KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 5 of 5

Some of the metrics are blocked by your
consent settings
Dynamic Ensemble Learning With Received Signal Strength Transformation for Robust Multi-Floor Wi-Fi Indoor Localization
(2026-01-01)
Yuli Martin Adiyatma, Farid
;
Cherntanomwong, Panarat
;
Joko Suroso, Dwi
Received Signal Strength (RSS)-based Wi-Fi localization offers a cost-effective solution for multi-floor indoor location estimation. However, its accuracy is often degraded by signal fading, multipath propagation, and device heterogeneity, posing major challenges to reliable localization. Recent studies have increasingly employed deep neural networks due to their ability to extract meaningful patterns from RSS data; however, these models require substantial computational resources and extensive parameter tuning, which limits their adaptability across diverse dynamic environments. To address these limitations, we propose DELLoc-RT, a localization framework integrating Dynamic Ensemble Learning (DELLoc) with RSS Transformation (RT) for accurate, efficient, and adaptable multi-floor indoor localization. The RT module applies Sigmoid-scaled normalization and confidence weighting to convert RSS values into compact, learnable features. DELLoc employs multiple base learners optimized via the Tree-structured Parzen Estimator with a pruning strategy (TPE-PS) that accelerates convergence by focusing on promising configurations. Additionally, Iterative Ensemble Optimization with Stepwise Selection (IEO-SS) selects complementary learners to enhance overall performance. Experimental results demonstrate that DELLoc-RT achieves floor classification accuracies of 93.32%, 94.38%, and 94.02% on the UJIIndoorLoc, UTSIndoorLoc, and Tampere datasets, respectively, with mean Euclidean errors (MEE) of 10.63 m, 7.87 m, and 8.19 m. These results highlight the model’s strong adaptability across diverse datasets. Evaluation on a self-constructed dataset further confirms that DELLoc-RT delivers high accuracy and efficiency while substantially reducing the need for manual tuning, enabling rapid deployment in practical scenarios.
Some of the metrics are blocked by your
consent settings
An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization
(2025-01-01)
Adiyatma, Farid Yuli Martin
;
Cherntanomwong, Panarat
The increasing adoption of indoor localization has increased demand for location-based services (LBS) to enhance daily efficiency. Fingerprinting is a widely used method due to its high accuracy and resilience to multipath fading, but it requires dense sampling and extensive data collection, making large-scale site surveys labor-intensive and often impractical. To alleviate these challenges, interpolation and regression-based methods have been explored for their scalability and reduced manual effort. However, these approaches often struggle to model the complex, multimodal nature of real-world Received Signal Strength Indicator (RSSI) distributions. To address this limitation, we propose a novel Region-Aware Adaptive Ensemble Regressor (RAER) for generating synthetic RSSI values. RAER adapts to the statistical characteristics of the training data to efficiently produce high-quality virtual RSSI maps with low computational cost. The model integrates two components: 1) a clustering algorithm that partitions the radio map based on fingerprint signal similarity, and 2) an adaptive ensemble regression framework that combines multiple base regressors using weighted averaging, where weights are inversely proportional to each regressor’s mean absolute error (MAE). By prioritizing regressors with lower localization error, RAER improves the realism of synthetic RSSI data and enhances radio map accuracy. Experimental evaluations conducted in a multi-story university building demonstrate that RAER can reconstruct radio maps, which reduces the need for site surveys by up to 25%. Furthermore, it improves localization accuracy by 5.06%, outperforming existing methods and offering a scalable and practical solution for fingerprint-based indoor positioning systems.
Some of the metrics are blocked by your
consent settings
C-MEL: Consensus-Based Multiple Ensemble Learning for Indoor Device-Free Localization Through Fingerprinting
(2024-01-01)
Suroso, Dwi Joko
;
Adiyatma, Farid Yuli Martin
The rise of location-aware services is enhancing the effectiveness of our daily tasks, especially within indoor environments where most activities take place. Wireless indoor localization systems are the predominant method for estimating locations indoors. These systems utilize two primary approaches: device-based and device-free. Device-based techniques are attracting considerable research attention due to their ability to offer highly accurate localization in most scenarios. Conversely, device-free techniques are increasingly popular because they can determine a target's location without the target carrying a device. This capability makes them suitable for certain applications such as elderly monitoring and intruder tracking. The most popular technique for both approaches is fingerprinting, which uses the uniqueness of spatial information to predict a target's location. This spatial information is stored in a fingerprint database, containing locations and their associated parameters. However, in device-free methods, the fingerprint technique encounters challenges in accurately recognizing the complexity of each parameter combination pattern, thus impacting the accuracy of the estimation. To overcome this issue, we introduce a novel indoor device-free localization (IDFL) pattern matching algorithm named Consensus-based Multiple Ensemble Learning (C-MEL). This algorithm incorporates consensus strategies, i.e., majority voting and average strategy, to integrate outputs from various ensemble learning algorithms, such as Random Forest, Gradient Boosting, XGBoost, and LightGBM. We validate our algorithm in an 18 m2 office space featuring stainless steel partitions, tables, chairs, and cabinets. Experimental results show that C-MEL using the average strategy (C-MEL-AV) enhances accuracy by up to 44.51%, 11.26%, and 37.85%, while C-MEL with majority voting (C-MEL-MV) improves by up to 40.56%, 4.95%, and 33.44% compared to Decision Tree, Gradient Boosting, and 1D CNN-BLSTM, respectively. Based on these results, C-MEL-AV emerges as a reliable approach for accurate IDFL based on the fingerprint technique, while C-MEL-MV remains a viable alternative for IDFL systems.
Some of the metrics are blocked by your
consent settings
Partial Discharge Localization Model in Power Transformer with Fingerprinting Technique
(2023-01-01)
Chaisang, Aditep
;
Tiengthong, Thanadol
;
Maw, Myo Myint
;
Promwong, Sathaporn
Partial discharge localization in power transformers is of utmost importance, requiring an effective evaluation method to identify the location of such events precisely. Antenna placement poses challenges within power transformers, as improper positioning can significantly affect localization precision. This paper introduces an evaluation of the fingerprinting method for ultra-high frequency partial discharge localization. The fingerprinting method, commonly employed in wireless localization systems, is utilized to assess the accuracy of partial discharge localization. The proposed method leverages fingerprinting analysis and received signal strength to evaluate partial discharge events in power transformers. Experimental partial discharge measurements are conducted on a power transformer model provided by Tesla Power Company. The results include the average received signal strength at each measurement position and the distance error of the partial discharge location determined using the fingerprinting method. This research contributes to assessing partial discharge in power transformers, offering valuable insights for enhancing their health and performance evaluation.
Some of the metrics are blocked by your
consent settings
Indoor Localization Model with Impulse Radio Transmission for Short-Range Wireless Communication Systems
(2022-01-01)
Muangmee, Yannopas
;
Maw, Myo Myint
;
Promwong, Sathaporn
Impulse radio is another wireless communication technology developed for short-range communications, with low transmission power and wideband frequency. Which compliance with communication systems that require the speed of transmitting large amounts of data. Therefore, this project has designed and fabrication a biconical antenna. Furthermore, perform the antenna's performance test for an indoor impulse radio localization in the frequency range of 3.1 GHz to 10.6 GHz with a vector network analyzer. The results of the measurements were analyzed and evaluated in comparison with the distance error with matched filter and time-gating techniques. The results obtained from the study of this project are based on theory and very useful for basic information in the research and development of short-range wireless communication systems and positioning by impulse radio transmission.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results