Abstract: Embodiments of the invention provide machine learning method and system. The method comprises: generating a group of sub-sequences based on a target sequence including n basic memory depth values, the group of sub-sequences includes at least one subset of composite sequences, and each composite sequence in any subset is generated based on an equal number of consecutive basic memory depth values (BMDV); determining weights of each sub-sequence, wherein initial weights for a composite sequence generated based on m BMDV are determined based on average of weights of at least two sub-sequences each having an equal number of BMDV which is less than and closest to m; determining weights of the target sequence based on an average of weights of at least two sub-sequences each having an equal number of BMDV which is closest to n; and solving the prediction problem based on weights of the target sequence.

Abstract: Embodiments of the invention provide a method and system for determining an error threshold value when a vector distance based error measure is to be used for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a target sequence to be used for machine failure prediction; calculating an average depth value based on the plurality of basic memory depth values; retrieving an elementary error threshold value, based on the average depth value, from a pre-stored table which is stored in a memory and includes a plurality of mappings wherein each mapping associates a predetermined depth value of an elementary sequence to an elementary error threshold value; and calculating an error threshold value corresponding to the target sequence based on both the retrieved elementary error threshold value and a standard deviation of the plurality of basic memory depth values.

Abstract: Embodiments of the invention provide a method and system for solving a prediction problem. The computer-implemented method comprises: decomposing a target sequence associated with the prediction problem into a binary sequence group including at least one binary sequence, wherein each binary sequence in the group is generated based on a corresponding multiplier value; generating a plurality of elementary sequences based on each composite sequence in the binary sequence group; determining initial weights of each composite sequence based on average of weights of the corresponding elementary sequences; determining a binary prediction value for each binary sequence, wherein the binary prediction value for each composite sequence is determined by modelling each composite sequence using RNN based on the determined initial weights; and determining a real prediction value for the target sequence based on a product of the binary prediction value for each binary sequence and the corresponding multiplier value.

Abstract: Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a composite sequence of machine failure history; ascertaining weight values for at least one of the identified basic memory depth values according to a pre-stored table which includes a plurality of mappings wherein each mapping relates a basic memory depth value to one set of weight values; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the ascertained weight values, wherein weight values related to a first basic memory depth value in the pre-stored table is ascertained based on a second set of weight values related to a second basic memory depth value which is less than the first basic memory depth value by a predetermined value.

Abstract: Embodiments of the invention provide a method and system for solving a prediction problem. The computer-implemented method comprises: decomposing a target sequence associated with the prediction problem into a binary sequence group including at least one binary sequence, wherein each binary sequence in the group is generated based on a corresponding multiplier value; generating a plurality of elementary sequences based on each composite sequence in the binary sequence group; determining initial weights of each composite sequence based on average of weights of the corresponding elementary sequences; determining a binary prediction value for each binary sequence, wherein the binary prediction value for each composite sequence is determined by modelling each composite sequence using RNN based on the determined initial weights; and determining a real prediction value for the target sequence based on a product of the binary prediction value for each binary sequence and the corresponding multiplier value.

Abstract: Embodiments of the invention provide a method and system for accelerating convergence of Recurrent Neural Network (RNN) for machine failure prediction. The method comprises: setting initial parameters in RNN wherein the initial parameters include an initial learning rate which is determined based on a standard deviation of a plurality of basic memory depth values identified from a machine failure sequence; training RNN based on the initial parameters and at the end of each predetermined time period, calculating current pattern error based on a vector distance between the machine failure sequence and current predicted sequence; and if the current pattern error is less than or not greater than a predetermined error threshold value, determining, by the processor, an updated learning rate based on the current pattern error, and updating weight values between input and hidden units in RNN based on the updated learning rate.

Abstract: Embodiments of the invention provide machine learning method and system. The method comprises: generating a group of sub-sequences based on a target sequence including n basic memory depth values, the group of sub-sequences includes at least one subset of composite sequences, and each composite sequence in any subset is generated based on an equal number of consecutive basic memory depth values (BMDV); determining weights of each sub-sequence, wherein initial weights for a composite sequence generated based on m BMDV are determined based on average of weights of at least two sub-sequences each having an equal number of BMDV which is less than and closest to m; determining weights of the target sequence based on an average of weights of at least two sub-sequences each having an equal number of BMDV which is closest to n; and solving the prediction problem based on weights of the target sequence.

Abstract: Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a machine failure history; ascertaining a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table including a plurality of mappings each mapping between a basic memory depth value and a basic weight range, or a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; ascertaining a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generating initial weights based on the composite initial weight range; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial weights.

Abstract: Embodiments of the invention provide a method and system for accelerating convergence of Recurrent Neural Network (RNN) for machine failure prediction. The method comprises: setting initial parameters in RNN wherein the initial parameters include an initial learning rate which is determined based on a standard deviation of a plurality of basic memory depth values identified from a machine failure sequence; training RNN based on the initial parameters and at the end of each predetermined time period, calculating current pattern error based on a vector distance between the machine failure sequence and current predicted sequence; and if the current pattern error is less than or not greater than a predetermined error threshold value, determining, by the processor, an updated learning rate based on the current pattern error, and updating weight values between input and hidden units in RNN based on the updated learning rate.

Abstract: Embodiments of the invention provide a method and system for determining an error threshold value when a vector distance based error measure is to be used for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a target sequence to be used for machine failure prediction; calculating an average depth value based on the plurality of basic memory depth values; retrieving an elementary error threshold value, based on the average depth value, from a pre-stored table which is stored in a memory and includes a plurality of mappings wherein each mapping associates a predetermined depth value of an elementary sequence to an elementary error threshold value; and calculating an error threshold value corresponding to the target sequence based on both the retrieved elementary error threshold value and a standard deviation of the plurality of basic memory depth values.

Abstract: The invention provides method and system for extracting rule specific data from a computer word. The method comprises: calculating at least one decimal value based on a rule representation associated with a rule, the rule representation is a byte array, value of each bit of the byte array representing whether a corresponding bit position in the computer word has a data component; identifying at least one result byte array based on the calculated decimal value from a preset look-up table, which includes a plurality of mappings, each between a result byte array and a decimal value, the result byte array indicating a set of reference bit positions for determining a set of bit positions in the computer word in which data components related to the rule are stored, and a last byte of the result byte array representing a bit count value associated with the set of reference bit positions.

Abstract: Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a machine failure history; ascertaining a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table including a plurality of mappings each mapping between a basic memory depth value and a basic weight range, or a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; ascertaining a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generating initial weights based on the composite initial weight range; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial weights.

Abstract: Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a composite sequence of machine failure history; ascertaining weight values for at least one of the identified basic memory depth values according to a pre-stored table which includes a plurality of mappings wherein each mapping relates a basic memory depth value to one set of weight values; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the ascertained weight values, wherein weight values related to a first basic memory depth value in the pre-stored table is ascertained based on a second set of weight values related to a second basic memory depth value which is less than the first basic memory depth value by a predetermined value.

Abstract: Embodiments of the invention provide a method and system for comparing a given number and a target number. The method comprises: generating a current index associated with the given number based on a current digit pair including a first current digit from the given number and a second current digit from the target number, and a current state information associated with the given number; looking up the generated current index in a preset state transition table to identify a next state information, wherein the preset state transition table maintains a plurality of mappings, each mapping is between an index and a next state information; if the current digit pair includes a last digit of any of the two numbers, determining a final comparison result based on the next state information; otherwise, taking on the next state information as the current state information for comparison of a next digit pair.

Abstract: Embodiments of the invention provide a method and system for comparing a given number and a target number. The method comprises: generating a current index associated with the given number based on a current digit pair including a first current digit from the given number and a second current digit from the target number, and a current state information associated with the given number; looking up the generated current index in a preset state transition table to identify a next state information, wherein the preset state transition table maintains a plurality of mappings, each mapping is between an index and a next state information; if the current digit pair includes a last digit of any of the two numbers, determining a final comparison result based on the next state information; otherwise, taking on the next state information as the current state information for comparison of a next digit pair.

Abstract: The invention provides method and system for extracting rule specific data from a computer word. The method comprises: calculating at least one decimal value based on a rule representation associated with a rule, the rule representation is a byte array, value of each bit of the byte array representing whether a corresponding bit position in the computer word has a data component; identifying at least one result byte array based on the calculated decimal value from a preset look-up table, which includes a plurality of mappings, each between a result byte array and a decimal value, the result byte array indicating a set of reference bit positions for determining a set of bit positions in the computer word in which data components related to the rule are stored, and a last byte of the result byte array representing a bit count value associated with the set of reference bit positions.