Abstract: Cathodes and lithium secondary batteries including the cathodes are disclosed. In some implementations, a cathode may include a cathode current collector and a cathode active material layer disposed on the cathode current collector and including cathode active material particles such that the cathode active material layer satisfies a specific equation. The cathode active material particles may include lithium metal oxide particles that include nickel, and may have a mole fraction of cobalt of 0.02 or less with respect to all elements except for lithium and oxygen.

Abstract: The present invention relates to a method and device for distributing idle UE by a carrier in eNB of a multi-carrier based mobile communication system. The method of distributing idle UE in a multi-carrier based mobile communication system according to the present invention includes a process of determining a search rate by a carrier on the basis of information representing load on the carrier, a step of determining a cell reselection priority on the idle UE on the basis of the determined search rate, and a process of transmitting the determined cell reselection priority to the idle UE.

Abstract: A cathode for a lithium secondary battery includes a cathode current collector, and a cathode active material layer formed on the cathode current collector. The cathode active material layer includes cathode active material particles. The cathode active material particles include a lithium metal oxide particle containing nickel and having a mole fraction of cobalt of 0.02 or less among all elements except lithium and oxygen.

Abstract: Provided is a process proximity effect correction method capable of efficiently improving the dispersion of patterns. There is a process proximity effect correction method according to some embodiments, the process proximity effect correction method of a process proximity effect correction device for performing process proximity effect correction (PPC) of a plurality of patterns using a machine learning module executed by a processor, comprising: training a sensitivity model by inputting a layout image of the plurality of patterns and a layout critical dimension (CD) of the plurality of patterns into the machine learning module; estimating an after cleaning inspection critical dimension (ACI-CD) sensitivity prediction value of the plurality of patterns by inferring an ACI-CD prediction value of the plurality of patterns; and determining a correction rate of the layout CD of the plurality of patterns using the estimated sensitivity prediction value.

Abstract: An output distribution method of a power supply system includes: if a required output power of a power supply system exceeds a reference value, assigning the required output power equally to all PCSs; if the required output power does not exceed the reference value, assigning at least one maximum power driving PCS to be operated with a maximum driving ratio power, on the basis of a maximum driving ratio and a lowest driving ratio; if a remaining power unassigned during power assignment of the maximum power driving PCS exceeds a lowest driving ratio power, assigning a remaining power driving PCS for operation of the remaining power; and if the remaining power unassigned during the power assignment of the maximum power driving PCS does not exceed the lowest driving ratio power, equally dividing and reassigning the required output power to the maximum power driving PCS.

Abstract: Provided is a system of retrieving a document. The system includes a DB storing a document, a document feature writing unit, a query input unit, a keyword extraction unit, a weighting unit, a document retrieving unit, and an output unit. The document feature writing unit extracts attribute information of the document stored in the document DB and writes an index for the document based on the attribute information. The query input unit receives a query input by a user. The keyword extraction unit extracts a keyword from the query. The weighting unit assigns a weight to the extracted keyword using the index. The document retrieving unit retrieves a similar document using the keyword with the assigned weight. The output unit displays the similar document retrieved by the document retrieving unit to a user.

Abstract: Provided are a document management system and method. The document management system including a database storing documents and a document classification unit for automatically classifying the documents stored in the database, wherein the document classification unit comprises a feature extraction module extracting features based on a keyword included in the documents and vectorizing the extracted features, a similarity judgment module judging similarity among the documents using vectors formed by the feature extraction module, and a classification system module classifying the documents stored in the database according to a preset classification system, the document classification unit performing document classification according to the classification system with respect to documents provided to the database.

Abstract: A document analysis system includes a database that stores documents, a document evaluation module that evaluates the documents by using features of the documents, and a user interface (UI) output unit that provides an evaluation result of the documents, which is produced by the document evaluation module, upon call of the documents.

Abstract: Provided are a system and method of clustering documents. The system includes a document DB, a document feature writing unit storing documents, a document retrieving unit, a clustering unit, and a cluster DB. The document DB stores documents. The document feature writing unit extracts attribute information of documents stored in the document database, and writes indexes with respect to the respective documents on the basis of the attribute information. The document retrieving unit retrieves documents including a query input by a user, using the indexes. The clustering unit includes a representative vector calculator calculating feature vectors and a representative vector of the retrieved documents, and a similarity calculator calculating similarities between the documents using the feature vectors and the representative vector. The cluster database stores documents clustered by the clustering unit.

Abstract: Provided are a system and method of clustering documents. The system includes a document DB, a document feature writing unit storing documents, a document retrieving unit, a clustering unit, and a cluster DB. The document DB stores documents. The document feature writing unit extracts attribute information of documents stored in the document database, and writes indexes with respect to the respective documents on the basis of the attribute information. The document retrieving unit retrieves documents including a query input by a user, using the indexes. The clustering unit includes a representative vector calculator calculating feature vectors and a representative vector of the retrieved documents, and a similarity calculator calculating similarities between the documents using the feature vectors and the representative vector. The cluster database stores documents clustered by the clustering unit.

Abstract: Provided is a system of retrieving a document. The system includes a DB storing a document, a document feature writing unit, a query input unit, a keyword extraction unit, a weighting unit, a document retrieving unit, and an output unit. The document feature writing unit extracts attribute information of the document stored in the document DB and writes an index for the document based on the attribute information. The query input unit receives a query input by a user. The keyword extraction unit extracts a keyword from the query. The weighting unit assigns a weight to the extracted keyword using the index. The document retrieving unit retrieves a similar document using the keyword with the assigned weight. The output unit displays the similar document retrieved by the document retrieving unit to a user.

Abstract: Provided are a document management system and method. The document management system including a database storing documents and a document classification unit for automatically classifying the documents stored in the database, wherein the document classification unit comprises a feature extraction module extracting features based on a keyword included in the documents and vectorizing the extracted features, a similarity judgment module judging similarity among the documents using vectors formed by the feature extraction module, and a classification system module classifying the documents stored in the database according to a preset classification system, the document classification unit performing document classification according to the classification system with respect to documents provided to the database.