This paper presents two RFID localization methods based on a k-nn algorithm for multiple moving tracking tags attached to a concrete masonry unit (cinder block). This work uses passive RFID tags for localization and s...
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ISBN:
(纸本)9781665440905
This paper presents two RFID localization methods based on a k-nn algorithm for multiple moving tracking tags attached to a concrete masonry unit (cinder block). This work uses passive RFID tags for localization and seeks to provide rapid wireless analysis for future smart infrastructure projects where precast concrete modular structures are moved during transport and assembly. The RFID localization system uses four reader antennas, four tracking tags, and 28 reference tags in a realistic indoor assembly environment. Results show average error in the direction of movement as low as 10.5 cm. Increasing the number of nearest neighbors in the k-nn algorithm is shown to reduce error in all coordinate directions. Increasing k from 4 to 6 is shown to reduce error by 4 cm or 10%. The localization environment is analyzed, and reference tags 22, 9, 5, and 8 around the moving cinder block are seen most commonly as nearest neighbors. A modified k-nn algorithm, described here as a weighted Euclidian distance k-nn algorithm is presented that reduces total error from 41.1 cm to 32.5 cm.
Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals...
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The shipment industry is going through tremendous growth in volume thanks to technological innovation in e-commerce and global trade liberalization. Volume growth also means a rise in fraud cases involving smuggling a...
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The shipment industry is going through tremendous growth in volume thanks to technological innovation in e-commerce and global trade liberalization. Volume growth also means a rise in fraud cases involving smuggling and false declaration of shipments. Shipping companies and customs are mostly relying on routine random inspection thus finding fraud is often by chance. As the volume increases dramatically it would no longer be sustainable and effective for both shipment companies and customs to pursue traditional fraud detection strategies. Other related papers on this area have proven that intelligent data-driven fraud detection is proven to be far more effective than routine inspections. However, the challenge in data-driven detection is its effectiveness are often reliant on the availability of data and the various fraud mechanism used by fraudsters to commit shipment related fraud. As such in this paper, we review and subsequently identify the most optimized approaches and algorithms to detect fraud effectively within the shipping industry. We also identify factors that influence fraud activity, review existing fraud detection models, develop the detection framework and implement the framework using the Rapidminer tool.
Scratches, those usually generated during polishing the silicon wafer surface, are one of the major yield loss factors in semiconductor manufacturing industry. In order to determine the source of the scratches in real...
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Scratches, those usually generated during polishing the silicon wafer surface, are one of the major yield loss factors in semiconductor manufacturing industry. In order to determine the source of the scratches in real time and reduce the yield loss, it is critical for manufacturers to match and identify the same type of scratches automatically. In this paper, an improved k nearest neighbors (knn) algorithm to address this issue is presented. Firstly, a skeleton extraction method is used to depict the main lines of scratches. Then the clustering protocol is applied as a preliminary step to group these main lines so that some essential endpoints features of main lines, such as distance, slope and curvature, can be extracted. During feature extraction, a dynamic coordinate system is introduced and this greatly reduces the distortions arise due to the magnitude of tangent difference. An intelligent matching of similar scratches MSML-knnalgorithm is formulated. The experimental results show that the proposed matching method for wafer scratches has a good adaptability and robustness.
In chocolate production, post-harvest procedure is one of the most critical factors. Fermentation is a vital procedure to consider since exact generation of acid contemplate to aroma and quality of the final product. ...
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ISBN:
(纸本)9781450366113
In chocolate production, post-harvest procedure is one of the most critical factors. Fermentation is a vital procedure to consider since exact generation of acid contemplate to aroma and quality of the final product. This innovative study aims to classify the quality of the cacao beans after the post-harvest procedures. Classified sample beans from partner cacao trader were analyzed and became data sets of the device. Photographs are taken to the subjects and undergo image processing procedure then through k-Nearest Neighbors algorithm (k-nn). Beans are classified to be well-fermented under fermentation and over-fermentation process. Function test and statistical analysis using confusion matrix revealed 97.22 percent accuracy in analyzing well-fermented beans, 92.59 percent accuracy in under fermented, 75 percent in over-fermented and 80 percent in analyzing unknowns. These results generated 92.50 percent overall accuracy of the device.
This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-nn spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. Th...
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This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-nn spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. The attributes of scenic water spots previously visited by the tourists were knowledge-mined, to ascertain the tourists' interest-tendencies. A scenic water-spot classification model was constructed, to classify scenic water spots in tourist cities. Then, a scenic water spot spatial-accessibility optimization model was set up, to sequence the scenic spots. Based on the tourists' interest-tendencies, and the spatial accessibility of the scenic water spots, a spatial-decision algorithm was constructed for water-spot tourists, to make decisions for the tourists, in regard to the tour routes with optimal accessibility and lowest cost. An experiment was performed, in which the tourist city of Leshan was chosen as the research object. The scenic water spots were classified, and the spatial accessibility for each scenic spot was calculated;then, the optimal tour routes with optimal spatial accessibility and the lowest cost were output. The experiment verified that the tour routes that were output via the proposed algorithm had stronger spatial accessibility, and cost less than the sub-optimal ones, and were thus more environmentally friendly.
In the recent years, the number of smartphone users has increased dramatically every year. Smartphones produce a variety of services including indoor navigation and tracking using the Received Signal Strength Indicato...
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In the recent years, the number of smartphone users has increased dramatically every year. Smartphones produce a variety of services including indoor navigation and tracking using the Received Signal Strength Indicator (RSSI) value of the Wi-Fi (Wireless Fidelity) routers to estimate user position. In this research, we developed a navigation and tracking system using a Fingerprint map and k-Nearest Neighbor (k-nn) algorithm. In that way, we can help the user to go through the nearest path to user destination by using Dijkstra's algorithm. These features are displayed in the form of an RSSI-based navigation application on an Android smartphone. At the same time, estimated position of user of this navigation app will be sent to server and viewed in a real time website application. This system helps to assist visitors in finding their way in a complex building and at the same time it allows building owners record and analyze visitor movement. One key benefit of the system is its low initial cost. It only utilizes the existing Wi-Fi infrastructure. Experimental results show that this system can reach an accuracy up to 78% and distance errors less than 3 m.
Vibrations in electromechanical machines pose a risk of performance deterioration and mechanical failures, stressing the need for precise all-weather vibration detection and identification of modal parameters for pred...
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Vibrations in electromechanical machines pose a risk of performance deterioration and mechanical failures, stressing the need for precise all-weather vibration detection and identification of modal parameters for predictive and proactive maintenance. Using an experimental approach, a dataset of interferograms is generated from an optical sensor with labeled vibration amplitudes corresponding to frequencies ranging from 50 Hz to 250 Hz through voltages of 10 V and 15 V, respectively. The experimental setup integrates a Mach-Zehnder interferometer (MZI) with a vibrating motor to capture minute displacements induced by vibration frequencies and record them as fringe images via a CCD camera. The k-nearest neighbor (k-nn) machine learning and FFT algorithms are employed for analysis. The vibration modes and resonant frequency of the motor are determined from the fringe images using the FFT technique. The dataset is split into a 70% training set and a 30% validation set. Computer vision techniques are applied to extract the features of a local binary pattern (LBP) from the training fringe images. The machine learning model is trained to accurately detect the vibration amplitudes based on the LBP in each fringe image. The proposed approach achieves 98.5% accuracy in detecting the motor vibration frequency. Consequently, MZI has a potential for monitoring the real-time vibrations in electromechanical equipment.
It is a constant objective for manufacturing companies to improve the efficiency and effectiveness of their maintenance processes. Avoiding unexpected breakdowns that result in high costs and production losses is a ma...
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It is a constant objective for manufacturing companies to improve the efficiency and effectiveness of their maintenance processes. Avoiding unexpected breakdowns that result in high costs and production losses is a major concern. Not only the selection of appropriate maintenance strategies, but also the use of appropriate methods and tools to support decision making in this area is essential to achieve this goal. This article presents the possibility of using machine learning methods to develop predictive models to support decision making in maintenance management. For this purpose, three different machine learning methods have been considered: Neural Networks (nn), the k-Nearest Neighbours algorithm (knn) and Support Vector Machines (SVM). These models were constructed using variables identified through statistical analysis as having a significant impact on the effectiveness of maintenance processes, specifically the availability of machinery in the production process. The developed models were subjected to a qualitative evaluation, which led to the identification of the SVM model as the most suitable to support decision making in the planning and execution of maintenance processes. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
This paper addresses the enhancement of the Wi-Fi-based fingerprint technique for an indoor positioning system applied in an experimental area. The conventional Wi-Fi-based fingerprint technique utilizes a k-nearest n...
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ISBN:
(纸本)9798350351774;9798350351767
This paper addresses the enhancement of the Wi-Fi-based fingerprint technique for an indoor positioning system applied in an experimental area. The conventional Wi-Fi-based fingerprint technique utilizes a k-nearest neighbor (k-nn) algorithm for position estimation. The k-nn algorithm is a simple and intuitive classification algorithm based on distance metric, i.e., Euclidean distance (ED), but often demonstrates limited accuracy. To mitigate this constraint and enhance positioning precision, advanced machine learning algorithms in artificial neural networks (Anns) have been introduced. Although Annalgorithms are considered highly reliable, they are complex and resource-intensive algorithms, resulting in less suitable for a small-scale area that requires simple indoor positioning applications. In contrast, the random forest (RF) algorithm offers comparable positioning accuracy while being more computationally efficient, making it a favorable choice for such scenarios. The work in this paper enhances the accuracy of the Wi-Fi-based fingerprint technique for indoor positioning systems by adopting the RF algorithm over the k-nn alternative for position estimation accuracy. The number of received signal strength (RSS) data selected from appropriate access points (APs) in the area chosen by a feature selection method is a pivotal factor influencing accuracy improvements. The experimental results express the direct correlation between increased RSS data and accuracy improvement for both algorithms. Significantly, the application of the feature selection method using the information gain ratio augments the positioning accuracy specifically for the RF algorithm.
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