A multipart distributed modelling approach for an automated protection system in open-air cloth drying

There are different methods of drying wet clothes, but drying with direct sunlight is considered the best suited for the preservation of the quality and usability of the cloths. However, sudden rainfall during the drying period constitutes a major drawback of the method. This returns the cloths to a drenched state as it is practically impossible to watch the clothes dry off after washing. This paper has proposed a model for an automated system for controlled open-air fabric drying by detecting the rain and moisture status of the cloths in real-time, and capable of shielding them to safety from the rainfall and excess sun. The modelled part considers the sensing model, drying model, control model, and their validation. The implementation and evaluation stage relates the result of the validated results to the developed prototypes. The simulated results in the sensing unit indicate above 87.5% agreements with the analytical results, and the controller simulated result provides a relatively small overshoot and faster dynamic response. Manufacturers of hanger systems for cloth drying have a basis for the design and implementation of their products in the paper

Improving the performance of free space optical systems: a space-time orthogonal frequency division modulation approach

Free space optical (FSO) communication systems are known for high capacity and information security. The overall system performances of FSO systems are however significantly affected by atmospheric turbulence induced fading. This paper, therefore, proposes a technique to mitigate this effect through the introduction of an additional degree of error correction capacity by exploiting the spectral dimension in the coding space. A space-time trellis coded orthogonal frequency division modulation (OFDM) scheme was developed, simulated and evaluated for optical communication through a Gamma-Gamma channel. The evaluation of the coding gain obtained from the simulation results, the mathematical analysis and the truncation error analysis shows that the proposed technique is a promising and viable technique for improving the error correction performance of space-time codes for free space optical communication links

Artificial neural network model for microwave propagation in water melon

The propagation and attenuation of microwave traversing through water melon at 2.45GHz were modeled and validated. An attenuation experiment was carried out on water melon using free space transmission technique and an Artificial Neural Network (ANN) was designed, trained and deployed for the observed data from laboratory experiments. This generated a compact system against which existing mathematical models were compared. The results in both cases were found to be in congruence

Control Design and Management of a Distributed Energy Resources System

Energy generation, distribution, and transmission are crucial to the development and advancement of humans and their environment. Therefore, the need for a sustainable environment is essential. This study focuses on designing, building, testing, and commissioning an intelligent grid solar-powered distributed energy resource system to serve as an alternative to powering loads with conventional energy sources, creating a pollution-free and self-dependent system that can be built to the capacity of the required load. The solar panels, voltage regulator, microprocessor, solar charge controller, and batteries are all interconnected to automatically switch between the three solar substations. The simulation of the DER network system was executed with MATLAB, Simulink, and Simscape Electrical. The management system was created using the Visual Studio 2019 and ASP.NET MVC software. The management system was designed to keep tabs on the daily sales of the DER components to various clients. The results are achieved by subjecting a load (21W rated headlight bulb and a 5W rated fluorescent bulb) at specified time intervals (10, 20, 30 minutes). The results showed us a particular set threshold voltage for the sub-station switch. This project gives an insight into how good and reliable the distributed energy resource system can be as it provides a constant power supply to the equipment

Characterization of oil palm fruitlets using artificial neural network

Accurate data of the dielectric properties of oil palm fruitlets and the development of appropriate models are central to the quest of quality sensing and characterization, and Artificial Neural Network (ANN) and Adaptive Neurofuzzy Inference Systems (ANFIS) are becoming increasingly relevant for this purpose owing to their excellent pattern matching and generalization ability. In this study, a Layer Sensitivity-Based Artificial Neural Network (LSB_ANN) and a Multi-Adaptive Neurofuzzy Inference System (MultiANFIS) were designed to characterize oil palm fruitlets and to model the dielectric phenomena of microwave interacting with oil palm fruitlets within the frequency range of 2-4GHz. The LSB_ANN has a unique weight update mechanism which employs network layer input-output sensitivity analysis. The inputs of the networks are the frequency, the magnitude of the reflection coefficient and the phase of the reflection coefficient while the outputs are the dielectric constant, the loss factor and the oil content. The training data for the models were obtained from dielectric and moisture content measurements and the obtained data were fitted into the quasi-static wave Equations and optimized using MATLAB complex root finding technique to obtain the normalized conductance, susceptance and the complex permittivity of the fruitlets. To further validate the generalization accuracy of the LSB_ANN, its performance was compared with that of a Multi-ANFIS network as well as those of three different ANN training algorithms: Levenberg Marquardt (LM) algorithm, Resilient Backpropagation (RP) algorithm and Gradient Descent with Adaptive learning rate (GDA). Having a testing Variance-For (VAF) of 97.81 and Root Mean Square Error of 3.97, the LSB_ANN was found to possess a better post training generalization ability than the LM, RP and GDA algorithms which had VAF of 93.57, 96.26 and 94.09 respectively, and RMSE of 4.14, 4.38, and 7.98 respectively. The results also showed that contrary to the widely reported gap between the accuracy of the LM algorithm and other feed forward neural network training algorithms, the RP trained network performed as good as that of the LM algorithm for the range of data considered. A user friendly neural network based Graphical User Interface (GUI) was designed suitable for rapid determination of the dielectric constant and percentage oil content of oil palm fruitlets from measured magnitude and phase of reflection coefficient within a frequency range of 2-4GHz