A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application. It can be also generalized to apply on other energy systems in steel industry.

Aspects relate to apparatuses and methods for rating the quality of a posting. An exemplary apparatus includes at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to acquire a plurality of inputs from at least a posting, classify the posting to a posting category as a function of the plurality of inputs, calculate a quality metric as a function of the posting category and the plurality of inputs, wherein the quality metric reflects a level of completeness regarding the arrangement of inputs in a posting, and generate, as a function of the quality metric, a ordering of the posting, wherein the order relates to a probable level of focus a user may use to fill the posting.

A device is provided. The device includes a processor and a memory configured to store instructions executable by the processor. The processor is configured to execute the instructions to extract context information from displayed data based on an application which is being executed by the device, identify an identifier from the context information, search for at least one recommended contact related to the identifier based on the identifier and a relation graph obtained by inputting information regarding a communication between a plurality of users into a first training model for determining an association between the plurality of users, identify a priority of the at least one recommended contact, and control to display the at least one recommended contact according to the priority.

Methods and systems for optimizing models for deployment based on similar models. In one embodiment, a method is provided that includes receiving a model for deployment in a computing environment and extracting a plurality of operations from the model. The operations may be categorized based on predefined operation categories to form categorized operations and a model summary score may be calculated based on the categorized operations. A similar model may be identified with a similar model summary score to the model similarity score of the model. The model may be updated based on an optimization operation performed on the similar model and the model may be deployed within the computing environment.

The present invention relates generally to the field of providing a computer-implemented system and method that supports ad hoc multilingual Natural Language database questions that are transformed in realtime to Database queries using a suite of cascading Deep Neural Networks which are initially and continuously trained through schema information and question-query examples.

A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.

A method and a dishwasher capable of analyzing the closure state of the filter of the dishwasher by using a deep neural network model trained through machine learning of artificial intelligence is provided. The dishwasher may include a microphone configured to obtain a drainage sound signal generated by draining washing water through the filter during operation of the dishwasher, a processor configured to analyze the degree of closure of the filter based on the drainage sound signal, and an alarm generator configured to generate an alarm if the degree of closure is a greater than or equal to a predetermined level. The operational state of the dishwasher may be analyzed based on a drainage sound of the washing water drained to the filter by the degree of closure of the filter.

A system comprises a memory that stores computer-executable components; and a processor, operably coupled to the memory, that executes the computer-executable components. The system includes a receiving component that receives a corpus of data; a relation extraction component that generates noisy knowledge graphs from the corpus; and a training component that acquires global representations of entities and relation by training from output of the relation extraction component.

Described are techniques for automated anomaly detection including a technique comprising training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure. The technique further comprises inputting aggregated time series data to the ensemble of deep learning models and identifying anomalies in the aggregated time series data based on respective portions of the aggregated time series data that are indicated as anomalous by a majority of deep learning models in the ensemble of deep learning models. The technique further comprises grouping the anomalies according to relationships between the anomalies and performing a mitigation action in response to grouping the anomalies.