Abstract: An object detection device 10 includes an entire processing unit 110 that obtains first metadata for the entire input image by scaling the input image and performing object detection processing, a divided image narrowing unit 120 that narrows down the input image into a predetermined number of selected divided images from a group of divided images obtained by dividing the input image, a division processing unit 130 that obtains second metadata by performing object detection processing for each of the selected divided images, and a synthesis processing unit 140 that removes the second metadata obtained by the division processing unit 130 that overlaps the first metadata obtained by the entire processing unit 110, and synthesizes the first metadata not removed and the first meta data obtained by the entire processing unit 110 to output the meta data.

Abstract: The learning device 10D is learned to extract moving image feature amount Fm which is feature amount relating to the moving image data Dm when the moving image data Dm is inputted thereto, and is learned to extract still image feature amount Fs which is feature amount relating to the still image data Ds when the still image data Ds is inputted thereto. The first inference unit 32D performs a first inference regarding the moving image data Dm based on the moving image feature amount Fm. The second inference unit 34D performs a second inference regarding the still image data Ds based on the still image feature amount Fs. The learning unit 36D performs learning of the feature extraction unit 31D based on the results of the first inference and the second inference.

Abstract: An embodiment is a packet capture device including a first local timer synchronized with an external global timer, a second local timer, a time stamp assign unit for assigning a time stamp to a inputted packet signal based on the second local timer, a filter unit for selecting the packet signal to which the time stamp is assigned, a capture file generation unit for receiving the selected packet signal, and a storage unit for storing a capture file generated in the capture file generation unit, wherein the capture file generation unit calculate a difference between a timer value of the first local timer and a timer value of the second local timer to correct the time stamp value on the basis of the difference.

Abstract: A data sequence correction method for temporarily saving data with sequence information in a ring buffer and performing sequence correction is provided. The ring buffer includes a number of storage regions, a monitoring section having one or more continuous sequence numbers, and an acceptance section having a first or second sequence number of the monitoring section as a start sequence number and a sequence number immediately preceding the start sequence number of the monitoring section as an end sequence number. The method includes, when a value determined based on a remainder obtained by dividing a sequence number of received data by the number of storage regions is inside the acceptance section, writing the received data in a position of the storage region corresponding to the determined value, and when data are written in the entire monitoring section, reading out all the data in the monitoring section.

Abstract: A packet capture system for capturing packets flowing in a capture target network, and a plurality of stages of packet distribution devices for capturing packet of a specific flow are cascade-connected, packet distribution devices identify a capture target flow by analyzing inputted packets, packet distribution devices other than a last-stage packet distribution device are configured to distribute packets to capture packets of a flow to be captured and output packets of a flow not to be captured to a next-stage packet distribution device, and the last-stage packet distribution device is configured to filter the packets of the flow to be captured and to discard the packets of the flow not to be captured.

Abstract: A battery management system includes a usage history acquisition unit, a deterioration estimation unit, a battery load acquisition unit, a deterioration change degree identification unit, and a battery management unit. The usage history acquisition unit acquires usage history information. The deterioration estimation unit estimates a current deterioration state and a deterioration factor of the secondary battery based on the usage history information. The battery load acquisition unit acquires battery load information indicating a future battery load. The deterioration change degree identification unit identifies a deterioration change degree by using the current deterioration state, the deterioration factor and the battery load information of the secondary battery.

Abstract: The integration unit 26, using configuration information of the convolutional neural network model and each filter used in each convolutional layer of the convolutional neural network model as inputs, deletes one or more pieces of activation function processing performed between the plurality of convolutional layers and integrates a plurality of filters used in the plurality of convolutional layers.

Abstract: A state detection circuit compares a target connection with an immediate previous connection based on an identifier of an incoming packet and detects a post-transitional state of the target connection based on a control flag of the incoming packet and on a pre-transitional state of the target connection detected just before if the target connection is identical with the immediate previous connection; and a connection counting circuit increments or decrements the number of target connections only when the detected post-transitional state indicates a start or end of the target connection.

Abstract: A battery diagnostic system includes an interpolation processing unit, a degradation estimation unit, a degradation prediction unit, and an output unit. When a part of the battery load history is missing, the interpolation processing unit interpolates the missing part using the rest of the constituent data. The degradation estimation unit estimates, based on the battery load history, a present degradation state of the secondary battery and a cause of degradation that has brought about the degradation state. The degradation prediction unit predicts, using a predicted battery load, the present degradation state and the cause of degradation of the secondary battery, a predicted degradation state of the secondary battery that will occur in the future upon having been used in the use mode. The output unit outputs the predicted degradation state of the secondary battery.

Abstract: A control device includes a diagnosis unit configured to diagnose a deterioration state of a secondary battery. The diagnosis unit includes: a resistance calculator configured to calculate a direct current resistance and a reaction resistance of impedance components based on an impedance measurement result of the secondary battery; an index value calculator configured to calculate an index value related to the direct current resistance and the reaction resistance; and a deterioration determination unit configured to compare the index value with a determination threshold based on an impedance characteristic at the time of normal deterioration of the secondary battery so as to determine whether a specific deterioration occurs due to charge and discharge of the secondary battery.

Abstract: An object of the present invention is to provide a carbon fiber which exhibits excellent strength development rate when used in a composite material. The present invention that solves the problems is a carbon fiber which simultaneously satisfies the following formulae (1) and (2): Lc/d?3??(1) TS×d×Lc>6.0×105??(2) wherein: Lc is an X-ray crystallite size (?), d is a filament diameter (?m), and TS is a strand tensile strength (MPa).

Abstract: An embodiment is a data processor including a decimal point position control circuit configured to set a decimal point position of N-bit (N is a natural number of 2 or more) fixed-length data corresponding to each of a plurality of layers constituting a multilayered neural network, and an arithmetic processing circuit configured to perform arithmetic processing corresponding to each of the plurality of layers constituting the multilayered neural network according to a processing algorithm of the multilayered neural network on the N-bit fixed-length data for which the decimal point position has been set by the decimal point position control circuit.

Abstract: A secondary battery system includes a secondary battery having an electrode body impregnated with an electrolytic solution containing metal ions. The secondary battery system measures an impedance of the secondary battery. The secondary battery system detects high-rate deterioration caused by uneven concentration of the metal ions in the electrolytic solution impregnated into the electrode body.

Abstract: A packet capture apparatus includes a hardware processing unit including a filter that filters packets input from a network and an NIC and a packet storage that stores packets input from the hardware processing unit. The filter includes a packet input that receives packets input from the network, a header analysis unit that analyzes a header structure of each packet input to the packet input unit and extracts a field value of a header of the packet, a rule table in which rules including a field value of a flow to be captured are recorded, a flow identification unit that identifies a flow in which the field value extracted by the header analysis unit matches a rule in the rule table and/or does not match the rule, and a packet output that outputs a packet of the flow identified by the flow identification unit to the NIC.

Abstract: An embodiment packet capture device comprises: a packet receiver configured to receive a packet from a network; a packet retainer configured to store the received packet in a memory to temporarily retain the received packet; a failure detector configured to determine a communication failure is present in the network; a capture controller configured to determine an operation stop address such that retention of packets from the network in time periods before and after a detection time point of the communication failure is ensured when the communication failure is detected by the failure detector; and a capture data generator configured to output the packet stored in the memory as capture data when a storage destination address of the packet stored in the memory has reached the operation stop address or when at least a predetermined waiting time period has elapsed from the detection time point of the communication failure.

Abstract: A traffic monitoring apparatus that monitors traffic of a monitoring target network and includes a statistical information processor that acquires statistical information per specific flow of the traffic, and a packet capture unit that captures a packet of the specific flow, in which the statistical information processor includes a statistical information aggregation unit that aggregates the pieces of statistical information, and a statistical information file generation unit that generates a statistical information file based on the pieces of aggregated statistical information.

Abstract: A recognition loss calculation unit of a learning device calculates a recognition loss using: a recognition result with respect to recognition object data in a learning data set that is a set of a pair of the recognition object data and a weak label; a mixing matrix calculated based on the learning data set; and the weak label attached to the recognition object data. The recognition loss calculation unit includes: a difference calculation unit that calculates a difference between a mixing matrix and the recognition result; and a sum of squares calculation unit that calculates the recognition loss by calculating a sum of a square of the difference.

Abstract: A recognition loss calculation unit of a learning device calculates a recognition loss using a recognition result with respect to recognition object data in a dataset for learning that is a set of pairs of the recognition object data and a weak label, a mixing matrix calculated based on the dataset for learning, and a weak label attached to the recognition object data. The recognition loss calculation unit includes a conversion unit that converts the recognition result into a conjugate vector, a mixing matrix product calculation unit that calculates a product of the conjugate vector and the mixing matrix, a normalization term calculation unit that calculates a normalization term from the conjugate vector, and a total sum calculation unit.

Abstract: Provided is a propylene oxide production apparatus including a switching mechanism that is capable of switching a state of each reactor between an operating state where reaction raw materials are supplied and an epoxidation reaction is performed and a non-operating state where the supply of the reaction raw materials is shut off. The propylene oxide production apparatus is capable of changing a reactor in the non-operating state one by one, and performs switching in such a way that only reactors in the operating state are connected fluidically in series or in parallel, thereby enabling supplying the reaction raw materials to the reactors in the operating state. A sampling mechanism is also provided that samples part of the reaction mixture from each discharge line that is connected to each reactor.

Abstract: A battery diagnostic system includes a superimposed current applying unit, a current value acquiring unit, a voltage value acquiring unit, an impedance calculating unit, and a diagnostic unit. The superimposed current applying unit configured to apply a superimposed current formed by superimposing a plurality of frequency components to a battery. The current value acquiring unit acquires the current value of the superimposed current applied to the battery. The voltage value acquiring unit acquires a battery voltage of the battery to which the superimposed current is applied. The impedance calculating unit calculates impedance for each of a plurality of frequency components using discrete Fourier transform from the superimposed current and the battery voltage. The diagnostic unit diagnoses the battery based on the impedance.