Abstract: Some embodiments relate to an overlapping electric loads operating mode detection method. The method includes measuring an aggregated power signal of an electrical outlet and determining an operating mode for the aggregated power signal. The operating mode is determined to be one of an active mode, a lower power mode, or switched off. On determining the operating mode is an active mode, the method includes determining a load category for the aggregated power signal; selecting a corresponding load category signature power signal from a load category database; evaluating spectral coherence between the aggregated power signal and the load category signature power signal; determining an overall probability of coherence between frequency components of the aggregated power signal and the load category signature power signal; determining if a low power mode is present within the active mode aggregated power signal; and determining the operating modes of each of the overlapping electric loads.

Abstract: There is provided a method and a system to adaptively control a vehicle inverter system that is operable to power a vehicle powertrain, wherein the vehicle powertrain comprises a plurality of operating modes. The method and system comprises adaptively controlling the AC power output of the vehicle inverter system to vary the AC power output of the vehicle inverter system for the vehicle powertrain according to each operating mode of the vehicle powertrain. The method and system further comprises identifying the operating mode of the vehicle powertrain in real-time and adaptively controlling the AC power output of the vehicle inverter system according to the identified real-time operating mode of the vehicle powertrain.

Abstract: A disaggregation and identification method arranged to disaggregate and identify aggregated electrical load data. The method comprises obtaining operation data from an automated control management system; extracting a timing sequence from the operation data for each load in the automated system; storing each timing sequence in a datastore; streaming aggregated power data from a load center associated with the system, wherein the power data comprises measured electrical signals; and recording time stamps for each new event measured from the streamed aggregated power data, wherein an event is a change in power signal at a load. The method further comprises performing nearest neighbour comparison of the recorded power time series data to the timing sequence of the operation data; mapping each event timestamp to the nearest time sequence; classifying the load according to the mapped time data; and storing the classified load profile in a datastore.

Abstract: A method of detecting states of an electrical load from an electrical signal. This involves first providing a mathematical model for the electrical signal. A sliding window can then be used to es-timate parameters of the model. In doing this, a plurality of windows are determined for the electrical signal, a window function is applied for each of the windows, and parameters of the model are determined by interpola-tion. The waveform can then be reconstructed from the determined para-meters and subtracted from the reconstructed waveform from the original signal to obtain a residual. State transitions can then be determined where a difference between the reconstructed waveform and the original signal exceeds a threshold. States are detected and determined as existing for the time period between successive state transitions. Methods of monitoring an electrical system using this approach are also described, as are electrical devices adapted to perform such methods.

Abstract: There is provided a data transmission and compression method arranged to compress and transmit data between an edge device and a remote server. The method comprises collecting data at the edge device, wherein the data is attributed to a plurality of signal signatures; generating a data matrix at the edge device; transforming the data matrix, wherein transforming the data comprises using a wavelet transform; compressing the data, wherein compressing the data comprises utilising an autoencoder; and transmitting the encoded compressed data to the remote server via a communication channel. The method further comprises, at the remote server, decompressing the data utilising an autoencoder; reconstructing the signal signatures using an inverse wavelet transform; and storing the reconstructed data signatures in a datastore on the remote server.

Abstract: A method for compressing a signal, the method comprising: acquiring, via a signal recording module, a primary signal; modelling, via a processor, a model signal of the primary signal by: acquiring, via the processor, a sampled signal; acquiring, via the processor, a windowed signal; and extracting, via the processor: a fundamental frequency waveform having a fundamental magnitude and a fundamental phase; and at least one harmonic frequency waveform having a harmonic magnitude and a harmonic phase; wherein the model signal comprises the fundamental frequency waveform and the at least one harmonic frequency waveform; calculating, via the processor, an error signal between a reconstructed signal and the primary signal; determining, via the processor, an optimal gain from at least; an averaging step providing an average value, a predefined threshold, and a scaled signal.

Abstract: A method of estimating power usage in an electricity distribution apparatus for a plurality of electrical loads, the electricity distribution apparatus comprising an electrical circuit including a plurality of branch circuits arranged in parallel, each branch circuit being coupled to one or more of the plurality of electrical loads, the electrical distribution apparatus being configured to distribute electrical power, received via a supply line from a supply of electrical power, across the electrical circuit, the method comprising: measuring voltage across at least one of the plurality of branch circuits; measuring current in a monitored branch circuit of the plurality of branch circuits; and detecting a first type of load change event if there is a change in the measured current and a corresponding change in the measured voltage, wherein the change in the measured current and the corresponding change in the measured voltage correspond to a change of load.

Abstract: Methods of detecting and characterising a noise event are described, together with apparatus adapted to perform such methods. A method of characterising a noise event, involves locating microphones in an environment and generating a training event at a location in the environment as a reference for a noise event. A sound sample is recorded at each microphone and phase differences between the sound samples are used to establish a noise event signature for an event at that location. A noise event may subsequently be identified by taking sound samples from the microphones associated with the noise event and using phase differences between them to identify the noise event by matching against noise event signatures. A computing system adapted to perform such methods is also described.

Abstract: A sound source localization apparatus configured to identify a location for an active sound object within a defined physical space is described. The sound source localization apparatus may include an acoustic component, a thermal component, and an analysis component. The acoustic component determines an approximate location for at least one sound object within the defined physical space. The thermal component determines an approximate location for at least one thermal object within the defined physical space. The analysis component identifies the active sound object when the approximate locations for at least one acoustic object and at least one thermal object match. Other embodiments are described and claimed.

Abstract: A multimodal object tracking apparatus configured to identify a location for a target object within a defined physical space is described. The object tracking apparatus may include an acoustic component, a thermal component, and an analysis component. The acoustic component determines an approximate location for at least one sound object within the defined physical space. The thermal component determines an approximate location for at least one thermal object within the defined physical space. The analysis component identifies the target object when the approximate locations for at least one acoustic object and at least one thermal object match. Other embodiments are described and claimed.

Abstract: Orchestration and/or management of services on deployed computing, electronic, appliance and like devices are described. A service orchestration and/or management system (“SOMS”) may be configured to provide for orchestration and/or management of services to one or more deployed devices. The SOMS may obtain information from deployed devices, such as status, capabilities and service metadata. The SOMS may then encapsulate and store the obtained information for later use. The SOMS may also store service information, facilitate creation of services based on stored service definition meta-data, modeling or simulating a service prior to deployment, and deployment of a service to remotely deployed devices followed by activation to begin operation of the service. Other embodiments may be described and claimed.

Abstract: Techniques are disclosed for scene analysis including the use of acoustic imaging and computer audio vision processes for monitoring applications. In some embodiments, an acoustic image device is utilized with a microphone array, image sensor, acoustic image controller, and a controller. In some cases, the controller analyzes at least a portion of the spatial spectrum within the acoustic image data to detect sound variations by identifying regions of pixels having intensities exceeding a particular threshold. In addition, the controller can detect two or more co-occurring sound events based on the relative distance between pixels with intensities exceeding the threshold.