Goddar, Heinz J

Methods, apparatus, and computer programs are disclosed for address range filtering in performance monitoring. One method comprises generating a set of performance monitoring events that each specify an address range within one or more address ranges of memory of a computing system or an application executing in the computing system for which memory access data is to be collected and performing memory access data collection based on the set of performance monitoring events for the one or more address ranges.

Existing techniques for ontology generation and extraction associated rules typically involving custom pattern-matching algorithms are complex and require significant human intervention to design and implement. Embodiments herein provide a method and system for generation of the ontology and extracting the associated rules using meta-models. The method comprises user defining a domain using the meta-model in a JavaScript Object Notation (JSON) format for tailored and precise generation of the ontology. A plurality of prompts is generated using the meta-model, a configuration file, and a domain specific corpus, and processed using a large language model (LLM) to generate the ontology. Further the generated ontology along with the domain specific corpus is used to extract associated rules effectively.

Conventional techniques for multi-modal image segmentation are not computationally effective for image analysis. Embodiments herein provide a method and system for an organ prioritized Y-Net multi-modal image segmentation model using a joint modality encoder and decoders. The method trains a genetic Y-Net multi-modal image segmentation model comprising the joint modality encoder, a bottleneck, and a plurality of modality-specific decoders, with each of a plurality of the multi-modal medical images, for a plurality of predefined hyperparameters, to generate a trained Y-Net multi-modal image segmentation model. Further a multi-objective organ prioritization reward function with an organ prioritization is formulated for the plurality of predefined hyperparameters of the trained Y-Net multi-modal image segmentation model. Further the plurality of parameters of the trained Y-Net multi-modal image segmentation model is optimized using the multi-objective organ prioritization reward function, to generate the organ prioritized Y-Net multi-modal image segmentation model.

This disclosure relates generally to method and system for made-tea quality assessment in manufacturing process. Conventional tea quality assessment methods making it challenging for tea manufacturers to maintain consistent quality standards and meet evolving market demands. The present disclosure collects a physical grade of made-tea sample from the tea factory. Spectral camera captures spectral image of the made-tea sample for preprocessing to obtain improved contrasts. Further, from a plurality of features are extracted from each candidate region to detect a plurality of anomalies, and simultaneously a negative score is assigned based on detected discrepancies. Then, a quality level score is computed to determine a final grade value. The final grade value is assessed based on a quality level score (QLS) and a valuation grade score (VLS). Finally, the method generates a quality assessment report indicating personalized improvements for the tea factory.

Segmented images are prepared by dividing a large image. An intermediate target region is extracted from one segmented image selected, and an intermediate-region information item (82) indicating the range of the intermediate target region and an intermediate feature value (83) of the intermediate target region are acquired. This is repeated using each segmented image as the above one segmented image. Based on the acquired intermediate-region information items (82), an integrated feature value (83a) of a target region that integrates multiple intermediate target regions adjoining one another in the large image is obtained from intermediate feature values (83) of the multiple intermediate target regions.

While knowledge distillation has shown promise in general NLP contexts, its application to domain-specific text remains largely underexplored. Existing models often struggle to generalize complex domain-specific vocabulary, specialized terminology, and unique sentence structures, resulting in degraded performance when applied to domain-specific tasks. Thus, embodiments of present disclosure provide a method and system for domain-specific text processing with keyword knowledge distillation. The disclosed method overcomes the challenges in conventional methods by utilizing a dynamic, weighted ensemble approach for keyword extraction, which adaptively selects domain-specific keywords extracted using multiple keyword extraction techniques. The method then applies knowledge distillation, where a student model is trained to predict the masked domain-specific keywords using context from surrounding tokens. By computing three loss functions, including distillation loss, keyword masking loss, and cosine embedding loss, the student model is optimized for performance while retaining critical domain knowledge.

Disclosed is a battery manufacturing system, comprising a processing device configured to process a battery material that is provided as a continuous material, wherein the processing device is configured to process the battery material in successive segments by processing one segment of the battery material at a time before processing a subsequent segment, a transport device configured to advance the battery material in a stepwise manner in a transport direction, alternating between an advancing phase and a resting phase, the resting phase being synchronized with processing of a segment by the processing device, and a sensor device configured to detect a marking object on the battery material in a sensing area, wherein the sensor device is arranged so that the sensing area is located upstream of the processing device in the transport direction and aligned with a boundary between two successive segments of the battery material when the transport device is in the resting phase.

A method and system for contextual search on a database based on dynamic reward function using relevancy count is disclosed. Generative artificial intelligence has challenges in handling complex natural language queries for database transactions that require contextual rather than direct column mappings. The method disclosed extracts contextually relevant data from relational databases using a combination of Retrieval-Augmented Generation (RAG) and a contextual Monte Carlo Tree Search (CMCTS) with a dynamic reward function. The method disclosed uses the dynamic reward function during the simulation step of the CMCTS on a Monte Carlo Tree by calculating a contextually relevant score of a current node in the Monte Carlo Tree. The method also uses a modified upper confidence bound during selection step of the CMCTS. The method disclosed solves the real-world problem of searching for companies relevant to a given topic or capability.

Conventionally, applications using machine learning models apply same models throughout the working of the application but there is a need to select and switch to appropriate models based on the input data for providing better results. Hence, embodiments of present disclosure provide a method and system for dynamic selection of machine learning models. The method receives input data, extracts features from it and derives a set of parameters from the features. Then, a model fit vector is generated as a weighted combination of the set of parameters. The weights are determined using an entropy-SHAP hybrid technique and further fine-tuned based on a fairness metric. Next, machine learning models are trained using the parameters having weights greater than a threshold value and a model score is calculated for each of the models. Best model is selected based on the model score.

Industrial structures are prone to external conditions resulting in leakage, faults etc. While easy-to install structure monitoring methods exist, sensor maintenance and in-situ inspection becomes arduous, if the structure is at an inaccessible location. For example, localization of leak event and the ability to differentiate leak and non-leak becomes an even more daunting challenge. Method and system disclosed herein facilitate wideband beam forming based target localization. Further to the wideband forming, the system identifies and processes a single channel audio frame from an identified microphone that is nearest to an angle of interest to generate a spatially filtered single channel audio frame. Further, the spatially filtered single channel audio frame is fused with a set of single frequency acoustic maps. Further, a target localization of an event of interest in the angle of interest is performed by fusing the spatial-spectral filtered single channel audio frame with the composite acoustic map.