A Novel Method for the Fault Diagnosis of a Planetary Gearbox based on Residual Sidebands from Modulation Signal Bispectrum Analysis

This paper presents a novel method for the fault diagnosis of planetary gearboxes based on an accurate estimation of residual sidebands using a modulation signal bispectrum (MSB). The residual sideband resulting from the out-phase superposition of vibration waves from asymmetrical multiple meshing sources are much less influenced by gear errors than that of the in-phase sidebands. Therefore, with the accurate estimation by MSB they can produce accurate and consistent diagnosis, which are evaluated by both simulating and experimental studies. However, the commonly used in-phase sidebands have high amplitudes but include gear error effects, consequently leading to poor diagnostic results

Assessing English Language Instructors’ Knowledge and Use of Information and Communication Technology (ICT) In Taif University Campuses (TUCs)

The success of the integration of information and communication technology (ICT) into the teaching and learning of English language depends largely on the level of knowledge of ICT possessed by the instructors and actual utilization of these in the classroom. The study therefore assessed Taif University (TU) English Language instructors’ knowledge and use of ICT in English Language classrooms. Attempt was made to provide answers to these four research questions using survey research design: (1) What is the level of knowledge of English language instructor about ICT? (2) Are ICT facilities available in Taif University Campuses (TUCs) for the teaching and learning of English? (3) Do English language instructors use ICT in English? (4) Is there any significant difference in the knowledge of ICT between male and female English language instructors? The participants were 94 English Language instructors from 4 university campuses in and around Taif City. A self-designed questionnaire was used to collect pertinent data which were analyzed using frequency counts, simple percentage and t-test. Findings revealed that the level of knowledge of ICT possessed by English Language instructors was poor and as such, they rarely use ICT in English Language instruction. It was also found that there was significant difference in the male and female instructors’ knowledge of ICT with the males demonstrating a higher level of knowledge than their female counterparts. Based on these findings, it is recommended, among others, that English language instructors must attend periodic seminars, workshops and in-service trainings to equip them with knowledge of ICT and its utilization in classroom instruction while instructor education programs in tertiary establishments must be reviewed to incorporate ICT-assisted instruction. Keywords: language assessment, English language teaching, information technology, and language communication

Misalignment diagnosis of a planetary gearbox based on vibration analysis

As a critical power transmission system, planetary gearbox is widely used in many industrial important machines such as wind turbines, aircraft turbine engines, helicopters. Early fault detection and diagnosis of the gearbox will help to prevent unexpected breakdowns of this important equip-ment. Misalignment is one of the major operating problems in the planetary gearbox which may be caused by inadequate system integration, variable operating conditions and differences of elastic deformations in the system. In this paper, the effect of varying degrees of installation misalignment of planetary gearbox are investigated based on vibration measurements using spectrum analysis and modulation signal bispectrum (MSB) analysis. It has shown that the misalignment can be diagnosed in the low frequency range in which the adverse effect due to co-occurrence of amplitude modula-tion and frequency modulation (AM-FM) effect is low compared with the components around meshing frequencies. Moreover, MSB produces a more accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for all operating conditions. In contrast, spectrum can produce correct results for some of the operating conditions. Keywords: Planetary gearbox, Condition Monitoring, Misalignment, Modulation signal bispectrum

Diagnosis of Combination Faults in a Planetary Gearbox using a Modulation Signal Bispectrum based Sideband Estimator

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme

Simplified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. In this work, the impact of implementing Deep Reinforcement Learning (DRL) in predicting the channel parameters for user devices in a Power Domain Non-Orthogonal Multiple Access system (PD-NOMA) is investigated. In the channel prediction process, DRL based on deep Q networks (DQN) algorithm will be developed and incorporated into the NOMA system so that this developed DQN model can be employed to estimate the channel coefficients for each user device in NOMA system. The developed DQN scheme will be structured as a simplified approach to efficiently predict the channel parameters for each user in order to maximize the downlink sum rates for all users in the system. In order to approximate the channel parameters for each user device, this proposed DQN approach is first initialized using random channel statistics, and then the proposed DQN model will be dynamically updated based on the interaction with the environment. The predicted channel parameters will be utilized at the receiver side to recover the desired data. Furthermore, this work inspects how the channel estimation process based on the simplified DQN algorithm and the power allocation policy, can both be integrated for the purpose of multiuser detection in the examined NOMA system. Simulation results, based on several performance metrics, have demonstrated that the proposed simplified DQN algorithm can be a competitive algorithm for channel parameters estimation when compared to different benchmark schemes for channel estimation processes such as deep neural network (DNN) based long-short term memory (LSTM), RL based Q algorithm, and channel estimation scheme based on minimum mean square error (MMSE) procedure.This research received no external funding

Some microbial treatments against the tomato leaf miner; Tuta absoluta (Merick) under natural field conditions

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Synthesis, characterization and antimicrobial activities of some 5-bromouracilmetal ion complexes

Six new complexes, [Mn(Br-U)2(H2O)2]×4H2O (1), [Cd(Br-U)2]×2H2O (2), [Cu(Br-U)2(H2O)2]×2H2O (3), [Co(Br-U)2(H2O)2]×4H2O (4), [Ni(Br-U)2(H2O)2]×4H2O (5) and [Ag(Br-U)(Br-U-H)]×2(H2O) (6)  were prepared by the reaction of 5-bromoouracil with MnCl2·4H2O, CdCl2·2.5H2O, CuSO4·5H2O, (CH3COO)2Co·4H2O, (CH3COO)2Ni·4H2O and AgNO3 respectively. The complexes were characterized by melting point, elemental microanalyses, IR and 1H NMR spectroscopy. The obtained data indicated that the ligand interacted with the metal ions in its mononegatively charged enol form in a bidentate fashion. Thermogravimetric analyses (TGA and DTG) were also carried out. The data obtained agreed well the proposed structures and showed that the complexes were finally decomposed to the corresponding metal or metal oxide. The ligand and its metal-ion complexes were tested for their antimicrobial activities against four bacterial strains (B. subtillis, S. aureus, E. coli and P. aeruginosa) by the agar-well diffusion technique using DMSO as a solvent. The obtained data showed that the complexes were more potent antimicrobial agents than the parent ligand.               KEY WORDS: 5-Bromoouracil–M2+ complexes, IR, Thermal analyses, 1H NMR, Antimicrobial activity Bull. Chem. Soc. Ethiop. 2019, 33(2), 255-268.DOI: https://dx.doi.org/10.4314/bcse.v33i2.

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.This research received no external funding

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

Data Availability Statement: Not applicable.Copyright: © 2022 by the authors. In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.Funding: This research received no external funding