公开(公告)号：US20070044007A1

公开(公告)日：2007-02-22

申请号：US11205713

申请日：2005-08-17

申请人： Roy Cideciyan ,  Evangelos Eleftheriou ,  Glen Jaquette ,  Paul Seger

发明人： Roy Cideciyan ,  Evangelos Eleftheriou ,  Glen Jaquette ,  Paul Seger

IPC分类号： H03M13/00

CPC分类号： G11B20/1201 ,  G11B20/18 ,  G11B20/1866 ,  G11B20/20 ,  G11B2020/1282 ,  G11B2020/1284 ,  G11B2220/90

摘要： A method and apparatus for providing error correction capability to longitudinal position data are disclosed. Initially, data are encoded via a set of even LPOS words and a set of odd LPOS words. The encoded data are then decoded by generating a set of syndrome bits for each of the LPOS words. A determination is then made as to whether or not there is an error within one of the LPOS words based on its corresponding syndrome bits.

摘要翻译： 公开了一种向纵向位置数据提供纠错能力的方法和装置。 最初，通过一组偶数LPOS字和一组奇数LPOS字对数据进行编码。 然后通过为每个LPOS字生成一组校正子位来解码编码数据。 然后，基于其对应的校正子位确定是否存在LPOS字之一内的错误。

公开(公告)号：US20070101213A1

公开(公告)日：2007-05-03

申请号：US11261037

申请日：2005-10-28

申请人： Paul Seger

发明人： Paul Seger

IPC分类号： G01R31/28

CPC分类号： H03M13/01 ,  G11B20/1833 ,  G11B20/1866 ,  G11B2220/90 ,  H04B17/0087 ,  H04B17/391

摘要： A method, system and program product accurately model the error characteristics of a communications system, such as a tape storage system. Input parameters are entered which describe defect rates and sizes, Codeword Data Structure bytes, and any interleaving factor. Bit defects from simulated defect sources are generated, defined by the starting and ending bits of each defect within a codeword. Any codewords which are defect-free are filtered out and not processed further, thereby increasing the processing speed of the model. Within the defect streams, overlapping defects are merged, redefining defect regions by starting and ending bits. Because only the definitions are processed, not the entire length of the codewords or defects, processing efficiency is further enhanced. The number of defects that occur in each codeword is determined and the probability of the occurrence of N bytes in error per processed codeword may be computed. If desired, a histogram may be generated which includes the rate at which errors occurred and subsequently used to estimate the probability of an error event. Such information may then be incorporated into the design of an error correction code for the modeled system.

摘要翻译： 一种方法，系统和程序产品可以精确地建模诸如磁带存储系统之类的通信系统的错误特性。 输入输入参数，描述缺陷率和大小，代码字数据结构字节和任何交织因子。 生成来自模拟缺陷源的位缺陷，由码字内的每个缺陷的起始位和结束位定义。 无缺陷的任何码字被过滤掉，不进一步处理，从而增加了模型的处理速度。 在缺陷流中，重叠的缺陷被合并，通过开始和结束位重新定义缺陷区域。 因为仅处理定义，而不是码字或缺陷的整个长度，处理效率进一步提高。 确定每个码字中发生的缺陷的数量，并且可以计算每个经处理的码字出现错误的N个字节的概率。 如果需要，可以生成包括发生错误的速率并且随后用于估计错误事件的概率的直方图。 然后可以将这样的信息并入用于建模系统的纠错码的设计中。

公开(公告)号：US20070106485A9

公开(公告)日：2007-05-10

申请号：US10947934

申请日：2004-09-23

申请人： Paul Seger

发明人： Paul Seger

IPC分类号： G06F15/00

CPC分类号： G06N3/02

摘要： The state or condition of a system may be evaluated by comparing a set of selected parameter values, converted into a trial vector, with a number of model or exemplar vectors, each of which was represents a particular state or condition of a sample system. Examples of such conditions may include “good”, “marginal”, “unacceptable”, “worn”, “defective”, or other general or specific conditions. Sets of parameter values from the system are converted into input vectors. Unprocessed vectors are then processed against the input vectors in an artificial neural network to generate the exemplar vectors. The exemplar vectors are stored in a memory of an operational system. During operation of the system, the trial vector is compared with the exemplar vectors. The exemplar vector which is closest to the trial vector represents a state which most closely represents the current state of the system. Thus, a high similarity between the trial vector and an exemplar vector which represent a “good” system is likely to have come from a “good” system.

摘要翻译： 可以通过将转换成试验载体的所选参数值的集合与多个模型或示例性向量进行比较来评估系统的状态或状态，每个模型或示例向量表示样本系统的特定状态或状态。 这种情况的例子可能包括“良好”，“边缘”，“不可接受”，“磨损”，“缺陷”或其他一般或特定条件。 将系统中的参数值集合转换为输入向量。 然后针对人造神经网络中的输入向量处理未处理的向量以生成示例向量。 示例性向量存储在操作系统的存储器中。 在系统运行过程中，将试验向量与示例向量进行比较。 最接近试验向量的样本向量表示最接近地表示系统当前状态的状态。 因此，试验载体与代表“良好”系统的示范载体之间的高度相似性可能来自“良好”系统。

公开(公告)号：US20060074604A1

公开(公告)日：2006-04-06

申请号：US10947934

申请日：2004-09-24

申请人： Paul Seger

发明人： Paul Seger

IPC分类号： G06F15/00

CPC分类号： G06N3/02

摘要： The state or condition of a system may be evaluated by comparing a set of selected parameter values, converted into a trial vector, with a number of model or exemplar vectors, each of which was represents a particular state or condition of a sample system. Examples of such conditions may include “good”, “marginal”, “unacceptable”, “worn”, “defective”, or other general or specific conditions. Sets of parameter values from the system are converted into input vectors. Unprocessed vectors are then processed against the input vectors in an artificial neural network to generate the exemplar vectors. The exemplar vectors are stored in a memory of an operational system. During operation of the system, the trial vector is compared with the exemplar vectors. The exemplar vector which is closest to the trial vector represents a state which most closely represents the current state of the system. Thus, a high similarity between the trial vector and an exemplar vector which represent a “good” system is likely to have come from a “good” system.

摘要翻译： 可以通过将转换成试验载体的所选参数值的集合与多个模型或示例性向量进行比较来评估系统的状态或状态，每个模型或示例向量表示样本系统的特定状态或状态。 这种情况的例子可能包括“良好”，“边缘”，“不可接受”，“磨损”，“缺陷”或其他一般或特定条件。 将系统中的参数值集合转换为输入向量。 然后针对人造神经网络中的输入向量处理未处理的向量以生成示例向量。 示例性向量存储在操作系统的存储器中。 在系统运行过程中，将试验向量与示例向量进行比较。 最接近试验向量的样本向量表示最接近地表示系统当前状态的状态。 因此，试验载体与代表“良好”系统的示范载体之间的高度相似性可能来自“良好”系统。

公开(公告)号：US20060074820A1

公开(公告)日：2006-04-06

申请号：US10947692

申请日：2004-09-23

申请人： Paul Seger

发明人： Paul Seger

IPC分类号： G06F17/00 ,  G06N3/12 ,  G06F17/20 ,  G06F15/18 ,  G06N3/00

CPC分类号： G06N3/02 ,  G06F3/0634 ,  G06F11/2263

摘要： The state or condition of a data storage drive, or a subsystem within a drive, may be evaluated by comparing a set of selected parameter values, converted into a trial vector, with a number of model or exemplar vectors, each of which was represents a particular state or condition of a sample drive. Examples of such conditions may include “good”, “marginal”, “unacceptable”, “worn”, “defective”, or other general or specific conditions. Sets of parameter values from the drive are converted into input vectors. Unprocessed vectors are then processed against the input vectors in an artificial neural network to generate the exemplar vectors. The exemplar vectors are stored in a memory of an operational drive. During operation of the drive, the trial vector is compared with the exemplar vectors. The exemplar vector which is closest to the trial vector represents a state which most closely represents the current state of the drive. Thus, a high similarity between the trial vector and an exemplar vector which represent a “good” drive is likely to have come from a “good” drive.

摘要翻译： 数据存储驱动器或驱动器内的子系统的状态或状态可以通过将转换成试验向量的所选择的参数值的集合与多个模型或示例性向量进行比较来评估，每个模型或示例向量表示一个 样品驱动器的特定状态或状态。 这种情况的例子可能包括“良好”，“边缘”，“不可接受”，“磨损”，“缺陷”或其他一般或特定条件。 驱动器中的参数值集合将转换为输入向量。 然后针对人造神经网络中的输入向量处理未处理的向量以生成示例向量。 示例性矢量被存储在操作驱动器的存储器中。 在驱动器的操作期间，将试验载体与示例矢量进行比较。 最接近试验载体的样本向量表示最接近地表示驱动器的当前状态的状态。 因此，试验载体与代表“良好”驱动力的示范载体之间的高相似性可能来自“良好”的驱动力。

公开(公告)号：US20050044470A1

公开(公告)日：2005-02-24

申请号：US10646648

申请日：2003-08-21

申请人： Paul Seger

发明人： Paul Seger

IPC分类号： G11B20/18 ,  G11C29/00

CPC分类号： G11B20/1816 ,  G11B20/1879

摘要： The present invention differentiates between systematic and non-systematic conditions by observing a figure of merit over a series of many observation events. In a data storage recording environment, the particular figure of merit used is the number of data segments that must be re-written (due to errors) to a recording medium in order to assure that an entire data set is correctly written. A larger number of re-written segments is indicative of a significant error condition. After each data set is completely and correctly written, the number of re-written segments for the data set is reported as an “event.” A running history of the classified events (or the events themselves) is maintained. Then, at a predetermined time, the history is analyzed and a decision made as to whether any observed events meets predetermined criteria for a systematic condition.

摘要翻译： 本发明通过观察一系列观察事件的品质因素来区分系统和非系统条件。 在数据存储记录环境中，所使用的特定品质因数是为了确保整个数据集被正确写入而必须重写（由于错误）到记录介质的数据段的数目。 更多数量的重写段指示了重大的错误状况。 在每个数据集完全正确写入之后，数据集的重写段数被报告为“事件”。 分类事件（或事件本身）的运行历史被维护。 然后，在预定时间，对历史进行分析，并且确定任何观察到的事件是否满足系统条件的预定标准。