The Impact of Information Technology on Industrial Automation – A Critical Evaluation
Bibliographic record
Abstract
Information Technology (IT) now impacts on all aspects of modern society (personal, commercial and industrial). The advances in IT may broadly be categorized into the following subsets of technologies: integrated circuits, communications, software engineering and Graphical User Interfaces. In the context of Industrial Automation (IA) it can be seen that some communication protocols, for example Modbus which was first introduced in the 1970’s, has under gone numerous updates driven by IT developments with associated considerable improvements in performance and capability. By contrast some, for example HART which was first introduced in the 1980’s, whilst it has undergone revisions it does not fully utilize the capabilities that IT can provide. For higher level IA systems they can be divided into four generations which can each be associated with specific IT developments. The first generation IA system is based on simple Proportional, Integral and Derivative (PID) controllers and ruggedized microprocessor based Programmable Logic Controllers (PLC). IA systems expanded in both complexity and scope through these generations to include Distributed Control Systems (DCS’s) and Supervisory Control and Data Acquisition (SCADA). The fifth generation of IA systems may be associated with IT developments both on and over the horizon that include: Cloud Computing, Cyber Security and Internet of Things. Responses to these technologies include: General Electric (GE) Predix and Siemens MindSphere. Regardless of how IA is categorized there can be no doubt that IT is responsible for major technical developments – a process that is likely to continue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".