31 results about "Cybernetics" patented technology

3D music visualization process employing a novel method of real-time reconfigurable control of 3D geometry and texture, employing blended control combinations of software oscillators, computer keyboard and mouse, audio spectrum, control recordings and MIDI protocol. The method includes a programmable visual attack, decay, sustain and release (V-ADSR) transfer function applicable to all degrees of freedom of 3D output parameters, enhancing even binary control inputs with continuous and aesthetic spatio-temporal symmetries of behavior. A “Scene Nodes Graph” for authoring content acts as a hierarchical, object-oriented graphical interpreter for defining 3D models and their textures, as well as flexibly defining how the control source blend(s) are connected or “Routed” to those objects. An “Auto-Builder” simplifies Scene construction by auto-inserting and auto-routing Scene Objects. The Scene Nodes Graph also includes means for real-time modification of the control scheme structure itself, and supports direct real-time keyboard / mouse adjustment to all parameters of all input control sources and all output objects. Dynamic control schemes are also supported such as control sources modifying the Routing and parameters of other control sources. Auto-scene-creator feature allows automatic scene creation by exploiting the maximum threshold of visualizer set of variables to create a nearly infinite set of scenes. A Realtime-Network-Updater feature allows multiple local and / or remote users to simultaneously co-create scenes in real-time and effect the changes in a networked community environment where in universal variables are interactively updated in real-time thus enabling scene co-creation in a global environment. In terms of the human subjective perception, the method creates, enhances and amplifies multiple forms of both passive and interactive synesthesia. The method utilizes transfer functions providing multiple forms of applied symmetry in the control feedback process yielding an increased level of perceived visual harmony and beauty. The method enables a substantially increased number of both passive and human-interactive interpenetrating control / feedback processes that may be simultaneously employed within the same audio-visual perceptual space, while maintaining distinct recognition of each, and reducing the threshold of human ergonomic effort required to distinguish them even when so coexistent. Taken together, these novel features of the invention can be employed (by means of considered Scene content construction) to realize an increased density of “orthogonal features” in cybernetic multimedia content. This furthermore increases the maximum number of human players who can simultaneously participate in shared interactive music visualization content while each still retaining relatively clear perception of their own control / feedback parameters.

The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms during various stages of medical disease management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting. The invention enables both synchronous and asynchronous multiparty collaboration with multichannel, multiplexed streaming imagery data, including interactive curation, multisensory annotation and metadata tagging, as well as multi-formatted encapsulation, saving and sharing of collaborated imagery data as packetized augmented intelligence. The invention acquires both live stream and archived medical modality imagery from network-connected medical devices, cameras, signals and sensors, as well as multiomic data [phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic and radiogenomic] maps and clinical data sets from structured reports and clinical documents, including biometric maps and movies, hapmaps, heat maps and data stream visualizations. The invention also acquires both medical and non-medical streaming imagery data from image data repositories, documents and structured reports, workstations and mobile devices, as well as from wearable computing, signals and sensors. The invention enables networked teams to interactively communicate, concurrently collaborate and bi-directionally exchange multichannel multiplexed imagery data streams, singly or together, in real time or asynchronously, generally by curating, annotating and tagging imagery information objects. The invention encapsulates and saves collaborated imagery data, together with multisensory annotations and metadata tags, in standard file formats as packetized augmented intelligence. The invention enables recursive cognitive enrichment of clinical cognitive vismemes, and saves packetized imagery information objects, multisensory annotations and metadata tags in native file formats [PDF, MPEG, JPEG, XML, XMPP, QR,TIFF, RDF, RDF / XML, SVG and DAE] as well as in formats compliant with standards for digital communications in medicine [DICOM]. The invention enables live stream multicasting of multimodal cognitive instruction and collaborative knowledge exchange with multisensory [visual, auditory, haptic] annotation of streaming imagery data, as well as secure, encrypted transmission of streaming augmented intelligence across file sharing data networks for informatics-enabled learning, specialist skills acquisition and accelerated knowledge exchange.

The invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. These methods can leverage domain knowledge and clinical expertise with cognitive collaboration, augmented medical intelligence and cybernetic workflow streams for learning health care systems. The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms. It provides for both synchronous and asynchronous cognitive collaboration with multichannel, multiplexed imagery data streams during various stages of medical disease and injury management—detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs. The invention enables cognitive curation, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized medical intelligence. It can augment packetized medical intelligence through recursive cognitive enrichment, including multimodal annotation and [semantic] metadata tagging with resources consumed and outcomes delivered. Augmented medical intelligence can be saved and stored in multiple formats, as well as retrieved from standards-based repositories. The invention can incorporate and combine various machine learning techniques [e.g., deep, reinforcement and transfer learning, convolutional and recurrent neural networks, LSTM and NLP] to assist in curating, annotating and tagging diagnostic, procedural and evidentiary medical imaging. It also supports real-time, intraoperative imaging analytics for robotic-assisted surgery, as well as other imagery guided interventions. The invention facilitates collaborative precision medicine, and other clinical initiatives designed to reduce the cost of care, with precision diagnosis and precision targeted treatment. Cybernetic workflow streams—cognitive communications, collaboration, consultation and instruction with augmented medical intelligence—enable care delivery teams of medical minds and machines to ‘deliver the right care, for the right patient, at the right time, in the right place’—and deliver that care faster, smarter, safer, more precisely, cheaper and better.

The invention provides a feature extraction method for text categorization. The feature extraction method is used for solving the problem that the accuracy rate and the recall rate of text categorization need to be increased further. The feature extraction method is a strategic method. In consideration of the concept of entropy in statistical thermodynamics, entropy is used for describing the degree of disorder of a system and is significantly applied to the fields of cybernetics, probability theory, number theory, astrophysics, bioscience, information theory and the like. According to the feature extraction method, entropy can also be used in text categorization, a feature is regarded as an event, a category set of text is a system, and therefore entropy can be used for measuring the degree of disorder of features and categories and converted into the closeness degree of the relation between the features and the categories. According to the feature extraction method, on the basis of improved mutual information, the concept of entropy is combined, a new feature evaluation function is provided, feature extraction is conducted on the basis of the function, a superior feature subset can be selected for showing the text and building a categorizer, and therefore the accuracy rate and the recall rate of text categorization are increased.

Holistic cybernetic vehicle control enables the results of machine sensing and decision making to be communicated to a vehicle operator through the various senses of the operator. By providing machine advice to the operator through various vehicle functions and by integrating the machine advice with what the operator senses and perceives, holistic cybernetic control can result in much better and safer vehicle operation. The invention integrates human and machine vehicle control action to improve vehicle operation and, particularly, to avoid collision events.

The invention discloses an individualized emotion model applied to a child user playmate robot, which mainly comprises a random process model of an emotional state stimulation transfer process, a markov chain model of an emotional state spontaneous transfer process, a cybernetic model of a mood state stimulation transfer, and a dynamic balance model of a mood state spontaneous transfer. On the basis of the prior emotion model, the invention discloses the integration of a mood model fused with an individualized interactive technique and the four models, and also discloses an application method of the models for software realization.

Holistic cybernetic vehicle control enables the results of machine sensing and decision making to be communicated to a vehicle operator through the various senses of the operator. By providing machine advice to the operator through various vehicle functions and by integrating the machine advice with what the operator senses and perceives, holistic cybernetic control can result in much better and safer vehicle operation. The invention integrates human and machine vehicle control action to improve vehicle operation and, particularly, to avoid collision events.

The invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. These methods can leverage domain knowledge and clinical expertise with networked cognitive collaboration, augmented clinical intelligence and cybernetic workflow streams for learning health care systems. The invention enables multimodal clinical communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms. It provides for both synchronous and asynchronous cognitive collaboration with multichannel, multiplexed imagery data streams during various stages of medical disease and injury management—detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs. The invention enables cognitively-enriched, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized clinical intelligence.

The invention relates to the field of a distribution auxiliary system, in particular to a product distribution auxiliary system. The software belongs to a module augmented product of logistics software, and is a logistics product distribution auxiliary system that is determined through complete research and discussion on the basis of completely researching a product distribution module of logistics products. Logistics management, as an object that is jointly researched by modern enterprises, means that fundamental principles and scientific approaches of management are applied to planning, organizing, directing, coordinating, controlling and monitoring logistics activities in the process of society reproduction according to rules of material articles flows so that all logistics activities are perfectly coordinated and matched to reduce logistics cost and improve logistics efficiency and economic interest. Modern logistics management is based on the system theory, the information theory and the control theory.

The invention relates to a human-in-loop machine learning application development method and system, a machine learning application development process is regarded as a control process, a data flow isa signal flow, and according to a negative feedback adjustment principle in a control theory, a negative feedback loop comprising an online stage and an offline stage and three artificial assistantsis designed. Data of the on-line loop can enrich basic data samples, fault feedback of the on-line loop is matched with manual processing to position the off-line data or the model stage, then the problem is solved, and finally the model data flow is uploaded to the on-line state to form a continuous iterative development process. The invention provides a solution for developing a complete set ofprocesses of machine learning application. According to the method, based on the negative feedback adjustment principle in the control theory, manual experience and knowledge are applied to loops of all development stages at a relatively low cost, iterative and incremental development is supported, and the development quality of all components and stages is improved, so that the performance of thewhole application is improved.

The invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. These methods can leverage domain knowledge and clinical expertise with cognitive collaboration, augmented medical intelligence and cybernetic workflow streams for learning health care systems. The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among cognitive collaborants, including heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms. It provides for both synchronous and asynchronous cognitive collaboration with multichannel, multiplexed imagery data streams during various stages of medical disease and injury management—detection, diagnosis, prognosis, treatment, measurement and monitoring, as well as resource utilization and outcomes reporting. The invention acquires both live stream and archived medical imagery data from network-connected medical devices, cameras, signals, sensors and imagery data repositories, as well as multiomic data sets from structured reports and clinical documents. It enables cognitive curation, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized medical intelligence. The invention augments packetized medical intelligence through recursive cognitive enrichment, including multimodal annotation and [semantic] metadata tagging with resources consumed and outcomes delivered. Augmented medical intelligence can be saved and stored in multiple formats, as well as retrieved from standards-based repositories. The invention provides neurosynaptic network connectivity for medical images and video with multi-channel, multiplexed gateway streamer servers that can be configured to support workflow orchestration across the enterprise—on platform, federated or cloud data architectures, including ecosystem partners. It also supports novel methods for managing augmented medical intelligence with networked metadata repositories [inclduing imagery data streams annotated with semantic metadata]. The invention helps prepare streaming imagery data for cognitive enterprise imaging. It can be incorporate and combine various machine learning techniques [e.g., deep, reinforcement and transfer learning, convolutional neural networks and NLP] to assist in curating, annotating and tagging diagnostic, procedural and evidentiary medical imaging. It also supports real-time, intraoperative imaging analytics for robotic-assisted surgery, as well as other imagery guided interventions. The invention facilitates collaborative precision medicine, and other clinical initiatives designed to reduce the cost of care, with precision diagnosis [e.g., integrated in vivo, in vitro, in silico] and precision targeted treatment [e.g., precision dosing, theranostics, computer-assited surgery]. Cybernetic workflow streams—cognitive communications, collaboration, consultation and instruction with augmented medical intelligence—enable care delivery teams of medical minds and machines to ‘deliver the right care, for the right patient, at the right time, in the right place’ - and deliver that care faster, smarter, safer, more precisely, cheaper and better.

The invention discloses a teaching resource dynamic distribution system based on knowledge coding and an LFNN model. The system is based on a control theory model, and comprises a controller model formed by a professional teacher and an intelligent control module, and a controlled object model formed by three subsystems, namely a knowledge coding system, a learning condition management system and a teaching planning system. Knowledge codes are introduced into teaching, teaching content is converted into quantifiable data, an online and offline intelligent teaching system based on a control theory model is established based on an Ebbinghaus memory curve, overall and individual process learning conditions of students can be accurately analyzed, a teaching plan based on a scientific memory method and a personalized autonomous learning plan are adjusted in real time, teaching is intelligently driven through data, teaching according to materials is promoted, and the teaching efficiency is improved.

The invention discloses a system for planning, addressing and optimizing the value of an entity channel. The system is characterized by comprising a planning function, an addressing function and a value optimization function, wherein the planning function is used for planning a layout design of a regional entity channel based on urban planning and market analysis; the addressing function is used for analyzing and estimating a candidate address of the entity channel to obtain business prediction and benefit prediction of the candidate address; and the value optimization function is used for evaluating the value of the current entity channel and designing an optimal configuration scheme of personnel and equipment. Based on a system theory and a control theory, the system for planning, addressing and optimizing the value of the entity channel performs system analysis on all important factors relevant to the planning and the addressing of the entity channel and provides a key factor, so the transformation from a model to actual operation is realized and effective decision basis is provided for scientific addressing and adjustment on value optimization of the entity channel. Moreover, the system has high operability and practicality.

The invention provides a minimum driving node identification method based on complex network strict target controllability, relates to the technical field of complex network controllability, and provides a brand-new method for identifying minimum driving nodes required by complex network strict target control. According to the method, a PBH rank criterion and a Kalman rank criterion in a control theory are utilized; the upper bound and the lower bound of the number of the driving nodes are respectively estimated when the strict target of the complex network is controllable; a method for identifying the position of the driving node in the first method is provided; on the basis, a method for quickly identifying the number and positions of upper bound driving nodes is provided. According to the invention, the method has the advantages of simple operation and wide application range, can be applied to the fields of regulation and control of a biological network, flow control of a traffic network, information propagation of a social network, safety protection of an intelligent power grid, optimal scheduling of the Internet of Vehicles and the like, can further promote the development ofthe fields of machine learning and artificial intelligence, and has important economic and social values.

The invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. These methods can leverage domain knowledge and clinical expertise with cognitive collaboration, augmented medical intelligence and cybernetic workflow streams for learning health care systems. The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms. It provides for both synchronous and asynchronous cognitive collaboration with multichannel, multiplexed imagery data streams during various stages of medical disease and injury management—detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs. The invention enables cognitive curation, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized medical intelligence.

The invention discloses a natural cybernetics-based nuclear and chemical accident emergency optimizing control method, which belongs to the field of hazard control. The method comprises the following steps: firstly, performing prediction and pre-warning on a nuclear and chemical accident; and on the basis of the prediction on the consequence of the nuclear and chemical accident, developing a natural cybernetics-based optimizing control scheme. The natural cybernetics-based nuclear and chemical accident emergency optimizing control method disclosed by the invention has the advantages of fulfilling the aim of performing emergency control on the nuclear and chemical accident, minimizing cost paid by the control, and accidental loss and realizing optimal emergency control of the nuclear and chemical accident.

The invention discloses a solution of a product green optimization design problem containing uncertain factors. The solution comprises the steps of setting a product function-structure mapping tree, setting multiple generalized operator models for product green design, setting multiple uncertain optimization module configuration models, multiple uncertain optimization module configuration model conversion, setting certain type module optimization configuration models, and certain type module optimization configuration model solving. The solution comprises the specific steps that according to an existing modularized product of an enterprise, on the basis of the product function-structure mapping tree, and through the large scale system cybernetics, the multiple generalized operator models are set to solve the product green optimization design problem containing the uncertain factors; according to the product green optimization design criteria and a green level calculation method, the multiple uncertain optimization module configuration models corresponding to the multiple generalized operator models are set, the defuzzification method is used for converting the multiple uncertain optimization module configuration models to the certain type module optimization configuration models, and then the genetic algorithm is used for solving the certain type module optimization configuration models. The solution can effectively improve the resource utilization rate of the enterprise, lower energy consumption and reduce influences on the environment.

A system and method is provided for constructing optimal cybernetic based systems. The method is based on a process of formalized rational inquiry, measurement and planned action. More particularly, the method relies upon the precise determination of alternatives through inquiry, a formal logical model of measurement, and the subsequent matching of information obtained through measurement to possible courses of action to arrive at a realizable and useful cybernetic system architecture.

The invention belongs to the technical field of software engineering, in particular to a software quality run-time optimizing method based on control theory and goal inference. The invention introduces a value viewpoint in the software engineering based on value, defines a value measurement model specific to a goal system as a system total quality feedback from a client business point of view and achieves the software quality run-time optimization based on demand goal inference on the basis of the system total quality feedback. The invention uses a PID controller to dynamically regulate priority levels (quality preference) of relevant run-time quality attributes and takes the priority levels as input to conduct the demand goal inference of preference drive. A demand goal configuration project obtained by the goal inference is used for guiding dynamic reconfiguration of a run-time system structure, thereby achieving the optimal regulation of the system. The real time feedback-based software system run-time total quality optimization can be achieved. Compared with the traditional optimization method aiming at a specific quality attribute, the invention has larger superiority.

The invention discloses an emergency optimization control method for nuclear chemical accidents based on natural cybernetics, which belongs to the field of hazard control. The invention first predicts and warns the hazards of nuclear chemical accidents, and then formulates an optimal control scheme based on natural cybernetics on the basis of predicting the consequences of nuclear chemical accidents. An emergency optimization control method for nuclear chemical accidents based on natural cybernetics proposed by the present invention can not only achieve the goal of emergency control of nuclear chemical accidents, but also minimize the cost of control and accident losses, and realize emergency response to nuclear chemical accidents best control.

The invention relates to a human-in-loop machine learning application development method and system, a machine learning application development process is regarded as a control process, a data flow isa signal flow, and according to a negative feedback adjustment principle in a control theory, a negative feedback loop comprising an online stage and an offline stage and three artificial assistantsis designed. Data of the on-line loop can enrich basic data samples, fault feedback of the on-line loop is matched with manual processing to position the off-line data or the model stage, then the problem is solved, and finally the model data flow is uploaded to the on-line state to form a continuous iterative development process. The invention provides a solution for developing a complete set ofprocesses of machine learning application. According to the method, based on the negative feedback adjustment principle in the control theory, manual experience and knowledge are applied to loops of all development stages at a relatively low cost, iterative and incremental development is supported, and the development quality of all components and stages is improved, so that the performance of thewhole application is improved.

A system and method is provided for constructing optimal cybernetic based systems. The method is based on a process of formalized rational inquiry, measurement and planned action. More particularly, the method relies upon the precise determination of alternatives through inquiry, a formal logical model of measurement, and the subsequent matching of information obtained through measurement to possible courses of action to arrive at a realizable and useful cybernetic system architecture

The invention discloses an NBPP prevention and treatment method based on the disease control theory. The method accords with the objective law of occurrence and development of the NBPP which occurs sporadically. The occurrence rate of the NBPP is decreased to the minimum in an area by carrying out control through a prevention and treatment system with the area as a unit, and timely and scientific diagnosis and treatment plans and measures are provided for each NBPP case to decrease the number of physically-challenged newborns or lower the physically-challenged degree. The NBPP prevention and treatment system will provide solid guarantees for solving numerous NBPP problems.

The invention discloses a control and management method for tuberculosis on the basis of a disease control theory. The method accords with the objective law of occurring and developing of the tuberculosis. Through effect evaluation of the control and management of the tuberculosis, performance of the control and management of the tuberculosis is analyzed, new suggestions for improvement are proposed for control and management of the tuberculosis, disease control of the tuberculosis is achieved gradually, and the control and management of the tuberculosis tending to a perfect medical condition is achieved.

The invention relates to a ball screw grinding chatter stability limit graph determining method, comprising the steps: 1) establishing a dynamics model of a grinding process via an experimental method; 2) according to the stability criterion of cybernetics, determining a grinding chatter stability limit graph; and 3) according to the grinding chatter stability limit graph, determining the parameters of the grinding technique for ensuring the grinding stability. The invention mainly aims at solving the chatter inhibition problem of a numerical control thread grinder in the grinding process of a ball screw, adopts the experimental design method to accurately identify the parameters of the grinding dynamics model, determines the grinding chatter stability limit graph according to the parameters, and provides the parameters of the grinding technique to ensure the grinding stability, thereby effectively solving the chatter inhibition problem in grinding process. The curve can be used to determine the parameters of the grinding technique, namely the abrasion wheel main shaft speed n and the grinding depth h to avoid flutter, thus the invention can improve the grinding quality and efficiency of the ball screw.