1. A mold clamping device comprising:
a stationary platen that supports a stationary mold;
a movable platen that supports a movable mold;
a plurality of tie bars with ends that are detachably connected to the stationary platen and other ends that extend through the movable platen;
mold opening and closing means that moves the movable platen toward and away from the stationary platen so that the movable mold opens from and closes on the stationary mold;
tie bar connecting means that detachably connects each of the plurality of tie bars to the movable platen by having a split nut engaged with an engagement portion formed in each of the tie bars; and
a mold clamping cylinder that is provided around a tie bar insertion through hole in the movable platen and propels the movable platen toward the stationary platen side, using the split nut in the tie bar connecting means as a reaction point, so as to generate a mold clamping force, wherein
the mold clamping cylinder includes a secondary piston that defines, out of two chambers in front and in rear defined by a primary piston that abuts against the split nut, the chamber positioned on the stationary platen side into a front chamber and a rear chamber, and
the secondary piston integrally operates with the primary piston at a time of a mold contact and a mold clamping, but makes a movement relative to the primary piston at a time of a mold release.
2. The mold clamping device according to claim 1, wherein
the mold opening and closing means stops the movable platen at a position, immediately before the movable mold comes in contact with the stationary mold.
3. A molding method using the mold clamping device according to claim 2, wherein
after the mold opening and closing means moves the movable platen toward the stationary platen side and stops the movable platen at the position, immediately before the movable mold comes in contact with the stationary mold, the split nut in the tie bar connecting means is closed so that each of the plurality of tie bars is connected to the movable platen,
the movable platen is then propelled by an operation of the mold clamping cylinder so that the mold contact and the mold clamping are performed, and
when a predetermined period of cooling time has passed after completion of an injection process, a supply-discharge mode for pressure oil to and from the mold clamping cylinder is changed so that the mold release is performed with a piston stroke larger than a piston stroke used at the time of the mold contact and the mold clamping.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for determining suspect entities engaged in financial transactions, comprising the steps of:
a) selecting a focus entity F and a plurality of peripheral entities, each peripheral entity having one or more financial transaction with F within a period of time T;
b) partitioning the period of time T into a plurality of time intervals;
c) generating a unifocused directed graph for each time interval of the plurality of time intervals, each directed graph consisting of a focus node, a plurality of peripheral nodes, and edges between the focus node and the peripheral nodes, the focus node representing F, each peripheral node representing a peripheral entity having at least one directed financial transaction of said one or more directed financial transactions with F within the time interval, each edge having a weight, said weight being a function of the at least one directed financial transaction between F and the peripheral node within the time interval;
d) determining, from the directed graphs or from a representation of the directed graphs, whether any of said edges are out-of-norm edges, said determining including applying edge-selection criteria to the weights associated with the edges of the directed graphs; and
e) if any of said edges are so determined to be out-of-norm edges then identifying at least one potential suspect entity from the out-of-norm edges, followed by deriving at least one suspect entity from the at least one potential suspect entity.
2. The method of claim 1, said method further comprising prior to performing step a): identifying the one or more directed financial transactions which are recorded in, or managed by, a database of a financial institution.
3. The method of claim 1, wherein the partitioning in step b) includes taking into account the distribution over time within the period of time T of the one or more directed financial transactions.
4. The method of claim 1, wherein the partitioning in step b) does not include taking into account the distribution over time within the period of time T of the one or more directed financial transactions.
5. The method of claim 1, wherein the time intervals of the plurality of time intervals resulting from step b) are constant time intervals.
6. The method of claim 1, wherein the time intervals of the plurality of time intervals resulting from step b) are variable time intervals.
7. The method of claim 1, wherein the function in step c) is proportional to the sum of the directed financial transactions between F and the peripheral node within the time interval.
8. The method of claim 7, wherein the function in step c) is a linear function of said sum.
9. The method of claim 7, wherein the function in step c) is a nonlinear function of said sum.
10. The method of claim 1, wherein the determining in step d) is from the representation of the directed graphs, and wherein said representation is a matrix representation of the directed graphs.
11. The method of claim 1, wherein said determining at least one suspect entity in step e) includes determining whether the at least one potential suspect entity includes at least one valid entity, and if it is so determined that the at least one potential suspect entity includes the at least one valid entity then the at least one suspect entity consists of the at least one potential suspect entity exclusive of the at least one valid entity.
12. The method of claim 1, said method further comprising after step e) the step of:
f) generating a report that includes those transactions of the one or more directed financial transactions which the suspect entities determined in step e) have participated in.
13. The method of claim 1, said method further comprising prior to step a) the step of: selecting a plurality of focus entities from a database of directed financial transactions, wherein steps a), b), c), d), and e) are performed for each focus entity of the plurality of focus entities, and wherein F represents said each focus entity of the plurality of focus entities for which steps a), b), c), d), and e) are performed.
14. The method of claim 1, said method further comprising after performing step e) at level 1: performing steps a), b), c), d), and e) to level L for each suspect entity determined in step e) at levels 1, 2, . . . L\u22121, wherein F represents said each suspect entity for which steps a), b), c), d), and e) are performed, and wherein L is at least 2.
15. The method of claim 14, wherein L=2.
16. The method of claim 14, wherein L exceeds 2.
17. A computer system having a processor, said processor adapted to execute computer readable program code, said computer readable program code comprising an algorithm for determining suspect entities engaged in financial transactions, said algorithm adapted to execute the steps of:
a) selecting a focus entity F and a plurality of peripheral entities, each peripheral entity having one or more financial transaction with F within a period of time T;
b) partitioning the period of time T into a plurality of time intervals;
c) generating a unifocused directed graph for each time interval of the plurality of time intervals, each directed graph consisting of a focus node, a plurality of peripheral nodes, and edges between the focus node and the peripheral nodes, the focus node representing F, each peripheral node representing a peripheral entity having at least one directed financial transaction of said one or more directed financial transactions with F within the time interval, each edge having a weight, said weight being a function of the at least one directed financial transaction between F and the peripheral node within the time interval;
d) determining, from the directed graphs or from a representation of the directed graphs, whether any of said edges are out-of-norm edges, said determining including applying edge-selection criteria to the weights associated with the edges of the directed graphs; and
e) if any of said edges are so determined to be out-of-norm edges then identifying at least one potential suspect entity from the out-of-norm edges, followed by deriving at least one suspect entity from the at least one potential suspect entity.
18. The computer system of claim 17, said algorithm further adapted to execute prior to step a) the step of: identifying the one or more directed financial transactions which are recorded in, or managed by, a database of a financial institution.
19. The computer system of claim 17, wherein the partitioning in step b) includes taking into account the distribution over time within the period of time T of the one or more directed financial transactions.
20. The computer system of claim 17, wherein the partitioning in step b) does not include taking into account the distribution over time within the period of time T of the one or more directed financial transactions.
21. The computer system of claim 17, wherein the time intervals of the plurality of time intervals resulting from step b) are constant time intervals.
22. The computer system of claim 17, wherein the time intervals of the plurality of time intervals resulting from step b) are variable time intervals.
23. The computer system of claim 17, wherein the function in step c) is proportional to the sum of the directed financial transactions between F and the peripheral node within the time interval.
24. The computer system of claim 23, wherein the function in step c) is a linear function of said sum.
25. The computer system of claim 23, wherein the function in step c) is a nonlinear function of said sum.
26. The computer system of claim 17, wherein the determining in step d) is from the representation of the directed graphs, and wherein said representation is a matrix representation of the directed graphs.
27. The computer system of claim 17, wherein said determining at least one suspect entity in step e) includes determining whether the at least one potential suspect entity includes at least one valid entity, and if it is so determined that the at least one potential suspect entity includes the at least one valid entity then the at least one suspect entity consists of the at least one potential suspect entity exclusive of the at least one valid entity.
28. The computer system of claim 17, said algorithm further adapted to execute after step e) the step of:
f) generating a report that includes those transactions of the one or more directed financial transactions which the suspect entities determined in step e) have participated in.
29. The computer system of claim 17, said algorithm further adapted to execute prior to step a) the step of: selecting a plurality of focus entities from a database of directed financial transactions, wherein steps a), b), c), d), and e) are performed for each focus entity of the plurality of focus entities, and wherein F represents said each focus entity of the plurality of focus entities for which steps a), b), c), d), and e) are performed.
30. The computer system of claim 17, said algorithm further adapted to execute after step e) at level 1: executing steps a), b), c), d), and e) to level L for each suspect entity determined in step e) at levels 1, 2, . . . L\u22121, wherein F represents said each suspect entity for which steps a), b), c), d), and e) are performed, and wherein L is at least 2.
31. The computer system of claim 30, wherein L=2.
32. The computer system of claim 30, wherein L exceeds 2.
33. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code comprising an algorithm for determining suspect entities engaged in financial transactions, said algorithm adapted to execute the steps of:
a) selecting a focus entity F and a plurality of peripheral entities, each peripheral entity having one or more financial transaction with F within a period of time T;
b) partitioning the period of time T into a plurality of time intervals;
c) generating a unifocused directed graph for each time interval of the plurality of time intervals, each directed graph consisting of a focus node, a plurality of peripheral nodes, and edges between the focus node and the peripheral nodes, the focus node representing F, each peripheral node representing a peripheral entity having at least one directed financial transaction of said one or more directed financial transactions with F within the time interval, each edge having a weight, said weight being a function of the at least one directed financial transaction between F and the peripheral node within the time interval;
d) determining, from the directed graphs or from a representation of the directed graphs, whether any of said edges are out-of-norm edges, said determining including applying edge-selection criteria to the weights associated with the edges of the directed graphs; and
e) if any of said edges are so determined to be out-of-norm edges then identifying at least one potential suspect entity from the out-of-norm edges, followed by deriving at least one suspect entity from the at least one potential suspect entity.
34. The computer program product of claim 33, said algorithm further adapted to execute prior to step a) the step of: identifying the one or more directed financial transactions which are recorded in, or managed by, a database of a financial institution.
35. The computer program product of claim 33, wherein the partitioning in step b) includes taking into account the distribution over time within the period of time T of the one or more directed financial transactions.
36. The computer program product of claim 33, wherein the partitioning in step b) does not include taking into account the distribution over time within the period of time T of the one or more directed financial transactions.
37. The computer program product of claim 33, wherein the time intervals of the plurality of time intervals resulting from step b) are constant time intervals.
38. The computer program product of claim 33, wherein the time intervals of the plurality of time intervals resulting from step b) are variable time intervals.
39. The computer program product of claim 33, wherein the function in step c) is proportional to the sum of the directed financial transactions between F and the peripheral node within the time interval.
40. The computer program product of claim 39, wherein the function in step c) is a linear function of said sum.
41. The computer program product of claim 39, wherein the function in step c) is a nonlinear function of said sum.
42. The computer program product of claim 33, wherein the determining in step d) is from the representation of the directed graphs, and wherein said representation is a matrix representation of the directed graphs.
43. The computer program product of claim 33, wherein said determining at least one suspect entity in step e) includes determining whether the at least one potential suspect entity includes at least one valid entity, and if it is so determined that the at least one potential suspect entity includes the at least one valid entity then the at least one suspect entity consists of the at least one potential suspect entity exclusive of the at least one valid entity.
44. The computer program product of claim 33, further comprising after step e) the step of:
f) generating a report that includes those transactions of the one or more directed financial transactions which the suspect entities determined in step e) have participated in.
45. The computer program product of claim 33, further comprising prior to step a) the step of:
selecting a plurality of focus entities from a database of directed financial transactions, wherein steps a), b), c), d), and e) are performed for each focus entity of the plurality of focus entities, and wherein F represents said each focus entity of the plurality of focus entities for which steps a), b), c), d), and e) are performed.
46. The computer program product of claim 33, said algorithm further adapted to execute after step e) at level 1: executing steps a), b), c), d), and e) to level L for each suspect entity determined in step e) at levels 1, 2, . . . L\u22121, wherein F represents said each suspect entity for which steps a), b), c), d), and e) are performed, and wherein L is at least 2.
47. The computer program product of claim 46, wherein L=2.
48. The computer program product of claim 46, wherein L exceeds 2.