Abstract: Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined.

Abstract: Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.

Abstract: Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined.

Abstract: Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.

Abstract: Improved methods and systems for a neighborhood determination in computer aided engineering analysis are disclosed. According to one aspect, a list of neighbor elements is created for a base element of a grid model representing a structure or an engineering product. The representative node's coordinates of the base element are calculated using corner nodes of the base element. A characteristic length is assigned to the base element. The characteristic length can be determined by users of the computer aided analysis, or be calculated using geometry of the base element. The characteristic length and the representative node collectively define a surface boundary that divides elements in the grid model into two groups. The first group contains potential neighbors, while the second group contains non-neighbors.

Abstract: Improved topology optimization for engineering product design is disclosed. An engineering product including a design domain to be optimized is defined. Design domain can be a portion or the entire engineering product. Design objective and optional constraint are defined such that optimization goal is achieved. Additionally, initial configuration of the design domain is represented by a finite element analysis (FEA) mesh. Each element or element group is associated with a design variable. A set of discrete material models is created from the baseline material used for the design domain. The set of discrete material models is configured to cover entire range of the design variable and each discrete material model represents a non-overlapping portion. Each element representing the design domain is associated with an appropriate discrete material model according to the design variable. Structure response is obtained via FEA to evaluate design objective and update design variable.

Abstract: A method of identifying most influential design variables in a multi-objective engineering design optimization of a product is disclosed. According to one aspect of the present invention, a product is optimized with a set of design variables and a set of response functions as objectives and constraints. Representative product design alternatives (samples) are chosen from the design space and evaluated for responses. Metamodels are then used for fitting the sample responses to facilitate a global sensitivity analysis of all design variables versus the response functions. A graphical presentation tool is configured for allowing the user to conduct “what-if” scenarios by interactively applying respective weight factors to response functions to facilitate identification of most influential design variables. Engineering design optimization is then conducted in a reduced design space defined by the most influential design variables.

Abstract: Systems and methods of obtaining a set of better converged and diversified Pareto optimal solutions in an engineering design optimization of a product (e.g., automobile, cellular phone, etc.) are disclosed. According to one aspect, a plurality of MOEA based engineering optimizations of a product is conducted independently. Each of the independently conducted optimizations differs from others with parameters such as initial generation and/or evolutionary algorithm. For example, populations (design alternatives) of initial generation can be created randomly from different seed of a random or pseudo-random number generator. In another, each optimization employs a particular revolutionary algorithm including, but not limited to, Nondominated Sorting Genetic Algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA), etc. Furthermore, each independently conducted optimization's Pareto optimal solutions are combined to create a set of better converged and diversified solutions.

Abstract: The present invention discloses systems and methods of conducting multi-objective evolutionary algorithm (MOEA) based engineering design optimization of a product (e.g., automobile, cellular phone, etc.). Particularly, the present invention discloses an archive configured for monitoring the progress and characterizing the performance of the MOEA based optimization. Further, an optimization performance indicator is created using the archive's update history. The optimization performance indicator is used as a metric of the current state of the optimization. Finally, a stopping or termination criterion for the MOEA based optimization is determined using a measurement derived from the optimization performance indicators. For example, a confirmation of a “knee” formation has developed in the optimization performance indicators. The optimization performance indicators include, but are not limited to, consolidation ratio, improvement ratio, hypervolume.

Abstract: The present invention discloses systems and methods of conducting multi-objective evolutionary algorithm (MOEA) based engineering design optimization of a product (e.g., automobile, cellular phone, etc.). Particularly, the present invention discloses an archive configured for monitoring the progress and characterizing the performance of the MOEA based optimization. Further, an optimization performance indicator is created using the archive's update history. The optimization performance indicator is used as a metric of the current state of the optimization. Finally, a stopping or termination criterion for the MOEA based optimization is determined using a measurement derived from the optimization performance indicators. For example, a confirmation of a “knee” formation has developed in the optimization performance indicators. The optimization performance indicators include, but are not limited to, consolidation ratio, improvement ratio, hypervolume.

Abstract: Improved topology optimization for engineering product design is disclosed. An engineering product including a design domain to be optimized is defined. Design domain can be a portion of or the entire engineering product. Design objective and optional constraint are also defined such that optimization goal can be achieved. Additionally, initial configuration of the design domain is represented by a finite element analysis (FEA) mesh. Each element or element group is associated with a design variable. A set of discrete material models is created from the baseline material used for the design domain. The set of discrete material models is configured to cover entire range of the design variable and each discrete material model represents a non-overlapping portion. Each element representing the design domain is associated with an appropriate discrete material model according to the design variable.

Abstract: A sampling strategy using genetic algorithms (GA) in engineering design optimization is disclosed. A product is to design and optimize with a set of design variables, objectives and constraints. A suitable number of design of experiments (DOE) samples is then identified such that each point represents a particular or unique combination of design variables. The sample selection strategy is based on genetic algorithms. Computer-aided engineering (CAE) analysis or analyses (e.g., finite element analysis, finite difference analysis, mesh-free analysis, etc.) is/are performed for each of the samples during the GA based sample selection procedure. A meta-model is created to approximate the CAE analysis results at all of the DOE samples. Once the meta-model is satisfactory (e.g., accuracy within a tolerance), an optimized “best” design can be found by using the meta-model as function evaluator for the optimization method. Finally, a CAE analysis is performed to verify the optimized “best” design.

Abstract: Systems and methods of consuming radial basis function (RBF) based meta-models are described. In one aspect, a product is to be designed and optimized with a set of design variables, objectives and constraints. A number of design of experimentals (DOE) points are identified. Each of the DOE points represents a particular or unique combination of design variables. Computer-aided engineering (CAE) analysis/analyses is/are then performed for each of the DOE points. A RBF based meta-model is created to approximate the CAE analysis results at all of the DOE points. A crowding distance is calculated for each DOE point. The DOE points are sorted accordingly in a predetermined criterion such as descending order, from which a predefined number of the DOE points are chosen as RBF neuron centers. RBF parameters such as function type, width and weight factor are adjusted so that the meta-model can substantially match the CAE analysis results.