Engineering Transactions: A Research Publication of Mahanakorn University of Technology

Smart Warehouse Monitoring System Based on WEMOS D1 R2 Microcontroller

2025-09-12T20:07:36+07:00

To complement the "normal monitoring" function in smart warehouse security systems and address the limitations of traditional warehouses, such as the lack of real-time visualization of environmental data, difficulty tracking dynamic scenes, and lack of traceability of personnel access, this paper designs and implements a multi-dimensional smart warehouse monitoring system. Based on a Wemos D1 R2 and Arduino Uno controller, the system integrates a DHT11 temperature and humidity sensor, an MQ-2 smoke sensor, an ESP32-CAM camera module, and a TZM1026 semiconductor fingerprint door sensor. Warehouse environmental parameters are collected via the DHT11 and MQ-2 sensors, and the data is transmitted in real time via UDP to an OLED display module and a mobile app (developed by E4A), visualizing temperature, humidity, and smoke concentration. The ESP32-CAM module captures real-time images of the warehouse and broadcasts them over Wi-Fi IP, allowing remote access to the video stream from mobile devices. The TZM1026 fingerprint module and the door sensor module work together to verify personnel identities and record access control status. Test results show that the synchronization delay between the system's environmental data on the OLED and app is less than 0.5 seconds, the ESP32-CAM video stream transmission is stable (delay < 1 second), and the fingerprint recognition response speed is < 4ms. This effectively meets the warehouse's normal monitoring needs of "real-time environmental monitoring, scene visualization, and personnel access traceability," complementing the existing system focused on "abnormal alarms" and improving the smart warehouse security system.

IoT-Based Sign Language Recognition System

2026-03-13T10:28:41+07:00

This research aims to develop a real-time English alphabet sign language translation system based on a hybrid computing approach integrating a web client and an IoT device. The system employs MediaPipe to extract 42 hand landmark coordinates instead of raw images in order to reduce data complexity and improve processing efficiency. The extracted coordinates are classified using a lightweight Multilayer Perceptron (MLP) model deployed on an ESP32 microcontroller. To address the differences between left and right hands, the Canonical Form concept is applied by transforming right-hand inputs into a unified representation. Experimental results indicate that the proposed system can accurately recognize English alphabet signs from A to Z, excluding J and Z, achieving an average accuracy of 91.67% while maintaining stable performance under varying environmental conditions. The results demonstrate that combining structural hand features with a hybrid computing architecture effectively enhances recognition accuracy and is well suited for IoT-based sign language translation systems with limited computational resources.

Rogue Access Point Detection on Wireless Networks Using RTT Analysis and K-means

2026-02-14T16:36:17+07:00

While the rapid expansion of WLAN technology facilitates daily life, it has simultaneously increased cybersecurity risks, particularly from Rogue Access Points (RAP) using the IEEE 802.11 standard. This vulnerability exposes users to Man-in-the-Middle (MitM) attacks and data theft. This research investigates these threats using experimental and statistical methods. Utilizing the K-means algorithm on real-world data, the proposed model identifies malicious access points with 99.7% accuracy. Finally, the study offers actionable guidelines to strengthen network security for both individuals and organizations.

A feasibility study on the use of artificial intelligence to analyze pig health in livestock farms

2026-02-14T16:33:58+07:00

Tracking the daily behavior of pigs raised on farms using artificial intelligence technology helps farmers to automatically and quickly assess the health and growth of the pigs. This article presents a feasibility study on using artificial intelligence (AI) to analyze images from CCTV cameras to detect standing, eating, and lying behaviors of pigs in a farm, and to record numerical values of these behaviors over time for health analysis. The study trained the AI with numerous pig images sourced from social media, without using actual farm pig images, and developed a model using YOLO8 tools. The trained model can detect the three behaviors from video clips with approximately 70% accuracy. The analysis shows that this method has high potential for practical application in pig farms, with possible adjustments to the camera setup environment to achieve the best image quality for detection.

Incentive Framework for Academic Publication Reviewer System

2026-04-08T20:07:36+07:00

The contemporary academic publishing ecosystem is impeded by structural constraints in cost management and incentive design. The peer review landscape relies predominantly on voluntary contributions. Reviewer compensation is typically non-transferable across publishers. Additionally, there is a lack of a centralized repository with transparent quality metrics. These inefficiencies lead to inconsistent expert assignment and persistent delays in preprint evaluation. To alleviate the problems that are earlier mentioned. This paper proposes a novel incentive framework designed as a shared infrastructure for academic publishers. We introduce a Reviewer Index (RV-Index) that synthesizes review quality, timeliness, and participation history to establish auditable reviewer tiers. Furthermore, a centralized clearinghouse manages guarantees, transferable benefits, and rewards, while a standardized API facilitates reviewer requests based on tiers and Service Level Agreements (SLAs). Lastly, the validations confirm the framework’s soundness and its potential to enhance the quality and efficiency of the peer review ecosystem.

Stability Criteria of an Euler Column with a Continuous Elastic Restraint and Classical End Conditions

2026-03-12T12:54:58+07:00

The objective of this research work is aiming at the determination of the stability criteria and corresponding buckling shape function for a continuous elastically restrained Euler column in which the elastic restraint can be viewed and modeled as Winkler-type foundation. The column with 10 combinations of classical end condition is considered. The method of problem analysis starts with formulating the dimensionless governing equation for the column buckling and then analytical solving. It is founded that most of the obtained stability criteria are highly nonlinear transcendental equations represented in terms of the required implicit stability parameters.

Euler Column Lying on Winkler-Type Foundation with One End Elastically Restrained against Translation and Rotation

2026-03-12T21:08:18+07:00

In various practical applications, it can be founded that there is not only restraint condition at the ends of column, in some cases, the column may be restrained continuously along its length. Therefore, the aim of this paper is to analyse the buckling problem of an Euler column lying on the Winkler elastic foundation instead of a column with continuous elastic restraint that covered the all possible combinations of condition between one end as the elastically restrained and another end as the classically restrained. Based on an analytical method, this leads to the first time obtaining the exact stability criteria of column and corresponding of their analytical closed-form buckling shape functions, which are not appeared in the previous work.