Abstract: A simulator capable of communicating with an energy management system (EMS), the simulator including a simulator unit configured to execute a simulation of an algorithm program to be evaluated for the EMS, a performance determination unit configured to determine performance of the algorithm program to be evaluated based on a result of operations of the simulator unit, and a quality determination unit for the simulation unit, wherein an interface for when the algorithm program to be evaluated is incorporated into the simulator and an interface for when the algorithm program to be evaluated is incorporated into the EMS are commonalized.

Abstract: A molybdenum sulfide powder according to the invention contains molybdenum disulfide having a 3R crystal structure. A heavy-metal adsorbent according to the invention contains molybdenum sulfide particles, and the molybdenum sulfide particles have a median diameter D50 of 10 nm to 1,000 nm obtained by a dynamic light scattering type particle diameter distribution measuring device. A photothermal conversion material according to the invention contains a material containing molybdenum sulfide particles and generates heat by absorbing light energy.

Abstract: A system according to an embodiment of the present disclosure is a system for displaying a video captured by a camera connected to a network. The system is configured to set, in response to instructions from a user, a first threshold regarding the number of people staying in a target area and a second threshold regarding a period of time during which the people stay in the target area, measure the number of people staying in the target area and a period of time during which the people stay in the target area, generate a thumbnail showing a video when the number of people staying in the target area for a period of time equal to or longer than the second threshold is equal to or greater than the first threshold, and display the thumbnail generated.

Abstract: A vehicle drive system includes a motor (drive motor), a generator, a reduction drive that reduces output of the motor, and a housing. At a position above a motor shaft, an oil passage, through which oil is supplied to each of the motor, the reduction drive, and the generator, is integrally formed with the housing. An oil pipe is attached to the housing and supplies the oil from an oil reservoir to the oil passage. At a position between the motor and the generator, the reduction drive is offset from the motor shaft in a second direction that is orthogonal to the first direction. The oil pipe is located between the motor and the generator in the first direction, and at least part of the oil pipe is located on an opposite side of the reduction drive with the motor shaft being interposed therebetween in the second direction.

Abstract: A data complementing method implemented by a computer, the method includes: calculating degree of correlation of a data item corresponding to a missing data value with another data item, in a case where the missing data value exists in a plurality of data records including data values corresponding to a plurality of data items, respectively; performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predetermined correlation threshold; and performing complementation of the missing data value by a statistical method based on a data value other than the missing data value of a data item corresponding to the missing data value, in a case where the degree of correlation is not larger than the correlation threshold.

Abstract: A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

Abstract: A power train support structure includes a first front side frame disposed on one of left and right sides of a power train; a second front side frame disposed on the other of the left and right sides of the power train, and being greatly away from the power train with respect to the first front side frame; a mounting bracket having one of left and right ends connected to the power train; and a connecting member for connecting the mounting bracket and the second front side frame. The mounting bracket includes a front fixing portion and a rear fixing portion to be fixed to the power train. A strength of the front fixing portion and the rear fixing portion is set to such a strength that the rear fixing portion is broken by a collision load from a front side, and the front fixing portion is not broken.

Abstract: A learning method is executed by a computer. The method includes: obtaining a trained model in which training data having non-linear characteristics is learned by supervised learning using a first teacher label; classifying the training data by using the obtained trained model and calculating a score related to a factor of the obtainment of the classification result for the training data; clustering the training data based on the calculated score; applying a second teacher label based on clusters obtained from the clustering to the training data; and executing supervised learning of a decision tree by using the training data and the applied second teacher label.

Abstract: An inference method is executed by a computer. The method includes: obtaining a learned model in which learning data having non-linear characteristics is learned by supervised learning; creating a decision tree that includes nodes and edges in which intermediate nodes are associated with branch conditions and terminal nodes are associated with clustered learning data; identifying a terminal node associated with classification target data by following the intermediate nodes and the edges of the created decision tree based on the inputted classification target data; and outputting a prediction result obtained by applying the learning data associated with the identified terminal node to the learned model as a prediction result of the identified terminal node.

Abstract: A method includes: selecting a plurality of models by using data set and a prediction result of a prediction model for the data set, each model being configured to linearly separate data included in the data set input to the prediction model; creating a decision tree such that a leaf of the decision tree corresponds to each selected model and a node of the decision tree corresponds to each of logics classifying the data from a root to each leaf of the decision tree; specifying a branch to be pruned by using variation in the data belonging to each leaf of the created decision tree; recreating the decision tree by using the data set corresponding to the decision tree in which the specified branch has been pruned; and outputting each of the logics corresponding to each node of the recreated decision tree as a description result of the prediction model.

Abstract: A power train support structure includes a first front side frame disposed on one of left and right sides of a power train; a second front side frame disposed on the other of the left and right sides of the power train, and being greatly away from the power train with respect to the first front side frame; a mounting bracket having one of left and right ends connected to the power train; and a connecting member for connecting the mounting bracket and the second front side frame. The mounting bracket includes a front fixing portion and a rear fixing portion to be fixed to the power train. A strength of the front fixing portion and the rear fixing portion is set to such a strength that the rear fixing portion is broken by a collision load from a front side, and the front fixing portion is not broken.

Abstract: At least one of a pair of right and left motor unit support systems for mounting and supporting a motor unit (71) on a cross member (80) is fastened and fixed to an upper part of the motor unit (71) by a first shaft-like fastening member (a first bolt (101)). An axial direction of the first shaft-like fastening member (the first bolt (101)) is the same as a vehicle longitudinal direction in a state in which the at least one of the motor unit support systems is fastened and fixed to the upper part of the motor unit (72).

Abstract: A data complementing method implemented by a computer, the method includes: calculating degree of correlation of a data item corresponding to a missing data value with another data item, in a case where the missing data value exists in a plurality of data records including data values corresponding to a plurality of data items, respectively; performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predetermined correlation threshold; and performing complementation of the missing data value by a statistical method based on a data value other than the missing data value of a data item corresponding to the missing data value, in a case where the degree of correlation is not larger than the correlation threshold.

Abstract: A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

Abstract: At least one of a pair of right and left motor unit support systems for mounting and supporting a motor unit (71) on a cross member (80) is fastened and fixed to an upper part of the motor unit (71) by a first shaft-like fastening member (a first bolt (101)). An axial direction of the first shaft-like fastening member (the first bolt (101)) is the same as a vehicle longitudinal direction in a state in which the at least one of the motor unit support systems is fastened and fixed to the upper part of the motor unit (72).

Abstract: A vehicle front structure includes a front window glass, a washer nozzle, a cowl, and plate members. The front window glass includes angle-of-view areas at its upper portions. The angle-of-view areas cover angles of view of left and right imagers disposed on an interior of a vehicle. The washer nozzle ejects a washer fluid to an outer surface of the front window glass. The cowl is recessed and extends in a vehicle width direction between the front window glass and a front hood. The plate members stand erect in the cowl and extend in a vehicle front-rear direction. The plate members are disposed at least one at each outer side, in the vehicle width direction, of a widthwise center of the vehicle. The plate members extend toward an area interposed between outer edges of the angle-of-view areas in the vehicle width direction in a plan view of the vehicle.

Abstract: A vehicle front structure includes a front window glass, a washer nozzle, a cowl, and plate members. The front window glass includes angle-of-view areas at its upper portions. The angle-of-view areas cover angles of view of left and right imagers disposed on an interior of a vehicle. The washer nozzle ejects a washer fluid to an outer surface of the front window glass. The cowl is recessed and extends in a vehicle width direction between the front window glass and a front hood. The plate members stand erect in the cowl and extend in a vehicle front-rear direction. The plate members are disposed at least one at each outer side, in the vehicle width direction, of a widthwise center of the vehicle. The plate members extend toward an area interposed between outer edges of the angle-of-view areas in the vehicle width direction in a plan view of the vehicle.

Abstract: There is provided a communication device including a first transmitter configured to up-convert a frequency of a first transmission signal to a first frequency so as to be wirelessly transmitted, a second transmitter configured to up-convert a frequency of a second transmission signal to a second frequency different from the first frequency so as to be wirelessly transmitted, a receiver configured to receive a reception signal including a passive intermodulation signal generated due to the first and second transmission signals wirelessly transmitted, a memory, and a processor coupled to the memory and configured to estimate frequency errors of the up-converted first and second transmission signals, generate a cancellation signal into which any frequency of the first and second transmission signals has been corrected, based on the estimated frequency errors, and combine the cancellation signal with a down-converted reception signal.

Abstract: A communication device includes a transmitter to transmit a radio transmission signal having a frequency different from a frequency of a radio transmission signal transmitted by another transmitter, a receiver to receive a receiving signal including a primary signal and a first passive intermodulation signal generated by radio transmission signals, a memory, and a processor coupled to the memory and the processor to calculate a power of the primary signal, update a first coefficient for generating a cancel signal for canceling the first passive intermodulation signal, based on the receiving signal and transmission signals to be transmitted, generate the cancel signal based on the transmission signals and the first coefficient, and combine the receiving signal and the cancel signal, wherein the processor is further to adjust a step coefficient, which is a time constant in case of updating the first coefficient, based on the power of the calculated primary signal.