Enhancing Automatic Reasoning of human errors in an operating system using fuzzy logic

2019 ◽  
Author(s):  
Konstantina Chrysafiadi ◽  
Maria Virvou
Keyword(s):  
Operating System ◽  
Fuzzy Logic ◽  
Human Errors ◽  
Automatic Reasoning

Acquisition and Analysis for Customer Requirement Information

2010 ◽  
Vol 139-141 ◽  
pp. 1468-1471 ◽  
Author(s):  
Zheng Xiao ◽  
Quan Liu ◽  
Qing Song Ai
Keyword(s):  
Probability Theory ◽  
Fuzzy Logic ◽  
Real Time ◽  
Point Of View ◽  
The Internet ◽  
Uncertain Information ◽  
Customer Requirement ◽  
Human Errors ◽  
Current Classification ◽  
Online Experience

This paper summarized the popular methods for acquiring customer requirement (CR) information, where the internet-based method is especially efficient when an enterprise's web possesses the function of real-time communication, guiding investigation and online experience. However, current classification and analysis of CR mainly depends on a subjective judgment, awareness and assessment of the enterprise's staff, which exists with human errors. From the point of view of CR information, the authors classified it into certain information and uncertain information; there are two types of uncertain information----the random and the fuzzy. Based on this classification, CR information can be analyzed by means of probability theory and fuzzy logic. Therefore, the enterprise will acquire CR information more effectively and analyze it more accurately.

TOWARDS A UNIFIED PRINCIPLE FOR REASONING ABOUT HETEROGENEOUS DATA: A FUZZY LOGIC FRAMEWORK

2012 ◽  
Vol 20 (02) ◽  
pp. 281-302 ◽  
Author(s):  
LYAMINE HEDJAZI ◽  
JOSEPH AGUILAR-MARTIN ◽  
MARIE-VERONIQUE LE LANN ◽  
TATIANA KEMPOWSKY
Keyword(s):  
Fuzzy Logic ◽  
Heterogeneous Data ◽  
Learning Problems ◽  
Characteristic Functions ◽  
Partial Knowledge ◽  
Process Variables ◽  
Automatic Reasoning ◽  
Multiple Data ◽  
Monitoring Process ◽  
Data Representations

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.

Routine Digital Processing of Microscope Images

1990 ◽  
Vol 48 (1) ◽  
pp. 550-551
Author(s):  
E. Wisse ◽  
A. Geerts ◽  
R.B. De Zanger
Keyword(s):  
Operating System ◽  
Digital Processing ◽  
Hard Disks ◽  
The Sun ◽  
Tape Drive ◽  
High Definition ◽  
Fluorescent Microscope ◽  
Microscope Images ◽  
Helical Scan ◽  
Ethernet Connection

The slowscan and TV signal of the Philips SEM 505 and the signal of a TV camera attached to a Leitz fluorescent microscope, were digitized by the data acquisition processor of a Masscomp 5520S computer, which is based on a 16.7 MHz 68020 CPU with 10 Mb RAM memory, a graphics processor with two frame buffers for images with 8 bit / 256 grey values, a high definition (HD) monitor (910 × 1150), two hard disks (70 and 663 Mb) and a 60 Mb tape drive. The system is equipped with Imaging Technology video digitizing boards: analog I/O, an ALU, and two memory mapped frame buffers for TV images of the IP 512 series. The Masscomp computer has an ethernet connection to other computers, such as a Vax PDP 11/785, and a Sun 368i with a 327 Mb hard disk and a SCSI interface to an Exabyte 2.3 Gb helical scan tape drive. The operating system for these computers is based on different versions of Unix, such as RTU 4.1 (including NFS) on the acquisition computer, bsd 4.3 for the Vax, and Sun OS 4.0.1 for the Sun (with NFS).

Locus of Control, Safety Attitudes and Involvement in Hazardous Events in Indian Army Aviators

2013 ◽  
Vol 3 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Catherine Joseph ◽  
Suhasini Reddy ◽  
Kanwal Kashore Sharma
Keyword(s):  
Locus Of Control ◽  
Future Research ◽  
Research Needs ◽  
Negative Attitudes ◽  
Human Errors ◽  
Safety Attitudes ◽  
Positive Correlation ◽  
Self Confidence

Locus of control (LOC), safety attitudes, and involvement in hazardous events were studied in 205 Indian Army aviators using a questionnaire-based method. A positive correlation was found between external LOC and involvement in hazardous events. Higher impulsivity and anxiety, and decreased self-confidence, safety orientation, and denial were associated with a greater number of hazardous events. Higher external LOC was associated with higher impulsivity, anxiety, and weather anxiety and with lower self-confidence, safety orientation, and denial. Internal LOC was associated with increased self-confidence, safety orientation, and denial. Hazardous events and self-confidence were higher in those involved in accidents than those not involved in accidents. Future research needs to address whether training can effectively modify LOC and negative attitudes, and whether this would cause a reduction in, and better management of, human errors.

Hysteresis in Categorization: Fuzzy Logic Makes Fuzzy Boundaries

2012 ◽  
Author(s):  
Thomas M. Crawford ◽  
Justin Fine ◽  
Donald Homa
Keyword(s):  
Fuzzy Logic
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