Journal of Innovation, Technology and Sustainability

A voltage stability constrained optimal power flow using Multiobjective Particle Swarm Optimization Algorithm

2023-10-09T13:55:06+00:00

As the global demand for energy rises, power system networks are teetering on the verge of collapsing owing to a compromise in system stability. During system disturbances, the network's inability to supply adequate reactive power causes instability and eventual collapse. As such, optimized generation scheduling during system disturbances can improve the utilization of the power plants while lowering power loss, improving voltage regulation, reducing branch loading, and ensuring the secure operation of system equipment. Since power systems have conflicting and multiple objectives, this study proposes a multiobjective optimal
power flow incorporating three objective functions: generation cost, power loss, and the maximum value of the line Voltage Collapse Proximity Index. The Multiobjective Particle Swarm Optimization Algorithm is used to minimize these objectives on the IEEE 30-bus system for different case studies in normal, contingency, and stressed system conditions. Fuzzy Decision Theory is utilized for obtaining the best compromise solutions amongst a set of Pareto optimal solutions. The results show that the voltage stability of the system is improved by an average of 63.09% during system disturbances with multiobjective optimization. Simultaneous optimization of the three objective functions provides the most voltage stable condition for all system conditions, preventing possible collapse.

Baggage screening using colour extraction algorithm and encryption of screened results

2023-10-10T08:43:52+00:00

Threats to aviation security persist and continue to evolve. As a result, passenger and checked baggage security screening must continue to adapt to meet evolving threats and changes within the aviation industry. During peak hours in airports screeners have only a few seconds to decide whether a bag contains or not a prohibited item, and detection performance is only about 80-90%. This is a major susceptibility of air transport to security threats and illegal smuggling of goods. There is need for the aviation authorities to consider timely sharing of screening results. The use of ICT in electronic data exchange of information over open networks in particular requires implementation of Public Key Infrastructure to secure electronic exchange of information. The researcher developed a colour extraction tool that was integrated with encryption algorithm for securing screening results. The tool aims at providing a secure and timely mechanism of sharing travelers screening results with relevant authorities within the aviation eco-system.

Design of a classifier for tomato leaf disease identification

2023-10-10T10:11:37+00:00

Plants are the backbone of human existence for they are directly depended on for food. Plant infections and diseases are thus a major concern. Technology can promote food production in several ways through the application of computer vision technology that employs image processing to determine several aspects. Faster and timely plant disease recognition could immensely aid in the early application of appropriate treatment methods that fundamentally reduce economic losses. The introduction of machine learning techniques in image classification has revolutionized digital imaging and learning systems. Presently, convolutional neural networks have been found to provide the most accurate results while grey level cooccurrence is a popularly used descriptor. However, Convolution Neural Network (CNN) requires numerous learning iterations which lead to high computation costs whereas Grey Level Co-Occurrence Matrix (GLCM) cannot be used alone as a descriptor because a classifier is required to carry out the classification of the texture features extracted. This study proposed a hybrid model that combines CNN and GLCM techniques to classify plant diseases from a set of plant images. The research methodology used a systematic literature review and experimental research design. The systematic literature review was employed to determine and identify the existing techniques in digital plant images and the features to be used in classifying plant diseases. In experimentation, the study evaluated GLCM contrast, energy, and correlation features. The classification was carried out in three phases; 100, 150, and 200 iterations where the GLCM-CNN network had the best accuracy of 96.09% and F1 score of 0.8884 using energy texture images with 200 iterations.

A comparative study of the lexicographical complexity of Java, Python and C languages based on program characteristics

2023-10-10T11:27:53+00:00

In software engineering, software complexity measures how complicated it is to design, test, maintain, and comprehend a system or a program. Metrics have been appreciated over time as a measure of various attributes of software products. Some of the most well-known languages for scientific, object-oriented, and imperative programming are Python, Java, and C, respectively. However, it is not easy to distinguish the structural complexity of these programming languages and the existing studies have overlooked this issue. This study, therefore, uses a technique based on Halstead Software Science to conduct a comparative investigation to evaluate the lexicographical complexity of sequence, selection, and looping program structures in object-oriented, scientific, and imperative programming languages. Halstead Complexity Metrics were implemented utilizing sequence, selection, and loop control structures in Java, C, and Python to accomplish the study's goal. When subjected to the Halstead software science comprising of nine measurement criteria, the findings of the experiment demonstrated that in sequence and Loops program structures C language has the highest lexicographical complexity followed by Java, while in Selection program structures Java is more slightly complex than C. Python on the other hand, had the least lexicographical complexity across all three essential program structures—sequence, selection, and loops during the comparative study, therefore, it is the most appropriate programming language among the three that are being studied here in terms of program complexity. Using the results of this study, we intend to use effort prediction models in the future to estimate the programming effort. We also intend to do additional experiments with the same program structures using larger program samples in the future. A replication of the study using different programming languages is also suggested.