Abstract:
Problemstellung Die vorliegende Erfindung bezieht sich auf ein Verfahren, eine Einheit und ein Computerprogramm für das effiziente Identifizieren von Elementen mit einer hohen Auftrittshäufigkeit innerhalb von Elementen, die in einem umfangreichen Textdatenstrom enthalten sind. Mittel zur Problemlösung Identifizierungsdaten zum Identifizieren eines Elements und eines Zählwerts von Elementen werden in einer höheren Speicherebene gespeichert, und lediglich Identifizierungsdaten werden in einer tieferen Speicherebene unterhalb der höheren Speicherebene gespeichert. Eine Textdatenstrom-Eingabe wird empfangen, das Inkrement des Zählwerts eines Elements wird als Reaktion auf das Speichern von Identifizierungsdaten für ein Element, das in einem Intervall enthalten ist, der von der empfangenen Textdatenstrom-Eingabe getrennt ist, in dem Speicher einer höheren Ebene erhöht, die Identifizierungsdaten für das Element werden gemeinsam mit dem anfänglichen Zählwert als Reaktion auf das Speichern in der tieferen Speicherebene in die höhere Speicherebene übertragen, und die Identifizierungsdaten für das Element werden gemeinsam mit dem anfänglichen Zählwert als Reaktion darauf, dass sie in keiner Ebene gespeichert sind, in der höheren Speicherebene neu gespeichert.
Abstract:
PROBLEM TO BE SOLVED: To properly correct a mathematical programming problem.SOLUTION: An apparatus comprises: a first-time-point-solution generating unit generating at least one solution to a mathematical programming problem to be solved at a specified first time point; a second-time-point-problem generating unit generating a plurality of mathematical programming problems to be solved at a second time point later the first time point, on the basis of the solution to the mathematical programming problem to be solved at the first time point; a second-time-point optimum value calculating unit calculating, for each of the plurality of mathematical programming problems to be solved at the second time point, an optimum value of the mathematical programming problem to be solved at the second time point; a relational expression estimating unit estimating relational expressions between the solution to the mathematical programming problem to be solved at the first time point and the optimum values of the mathematical programming problems to be solved at the second time point; and a correcting unit correcting the mathematical programming problem to be solved at the first time point on the basis of the relational expressions.
Abstract:
PROBLEM TO BE SOLVED: To provide a battery state prediction technique capable of predicting a battery state to various deterioration environment and updating a model using a use history in various deterioration environment.SOLUTION: A model is divided into a time lapse portion and an energization portion of a battery. Namely, the model determines a deterioration amount of a capacity maintenance rate by a linear sum of a staying frequency (an energization amount during a stay) at each temperature and each SOC. Deterioration of the battery is predicted under various deterioration environment by resolving the deterioration amount into a deterioration component at each temperature and each SOC. The model divided into the time lapse portion and the energization portion of the battery and a computational model such as a root rule are combined to compose an objective function, and a table of a discharge coefficient h(T, S) and an energization coefficient a(T, S) is formed by setting a temperature to T and a SOC to S using solver and the like. Once such a table is formed, deterioration prediction of the battery can be calculated using the table.
Abstract:
PROBLEM TO BE SOLVED: To provide techniques to reduce the cost of variations in electric power usage by combination with charging and discharging of a storage battery on the basis of a Markov decision process.SOLUTION: A period to which a predicted value given notice of to an electric power company is applied is preferably evenly divided into subsections. Further, for each of the subsections, on the basis of a Markov decision process including a state that depends on an electric power usage amount error, charge amount, and set target, the amount of charging and discharging of a storage battery as an action at any given time is optimally decided depending on the electric power usage amount error, charge amount, time, and set target at that time. A predetermined time in a subsection is a target setting time, at which a future target is further set as the action. The action includes deciding the charging and discharging amount in that subsection and deciding a future target in a subsection whose target should be set.
Abstract:
PROBLEM TO BE SOLVED: To provide a method for determining an optimal action considering a risk for each of states in each of phases of a target term by using a computer system.SOLUTION: The method includes the steps of: a) selecting one of states which may be taken in a present phase; b) selecting one of executable action candidates; c) calculating a commission obtained by executing the selected one of action candidates and a probability distribution of evaluation values depending on an optimal value in the next phase; d) using the probability distribution of evaluation values to calculate a risk index; e) performing weighting in accordance with a predetermined preference considering the risk index to calculate a value index in the case of executing the action candidate; f) repeating the steps b) to e) for non-selected action candidates; and g) comparing the value indexes for determining one of the action candidates as an optimal action.
Abstract:
Ein neuromorpher Chip enthält synaptische Zellen, die entsprechende resistive Einheiten, Axonleitungen, Dendritenleitungen und Schalter umfassen. Die synaptischen Zellen sind mit den Axonleitungen und Dendritenleitungen zu einer Kreuzschienenanordnung verbunden. Die Axonleitungen sind so konfiguriert, dass sie Eingangsdaten empfangen und die Eingangsdaten an die synaptischen Zellen liefern. Die Dendritenleitungen sind so konfiguriert, dass sie Ausgangsdaten empfangen und die Ausgangsdaten über eine oder mehrere entsprechende Ausgangsleitungen liefern. Ein gegebener einer der Schalter ist so konfiguriert, dass er einen Eingangsanschluss mit einer oder mehreren Eingangsleitungen verbindet und seinen einen oder seine mehreren Ausgangsanschlüsse mit einer gegebenen einen oder mehreren gegebenen Axonleitungen veränderlich verbindet.
Abstract:
PROBLEM TO BE SOLVED: To implement a function for learning a decision-making model while suppressing an unnecessary increase in mixing time.SOLUTION: A technique for updating a parameter (policy parameter) defining a policy under a Markov decision process system environment includes updating the policy parameter according to an update equation. The update equation includes a term for decreasing a weighted sum (weighted expected hitting time sum) over a first state (s) and a second state (s') of a statistic (expected hitting time function) on the number of steps (hitting time) required to make a first state transition from the first state (s) to the second state (s').
Abstract:
PROBLEM TO BE SOLVED: To provide a method and system for accurately predicting data for explained variables.SOLUTION: This method includes: receiving explanatory variables as input data; searching training data for elements whose discrete variables match those of each element of sets contained in the input data; applying a function weighted by a scaling variable to each element in the input data and to each of one or more elements found for the element in the input data in order to calculate each function value; calculating the sum of the function values for every element in the input data; and applying every calculated sum to a prediction equation in order to calculate a prediction value of the explained variable.
Abstract:
PROBLEM TO BE SOLVED: To provide a method for more efficiently determining an optimum policy compared to an existing calculation method when a Markov decision process has cyclicity, and a device and a computer program therefor.SOLUTION: Provided is a method for determining an optimum policy by using a Markov decision process in which T (T is a natural number) pieces of subspaces, that have at least one states, have cyclic structure, respectively. The method includes steps of: identifying subspaces which are parts of a state space; receiving selection of t-th (t is a natural number and t≤T) subspace among the identified subspaces; calculating a probability and an expected value in costs of reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace of a following cycle; and recursively calculating a value and an expected value in costs on the basis of the calculated probability and expected value in costs, in a sequential manner starting from the (t-1)th subspace.