Prototyping an IoT-Platform Embedded Device to Prevent the Failure of the Battery System at the Kedungbadak-Bogor Substation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it