Abstract: In an abnormality detection method for detecting an abnormality in vehicle behaviors based on an abnormality degree indicating a degree of difference between first driving information representing a vehicle behavior undergoing abnormality detection and second driving information obtained when no abnormality is found in a travel environment, an abnormality threshold is estimated using an abnormality degree distribution generated from a set of preaccumulated abnormality degrees. The abnormality threshold is an abnormality degree allowing a probability of erroneous determination as to presence or absence of an abnormality to be a predefined probability. Further, in the abnormality detection method, it is detected whether an abnormality is found in the vehicle behavior by comparing the abnormality degree for the first driving information and the estimated abnormality threshold.

Abstract: According to at least one embodiment, there is provided a hard coat laminated film, including, from a surface layer side, a second hard coat, a first hard coat, and a transparent resin film layer, where the first hard coat and the transparent resin film layer are laminated directly, where the first hard coat is formed of a coating material including: (A) 100 parts by mass of a polyfunctional (meth)acrylate; and (B) 1 to 100 parts by mass of an N-substituted (meth)acrylamide compound, where the second hard coat is formed of a coating material containing no inorganic particles, and where the transparent resin film is a transparent multilayer film or a transparent monolayer film made of a poly(meth)acrylimide resin, where the transparent multilayer film includes a surface layer made of a poly(meth)acrylimide resin, the first hard coat being formed on the surface layer.

Abstract: Embodiments relate to a hard coat laminated film having a first hard coat, a second hard coat, and a transparent resin film layer, where: the first hard coat is formed from paint which contains predetermined amounts of (A) a multifunctional (meth)acrylate, (B) a water repellent, and (C) a silane coupling agent, and which does not contain inorganic particles; and the second hard coat is formed from paint containing predetermined amounts of (D) a polymerizable compound and (E) inorganic fine particles having an average particle size of 1-300 nm. The (D) polymerizable compound contains: (d1) a multifunctional (meth)acrylate having three or more (meth)acryloyl groups in one molecule; (d2) a compound having two or more secondary thiol groups in one molecule; and optionally, (d3) at least one (meth)acrylate selected from the group consisting of a (meth)acrylate having two (meth)acryloyl groups in one molecule, a (meth)acrylate having one (meth)acryloyl group in one molecule, and a urethane(meth)acrylate.

Abstract: The signal collection method is a method that collects internal states indicated by signals in an electronic circuit device including a bus, the signal collection method including: storing the internal states with a fine resolution data storage by obtaining the internal states per a first period; per a second period, which is longer than the first period: obtaining a first data transfer amount, which is a data amount transferred by the bus, via a coarse resolution data storage; calculating a difference between a second data transfer amount and the first data transfer amount obtained in the obtaining, the second data transfer amount being calculated in advance and obtained from a cycle pattern generator; and determining whether the difference calculated in the calculating is within a predetermined range to stop storing in the storing when it is determined that the difference is not within the predetermined range.

Abstract: In an accident pattern determination apparatus including a storage storing attributes assigned to respective ones of a plurality of predefined traffic situations, an acquirer acquires, for each of vehicle-related accident cases, an accident pattern that is a combination of traffic situations in the accident case, from the plurality of predefined traffic situations. A determiner determines, for each accident pattern acquired by the acquirer, whether the accident pattern is an accident pattern of high accident risk or an accident pattern of low accident risk for specific vehicles, based on the accident patterns acquired for the respective accident cases and the attributes assigned to respective ones of the plurality of predefined traffic situations.

Abstract: A computer calculates, in accordance with a maximum mean discrepancy, a similarity level between a first feature distribution correlating to a first distribution information item stored in a distribution database and a second feature distribution correlating to a second distribution information item stored in the distribution database. The second distribution information item is different from the first distribution information item. The maximum mean discrepancy is a distance measure indicative of the similarity level between the first and second feature distributions. The computer determines whether the calculated similarity level is equal to or higher than a predetermined threshold, and integrates the first feature distribution and the second feature distribution into a common feature distribution upon determining that the calculated similarity level is equal to or higher than the predetermined threshold.

Abstract: The signal collection method is a method that collects internal states indicated by signals in an electronic circuit device including a bus, the signal collection method including: storing the internal states with a fine resolution data storage by obtaining the internal states per a first period; per a second period, which is longer than the first period: obtaining a first data transfer amount, which is a data amount transferred by the bus, via a coarse resolution data storage; calculating a difference between a second data transfer amount and the first data transfer amount obtained in the obtaining, the second data transfer amount being calculated in advance and obtained from a cycle pattern generator; and determining whether the difference calculated in the calculating is within a predetermined range to stop storing in the storing when it is determined that the difference is not within the predetermined range.

Abstract: In an abnormality detection method for detecting an abnormality in vehicle behaviors based on an abnormality degree indicating a degree of difference between first driving information representing a vehicle behavior undergoing abnormality detection and second driving information obtained when no abnormality is found in a travel environment, an abnormality threshold is estimated using an abnormality degree distribution generated from a set of preaccumulated abnormality degrees. The abnormality threshold is an abnormality degree allowing a probability of erroneous determination as to presence or absence of an abnormality to be a predefined probability. Further, in the abnormality detection method, it is detected whether an abnormality is found in the vehicle behavior by comparing the abnormality degree for the first driving information and the estimated abnormality threshold.

Abstract: According to at least one embodiment, there is provided a hard coat laminated film, including, from a surface layer side, a second hard coat, a first hard coat, and a transparent resin film layer, where the first hard coat and the transparent resin film layer are laminated directly, where the first hard coat is formed of a coating material including: (A) 100 parts by mass of a polyfunctional (meth)acrylate; and (B) 1 to 100 parts by mass of an N-substituted (meth)acrylamide compound, where the second hard coat is formed of a coating material containing no inorganic particles, and where the transparent resin film is a transparent multilayer film or a transparent monolayer film made of a poly(meth)acrylimide resin, where the transparent multilayer film includes a surface layer made of a poly(meth)acrylimide resin, the first hard coat being formed on the surface layer.

Abstract: A hard coat laminated film according to at least one embodiment has, from the surface layer side, a first hard coat and a transparent resin film layer. The first hard coat is formed from a coating material including: (A) 100 parts by mass of a polyfunctional (meth)acrylate; and (B) 1-100 parts by mass of an N-substituted (meth)acrylamide compound. The transparent resin film has a first hard coat forming surface which is a poly(meth)acrylic imide resin. A hard coat laminated film according to another embodiment has, from the surface layer side, a second hard coat, the first hard coat and the transparent resin film layer. The second hard coat is formed from a coating material which includes: (A) 100 parts by mass of a polyfunctional (meth)acrylate; (E) 0.01-7 parts by mass of a water repelling agent; and (F) 0.01-10 parts by mass of a silane coupling agent; without inorganic particles.

Abstract: Embodiments relate to a hard coat laminated film having a first hard coat, a second hard coat, and a transparent resin film layer, where: the first hard coat is formed from paint which contains predetermined amounts of (A) a multifunctional (meth)acrylate, (B) a water repellent, and (C) a silane coupling agent, and which does not contain inorganic particles; and the second hard coat is formed from paint containing predetermined amounts of (D) a polymerizable compound and (E) inorganic fine particles having an average particle size of 1-300 nm. The (D) polymerizable compound contains: (d1) a multifunctional (meth)acrylate having three or more (meth)acryloyl groups in one molecule; (d2) a compound having two or more secondary thiol groups in one molecule; and optionally, (d3) at least one (meth)acrylate selected from the group consisting of a (meth)acrylate having two (meth)acryloyl groups in one molecule, a (meth)acrylate having one (meth)acryloyl group in one molecule, and a urethane(meth)acrylate.

Abstract: In a driving-state data storage apparatus, a collector collects, from each of vehicles on a target travelling road, a value of data indicative of a driving state of the corresponding vehicle to correspondingly obtain driving-state data values for the target road. A data allocator divides, based on similarity among the driving-state data values, the target traveling road into a plurality of traveling segments, and extracts, from the driving-state data values, data values for each of the divided travelling segments. The data values extracted for each of the travelling segments are similar to each other. The data allocator allocates a distribution of the extracted data values for each of the divided travelling segments to the corresponding one of the divided travelling segments as a feature distribution. A storage unit stores the feature distribution allocated for each of the travelling segments.

Abstract: A steam turbine includes a rotating shaft (1); a rotor blade (4) having a platform (43) and a rotor blade main body (40); a casing (2); a stationary blade (7) having a stationary blade main body (70) and a stator shroud (71); and a projecting portion (45A) that projects from the platform (43) toward a upstream side in a central axis direction (Da). The projecting portion (45A) has, on a side thereof facing a radially inner side, a guide surface (45f) that gradually inclines or curves radially inward from a base end portion (45s) on the platform (43) side to a tip end portion (45t) on an upstream side in the central axis direction (Da).

Abstract: An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point.

Abstract: In a driving assist system, a driving evaluator compares a driving feature data item sampled at each predetermined sampling point and obtained from a target vehicle with historical driving data items for the corresponding sampling point. The driving evaluator obtains, based on a result of the comparison, an evaluation value of the driving feature data item for the target vehicle at each predetermined sampling point. An unusual driving determiner obtains a cumulative sum of selected values in the evaluation values of the driving feature data items for the target vehicle. The unusual driving determiner determines whether the cumulative sum is larger than a predetermined threshold, and determines that driving of a driver of the target vehicle is unusual upon determining that the cumulative sum is larger than the predetermined threshold.

Abstract: A computer calculates, in accordance with a maximum mean discrepancy, a similarity level between a first feature distribution correlating to a first distribution information item stored in a distribution database and a second feature distribution correlating to a second distribution information item stored in the distribution database. The second distribution information item is different from the first distribution information item. The maximum mean discrepancy is a distance measure indicative of the similarity level between the first and second feature distributions. The computer determines whether the calculated similarity level is equal to or higher than a predetermined threshold, and integrates the first feature distribution and the second feature distribution into a common feature distribution upon determining that the calculated similarity level is equal to or higher than the predetermined threshold.

Abstract: In an apparatus for detecting an anomaly of an evaluation target condition of a road, a storage stores a reference data set correlating to a predetermined attribute and being comprised of reference data samples. An anomaly level calculator obtains, from target vehicles, an evaluation data set correlating to the predetermined attribute and being comprised of evaluation data samples. The evaluation data samples are collected from the respective target vehicles as target driving data items at the predetermined attribute under the evaluation target condition of the road. The target driving data item for each of the target vehicles represents at least one driving operation of the corresponding one of the target vehicles. The anomaly level calculator compares the reference data set with the evaluation data set to thereby calculate an anomaly level of the evaluation target condition of the road.

Abstract: A drive assist apparatus includes a data collection part that collects driving behavior data representing driving maneuvers and vehicle behaviors caused by the driving maneuvers for each drivers, a classification part that classifies the driving behavior data into a plurality of clusters each showing a tendency of driving behavior of the drivers by clustering the driving behavior data, a storage part that stores cluster information representing a driving behavior characteristic of each of the clusters, a subject data acquisition part that acquires, as subject data, the driving behavior data for a subject driver to be assisted, an estimation part that estimates, as a corresponding cluster, one of the clusters to which the subject driver is assumed to belong by comparing the subject data with the cluster information stored in the storage part, and an assist providing part that provides a drive assist depending on the estimated corresponding cluster.

Abstract: A hard coat laminated film according to at least one embodiment has, from the surface layer side, a first hard coat and a transparent resin film layer. The first hard coat is formed from a coating material including: (A) 100 parts by mass of a polyfunctional (meth)acrylate; and (B) 1-100 parts by mass of an N-substituted (meth)acrylamide compound. The transparent resin film has a first hard coat forming surface which is a poly(meth)acrylic imide resin. A hard coat laminated film according to another embodiment has, from the surface layer side, a second hard coat, the first hard coat and the transparent resin film layer. The second hard coat is formed from a coating material which includes: (A) 100 parts by mass of a polyfunctional (meth)acrylate; (E) 0.01-7 parts by mass of a water repelling agent; and (F) 0.01-10 parts by mass of a silane coupling agent; without inorganic particles.

Abstract: An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point.