Journal of Applied Research and Technology

Tuning a PID controller using genetic algorithms

2024-11-04T11:21:03-06:00

This paper details the development of PID controller tuning, based on the implementation of advanced optimization techniques in MATLAB to find the optimal gains for control actions. The methodology used to create the solutions was that of genetic algorithms, an artificial intelligence technique developed in the 1970s and inspired by Darwin's natural selection, within the field of evolutionary computing. Its implementation is based on selection, crossover, and mutation processes, which allow the solutions to iteratively converge towards increasingly optimal results. Two different genetic algorithms were programmed and designed. The first focused exclusively on a single objective, which was the settling time; while the second was based on a multi-objective technique that additionally considered the maximum overshoot, rise time, and delay time. Different fitness functions were developed to create these neural models; subsequently, the gain results obtained from these genetic methods were compared with those proposed by analytical and experimental methods, both in the field of simulation and in physical implementation. The analysis of the responses validated the
efficiency and effectiveness of the proposed algorithms for controller tuning, showing better performance with the gains obtained through genetic algorithms.

Enhanced secure path selection model for underwater acoustic sensor networks using advanced machine learning and optimization techniques

2024-09-30T11:19:31-06:00

The underwater acoustic sensor network is a large network consisting of many operating sensor nodes that surround a transmitting node. The communication process faces substantial disturbances caused by the everchanging nature of the underwater acoustic channel, which is characterized by fluctuating properties in both time and location. Therefore, the underwater acoustic communication system has difficulties in reducing interference and improving communication efficiency and quality by using adaptive modulation. This work presents a model that aims to tackle these difficulties by suggesting an optimum route selection and safe data transmission
approach in UASN using sophisticated technology. The suggested approach for transferring safe data in UASN via optimum route selection consists of two main stages. Nodes are first chosen based on restrictions such as energy, distance, and connection quality, which are quantified in terms of throughput. Moreover, the process of forecasting energy is made easier by using sophisticated machine learning methods like transformer models. The ideal route is generated using a hybrid optimization technique called enhanced swarm optimization, which combines ideas from particle swarm optimization and genetic algorithms. Afterward, data is safely transported via the most efficient route by using fully homomorphic encryption. Finally, the ESO+ transformer model that was created is tested against established benchmark models, showcasing its strong and reliable performance. The proposed model demonstrates remarkable performance with an accuracy of 95.12%, precision of 94.83%, specificity of 93.65%, sensitivity of 95.28%, false positive rate of 4.72%, F1 score of 94.95%, Matthews correlation coefficient of 94.85%, false negative rate of 4.72%, negative predictive value of 95.15%, and false discovery rate of 5.15% when trained on a learning percentage of 70%.

Kinematic and dynamic analysis through the robotic formulation of the clavicle-shoulder joint of a human upper extremity

2024-09-17T17:23:28-06:00

This paper presents the kinematic and dynamic modeling of the shoulder-clavicle assembly constituting a four–degrees-of-freedom mechanical system. The models are obtained through robotic concepts and formulations, applied to a specific case of arm abduction with movement in the acceleration and deceleration phase, and compared with its equivalent static model. The influence of a suspended mass at the arm´s end is also analyzed. Subsequently, a biomechanical model considering the muscular action of the deltoid muscle is created based on the dynamic model obtained, allowing estimation of the force exerted by the moving muscle.

Characterization of urban mobility in Bogotá: A spatial autocorrelation analysis

2024-09-25T10:49:50-06:00

This paper studies the variables influencing urban mobility in Bogotá's Urban Planning Zones (UPZs) through spatial autocorrelation analysis. Initially, data from various databases were compiled, covering 13 variables across 111 UPZs. A descriptive analysis identified significant variables, revealing a positive skewness trend. Higher social strata (4, 5, and 6) correlated with better mobility indices and more automobiles per family. The Moran Index showed strong spatial autocorrelation in mobility indices, indicating that nearby UPZs have similar mobility patterns. Areas like Suba and Usaquén, with better infrastructure, showed higher mobility indices, while Ciudad Bolívar and Usme had poor infrastructure and low mobility. The study highlights the correlation between mobility and factors like social strata, automobile numbers, and infrastructure, providing a foundation for future transport and urban planning policies.

Fuel rod design for a high burnup small modular nuclear reactor

2024-09-24T17:23:04-06:00

PWR is a type of nuclear reactor that is widely used as a nuclear power plant. Even though PWR has been around for a long time, technology continues to develop. The direction of development of PWR technology is to create a more compact design with a modular system (SMR) and more efficient fuel. More efficient fuel can be obtained by increasing fuel burnup. By increasing burnup, the fuel usage period is longer, thereby increasing the economic value of the fuel and reducing the volume of radioactive waste produced from spent fuel. High burnup means the fuel will be exposed to radiation for longer. Therefore, it is necessary to calculate both thermal and mechanical aspects with the new fuel rod design, to see whether the fuel can be used until the end of the fuel cycle. Calculations were
conducted using the Femaxi version 6 code. From the calculation results, it was obtained that the dimensions of the fuel rods were capable of reaching a burnup of 60 GWd/TU. The dimensions obtained include the diameter and length of the pellets of 7.4 mm and 10 mm, the diameter and depth of the disc of 4.7 mm and 0.51 mm, and the inner and outer diameters of the cladding of 7.8 mm and 9.3 mm. The calculation results show that the temperature distribution in the fuel rods during reactor operation is still within safe limits, and pellet cladding interaction (PCI) does not occur until the end of the fuel consumption cycle.

Maximizing transmission efficiency through WDM-DCF Integration in optical fiber communication systems

2024-10-03T05:34:01-06:00

In the realm of optical fiber communication systems, maximizing transmission efficiency stands as a paramount objective. This study embarks on an innovative approach, merging wavelengthdivision multiplexing (WDM) with dispersion compensation fiber (DCF), to address the persistent challenges of signal degradation due to dispersion. Drawing from comprehensive simulations and meticulous analysis, our research reveals the transformative potential of this integrated solution. By seamlessly integrating WDM and DCF, we achieve remarkable enhancements in transmission performance, characterized by superior signal fidelity, unprecedented transmission distances, and unparalleled data rates. This study not only underscores the technological advancements propelling optical communication systems into a new era of efficiency but also heralds the dawn of a paradigm shift in high-speed, long-distance communication networks.

Acoustic and thermal study of coconut fiber agglomerated with cassava starch

2024-10-04T13:36:47-06:00

In this work, a thermal and acoustic study of specimens made from coconut fiber agglomerated with cassava starch is carried out. Sound absorption was measured in a transmission tube according to ISO 10534-2: 2001, in order to obtain the sound absorption coefficient (α). In addition, procedures described in ASTM E2611–19 were implemented to determine the sound transmission loss (STL). The results demonstrate the capacity of the tested specimen as a sound absorber, with absorption coefficients greater than 70% for a considerable range of frequencies starting at around 1000 Hz and above. Similarly, the thermal study of the material based on ASTM C-177 indicates an average thermal conductivity coefficient of 0,174 W/m.K, in a range of inlet temperatures between 52°C and 137°C, confirming that it has qualities that are similar to good thermal insulators, although still not comparable to some industrial materials.

UV protection and color constants of cotton fabrics dyed with natural dyes

2024-09-14T15:02:02-06:00

In this research, cotton fabrics were dyed with various dye solutions obtained from natural materials, including turmeric, hibiscus and spinach, as well as mixtures of them. Pre-mordanting was applied using three metal salts. After the dyeing process, color constants were determined using the imageJ program, and the UVB/UVA transmission ratio for the dyed fabrics was measured. The results show that fabrics dyed with natural dyes, as well as those dyed with mixtures of natural dyes and different mordants, produced a variety of colors; additionally, the UV transmission values were found to be different and lower than those of the undyed fabrics.

Design of an improved model for natural image classification using AugMix, SE-ResNeXt, and MAML

2024-10-14T00:41:59-06:00

Retaining the effectiveness but improving the efficiency of natural image classification is of prime necessity in recent times, with the surge in demand for deploying these models in practical applications, ensuring accuracy and generalization. Classic deep learning classifiers suffer from limited robustness, generalization, and failure to adapt to new tasks and domains. These shortcomings restrict their practically effective deployment by the availability of different diversified and unseen data. In this work, the authors introduce an optimized deep learning classifier framework, leveraging state-of-the-art techniques in various key domains. The proposed model harnesses a combination of techniques ranging from AugMix, SE-ResNeXt, MAML, Hyperband, and finally Domain-Adversarial Neural Network (DANN) for performance improvement. AugMix integrates Mixup and CutMix with the stochastic augmentation technique of complex augmentation chains to enhance the model's robustness and generalization. Mixing images with stochastic augmentations and the use of Mixup and CutMix bring further strong regularizations, boosting the robustness metrics by 15-20% and classification accuracy by 3-5% on the unseen natural images and samples. SE-ResNeXt introduces the use of channel-wise attention to enhance the representational power of the model. Squeeze-and-Excitation (SE) blocks are introduced to recalibrate the channel-wise feature responses by weighting informative features and suppressing less useful ones. It boosts the accuracy of models on benchmark CIFAR-100 dataset samples by 2-3% over standard ResNeXt. Execution of Model-Agnostic Meta-Learning enables a model to adapt quickly to a new task based on a small number of examples. MAML meta-learns updated models based on examples of tasks instead of direct model parameters. A 5-7% improvement in accuracy is achieved for different scenarios. Hyperband performs tension-free search of optimal hyperparameters via adaptive resources dealing, which configures the resources only for the promising configurations. Reducing the computational cost of hyperparameter tuning to at most 50% ensures an increase in model accuracy of 2- 3%. The DANN technique uses adversarial training in order to suppress the domain shift between source and target datasets. DANN uses a gradient reversal layer to train feature extractors to produce domain-invariant features, leading to a 10~15% increase in accuracy on target domain datasets compared to non-adaptive methods.

Development and implementation of a microstrip antenna for autonomous vehicles and IoT in 5G communication systems

2024-08-08T10:43:05-06:00

This research paper shows the design and implementation of a 5.8 GHz microstrip patch antenna (MPA). Experimental investigations have concluded that employing a 0.8 mm high Rogers Rt-duroid substrate along with an inset feeding technique produces optimal outcomes to fulfill the demands of 5G applications. The designed antenna demonstrates 8.09 dBi directivity, a 7.38 dB gain, and -20 dB return loss at its resonant frequency. To further verify the performance of antenna, it was fabricated and evaluated using the CST Microwave Studio program, a 3D simulation tool specifically designed for antenna design and parameter calculation