Abstract: A method and an apparatus are provided that may be applicable to refrigeration assets, including refrigeration plants and cold-storage facilities comprising large numbers of refrigeration assets. The performance of refrigerators and refrigeration devices may be monitored based on temperature and electrical current and other measurements known or deemed to be correlated with refrigeration performance. Refrigeration assets in need of repair may be identified and a repair process may be specified, classified, managed and optimized. The immediate effectiveness and long-term persistence of repairs may be determined over time. The refrigeration assets may be classified according to reliability, performance, make, model and manufacturer. The effectiveness of repairs may be classified based on measured performance results following repairs.

Abstract: Systems and methods for remote monitoring of mechanical fire suppression systems are provided. For example, some embodiments generally provide for a smart fire suppression system with integrated sensors and communication technology to monitor the current state of the fire suppression system and notify various monitoring platforms, service providers, equipment manufacturers and/or others of the current state. In some embodiments, various sensors (e.g., micro switches) can be used to detect and report the status of the cartridge, activation status of the system, and/or the status of the detection line. Additional sensors and actuators may be also be included within the smart fire suppression system to allow monitoring of different states (e.g., temperature) and control the appliance and/or building utilities.

Abstract: Real-time quality control of a culture for bioproduction is facilitated using machine learning. In this approach, real-time process data for a set of parameters for a current production run is received. Based on this process data, a prediction is made using an instance of a machine learning model that has been trained on process data from past production or development runs. The instance is uniquely associated to a particular culture day and thus independent of any other instance of the machine learning model (for other culture days). Based on the prediction, a quality control recommendation for the current production run is then made. Several different types of predictions are enabled, and various different recommendations are provided based on the predictions.

Abstract: A temperature demodulation method oriented toward a distributed fiber Raman temperature sensing system, the method comprising the following steps: step 1 of constructing a high-precision temperature detection device oriented towards a distributed fiber Raman sensing system; step 2 of performing signal processing with respect to Stokes light and anti-Stokes light at a calibration stage; step 3 of performing signal processing with respect to Stokes light and the anti-Stokes light at a measurement stage; and step 4 of obtaining a high-precision temperature demodulation technique oriented toward the distributed fiber Raman sensor. The method is used to effectively resolve the issue of low temperature measuring accuracy caused by Rayleigh crosstalk in existing distributed fiber Raman temperature measurement systems, and temperature measurement accuracy thereof is expected to fall within ±0.1° C. The method is applicable to distributed fiber Raman temperature measurement systems.

Abstract: A method of using an inspection processor includes receiving one or more articles at an inspection station, and identifying, by the inspection processor, one of the articles by reading a unique identifier associated with the one of the articles. The method also includes inspecting the article based on at least one quality criterion, and updating an article tracking system based on the result of the inspection and the identifier.

Abstract: Techniques described herein use backpropagation to train one or more machine learning (ML) models of a dialog system. For instance, a method includes accessing seed data that includes training tuples, where each training tuple comprising a respective logical form. The method includes converting the logical form of a training tuple to a converted logical form, by applying to the logical form a text-to-speech (TTS) subsystem, an automatic speech recognition (ASR) subsystem, and a semantic parser of a dialog system. The method includes determining a training signal by using an objective function to compare the converted logical form to the logical form. The method further includes training the TTS subsystem, the ASR subsystem, and the semantic parser via backpropagation based on the training signal. As a result of the training by backpropagation, the machine learning models are tuned work effectively together within a pipeline of the dialog system.

Abstract: A system and method for quantitative image quality assessment for photogrammetry are disclosed.

Abstract: A communication transmission device is provided with an input sound level detection unit for dividing, per unit time, sound data of a predetermined period of time and converting the sound data into a bit string according to whether or not a sound level thereof exceeds a predetermined threshold value, an arithmetic processing unit for performing predetermined arithmetic processing on the sound data an output sound level detection unit for dividing, per the unit time, sound data of the predetermined period of time after the arithmetic processing and converting the sound data into a bit string according to whether or not a sound level thereof exceeds the predetermined threshold value, and a comparison determination unit for determining whether or not a sound failure has occurred on the basis of a predetermined logic by which the bit string before the arithmetic processing and the bit string after the arithmetic processing are compared.

Abstract: Methods, systems, and apparatuses are described for monitoring of one or more A/V collection devices within a premises. Based on the monitoring, one or more computing devices may determine whether to route or block a data transmission outside a premises. The data transmission may be routed or blocked based on, for example, one or more of the following: whether a transmission window associated with an A/V collection device is open or closed; whether the data transmission is being sent to an authorized destination device; whether the data transmission is encrypted, and the like.

Abstract: An anomaly data detecting method performed by a computing device having at least one processor includes acquiring first time-series data, dividing the first time-series data into a plurality of sub time-series data, adjusting scales of variable values included in at least one sub time-series data among the plurality of sub time-series data and determining whether the first time-series data is abnormal by inputting scaled first time-series data to a neural network based detection model.

Abstract: An audio device for improved talker discrimination is provided. To improve suppression of close talker interference, i.e., of a person talking in close proximity to the user of the audio device, the audio device comprises at least an audio input to receive a voice input signal; a filter bank, configured to provide a plurality of sub-band signals from the voice input signal; a multi-band audio expander, arranged to receive at least a group of the sub-band signals and configured to conduct audio expansion on the group of sub-band signals to provide gain-controlled sub-band signals; and an audio output, configured to provide a voice output signal from at least the gain-controlled sub-band signals.

Abstract: A method of determining a user's exposure to pollution, the method comprising sensing environmental conditions by a detector device, transmitting sensed environmental conditions data from the detector device to a smart device, and alerting the user about personal pollutant exposure by the smart device.

Abstract: Techniques enable an automatic adjustment of a muted response setting of an automated assistant based on a determination of an expectation by a user to hear an audible response to their query, despite the muted setting. Determination of the expectation may be based on historical, empirical data uploaded from multiple users over time for a given response scenario. For example, the system may determine from the historical data that a certain type of query has been associated with a user both repeating their query and increasing a response volume setting within a given timeframe. Metrics may be generated, stored, and invoked in response to attributes associated with identifiable types of queries and query scenarios. Automated response characteristics meant to reduce inefficiencies may be associated with certain queries that can otherwise collectively burden network bandwidth and processing resources.

Abstract: A radio frequency connection between a far field voice detection device and a further device is used to determine a first angular direction from the far field voice detection device to the further device. The determined first angular direction is then used to emphasize, during a noise processing of a plurality of sounds received via use of a plurality of microphones of the far field voice detection device, a first one of the plurality of sounds relative to a remainder of the plurality of sounds.

Abstract: Provided are a diagnosing device, a diagnosing method, and a program with which it is possible to detect abnormalities accurately even if there is a small amount of data or the number of data points varies. This diagnosing device is provided with a Mahalanobis distance calculating unit which calculates the Mahalanobis distance (referred to as ‘MD value’ hereinbelow) of a detected value, and an abnormality determining unit which determines the presence or absence of an abnormality on the basis of the MD value, wherein the abnormality determining unit determines the presence or absence of an abnormality by arranging that a determination that there is no abnormality is more likely to occur if the number of samples per unit space is small than if the number of samples per unit space is large.

Abstract: Methods and systems for answering frequently asked questions are described. An utterance is received. A decision score that is indicative of the likelihood that the utterance is answerable according to a set of frequently asked questions and associated answers is determined for the utterance. A candidate answer from the associated answers and a selection score for the candidate answer are determined for the utterance. A total score for the candidate answer is determined based on the decision score and the selection score. The total score is indicative of the likelihood that the candidate answer is a correct answer for the utterance according to the set of frequently asked questions and associated answers.

Abstract: Described herein is a system for sentiment detection in audio data. The system is trained using acoustic information and lexical information to determine a sentiment corresponding to an utterance. In some cases when lexical information is not available, the system (trained on acoustic and lexical information) is configured to determine a sentiment using only acoustic information.

Abstract: A quality assessment method for automatic annotation of speech data is provided and includes: building a base rule-base of automatically annotated speech data based on quality key indicators; reading automatically annotated speech data to be detected, and performing quality detection on the automatically annotated speech data to be detected according to the quality key indicators to thereby complete quality measurement; updating an automatically annotated speech dataset according to a result of the quality measurement; and importing the automatically annotated speech dataset after the updating into the base rule-base. The shortcomings of using traditional quality assessment methods for data annotation in automatic machine annotations can be overcome, and it can play a very positive supporting role in promoting the development of ethnic minority speech intelligence.

Abstract: Techniques for detecting certain acoustic events from audio data are described. A system may perform event aggregation for certain types of events before sending an output to a device representing the event is detected. The system may bypass the event aggregation process for certain types of events that the system may detect with a high level of confidence. In such cases, the system may send an output to the device when the event is detected. The system may be used to detect acoustic events representing presence of a person or other harmful circumstances (such as, fire, smoke, etc.) in a home, an office, a store, or other types of indoor settings.

Abstract: Methods, apparatuses, and systems are provided for detecting overhead obstructions along a path segment. One exemplary method includes receiving three-dimensional data collected by a depth sensing device traveling along a path segment, wherein the three-dimensional data comprises point cloud data positioned above a ground plane of the path segment. The method further includes identifying data points of the point cloud data positioned within a corridor positioned above the ground plane. The method further includes projecting the identified data points onto a plane. The method further includes detecting the overhead obstruction based on a concentration of point cloud data positioned within a plurality of cells of the plane. The method further includes storing the detected overhead obstruction above the path segment within a map database.