Abstract: An underwater detection device includes a surface drive boat and an unmanned underwater vehicle. The surface drive boat includes: a hull; transverse attitude-stabilizing thrusters and orbit vectored thrusters arranged at the bottom of the hull; a control box arranged on the hull and electrically connected with the transverse attitude-stabilizing thrusters and the orbit vectored thrusters; a cable and a cable winding assembly arranged on the hull, the control box being connected with the unmanned underwater vehicle by the cable, the cable winding assembly being electrically connected with the control box; and a positioning assembly arranged on the hull and electrically connected with the control box.

Abstract: A modular mechanical arm of an underwater pile foundation structure includes a modular interface disposed at one end of the transmission arm rod, a mechanical arm connecting part disposed at the other end of the transmission arm rod. The mechanical arm connecting part is separately provided with two arm frames, each of the arm frames comprises an inner arm frame and an outer arm frame, the inner arm frame and the outer arm frame are coupled by a joint, and a soft moving and cleaning part is disposed on an inner side of each of the arm frames; the modular interface is coupled with an underwater UAV interface to transmit current and a control signal, a waterproof steering gear is built in the transmission arm rod to receive the control signal.

Abstract: Provided are a data storage method, a storage device, a server and a storage medium, the method includes: obtaining, by a server, image data of different dimensions (S11); according to the association identity carried by each of the image data, determining, by the server, the image data of the different dimensions with the same association identity as association data (S12); sending the image data of the different dimensions included in the association data to target data tables of databases with different dimensions for storage, respectively (S13), wherein, the databases of different dimensions are located in the same storage apparatus, regarding the target data tables of the database of different dimensions, the table name prefixes of the target data tables are different, but the table name suffixes are the same. This method can store image data in association, meanwhile, when performing associated query, the loss of associated data can be avoided.

Abstract: The present invention discloses a preparation method of a cotton bast fiber and chitosan composite non-woven fabric for filtration and belongs to the technical field of fiber materials. The preparation method adopts steam flash explosion for pretreatment to separate cotton bast first, then a papermaking method is used for forming, finally chitosan is added to enhance the mechanical performance and improve the filtration efficiency, and a technological process is simple, environmentally friendly and suitable for industrial production. The non-woven fabric adopts lignin-rich cotton bast fibers and chitosan as raw materials, so that the non-woven fabric has good antibacterial performance. The cotton bast and chitosan used in the preparation method are both bio-based materials which can be degraded in the natural environment after use, so that burdens caused to the environment are reduced.

Abstract: Provided are a data storage method, a storage device, a server and a storage medium, the method includes: obtaining, by a server, image data of different dimensions (S11); according to the association identity carried by each of the image data, determining, by the server, the image data of the different dimensions with the same association identity as association data (S12); sending the image data of the different dimensions included in the association data to target data tables of databases with different dimensions for storage, respectively (S13), wherein, the databases of different dimensions are located in the same storage apparatus, regarding the target data tables of the database of different dimensions, the table name prefixes of the target data tables are different, but the table name suffixes are the same. This method can store image data in association, meanwhile, when performing associated query, the loss of associated data can be avoided.

Abstract: The present invention discloses a preparation method of a cotton bast fiber and chitosan composite non-woven fabric for filtration and belongs to the technical field of fiber materials. The preparation method adopts steam flash explosion for pretreatment to separate cotton bast first, then a papermaking method is used for forming, finally chitosan is added to enhance the mechanical performance and improve the filtration efficiency, and a technological process is simple, environmentally friendly and suitable for industrial production. The non-woven fabric adopts lignin-rich cotton bast fibers and chitosan as raw materials, so that the non-woven fabric has good antibacterial performance. The cotton bast and chitosan used in the preparation method are both bio-based materials which can be degraded in the natural environment after use, so that burdens caused to the environment are reduced.

Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.

Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.

Abstract: Described is a technology by which software instrumentation data collected from user program sessions are analyzed to output an analysis report or the like via example methods and an architecture configured for efficient operation. A client component queries a service for analysis related information. To process the query, the service works with a data manager, and via a high dimensional analysis component may use information processed from the software instrumentation data, such as in the form of one or more inverted indexes and/or raw value files. The service may include a usage analysis component, a feature recognition component that locates features from command sequences, a user recognition component and/or a program reliability component. One or more counterpart components at the client may generate analysis reports or the like based on the query results. The client also may maintain user libraries and feature libraries to facilitate analyses.

Abstract: In a distributed system environment, a system profiling log can be used at a central server to collect and analyze log data. The log data can be used to gauge performance of software applications. In particular, the log data includes different activities (i.e., tasks) that are executed to implement the software applications. Correlation of the different activities versus a timeline is an important parameter in the system profiling log. For example, where the correlation of the different activities is represented in colored graphs at a user interface, a user may easily pinpoint a bottleneck. The bottleneck at the one or more activities may encourage the user to adopt system improvement in the distributed system environment.

Abstract: Described is a technology by which high dimensional source data corresponding to rows of records with identifiers, and columns comprising dimensions of data values, are processed into a file model for efficient access. An inverted index corresponding to any dimension is built by mapping data from raw dimension values to mapped values based on mapping entries in a dimension table. The record identifiers are arranged into subgroups based on their mapped value; a count and/or an offset may be maintained for locating each of the subgroups. The raw values for a dimension are maintained within a raw value file. For sparse data, the raw value file may be compressed, e.g., by excluding nulls and associating a record identifier with each non-null. A data manager provides access to data in the data files, such as by offering various functions, using caching for efficiency.

Abstract: Copper alloys, methods for producing copper alloys, and copper tubes are provided. The copper alloys include an alpha solid solution formed from phosphorous deoxidized copper and at least one trace element comprising tin. The content of tin ranges from about 0.1% to about 2.0% by weight of the copper alloy. The trace element can further include zinc in an amount from about 0.05% to about 1.0% by weight of the copper alloy. The copper alloys have an increased tensile strength due to the strengthening effect by the alpha solid solution formed by phosphorous deoxidized copper and tin as the trace element, and accordingly a copper tube made from the copper alloy has a significantly improved pressure resistance.

Abstract: Described is a technology by which high dimensional data may be efficiently analyzed, including by filtering, grouping, aggregating and/or sorting operations to provide an analysis result. For efficiency in the analysis, an inverted index may be built (e.g., as part of filtering), and/or a hash structure (e.g., as part of grouping). Analysis parameters specify dimensions, on which union and/or intersection operations are performed to provide a final dataset. The analysis tool provides a user interface for inputting analysis parameters and outputting information corresponding to an analysis result. The analysis tool may sort the information corresponding to the analysis result, e.g., to output the topmost or bottommost results.

Abstract: Described is a technology for analyzing usage of a software program's features. Software instrumentation data is collected during actual user program usage sessions. The collected data is then processed to determine various feature usage counts and other information, cross-feature usage (e.g., among users who use a feature, how many use another feature or program), and characteristics of feature users, e.g., how long, how much, how often and how extensive feature users use a program. Session analysis may be performed to provide information about the number of sessions in which a set of features occur. Feature usage trends over time may also be determined via analysis. A user interface is described for facilitating selection of one or more features to analyze, for facilitating selection of a group of users, and/or for outputting results corresponding to the analysis.

Abstract: Described is a technology by which software program feature usage is located within a sequence of commands collected during program usage sessions. For example, feature generally corresponds to a series of commands, such as copy and paste. A visual modeling component is controlled via drag-and-drop operations to describe a feature model, which is then compiled by a compiler into a finite state machine. Noise models may be used to exclude any command in the sequence that is irrelevant to the feature usage. A recognition process uses the finite state machine to locate program feature usage within the sequence of recorded commands by matching command sub-sequences corresponding to the feature model via the state machine. An analyzer may then use the located matches to provide an analysis report on feature usage.

Abstract: Described is a technology by which high dimensional data may be efficiently analyzed, including by filtering, grouping, aggregating and/or sorting operations to provide an analysis result. For efficiency in the analysis, an inverted index may be built (e.g., as part of filtering), and/or a hash structure (e.g., as part of grouping). Analysis parameters specify dimensions, on which union and/or intersection operations are performed to provide a final dataset. The analysis tool provides a user interface for inputting analysis parameters and outputting information corresponding to an analysis result. The analysis tool may sort the information corresponding to the analysis result, e.g., to output the topmost or bottommost results.

Abstract: Described is a technology by which high dimensional source data corresponding to rows of records with identifiers, and columns comprising dimensions of data values, are processed into a file model for efficient access. An inverted index corresponding to any dimension is built by mapping data from raw dimension values to mapped values based on mapping entries in a dimension table. The record identifiers are arranged into subgroups based on their mapped value; a count and/or an offset may be maintained for locating each of the subgroups. The raw values for a dimension are maintained within a raw value file. For sparse data, the raw value file may be compressed, e.g., by excluding nulls and associating a record identifier with each non-null. A data manager provides access to data in the data files, such as by offering various functions, using caching for efficiency.