Design of a Temperature Control and Object Motion Detection System in the Server Room Using IOT-Based Wemos D1
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
Servers play an important role in managing information and managing network traffic within an agency. It contains various types of network devices that must be kept on every day to provide the best service. This leads to the importance of maintaining server presence, such as paying attention to room humidity, regulating temperature and monitoring server room movements to keep it under control. The air temperature in the server room is set within 20 - 25 degrees Celsius with a relative humidity of 40 - 55%. In this context, the concept of the Internet of Things (IoT) emerges as a potential solution by connecting physical objects via the internet. This research designs and builds an IoT-based temperature control and object motion detection system using a DHT22 sensor to measure the temperature in the room and a PIR sensor to detect human movement. Hardware components such as Wemos D1, Buzzer, Relay 2 channel 5v, and others are used to control the system via a smartphone. The software used includes the Arduino IDE. This system aims to monitor and control room temperature and object movement detection systems intelligently, prevent overheating, reduce the risk of loss and combine the advantages of IoT technology to create a temperature control and motion detection system that is responsive to room temperature and the movement of objects in the room.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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