The present work concerns the study of the dielectric relaxation of dielectric oil based on Lagenaria siceraria (calabash) seeds. Dielectric spectroscopy was used to measure the loss angle, the dielectric constant and...The present work concerns the study of the dielectric relaxation of dielectric oil based on Lagenaria siceraria (calabash) seeds. Dielectric spectroscopy was used to measure the loss angle, the dielectric constant and the electrical modulus. Three relaxation processes in calabash oil were identified. It was also found that the relative permittivity decreases with increasing temperature and frequency. A study of the imaginary part of the electrical modulus was done and revealed a relaxation process at low frequencies. At higher frequencies, the dielectric relaxation is thermally activated. The increase in temperature leads to a decrease in the relaxation rate. The result obtained indicates that relaxation type is not of the Debye type in the high-frequency region. The Cole-Cole model of the imaginary part of the permittivity as a function of its real part in calabash oil for different temperatures was drawn and analyzed. It shows the existence of a negative temperature coefficient of resistance in the fluid and helps identifying a relaxation process in the conductivity of the sample studied. It highlights the presence of Debye relaxation which characterizes the presence of an abnormal dispersion of the dielectric constant over a frequency range. Calabash seed oil exhibits better dielectric constant (relative permittivity) compared to other oils.展开更多
The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-bas...The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.展开更多
In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to m...In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.展开更多
This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve co...This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve complex power flow equations. This study simulates a series of outage scenarios to evaluate the responsiveness of the grid. The results obtained underline the crucial importance of reactive power management and highlight the urgent need to consolidate the grid infrastructure of North Cameroon. To increase grid resilience and stability, the paper recommends the strategic integration of renewables and the development of interconnections with other power grids. These measures are presented as viable solutions to meet current and future energy distribution challenges, ensuring a reliable and sustainable power supply for Cameroon.展开更多
Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency.However,classical maintenance methods rely on assumed lifetime distribu...Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency.However,classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity.The advent of Industry 4.0 has increased the use of sensors for monitoring systems,while deep learning(DL)models have allowed for accurate system health predictions,enabling data-driven maintenance planning.Most intelligent maintenance literature has used DL models solely for remaining useful life(RUL)point predictions,and a substantial gap exists in further using predictions to inform maintenance plan optimization.The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models.Hence,this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem(SMP).The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system.Numerical experiments compare the framework’s performance against prior SMP methods and highlight its strengths.When minimizing cost,maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs.The proposed method is usable in large-scale,complex scenarios and various industrial contexts.The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.展开更多
文摘The present work concerns the study of the dielectric relaxation of dielectric oil based on Lagenaria siceraria (calabash) seeds. Dielectric spectroscopy was used to measure the loss angle, the dielectric constant and the electrical modulus. Three relaxation processes in calabash oil were identified. It was also found that the relative permittivity decreases with increasing temperature and frequency. A study of the imaginary part of the electrical modulus was done and revealed a relaxation process at low frequencies. At higher frequencies, the dielectric relaxation is thermally activated. The increase in temperature leads to a decrease in the relaxation rate. The result obtained indicates that relaxation type is not of the Debye type in the high-frequency region. The Cole-Cole model of the imaginary part of the permittivity as a function of its real part in calabash oil for different temperatures was drawn and analyzed. It shows the existence of a negative temperature coefficient of resistance in the fluid and helps identifying a relaxation process in the conductivity of the sample studied. It highlights the presence of Debye relaxation which characterizes the presence of an abnormal dispersion of the dielectric constant over a frequency range. Calabash seed oil exhibits better dielectric constant (relative permittivity) compared to other oils.
文摘The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.
文摘In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.
文摘This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve complex power flow equations. This study simulates a series of outage scenarios to evaluate the responsiveness of the grid. The results obtained underline the crucial importance of reactive power management and highlight the urgent need to consolidate the grid infrastructure of North Cameroon. To increase grid resilience and stability, the paper recommends the strategic integration of renewables and the development of interconnections with other power grids. These measures are presented as viable solutions to meet current and future energy distribution challenges, ensuring a reliable and sustainable power supply for Cameroon.
文摘Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency.However,classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity.The advent of Industry 4.0 has increased the use of sensors for monitoring systems,while deep learning(DL)models have allowed for accurate system health predictions,enabling data-driven maintenance planning.Most intelligent maintenance literature has used DL models solely for remaining useful life(RUL)point predictions,and a substantial gap exists in further using predictions to inform maintenance plan optimization.The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models.Hence,this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem(SMP).The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system.Numerical experiments compare the framework’s performance against prior SMP methods and highlight its strengths.When minimizing cost,maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs.The proposed method is usable in large-scale,complex scenarios and various industrial contexts.The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.
