A coil spring modeling apparatus includes a first attachment member disposed on a lower spring seat, a second attachment member disposed on an upper spring seat, an actuator unit formed of a Stewart-platform-type parallel mechanism, a hydraulic pressure supply device, a torsion detection mechanism, and a controller. The torsion detection mechanism is constituted of displacement gauges such as a linear variable differential transformer. The displacement gauges are provided on hydraulic cylinders, and detect amounts of displacement relative to the reference lengths of the hydraulic cylinders, respectively. The controller calculates a relative torsional angle between the first attachment member and the second attachment member based on the detected displacement.

An air conditioner controlling method includes: deciding on a supply-air temperature Ts and a return-air temperature Tr as specified design values; deciding that the total heat-exchange efficiency η is zero; selecting two or more stepped levels of the outdoor-air volume percentage α; deriving a linear function which expresses a relation between an outdoor-air temperature To and a mixed-air temperature Tm for each level of the outdoor-air volume percentage α by using the return-air temperature Tr and the total heat-exchange efficiency η; and deciding on the range where the mixed-air temperature Tm is lower than the supply-air temperature Ts for each of the linear functions and the range where the mixed-air temperature Tm in each linear function comes closest to the supply-air temperature Ts as compared with the mixed-air temperatures Tm in the other linear functions.

An on-line control and reaction process for pH adjustment and a control device for automatically adjusting pH value are provided. The process includes mixing a first enhancer and a second enhancer, optionally after diluting the first enhancer and/or the second enhancer with water, to form a mixture, setting a base pH value (δ1) and a target pH value (δ2), and adding pH adjuster into the mixture via a pH control unit and mixing to obtain a product with the target pH value. The pH control unit adjusts the adding of the pH adjuster by measuring or inputting certain parameters.

A method may be provided for controlling a laundry treating apparatus. A first speed rotating may control the drum to accelerate the drum to rotate at a first speed, a braking may apply a brake to stop the drum, a second speed accelerating may accelerate the drum to a second speed, a current sensing may sense a current being applied to the motor during the drum is accelerated to the second speed, and a laundry amount determining may determine a laundry amount based on the current sensed in the current sensing.

A sensor device to detect an audio wave and determine whether the audio wave is a gunshot is provided. The sensor device may include microphone, analog-to-digital converter (ADC), microcontroller, and switch. The ADC may convert audio waves received from microphone into a digital signal. The microcontroller connected to the ADC may compare the digital signal of the audio waves with predetermined audio wave to determine whether the audio waves meet audio threshold parameters. The switch operatively connected to the microcontroller may transmit a trip signal to a predetermined emergency management and response system upon the microcontroller determining that the audio waves meet the audio threshold parameters. A gunshot detection system including the sensor device and a method of detecting a gunshot are also provided.

The present disclosure provides an artificial neural network communicatively-coupled to at least one computer having one or more processors, including a plurality of neurons arranged in layers. The artificial neural network is arranged to receive a new neuron into a layer of the artificial neural network during training; the new neuron being added to the neural network when no other neuron in that layer for a selected output can learn a relationship associated with an input vector of a data set being learnt. The new neuron is updated with both the relationship which could not be learnt by any other neuron in that layer and a modified data set from a last trained neuron in that layer that contributes to the selected output of the neural network, wherein the modified data set is formed by copying all learnt relationships from the last trained neuron into the new neuron and modifying the copied relationship based upon the relationship which could not be learnt by any other neuron in that layer; and, one or more output neurons are updated to accept input from the new neuron. Methods and computer-readable media are also disclosed.

The present disclosure provides an artificial neural network communicatively-coupled to at least one computer having one or more processors, including a plurality of neurons arranged in layers. The artificial neural network is arranged to receive a new neuron into a layer of the artificial neural network during training; the new neuron being added to the neural network when no other neuron in that layer for a selected output can learn a relationship associated with an input vector of a data set being learnt. The new neuron is updated with both the relationship which could not be learnt by any other neuron in that layer and a modified data set from a last trained neuron in that layer that contributes to the selected output of the neural network, wherein the modified data set is formed by copying all learnt relationships from the last trained neuron into the new neuron and modifying the copied relationship based upon the relationship which could not be learnt by any other neuron in that layer; and, one or more output neurons are updated to accept input from the new neuron. Methods and computer-readable media are also disclosed.

In some embodiments, apparatuses and methods are provided herein useful to assess monitor, improve, and/or modify health and well-being as it relates to people associated with a habitable or other built environments or spaces therein. In some embodiments, an intervention assessment system and methods include one or more sensors for measuring aspects related to the built environment, a personal user device, and a control circuit configured to receive one or more measurements form the sensor(s), identify a problem with the built environment, and identify potential interventions based on the indicators associated with the problem. By one approach, a plurality of potential interventions may be ranked based on, for example, the ability to reduce the prevalence of the problem or indicator in the built environment, feasibility, cost, and timeliness. In some approaches, the system and method also select and may implement one or more interventions.

A rotating equipment system with in-line drive-sense circuit (DSC) electric power signal processing includes rotating equipment, in-line drive-sense circuits (DSCs), and one or more processing modules. The in-line DSCs receive input electrical power signals and generate motor drive signals for the rotating equipment. An in-line DSC receives an input electrical power signal, processes it to generate and output a motor drive signal to the rotating equipment via a single line and simultaneously senses the motor drive signal via the single line. Based on the sensing of the motor drive signal via the single line, the in-line DSC provides a digital signal to the one or more processing modules that receive and process the digital signal to determine information regarding one or more operational conditions of the rotating equipment, and based thereon, selectively facilitate one or more adaptation operations on the motor drive signal via the in-line DSC.

The present disclosure provides an artificial neural network communicatively-coupled to at least one computer having one or more processors, including a plurality of neurons arranged in layers. The artificial neural network is arranged to receive a new neuron into a layer of the artificial neural network during training; the new neuron is added to the neural network when no other neuron in that layer for a selected output can learn a relationship associated with an input vector of a data set being learnt. The new neuron is updated with both the relationship which could not be learnt by any other neuron in that layer and a modified data set from a last trained neuron in that layer that contributes to the selected output of the neural network. Methods and computer-readable media are also disclosed.