Prototyping an IoT-Platform Embedded Device to Prevent the Failure of the Battery System at the Kedungbadak-Bogor Substation
Bibliographic record
Abstract
To increase the efficiency of the time used when measuring the voltage value on the battery system and each battery cell, it is necessary to maintain the condition of the battery so that it can work optimally through the build and installation of an embedded system prototype based on the Internet of Things (IoT) platform for preventing the failure on the battery system at the Substation of Kedungbadak-Bogor in a real-time condition.There are two subjects in this article are related to the research objectives, namely i) device manufacturing and programming and ii) device prototype performance measurement.The research implementation algorithm as a form of the research method was chosen.The formation of the subsystem is carried out through integrated wiring between electronic devices, in order to obtain the hardware handshaking process and conditions, whereas the subsystem programming is done through making algorithms and compiling syntax, in order to obtain handshaking by software.The performance of the subsystem is measurable when integrated into the smartphone via the Blynk IoT application, in order to obtain hardware and software handshaking processes.The performance of the device prototype when monitoring the voltage in the form of information about the measured voltage value of each battery cell, namely the voltage value displayed on the 'client system', 'server system', and 'smart-phone', while the battery cell voltage drop alarm is in the form of notifications on smart-phones and emails containing notifications voltage drop in one of the battery cells.Fabricating the embedded device prototype can provide measurement efficiency and early detection of anomalies in battery cells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".