Abstract: A plurality of UPSs are connected in parallel between an AC power supply and a load. The UPSs each include a first switch connected between the AC power supply and an AC node. During the first power feeding mode, a control device turns on the first switch, and controls a power conversion device so as to receive AC power from the AC node and generate AC power. During a second power feeding mode, the control device turns off the first switch, and controls the power conversion device so as to receive DC power from a power storage device and generate AC power. When the AC power supply recovers during the second power feeding mode, the control device successively switches the plurality of UPSs to the first power feeding mode by successively turning on a plurality of the first switches each of which corresponds to each of the plurality of UPSs.

Abstract: A DC power feeding device includes a power feed line connected to a DC load, a first DC/DC converter provided between a solar battery and the power feed line, and a second DC/DC converter provided between a battery and the power feed line. A first controller controls the first DC/DC converter such that maximum power tracking control of the solar battery is performed in a normal state of a commercial AC power source. The first controller controls the first DC/DC converter such that the solar battery outputs a power smaller than a load power in a power failure. A second controller controls the second DC/DC converter such that the battery is charged in the normal state of the commercial AC power source. The second controller controls the second DC/DC converter such that DC voltage on the power feed line attains a reference voltage in the power failure.

Abstract: An air condition control device includes a calculating circuitry to calculate a control influence degree, intended for controlling air condition changing devices, at each of control points in a room space on the basis of positional information that indicates a position where each of the air condition changing devices is located, influence degree information that indicates an influence degree of an air condition exerted on each of locations by each of the air condition changing devices, and running condition information that indicates a running condition of each of the air condition changing devices. The control influence degree is an influence degree of an air condition exerted by the air condition changing devices. The air condition control device also includes a controlling circuit to control the air condition changing devices to increase the control influence degree at a control point, which is relatively low.

Abstract: A fluid coupling includes a cover, an impeller, a turbine, an output hub, a lock-up piston, and a wall member. The impeller is fixed to the cover. The turbine is disposed opposite to the impeller. The output hub outputs a torque transmitted thereto from the turbine. The lock-up piston is disposed between the cover and the turbine. The lock-up piston is disposed to be axially slidable on the output hub. The lock-up piston is configured to be engaged by friction with the cover. The wall member is attached to the output hub. The wall member defines a hydraulic chamber in cooperation with the lock-up piston.

Abstract: A power conversion device includes: a dead time application unit which applies a dead time to only one of a pair of pulse signals; a current polarity detection unit which detects a polarity of an output current; and a gate signal selection unit which, if the polarity of the output current is positive, selects the one pulse signal, to which the dead time has been applied, as a gate signal for a positive arm and selects the other pulse signal as a gate signal for a negative arm, and, if the polarity of the output current is negative, selects the one pulse signal, to which the dead time has been applied, as the gate signal for the negative arm and selects the other pulse signal as the gate signal for the positive arm.

Abstract: A learning apparatus comprises a learning part that learns an error correction model by a set of a speech recognition result candidate and a correct text of speech recognition for given audio data, wherein the speech recognition result candidate includes a speech recognition result candidate which is different from the correct text, and the error correction model is a model that receives a word sequence of the speech recognition result candidate as input and outputs an error correction score indicating likelihood of the word sequence of the speech recognition result candidate in consideration of a speech recognition error.

Abstract: Provided a speech recognition device capable of implementing end-to-end speech. recognition considering a context.

Abstract: Without dividing speech into a unit such as a word or a character, text corresponding to the speech is labeled. A speech distributed representation sequence converting unit 11 converts an acoustic feature sequence into a speech distributed representation. A symbol distributed representation converting unit 12 converts each symbol included in the symbol sequence corresponding to the acoustic feature sequence into a symbol distributed representation. A label estimation unit 13 estimates a label corresponding to the symbol from the fixed-length vector of the symbol generated using the speech distributed representation, the symbol distributed representation, and fixed-length vectors of previous and next symbols.

Abstract: A power conversion device includes: a three-phase inverter including a plurality of switching elements; and a controller configured to PWM-control the three-phase inverter based on three phase voltage commands. The controller generates a zero-phase voltage command using a two-phase modulation scheme and a third harmonic component of the three phase voltage commands. The controller adds the generated zero-phase voltage command to each of the three phase voltage commands to thereby correct each of the three phase voltage commands. The controller compares each of the corrected three phase voltage commands with a carrier wave, to thereby generate a control signal for controlling switching of the plurality of switching elements.

Abstract: A power conversion device includes: an inverter that includes a switching element and converts DC power into AC power; a current detector that detects an output current of the inverter; and a controller that controls the inverter to allow the output current detected by the current detector to follow a current command value having a sinusoidal waveform. The controller compares a current deviation of the output current from the current command value with a hysteresis band to control switching of the switching element. The controller sets the hysteresis band so that a hysteresis band in a zero-crossing area of the current command value is smaller than a hysteresis band in a peak area of the current command value.

Abstract: A composition includes cells of Lactobacillus paracasei MCC1849 (NITE BP-01633). The composition is in the form of a pharmaceutical composition, a food, a drink, or a feed. The pharmaceutical composition can be used for treating an inflammatory disease, ulcer, food allergies, bronchial asthma, urticaria, pollinosis, anaphylactic shock or an opportunistic infection.

Abstract: A tag estimation device capable of estimating, for an utterance made among several persons, a tag representing a result of analyzing the utterance is provided. The tag estimation device includes an utterance sequence information vector generation unit that adds a t-th utterance word feature vector and a t-th speaker vector to a (t?1)-th utterance sequence information vector ut-1 that includes an utterance word feature vector that precedes the t-th utterance word feature vector and a speaker vector that precedes the t-th speaker vector to generate a t-th utterance sequence information vector ut, where t is a natural number, and a tagging unit that determines a tag lt that represents a result of analyzing a t-th utterance from a model parameter set in advance and the t-th utterance sequence information vector ut.

Abstract: A surveying instrument includes: a survey system; an image sensing system, including first and second image sensing units, the second having an angle of view wider than that of the first; horizontal and vertical angle drivers to rotate the survey and image sensing systems around a surveying instrument vertical and horizontal axes, respectively; a data storage part; an angle detecting part; and a control unit to cause an image, based on image data the first or second generates after imaging, a design data object for showing the design data portion locations included in the image, and coordinate measurement point objects for showing the coordinate measurement points locations, to be surveyed, corresponding to the design data portion included in the image, to appear on a representation device in response to the design data stored in the data storage part and the detected angle.

Abstract: An air condition control device includes a calculation unit to calculate a control influence degree, intended for controlling air condition changing devices, at each of control points in a room space on the basis of positional information that indicates a position where each of the air condition changing devices is located, on the basis of influence degree information that indicates an influence degree of an air condition exerted on each of locations by each of the air condition changing devices, and on the basis of running condition information that indicates a running condition of each of the air condition changing devices, the control influence degree being an influence degree of an air condition exerted by the air condition changing devices, and a control unit to control the air condition changing devices so as to increase the control influence degree at a control point, which is relatively low.

Abstract: The present invention improves the accuracy of language prediction. A history speech meta-information understanding unit 11 obtains a history speech meta-information vector from a word string of a preceding speech using a meta-information understanding device. A history speech embedding unit 12 converts the word string of the preceding speech and a speaker label into a history speech embedding vector. A speech unit combination vector construction unit 13 obtains a speech unit combination vector by combining the history speech meta-information vector and the history speech embedding vector. A speech sequence embedding vector calculation unit 14 converts a plurality of speech unit combination vectors obtained for the past speech sequences to a speech sequence embedding vector. A language model score calculation unit 15 calculates a language model score of a current speech from a word string of the current speech, a speaker label, and a speech sequence embedding vector.

Abstract: A keyword is extracted robustly despite a voice recognition result including an error. A model storage unit 10 stores a keyword extraction model that accepts word vector representations of a plurality of words as an input and extracts and outputs a word vector representation of a word to be extracted as a keyword. A speech detection unit 11 detects a speech part from a voice signal. A voice recognition unit 12 executes voice recognition on the speech part of the voice signal and outputs a confusion network which is a voice recognition result. A word vector representation generating unit 13 generates a word vector representation including reliability of voice recognition with regard to each candidate word for each confusion set. A keyword extraction unit 14 inputs the word vector representation of the candidate word to the keyword extraction model in descending order of the reliability and obtains the word vector representation of the keyword.

Abstract: A voice recognition device 10 includes: a phonological awareness feature amount extraction unit 11 that transforms an acoustic feature amount sequence of input voice into a phonological awareness feature amount sequence for the language 1 using a first model parameter group; a phonological awareness feature amount extraction unit 12 that transforms the acoustic feature amount sequence of the input voice into a phonological awareness feature amount sequence for the language 2 using a second model parameter group; a phonological recognition unit 13 that generates a posterior probability sequence from the acoustic feature amount sequence of the input voice, the phonological awareness feature amount sequence for the language 1, and the phonological awareness feature amount sequence for the language 2 using a third model parameter group; and a voice text transformation unit 14 that performs voice recognition based on the posterior probability sequence to output text of a voice recognition result.

Abstract: The vehicle notification apparatus comprises: an operation element operated by a driver to activate a turn signal indicator; an information obtaining device that obtains information on an object around the vehicle; an auditory notification device configured to give an auditory notification; a visual notification device configured to give a visual notification; and a control unit.

Abstract: The vehicle notification apparatus comprises: an operation element operated by a driver to activate a turn signal indicator; an information obtaining device that obtains information on an object around the vehicle; an auditory notification device configured to give an auditory notification; a visual notification device configured to give a visual notification; and a control unit.

Abstract: Learning data is generated automatically without manually applying rules. An acoustic model learning data generation device 20 includes a stochastic attribute label generation model 21 that generates attribute labels from a first model parameter group according to a first probability distribution; a stochastic phoneme sequence generation model 22 that generates a phoneme sequence from a second model parameter group and the attribute labels according to a second probability distribution; and a stochastic acoustic feature quantity sequence generation model 23 that generates an acoustic feature quantity sequence from a third model parameter group, the attribute labels, and the phoneme sequence according to a third probability distribution.