aspect that enables this multiple spatial scale within the information dynamic framework is a flow-rate optimization method that merges graph nodes into clusters. The activities can then be summarized on the coarse-grained graph derived from multiscale of derived clusters, which in turn allows the system to cue a region of interest for multiscale monitoring of the dynamics of the system.
A purpose of this invention is to efficiently direct computing resources to monitor and analyze emerging activities from multiple sources at multiple scales. Building upon this team's multilayer information dynamic framework, the advantages of the new feature of the multiscale, multilayer dynamic information are two-fold: 1) It reduces computations without losing the ability to find activity dependencies. The coarse resolution corresponds to sparse activity dependency; therefore, it provides better abstraction and enables coverage of larger graphs. 2) It has the ability to zoom-in or zoom-out of regions of interest in order to provide better actionable insights to an analyst or other system operations.
The system described herein can be deployed as embedded decision support modules in the cloud computing infrastructures or a stand-alone system for the application areas of complex systems, such as intelligence surveillance and reconnaissance (ISR) for posturing maritime activities (as demonstrated), crisis management, social unrests, and financial markets. The successful deployment of this technology is expected to result in detection and inference of system behaviors, activities, and dependency. Further details are provided below.
(4.1) Method Overview: Multilayer Information Dynamics with Multiple (Spatial) Scales
This disclosure provides a multi-scale multilayer graph representation for information dynamics, which from a time series of different observable (or measurement) modalities detects and infers their dependencies that cannot be directly observed (or measured). The multiple spatial scale within the information dynamic framework is developed using a flow-rate optimization model that merges graph nodes into clusters. This process if further illustrated in
(4.2) Mixed Coarse-Scale Multilayer Network: Example Scenario (Maritime Activities)
For further understanding, provided below is an example scenario as related to surveillance of maritime activities. As shown in
where G1 denotes vessel graph, G2 denotes communication graph, t denotes time, ε denotes reaction time delay, V denotes vessel density, α denotes diffusion constant, and β denotes coefficient of weighting communication information.
This semi-discrete (continuous in time and discrete in space) partial differential equation describes that the change of vessel density (left-hand side) depends on 1) diffusion of the vessels with the graph Laplacian operator with the vessel graph G1 and 2) advection of vessels with the graph gradient operator coming from the communication graph G2 with a small reaction time delay ε. This model generates data in a way that the vessel flows between certain zones depend on the communication activities.
A goal of this multilayer information framework is to discover the hidden dependencies between vessel flows and communication activities (the input data are time series of these), without knowing the model (the equation above) that generates the data. This method was demonstrated with flow transfer entropy (defined below in the flow transfer entropy section) to detect the hidden dependencies, i.e. identification of the vessel flows that depend on certain communication activities.
This multilayer information dynamic framework is extended to multiple spatial scales. As noted above, an advantage to extending the framework to multiple spatial scales is that it simplifies the framework and reduces computations without losing the ability to find dependencies (as shown in
Region of interest 504 are identified by the across-layer dependency links 506 (directed edges). As shown on the right of
Additionally,
(4.3) Vessel Flow Clustering:
Suppose the adjacency matrix for the vessel flow graph G1 is A=(aij)i,j=1, . . . , N, i.e., aij indicates the amount of vessels flowing from zone i to zone j. An approach to multiple spatial scales is the application of flow clustering to merge nodes into k clusters πc, for c=1, 2, . . . , k that highlights the largest flows ξs, for s=1, 2, . . . ,
, within and across clusters. Flow ξs is the collection of all links from nodes in cluster πc(s) to nodes in cluster πd(s). The flow rate of flow ξs is defined as:
where ν denotes node, c(s) denotes from nodes, d(s) denotes to nodes, i and j denote zones i and j, respectively, with aij denoting the amount of vessels flowing from zone i to zone j. The flow clustering problem is posed as finding k clusters that maximizes the sum of the flow rate in largest intra-cluster or inter-cluster flows. The numbers of clusters k and flows
are pre-defined. The flow rate maximization problem is optimization problem is as follows:
The solution to this can be approximated with kernel k-mean clustering (see Literature Reference No. 8), because both aim to maximize the weighted sum of the graph adjacency matrix entries. The kernel k-mean clustering is equivalent to the symmetric nonnegative matrix factorization (NMF) (see Literature Reference No. 3) and can be efficiently solved by coordinate descent methods (see Literature Reference No. 9).
Let M be the symmetrized matrix of the vessel flow adjacency matrix A: M=A+AT. The symmetric NMF aims to find an N×k matrix H (where k<N) with nonnegative entries Hij≥0 that minimizes ∥M−HHT∥F2, where ∥·∥F indicates the Frobenius norm.
The difference between the problem being solved here and the problem in Literature Reference No. 8 is the following: the present problem summarizes more general directed graphs, while Literature Reference No. 8 summarizes the influence flows from a single source node in the reversed publication citation graph. In the problem addressed by the present disclosure, the number of sources can be arbitrary.
(4.4) Flow Transfer Entropy
The flows from region Ri to region Rj are denoted as: VR
(4.5) Flow Clustering Algorithm
As depicted in
Inputs. V and k: An N×N×T vessel flow tensor V where each entry Vijt indicates the amount of vessels flowing from node i to node j at time t. The number of clusters k.
H is an N×k matrix.
the argument of the largest entry in Hi (the ith row of H).
Output. d: An N×1 vector d that indicates the cluster membership with entries from {1, 2, . . . , k}.
(4.6) Vessel Flow Clustering Example Result
The vessel flow clustering process was performed with a set of data to validate the system and process. Provided below is an example to illustrate that flow clustering summarizes vessel flow and reduces the number of flows. The example graph in
(4.7) Multi-Scale Multilayer Information Dynamics Algorithm
The system described herein detects communication and vessel flow dependency with low resolution and cue regions of interest with TE for multiscale monitoring (depicted as element 302 in
Inputs. V, C and k: An N×N×T vessel flow tensor V where entry Vijt indicates the amount of vessels flowing from node i to node j at time t. An N×N×T communication tensor C where entry Cijt indicates the amount of communication from node i to node j at time t. The number of clusters is k.
The output provides a unique abstraction and representation of flow dependency which enable decision making tools (e.g. situation awareness tool in monitoring vessel movements in/out of contested water) to support exploratory analysis (e.g. drill down to high-flow entropy zones based on dependent clusters), to refine units of analysis for tracking purpose (e.g., use corresponding multiscale flow and corresponding dependency links).
(4.8) Multi-Scale Multilayer Information Dynamics Example Result
The multi-scale multilayer information dynamic process was performed with the clustered data to further validate the system and process.
(4.9) Control of a Device.
As shown in
Finally, while this invention has been described in terms of several embodiments, one of ordinary skill in the art will readily recognize that the invention may have other applications in other environments. For example, while the system was described with respect to an ocean vessel, the system is not intended to be limited thereto and can be equally applied to an area in which objects may be mobile, such as automobiles in a street, people in a battlefield, etc. It should be noted that many embodiments and implementations are possible. Further, the following claims are in no way intended to limit the scope of the present invention to the specific embodiments described above. In addition, any recitation of “means for” is intended to evoke a means-plus-function reading of an element and a claim, whereas, any elements that do not specifically use the recitation “means for”, are not intended to be read as means-plus-function elements, even if the claim otherwise includes the word “means”. Further, while particular method steps have been recited in a particular order, the method steps may occur in any desired order and fall within the scope of the present invention.
The present application is a Continuation-in-Part application of U.S. application Ser. No. 15/497,202, filed on Apr. 25, 2017, which is a non-provisional application of U.S. Provisional Application No. 62/376,220, filed on Aug. 17, 2016, the entirety of which are incorporated herein by reference. The present application is ALSO a non-provisional patent application of U.S. Provisional Application No. 62/557,733 filed on Sep. 12, 2017, the entirety of which is hereby incorporated by reference.
This invention was made with government support under U.S. Government Contract Number PC 1141899 issued by the National Reconnaissance Office via the Boeing Company. The government has certain rights in the invention.
Number | Date | Country | |
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62376220 | Aug 2016 | US | |
62557733 | Sep 2017 | US |
Number | Date | Country | |
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Parent | 15497202 | Apr 2017 | US |
Child | 16033178 | US |