Integrating Offline Object Tracking, Signal Processing, and Artificial Intelligence to Classify Relevant Events in Sawmilling Operations

Sawmilling operations are typically one of the most important cells of the wood supply chain as they take the log assortments as inputs to which they add value by processing lumber and other semi-finite products. For this kind of operations, and especially for those developed at a small scale, long-term monitoring data is a prerequisite to make decisions, to increase the operational efficiency and to enable the precision of operations. In many cases, however, collection and handling of such data is limited to a set of options which may come at high costs. In this study, a low-cost solution integrating offline object tracking, signal processing and artificial intelligence was tested to evaluate its capability to correctly classify in the time domain the events specific to the monitoring of wood sawmilling operations. Discrete scalar signals produced from media files by tracking functionalities of the Kinovea® software (13,000 frames) were used to derive a differential signal, then a filtering-to-the-root procedure was applied to them. Both, the raw and filtered signals were used as inputs in the training of an artificial neural network at two levels of operational detail: fully and essentially documented data. While the addition of the derived signal made sense because it improved the outcomes of classification (recall of 92–97%) filtered signals were found to add less contribution to the classification accuracy. The use of essentially documented data has improved substantially the classification outcomes and it could be an excellent solution in monitoring applications requiring a basic level of detail. The tested system could represent a good and cheap solution to monitor sawmilling facilities aiming to develop our understanding on their technical efficiency.

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Low-cost Large Scale Vermicompost Unit

Journal of Physics Conference Series ◽

10.1088/1742-6596/2115/1/012026 ◽

2021 ◽

Vol 2115 (1) ◽

pp. 012026

Author(s):

Sonam Solanki ◽

Gunendra Mahore

Keyword(s):

Large Scale ◽

Low Cost ◽

Small Scale ◽

Scale Production ◽

Key Innovation ◽

Main Challenge ◽

Long Run ◽

Large Scale Production ◽

Term Maintenance

Abstract In the current process of producing vermicompost on a large-scale, the main challenge is to keep the worms alive. This is achieved by maintaining temperature and moisture in their living medium. It is a difficult task to maintain these parameters throughout the process. Currently, this is achieved by building infrastructure but this method requires a large initial investment and long-run maintenance. Also, these methods are limited to small-scale production. For large-scale production, a unit is developed which utilises natural airflow with water and automation. The main aim of this unit is to provide favourable conditions to worms in large-scale production with very low investment and minimum maintenance in long term. The key innovation of this research is that the technology used in the unit should be practical and easy to adopt by small farmers. For long-term maintenance of the technology lesser number of parts are used.

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Instrumentation for mechanical vibrations analysis in the time domain and frequency domain using the Arduino platform

Revista Brasileira de Ensino de Física ◽

10.1590/s1806-11173812063 ◽

2016 ◽

Vol 38 (1) ◽

Cited By ~ 2

Author(s):

Marcus Varanis ◽

Anderson Langone Silva ◽

Pedro Henrique Ayres Brunetto ◽

Rafael Ferreira Gregolin

Keyword(s):

Signal Processing ◽

Finite Elements ◽

Time Domain ◽

Good Accuracy ◽

Low Cost ◽

Analytical Models ◽

Experimental Setup ◽

Mechanical Vibrations ◽

Python Language ◽

The Time Domain

In this paper, we use the Arduino platform together with sensors as accelerometer, gyroscope and ultrasound, to measure vibrations in mechanical systems. The main objective is to assemble a signals acquisition system easy to handle, of low cost and good accuracy for teaching purposes. It is also used the Python language and its numerical libraries for signal processing. This paper proposes the study of vibrations of a beam, which is measured by position, velocity and acceleration. An experimental setup was implemented. The results obtained are compared with analytical models and computer simulations using finite elements. The results are in agreement with the literature.

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Evaluating the long-term performance of low-cost adsorbents using small-scale adsorption column experiments

Water Research ◽

10.1016/j.watres.2016.05.093 ◽

2016 ◽

Vol 101 ◽

pp. 429-440 ◽

Cited By ~ 27

Author(s):

O. Callery ◽

M.G. Healy ◽

F. Rognard ◽

L. Barthelemy ◽

R.B. Brennan

Keyword(s):

Low Cost ◽

Small Scale ◽

Column Experiments ◽

Adsorption Column ◽

Long Term Performance ◽

Term Performance

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Upgrading of waste stabilization ponds using a low-cost small-scale fine bubble diffused aeration system

Water Science & Technology ◽

10.2166/wst.2021.330 ◽

2021 ◽

Author(s):

Ramazan Vagheei

Keyword(s):

Low Cost ◽

Small Scale ◽

Waste Stabilization Ponds ◽

Efficient Treatment ◽

Stabilization Ponds ◽

Aeration System ◽

Waste Stabilization ◽

Fine Bubble ◽

Inlet Zone

Abstract Waste Stabilization Ponds (WSPs) are known for the economical treatment of wastewater, especially if low-cost land is available. In this research to overcome some common operational problems such as undesirable color changes in ponds, severe odor problems, and most importantly, deviations from the effluent standards, the performance of a novel installation of a small-scale fine bubble diffused aeration system in the inlet zone of the facultative pond has been investigated. The long-term operational data of the system in two wastewater treatment plants in the east of Iran demonstrated that this system can significantly improve the efficiency of the treatment plant in addition to eliminating the mentioned operational problems. Pre-aeration of the inlet zone of the facultative ponds (Birjand WSPs) by the aeration system consists of 250 fine bubble disk diffusers (12 inches in diameter) and one 22 kW roots blower showed that purple color and odor problem can eliminate after almost two weeks and organic matter removal efficiency increased from 58 ± 15% to about 85 ± 10% based on BOD5. Almost similar results were obtained from the WSPs of Neyshabur. Long-term experimental results showed this system can be used successfully to control the process and upgrade these natural and efficient treatment processes, especially in developing countries.

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Testing the Capability of Low-Cost Tools and Artificial Intelligence Techniques to Automatically Detect Operations Done by a Small-Sized Manually Driven Bandsaw

Forests ◽

10.3390/f11070739 ◽

2020 ◽

Vol 11 (7) ◽

pp. 739

Author(s):

Marius Cheţa ◽

Marina Viorela Marcu ◽

Eugen Iordache ◽

Stelian Alexandru Borz

Keyword(s):

Artificial Intelligence ◽

Low Cost ◽

Electrical Power ◽

Experimental System ◽

Video Camera ◽

Business Failure ◽

Small Scale ◽

Wood Processing ◽

Artificial Neural Network Ann ◽

Level Meter

Research Highlights: A low-cost experimental system was developed to enable the production monitoring of small-scale wood processing facilities by the means of sensor-collected data and the implementation of artificial intelligence (AI) techniques, which provided accurate results for the most important work operations. Background and Objectives: The manufacturing of wood-based products by small-scale family-held business is commonly affected by a lack of monitoring data that, on the one hand, may prevent the decision-making process and, on the other hand, may lead to less technical efficiency that could result in business failure. Long-term performance of such manufacturing facilities is limited because data collection and analysis require significant resources, thus preventing the approaches that could be pursued for competitivity improvement. Materials and Methods: An external sensor system composed of two dataloggers—a triaxial accelerometer and a sound pressure level meter—was used in combination with a video camera to provide the input signals and meta-documentation for the training and testing of an artificial neural network (ANN) to check the accuracy of automatic classification of the time spent in operations. The study was based on a sample of ca. 90 k observations collected at a frequency of 1 Hz. Results: The approach provided promising results in both the training (ca. 20 k) and testing (ca. 60 k) datasets, with global classification accuracies of ca. 85%. However, the events characterizing the effective sawing, which requires electrical power, were even better recognized, reaching a classification accuracy of 98%. Conclusions: The system requires low-cost devices and freely available software that could enable data feeding on local computers by their direct connection to the devices. As such, it could collect, analyze and plot production data that could be used for maintaining the competitiveness of traditional technologies.

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A Transputer Processing System Interface for The Evaluation of Digital Signal Processing Algorithms

International Journal of Electrical Engineering Education ◽

10.1177/002072099403100106 ◽

1994 ◽

Vol 31 (1) ◽

pp. 54-65

Author(s):

C. T. Pointon ◽

R. A. Carrasco

Keyword(s):

Signal Processing ◽

Digital Signal Processing ◽

Low Cost ◽

Processing System ◽

Digital Signal ◽

Computational Performance ◽

System Input ◽

Signal Processing Algorithms ◽

Processing Algorithms ◽

The Time Domain

A transputer processing system interface for the evaluation of digital signal processing algorithms The design and construction of a low-cost transputer processing system input/output (I/O) interface for the acquisition and retrieval of data, and its use in the evaluation of the computational performance characteristics of discrete convolution in the time domain, and fast convolution, using a non-look-up table fast Fourier transform (FFT) algorithm, is presented.

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Determination of an Indoor Target Position: An Accurate and Adaptable Approach Based on Differential Positioning

International Journal of Antennas and Propagation ◽

10.1155/2019/6202095 ◽

2019 ◽

Vol 2019 ◽

pp. 1-19 ◽

Cited By ~ 1

Author(s):

Siye Wang ◽

Chang Ding ◽

Weiqing Huang ◽

Yanfang Zhang ◽

Jianguo Jiang ◽

...

Keyword(s):

Indoor Localization ◽

Target Position ◽

Signal Strength ◽

Small Scale ◽

Localization Algorithm ◽

Accurate Identification ◽

Differential Signal ◽

Wireless Signal ◽

Communication Environments ◽

The Time Domain

The growing demand for new products that rely on the accurate identification of a target’s location indoors, while remaining mindful of cost, continues to drive research in this important and challenging area. Researchers are actively pursuing algorithmic improvements to eliminate errors introduced from complex interference factors present in indoor, wireless communication environments. In this work, we adopt a differential signal strength method in the design of our new indoor localization algorithm. The proposed algorithm reduces errors in the time domain by smoothing out the wireless signal fluctuations, thus stabilizing the signal; a single exponential algorithm is applied to the signal strength parameters collected to accomplish this. The target’s position is then computed by utilizing both the plane geometric method and difference localization theory. This combination of techniques is reasonable for the environment under consideration (small scale, wireless), as the multipath effects for the signal are approximately equal under these conditions. In addition, the proposed approach is compatible with a wide variety of technologies (e.g., RFID and Bluetooth); it can be cost-effectively deployed by leveraging an existing hardware infrastructure. The proposed approach has been implemented and experimentally validated. The test results are very promising: they indicate that our algorithm improves the positioning accuracy by 70%–80% in comparison with the trilateration and LANDMARC positioning algorithms.

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Design and Demonstration of a Low-Cost Small-Scale Fatigue Testing Machine for Multi-Purpose Testing of Materials, Sensors and Structures

Machines ◽

10.3390/machines6030030 ◽

2018 ◽

Vol 6 (3) ◽

pp. 30 ◽

Cited By ~ 4

Author(s):

Jack McAlorum ◽

Tim Rubert ◽

Grzegorz Fusiek ◽

Pawel Niewczas ◽

Giorgio Zorzi

Keyword(s):

Fatigue Testing ◽

Low Cost ◽

Testing Machine ◽

Industrial Applications ◽

Fatigue Tests ◽

Small Scale ◽

Mechanical Fatigue ◽

The Uk ◽

Sample Experiment

Mechanical fatigue testing of materials, prototype structures or sensors is often required prior to the deployment of these components in industrial applications. Such fatigue tests often requires the continuous long-term use of an appropriate loading machine, which can incur significant costs when outsourcing and can limit customization options. In this work, design and implementation of a low-cost small-scale machine capable of customizable fatigue experimentation on structural beams is presented. The design is thoroughly modeled using FEM software and compared to a sample experiment, demonstrating long-term endurance of the machine. This approach to fatigue testing is then evaluated against the typical cost of outsourcing in the UK, providing evidence that, for long-term testing of at least 373 h, a custom machine is the preferred option.

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