Abstract: An engine intake system control device configured to control an intake system of an engine, having a map function that inputs at least a fuel injection pressure of the engine, a fresh air flow, and a compressor outlet temperature of a supercharger, and outputs a control gain; and a control unit that inputs the control gain and a deviation between a controlled variable of the intake system of the engine and a target value thereof, and controls a manipulated variable of the intake system of the engine.

Abstract: A non-linear echo is allowed to be suppressed and voice deterioration is allowed to be suppressed. An echo suppression device for suppressing an echo generated by inputting a voice signal output from a speaker to a microphone includes a compressor on a receiving signal path for transmitting a signal of a receiving signal to the speaker. When a double-talk state has been detected, the compressor performs a compression process on a signal greater than a first threshold among the receiving signals.

Abstract: An engine control device includes a model that, based on engine operating condition and first-type operation amount, reproduces at least one index from among various indexes of combustion state of engine, and a processor that executes a process including deciding on second-type operation amount, by optimization using the model so as to treat at least one of the indexes, which are reproduced by the model, as estimated value of control amount, and ensure that the estimated value of the control amount follows control target value, associating the second-type operation amount with the control target value and the engine operating condition, rewriting a learning control table in which operation amount corresponding to the control target value and the engine operating condition is registered, and calculating operation amount according to the learning control table based on the control target value and the engine operating condition.

Abstract: A method including acquiring a first test pattern in which an operation variable for an engine test is changed in time series, inputting a first operation variable to a mathematical model representing a time series response obtained by inputting a test pattern as a simulation of the engine test, monitoring a first monitoring parameter obtained by inputting the first operation variable to the mathematical model, holding the first operation variable until the first monitoring parameter is less than a first threshold value, creating a history of the first operation variable in the simulation as a second test pattern, monitoring a second monitoring parameter obtained by inputting a second operation variable to a real engine based on the second test pattern, holding the second operation variable until the second monitoring parameter is less than a second threshold value, acquiring time series data of the second operation variable and a controlled variable.

Abstract: This ground short failure detection device can improve the accuracy of determining a ground short failure. The ground short failure detection device changes the potential of each of two signal lines, and transmits or receives data between a plurality of node devices. The ground short failure detection device comprises: a current measurement unit that measures a current value of a current flowing through each of the two signal lines; a determination unit that determines whether a difference between the measured current values exceeds a predetermined value, and when the difference exceeds the predetermined value, outputs a result indicating that a ground short failure occurs in a low-potential side signal line that has a relatively low potential in a dominant level among the two signal lines; and a control unit that controls the determination unit to determine whether the difference exceeds the predetermined value when a predetermined signal is completely transmitted.

Abstract: A control device includes a controller configured to: obtain a driving condition and an actual value of a controlled amount of an engine; use a transformed engine model to calculate an estimate value of a future controlled amount, from the obtained driving condition and a candidate value of an operation amount for controlling the controlled amount, the transformed engine model in which an activation function of each neuron is transformed using a linear inequality function having a binary variable, the transformed engine model for which a weight coefficient, bias, and upper and lower limits of an input/output variable of the each neuron are set based on an engine model that has a multilayer neural structure and the activation function of a ReLU structure, receives the driving condition and operation amount as input, and predicts dynamic characteristics of the engine; determine a value of the operation amount, based on error between the calculated estimate value and a target value of the controlled amount ident

Abstract: A small-sized onboard device that includes a speaker and a microphone and is configured to reduce noise caused by an echo is provided. The speaker is provided inside a housing that forms a cavity inside so that a sound emission surface of the speaker faces a direction substantially orthogonal to a front surface, and a first opening is formed in a front surface of the housing. A microphone case and a sound guide member are juxtaposed on the front surface of the housing. A microphone is housed inside the microphone case. The microphone case has a sound pickup hole in a front surface of the microphone case. The sound guide member has a sound guide hole that passes through the sound guide member. The first opening is provided in a vicinity of a top surface or a bottom surface of the housing.

Abstract: A control device includes a controller configured to: obtain a driving condition and an actual value of a controlled amount of an engine; use a transformed engine model to calculate an estimate value of a future controlled amount, from the obtained driving condition and a candidate value of an operation amount for controlling the controlled amount, the transformed engine model in which an activation function of each neuron is transformed using a linear inequality function having a binary variable, the transformed engine model for which a weight coefficient, bias, and upper and lower limits of an input/output variable of the each neuron are set based on an engine model that has a multilayer neural structure and the activation function of a ReLU structure, receives the driving condition and operation amount as input, and predicts dynamic characteristics of the engine; determine a value of the operation amount, based on error between the calculated estimate value and a target value of the controlled amount ident

Abstract: A control device calculates a first estimate value of a first monitored amount of a mobile object, from sensor information of a controlled object, using a transformed first model obtained by transformation of a first model having a multi-layered neural structure and activation functions of a ReLU structure, and predicting dynamic behavior of the mobile object. The transformed first model is a model for which weight coefficients, biases, and upper and lower limit values of input/output variables of neurons are set based on the first model and in which activation functions of the neurons are transformed using a linear inequality function having a binary variable. The control device calculates a second estimate value of a second monitored amount of the controlled object from a candidate value of an operation amount for controlling the second monitored amount.

Abstract: An engine control device includes a model that, based on engine operating condition and first-type operation amount, reproduces at least one index from among various indexes of combustion state of engine, and a processor that executes a process including deciding on second-type operation amount, by optimization using the model so as to treat at least one of the indexes, which are reproduced by the model, as estimated value of control amount, and ensure that the estimated value of the control amount follows control target value, associating the second-type operation amount with the control target value and the engine operating condition, rewriting a learning control table in which operation amount corresponding to the control target value and the engine operating condition is registered, and calculating operation amount according to the learning control table based on the control target value and the engine operating condition.

Abstract: An engine test method that causes a computer to execute a process including, acquiring, by a processer on the computer, a first test pattern in which an operation variable that is used for an engine test is changed in time series, inputting, based on the first test pattern, a first operation variable to a mathematical model that represents a time series response of an engine obtained by inputting a test pattern as a simulation of the engine test, monitoring, as a first monitoring parameter of engine abnormality, at least one of an air excess ratio, pressure and temperature of an intake manifold, pressure and temperature of an exhaust manifold, and a maximum cylinder pressure rise rate that are obtained by inputting the first operation variable to the mathematical model, holding, when the first monitoring parameter exceeds a first threshold value, the first operation variable until the first monitoring parameter is less than the first threshold value, creating, a history of the first operation variable in the

Abstract: An engine model construction method includes generating test patterns in which a plurality of manipulated variables used for an engine test are changed with time, correcting the test patterns based on first coverage of a first space of manipulated variables are allowed to take and second coverage of a second space of change rate values of the manipulated variables are allowed to take, acquiring pieces of time series data of operation amounts of the manipulated variables and controlled amounts with respect to the manipulated variables by performing an engine test using the corrected test patterns, and constructing a first engine model by performing machine learning on training data in which the corrected test patterns are adopted as input and the pieces of time series data are adopted as correct answers, by a processor.

Abstract: An engine torque estimating apparatus having a processor and a memory accessed by the processor. The processor performs a torque estimating that calculates time series data of an estimated indicated torque, based on a crank angle having been extracted from an output of a crank angle sensor of an engine including a plurality of cylinders; an estimated indicated torque-related value extracting that extracts an estimated indicated torque-related value, for each of the cylinders, from the time series data of the estimated indicated torque, for each of the cylinders; and average indicated torque correct value acquiring that converts, for each of the cylinders, the estimated indicated torque-related value into an average indicated torque correct value having been calculated based on a cylinder internal state of the engine in correspondence to the estimated indicated torque-related value.

Abstract: An echo is allowed to be effectively removed while a processing load on an arithmetic device is reduced. When a magnitude of a reference signal transmitted to a speaker is equal to or greater than a first threshold, a learning algorithm using nonlinear processing is selected. When the magnitude of the reference signal is smaller than the first threshold, a learning algorithm using linear processing is selected. The echo is learnt using the selected learning algorithm to remove an echo included in an input signal picked up by a microphone based on a learning result.

Abstract: An engine test method includes generating a test pattern in which a plurality of manipulated variables used for an engine test change in chronological order, correcting the test pattern based on an excess air ratio, and performing an engine test using the corrected test pattern to acquire time-series data on the manipulated variables and controlled amounts of the manipulated variables.

Abstract: An engine model construction method includes generating test patterns in which a plurality of manipulated variables used for an engine test are changed with time, correcting the test patterns based on first coverage of a first space of manipulated variables are allowed to take and second coverage of a second space of change rate values of the manipulated variables are allowed to take, acquiring pieces of time series data of operation amounts of the manipulated variables and controlled amounts with respect to the manipulated variables by performing an engine test using the corrected test patterns, and constructing a first engine model by performing machine learning on training data in which the corrected test patterns are adopted as input and the pieces of time series data are adopted as correct answers, by a processor.

Abstract: A method for generating a neural network model, the method includes acquiring first time series data having a first period that is shorter than an operation period of the neural network model; extracting, from the first time series data, a plurality of sets of second time series data having a second period that is longer than the first period; and executing training on the neural network model using training data that include the plurality of sets of second time series data.

Abstract: An engine control device includes an engine model having a neural network that inputs a manipulated variable of the engine and computes a controlled variable; and a controller that computes the manipulated variable so as to reduce a deviation between the controlled variable and a target controlled variable. The neural network includes an input layer to which the manipulated variable are input; a first hidden layer including a first fully connected layer; a second hidden later including a second fully connected layer that generates a plurality of second output values at a first time and has a return path on which the plurality of second output values at a second time, earlier than the first time, are input into the second fully connected layer; and an output layer from which the plurality of second output values at the first time are output as the controlled variable.

Abstract: An engine torque estimating apparatus having a processor and a memory accessed by the processor. The processor performs a torque estimating that calculates time series data of an estimated indicated torque, based on a crank angle having been extracted from an output of a crank angle sensor of an engine including a plurality of cylinders; an estimated indicated torque-related value extracting that extracts an estimated indicated torque-related value, for each of the cylinders, from the time series data of the estimated indicated torque, for each of the cylinders; and average indicated torque correct value acquiring that converts, for each of the cylinders, the estimated indicated torque-related value into an average indicated torque correct value having been calculated based on a cylinder internal state of the engine in correspondence to the estimated indicated torque-related value.

Abstract: An injector injection amount control device includes: acquiring a measured value of a crank angle that is a rotation angle of a crankshaft of an engine, determining instruction injection amounts of fuel from respective injectors, the respective injectors corresponding to a plurality of cylinders of the engine, estimating a torque arising in each of the plurality of cylinders of the engine based on a non-linear Kalman filter employing an error between the acquired measured value of the crank angle and an estimate value of the crank angle, and correcting the instruction injection amounts for the respective injectors corresponding to the plurality of cylinders such that each of the plurality of estimated torques falls within the same range.