Abstract: A method of treating the surface of a metal base which is conducted prior to cationic electrodeposition coating and is used for improving throwing power in the cationic electrodeposition coating; a metallic material treated by the surface treatment method; and a method of coating this metallic material.

Abstract: A positioning device operates in cooperation with a signal receiver for receiving a plurality of signal waves arriving from at least one synchronous transmission unit including a plurality of signal transmitters that operates in synchronization with each other. The positioning device includes: a time-of-arrival detecting unit for detecting time of arrival of each of the plurality of signal waves on the basis of a reception signal output from the signal receiver and a distance difference calculating unit for calculating a difference in distance from the plurality of signal transmitters to the signal receiver as a set of observation values on the basis of a difference in the time of arrival that has been detected; and a positioning arithmetic unit.

Abstract: The state prediction device smoothes a state vector including a flow rate and a water level of a tsunami at each of a plurality of points set two-dimensionally in a region including a coverage area, by using flow velocity observed values of a sea surface in a plurality of cells extending in a plurality of range directions and a plurality of beam directions in the coverage area of a radar.

Abstract: A detection area determining unit (6) determines a detection area where there is a possibility that a tsunami occurs. A current velocity estimating unit (7) estimates, as a sea-surface velocity of current in the detection area, a value obtained by smoothing sea-surface velocity of currents corresponding to cells included in the detection area. A tsunami detecting unit (8) detects occurrence of the tsunami in the detection area, based on the sea-surface velocity of current estimated by the current velocity estimating unit (7).

Abstract: A radar device includes processing circuitry to select multiple combinations of one or more cells consecutively arranged, out of multiple cells included in an observation region, assume that an object of observation is present in each of the selected cell combinations, and set up each of objects of observation as a target candidate, and calculate the flow rate of a cell in which each target candidate is present, by using the flow velocity of the cell in which the target candidate is present, out of the calculated flow velocities and determine whether each target candidate has a possibility of being an object of observation on the basis of the flow rate are disposed, and specify a target candidate which has been determined continuously with respect to time to have a possibility of being an object of observation, and recognize the specified target candidate as an object of observation.

Abstract: A detection area determining unit (6) determines a detection area where there is a possibility that a tsunami occurs. A current velocity estimating unit (7) estimates, as a sea-surface velocity of current in the detection area, a value obtained by smoothing sea-surface velocity of currents corresponding to cells included in the detection area. A tsunami detecting unit (8) detects occurrence of the tsunami in the detection area, based on the sea-surface velocity of current estimated by the current velocity estimating unit (7).

Abstract: A route prediction unit estimates a route of an object of interest with respect to a target object based on collision avoidance models. A collision risk estimation unit calculates collision risks between the object of interest and target object for each collision avoidance model. A collision deciding unit decides the presence or absence of a collision from the collision risks and feeds back a collision avoidance model correction value to the route prediction unit when it is determined that the collision occurs. A collision avoidance route selector selects any of the plurality of collision avoidance models in which the absence of collision is decided by the collision deciding unit, and selects a route of the collision avoidance model as a route for avoiding the collision between the objects. The route prediction unit performs a new route prediction using the collision avoidance model correction value.

Abstract: A configuration includes: a target-motion prediction unit (5) that calculates predicted movement ranges of one or more other vehicles (60) based on target tracking; an inter-target collision possibility estimator (6) that estimates a collision possibility based on overlap of predicted movement ranges of the other vehicles (60); a target-motion re-prediction unit (7) that again calculates predicted movement ranges to avoid collision when the collision possibility between the other vehicles (60) exists; an own-motion prediction unit (10) that calculates a predicted movement range of a user's vehicle (50) based on an observation result; and an own-collision possibility estimator (11) that estimates collision possibilities between the user's vehicle (50) and the other vehicles (60) based on overlap of the final predicted movement ranges of the other vehicles (60) and the predicted movement range of the user's vehicle (50).

Abstract: A light-emitting device includes a light source unit including a plurality of laser light sources; a dioptric system that refracts each light ray input from each of the plurality of laser light sources; a condensing optical system that condenses a plurality of refracted light rays input from the dioptric system, respectively, wherein the dioptric system is configured to refract a plurality of center light beams that are output along optical axes of the plurality of laser light sources, respectively, to proceed in directions each departing from an optical axis of the condensing optical system as proceeding toward the condensing optical system.

Abstract: A waveform estimation device includes: tracking filter processing units 11 and 12 that execute tracking filter processing on observation values using tracking filters having different drive noises; and a filter output selection unit 14 that selects a regular waveform component predicted by the tracking filter processing unit 11, in an irregular section, and selects a regular waveform component predicted by the tracking filter processing unit 12, in a regular section. A residual processing unit 16 calculates a residual between observation values extracted by an observation value extraction unit 1, and a regular waveform component selected by the filter output selection unit 14. An irregular waveform detection unit 17 determines whether the residual is an irregular waveform component.

Abstract: A waveform estimation device includes: tracking filter processing units 11 and 12 that execute tracking filter processing on observation values using tracking filters having different drive noises; and a filter output selection unit 14 that selects a regular waveform component predicted by the tracking filter processing unit 11, in an irregular section, and selects a regular waveform component predicted by the tracking filter processing unit 12, in a regular section. A residual processing unit 16 calculates a residual between observation values extracted by an observation value extraction unit 1, and a regular waveform component selected by the filter output selection unit 14. An irregular waveform detection unit 17 determines whether the residual is an irregular waveform component.

Abstract: A configuration includes: a target-motion prediction unit (5) that calculates predicted movement ranges of one or more other vehicles (60) based on target tracking; an inter-target collision possibility estimator (6) that estimates a collision possibility based on overlap of predicted movement ranges of the other vehicles (60); a target-motion re-prediction unit (7) that again calculates predicted movement ranges to avoid collision when the collision possibility between the other vehicles (60) exists; an own-motion prediction unit (10) that calculates a predicted movement range of a user's vehicle (50) based on an observation result; and an own-collision possibility estimator (11) that estimates collision possibilities between the user's vehicle (50) and the other vehicles (60) based on overlap of the final predicted movement ranges of the other vehicles (60) and the predicted movement range of the user's vehicle (50).