Digital Commons Network^™

Parameter Identification Of A Pi Controller For A Wind Energy Conversion System Based On Dfig, Mariam Hesham, Dina S. M. Osheba, Mahmoud M. Khater

Mansoura Engineering Journal

In this work, three methods are proposed to identify parameters of PI controllers used for wind energy conversion systems based on DFIG. Mathematical model of generator and grid side system is depicted. Three tuning methods; bode-plot, pole-placement, and MATLAB tuner are used for grid side and rotor side converter controllers’ design. Wind turbine system with PI controller parameters obtained using the mentioned method is investigated via MATLAB/SIMULINK under three different scenarios. The wind system is subjected to step wind speed change, random wind speed, and sever symmetrical voltage dip in the grid. The handled comparison illustrates that the pole-placement tuning …

Analog Systems Of Physical Quantities And Their Graph Models In Determining The Parameters Of Mechatronic Modules, Temurbek Omonboevich Rakhimov, Elmira Eshmurod Qizi Raxmanova, Guzal Khujaniyazova

Chemical Technology, Control and Management

This article is dedicated to the analogy systems of physical quantities and their graph models in determining the parameters of mechatronic modules. It presents the general sets and fundamental laws of analogy systems of physical quantities and their elements for determining the parameters of mechatronic modules. Based on the general sets and fundamental laws of analogy systems of physical quantities and their elements, principles have been developed for mechatronic modules with a heterogeneous structure consisting of electrical, magnetic, and mechanical parts. In these modules, the field as a form of matter exists, forming the basis for developing mathematical models to …

Models And Algorithms Of Control Mechanisms In Information Exchange Processes, Madina M. Fozilova, Dilshoda N. Uchqunova

Chemical Technology, Control and Management

In modern digital systems, efficient and reliable information exchange is essential for the stability of corporate systems. Traditional data management models struggle to detect and eliminate invalid, incomplete data at early stages, resulting in reduced accuracy and system inefficiency. This article proposes an advanced framework for controlling information exchange processes through the development of a Verification and Filtering algorithm. The algorithm operates within a multi-layered conceptual model that includes data input, control, validation, optimization, and decision layers. Acting as the core component, the Verification and Filtering algorithm distinguishes valid from invalid records in real time, ensuring data integrity before storage. …

Using Machine Learning To Predict Women At Risk Having A Child With Congenital Heart Defects, Amany M. Abdo Prof., Asmaa M. Mosallam Ms., Laila M. Abdelhamid Assoc.Prof.

Information Systems

Congenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and …

Exploring Math Word Problem Generation With Llms, Trung Hieu Vuong

Master's Theses

Math Word Problem (MWP) is an important building block for learning math. This type of problem is particularly useful for younger audiences because solving it involves two simultaneous skill sets: reading comprehension and mathematical reasoning. With publicly available large language models (LLMs), generating additional MWPs is readily achievable. While researchers have started using LLMs as MWP facilitators, there still exists a gap in studies about the diversity of MWPs generated by unmodified, publicly accessible LLMs. For that reason, our study focused on two goals: (1) to evaluate the diversity of MWPs generated by publicly available LLMs when provided with examples …

Performance Assessment Of Operators Under Digital Simulator-Based Training In Automated Industrial Systems, Kamola Abdullaeva

Chemical Technology, Control and Management

This article proposes a comprehensive approach to training process operators, based on computer simulators integrated with digital twins, SCADA/DCS systems, real production data, and artificial intelligence algorithms. This approach is particularly relevant given the increased requirements for safety, productivity, and reliability of industrial facilities, as human factors are often the main cause of accidents (up to 60-80% of cases). The architecture of the simulator complex is designed to accurately emulate steady-state, transient, and pre-emergency operating modes of equipment, which are not achievable under actual production conditions. The experiment compared the training effectiveness of two groups of operators: one that used …

Investigation Of Nonlinear Magnetic Circuits Of Measuring Transducers With A Special Parameter Distribution Structure, Javhar Sulton O'G'Li Fayzullayev

Chemical Technology, Control and Management

The article proposes a new analytical method for investigating nonlinear magnetic circuits with a special structure of parameter distribution. The method is based on introducing into the system of nonlinear differential equations of such circuits the condition that the second derivative of the magnetic flux along the length of the circuit is equal to zero, as well as on the assumption that one of the geometric parameters of the studied magnetic circuit – the size of the air gap between the ferromagnetic rods, their thickness, width, or the linear value of the number of turns of the distributed excitation winding …

Integrable Discrete Massive Thirring Model, Junchao Chen, Bao-Feng Feng

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we are concerned with integrable semi- and fully discrete analogues of the massive Thirring model in light core coordinates. By using the Hirota’s bilinear approach and the Kadomtsev-Petviashvili (KP) hierarchy reduction method, we propose both the semi- and fully discrete massive Thirring models and construct their multi-bright soliton solutions.

Seemingly Unrelated Exponetiated Exponential Geometric Regression Model, Oluwaseun Michael Famoni, Bamidele Mustapha Oseni

Al-Bahir

In the class of seemingly unrelated regression models, the dispersion nature of the dependent variable can greatly impact the efficiency and reliability of the parameter estimates for the model. Despite this, the seemingly unrelated Poisson regression model and seemingly unrelated negative binomial model are two most commonly used count data models for these class of regression models. This study introduces the seemingly unrelated exponentiated exponential geometric regression (SUEEGR) for modelling count data which might be equi-, under, or over-dispersed. Parameters estimation for the model was carried out using the method of maximum likelihood. A simulation study was carried out to …

Folding Architecture “Origami Art-Inspired” Applicability To Sustainable Architecture- Biomuseo As A Case Study, Vitta A. Ibrahim

Mansoura Engineering Journal

The growing need for sustainable architectural solutions in the contemporary era underscores the necessity for interactive architectural applications that can adapt to changing requirements. In architecture, folding systems refer to three-dimensional, foldable structural forms that create unique spatial configurations and possess a wide range of capabilities. Drawing inspiration from origami techniques, this study emphasizes the role of folding systems in generating innovative spatial designs. The research problem arises from the fact that the built environment is a significant contributor to greenhouse gas emissions and energy consumption, necessitating the implementation of smart solutions. The goal of this study is to identify …

Context Matching Is Not Reasoning When Performing Generalized Clinical Evaluation Of Generative Language Models, Andrew Wen, Qiuhao Lu, Yu-Neng Chuang, Guanchu Wang, Jiayi Yuan, Jiamu Zhang, Liwei Wang, Sunyang Fu, Kurt D Miller, Heling Jia, Steven D Bedrick, William R Hersh, Kirk E Roberts, Xia Hu, Hongfang Liu

Faculty, Staff and Student Publications

Current discussion surrounding the clinical capabilities of generative language models(GLMs) predominantly centers around multiple-choice question-answer(MCQA) benchmarks derived from clinical licensing examinations. While accepted for human examinees, characteristics unique to GLMs bring into question the validity of such benchmarks. Here, we validate five benchmarks using eight GLMs, ablating for parameter size and reasoning capabilities, validating via prompt permutation three key assumptions that underpin the generalizability of MCQA-based assessments: that knowledge is applied, not memorized, that semantic consistency will lead to consistent answers, and that situations with no answers can be recognized. While large models are more resilient to our perturbations compared …

Context Matching Is Not Reasoning When Performing Generalized Clinical Evaluation Of Generative Language Models, Andrew Wen, Qiuhao Lu, Yu-Neng Chuang, Guanchu Wang, Jiayi Yuan, Jiamu Zhang, Liwei Wang, Sunyang Fu, Kurt D Miller, Heling Jia, Steven D Bedrick, William R Hersh, Kirk E Roberts, Xia Hu, Hongfang Liu

Faculty, Staff and Student Publications

Current discussion surrounding the clinical capabilities of generative language models(GLMs) predominantly centers around multiple-choice question-answer(MCQA) benchmarks derived from clinical licensing examinations. While accepted for human examinees, characteristics unique to GLMs bring into question the validity of such benchmarks. Here, we validate five benchmarks using eight GLMs, ablating for parameter size and reasoning capabilities, validating via prompt permutation three key assumptions that underpin the generalizability of MCQA-based assessments: that knowledge is applied, not memorized, that semantic consistency will lead to consistent answers, and that situations with no answers can be recognized. While large models are more resilient to our perturbations compared …

Intrusion Detection System For Iot/Cloud Networks Using Federated Learning And Lightweight Cryptography, Ayad Al-Adhami, Rajaa K. Hasoun, Sanaa Ali Jabber, Soukaena H. Hashem

Baghdad Science Journal

This study presents a secure solution that utilizes lightweight cryptography (LWC) and intrusion detection systems (IDS) to safeguard cloud networks and Internet of Things (IoT) from cyberattacks. Federated Learning (FL) is suggested for identifying zero-attacks to guarantee the security of different local IoT networks connected to global server of cloud. The proposed federated learning utilizing data from all local IoT network devices to create a generalized Intrusion Detection System (IDS). Local IoT networks consist of clients that communicate updates to their parameters with a central server located in the global cloud. This server integrates these changes and deploys an improved …

Enhanced Network Anomaly Detection Using Hybrid Deep Learning Network Based On Interactive Threshold, Maythem S. Derweesh, Sundos A. Hameed Alazawi, Anwar H. Al-Saleh

Baghdad Science Journal

In recent years, the growing use of the internet by both governments and private companies has led to a major increase in individual online activity. This expand lead to make the systems more effected to the threats and cyber attacks, and need more strong solutions to cyber security. Recently, deep learning (DL) and machine learning (ML) have become powerful tools in the cybersecurity field, especially for tasks such as detecting malware and filtering spam. This study present new multi layer method to detect the abnormal activities by busing advanced deep learning techniques. The proposed system work in tow main steps. …

Optimizing Runtime Memory Size Of Smith-Waterman Algorithm For Long Sequences Alignment, Imad Qasim Habeeb, Zeyad Qasim Habeeb, Hanan Najm Abdulkhudhur

Baghdad Science Journal

Sequence alignment is used to help researchers see areas of similarity between two sequences. Hence, it is a key component of many applications, such as DNA matching, plagiarism detection, and spelling correction. The Smith-Waterman algorithm (SWA) is widely used to calculate the sequence alignment because it is guaranteed to find an optimal solution. This algorithm creates a matrix of the size n * m where the symbols n, m refers to the lengths of two sequences needed to be aligned. Therefore, it requires impersonal hardware with a large amount of main memory at runtime to align long sequences. Furthermore, it …

Comparing The Effectiveness Of Eggshell Spectra From Laser-Induced Break-Down Spectroscopy And Near-Infrared Spectroscopy Using Principal Compo-Nent Analysis To Determine The Authenticity Of Organic Eggs, Ahmad Qusthalani, Rara Mitaphonna, Muliadi Ramli, Rajibussalim Rajibussalim, Kurnia Lahna, Nasrullah Zaini, Nasrullah Idris

Makara Journal of Science

This study aimed to explore the potential of modern spectroscopy in the authentication of organic and non-organic chicken eggs using near-infrared spectroscopy (NIRS) and laser-induced breakdown spectroscopy (LIBS) spectra. A total of 175 eggs were analyzed, which were grouped into seven categories based on the source of feed given: 100% organic, 100% non-organic, 75% organic, 75% non-organic, 50% organic, free-range chickens, and eggs obtained from the local traditional market. Each group consisted of 25 eggs. NIRS spectra were recorded in the wavelength range of 350–2500 nm, whereas LIBS spectra were recorded in the range of 200–900 nm. A total of …

Supply Chain Network Based On Blockchain And Intelligent Agent, Hiba Hamdi Hassan, Rana Fareed Ghani

Journal of Soft Computing and Computer Applications

In agricultural supply chains, the complexity and indeterminacy pose serious challenges to traceability, reliability and confidence today. This challenge is especially acute in the olive oil industry where adulteration, wrong labeling, and uneven chemical quality threaten the actual well-being of producers and consumers. The project aims to design a blockchain-based hybrid architecture with intelligent agents (FNNs) to enhance transparency, reliability and responsiveness in the olive oil supply chain. The Blockchain component enables a completely open, tamper-proof ledger to be built in a very decentralized way and preserved as an archive of every account of its transactions. The intelligent agents contribute …

Intelligent Extensible Markup Language Encryption Using Type-2 Fuzzy Logic, Faiez Musa Lahmood Alrufaye, Seham Ahmed Hashem

Journal of Soft Computing and Computer Applications

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic …

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

Review Of Video Steganography By Using Deep Learning Methods: Datasets, Techniques, And Evaluations, Noor Fahem Sahib, Soukaena Hassan Hashem, Ekhlas Falih Naser

Journal of Soft Computing and Computer Applications

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a …

Real-Time Hand Gesture Recognition System For Abductees Rescue Using Deep Learning Techniques, Aws Saood Mohamed, Nidaa Flaih Hassan, Abeer Salim Jamil

Journal of Soft Computing and Computer Applications

Hand gesture recognition is a challenging problem in computer vision, particularly in terms of security surveillance applications. This study presents the first efficient system for abduction-related hand gesture real-time detection based on deep learning. The most critical problem is to detect and recognize hand gestures in real surveillance conditions and to be computationally effective for real-time multi-hand tracking in various lighting situations while allowing reliable surveillance beyond the 1–4 meters limitation. The proposed system consists of three main parts: The adaptive hand tracking algorithm, which has been used to create the Abductees-Rescue dataset. Introduced pose estimation You Only Look Once …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

Spotlight, Robert F. Manning

Tax Adviser

No abstract provided.

Choosing A Settlement Option Under A Defined Contribution Plan, W. Eugene Seago, Wayne E. Leininger

Tax Adviser

No abstract provided.

(R2141) Analysis Of Map^I_1 , Ph^(Oa)_2 / Ph^I_1 , Ph^O_2 / 1 Retrial Inventory Queue With Two Way Communication, (S, S) Replenishment Policy, Feedback, Bernoulli Vacation And Impatient Customers, G. Ayyappan, V. Ganesan

Applications and Applied Mathematics: An International Journal (AAM)

This work discusses about the topic as the two-way communication retrial inventory queue model, the (s, S) replenishment policy, immediate feedback, Bernoulli vacations, and impatient customers. The assumption we make is that arrivals follow a Markovian arrival process, and the server provides phase type services. When the server is idle and there is a positive inventory, an arriving customer immediately receives service. If not, arriving customers goes to orbit with infinite capacity. Only in the account of positive inventory the server renders rapid feedback for incoming call arrivals, otherwise customer departs. Outgoing calls will only be made by the server …

(R2152) New Bivariate Type-2 Gumbel Distribution Based On The Farlie-Gumbel-Morgenstern Copula: Properties And Its Application In Survival Analysis, Muneeb Javed, Said Farooq Shah, Muhammad Osama, Muhammad Atif, Muhammad Farooq

Applications and Applied Mathematics: An International Journal (AAM)

We introduce a new bivariate probability distribution, termed the Bivariate FGM Type-2 Gumbel Distribution, constructed by combining the Farlie–Gumbel–Morgenstern (FGM) copula with the Type-2 Gumbel marginal distributions. This proposed distribution provides a flexible framework for modeling bivariate data and offers a viable alternative to several existing bivariate distributions, especially in scenarios where capturing dependence between variables is crucial. The theoretical properties of the distribution are thoroughly explored. We derive the marginal and conditional distributions, conditional expectations, moment generating function, and product moments. Procedures for random number generation from the distribution are discussed. Reliability-based characteristics, such as the survival function and …

(R2150) Impact Of Specialist Predator Harvesting On The Stability Of A Three-Species Food Chain Model With A Generalist Predator, S. Ganga, S. Vijaya

Applications and Applied Mathematics: An International Journal (AAM)

In this study, the stability of a three-species food chain model comprising a prey, an intermediate specialist predator, and a top predator exhibiting generalist behavior is examined, with harvesting applied to the intermediate predator. The main objective is to investigate how variations in the harvesting rate influence system stability and the coexistence of all three species. The positivity, boundedness, and equilibrium points of the model are analyzed to ensure biological feasibility. The equilibrium point involving the prey and the top predator satisfies the criteria for both local and global asymptotic stability. The coexistence equilibrium point attains local asymptotic stability based …

Improved Pid Search Algorithm For Uav Path Planning In Mountainous Environments, Yi Peng, Yunkui Lei, Qingqing Yang, Hui Li, Jianming Wang

Journal of System Simulation

Abstract: To address the challenges of UAV path planning in mountainous environments, including high computational complexity and suboptimal optimization performance, and the disadvantages of the PIDbased search algorithm, such as low optimization accuracy and slow convergence rate, this paper proposed an improved PID search algorithm (IPSA). The method introduced a good point set to ensure a more uniform population distribution, thereby enhancing population diversity and global search capability. The Q-learning algorithm was employed to adapt PID parameter adjustments, incorporating an exploration rate factor to further improve the algorithm's exploration and computational capabilities. A lens imaging opposition-based learning mechanism was also …

Dynamic Characteristic Simulation And Optimization Of Ground Test System For Airborne Launch Rack, Yuguang Bai, Sheng Zhang, Yushun Cao, Xiaoshi Zhang, Hu Huang

Journal of System Simulation

Abstract: To solve the ground equivalent test problem of the airborne launch system, an optimization method for the dynamic characteristics of the ground launch rack test system based on a multi-variable optimization approach was proposed. Through the discussion on the boundary conditions of the foundation, an effective dynamic simulation model of the ground launch test system was established. By comparing the dynamic characteristics of the launch rack structure in the airborne state and the ground test state, the objectives and constraints of the optimization design were determined. The dynamic characteristics of the ground test system were optimized and designed. …

Nuclear Binding Energy Prediction For Some Odd-Mass Number Nuclei By Artificial Neural Network (Ann), Ruya H. Ibrahim, Akram Mohammed Ali

Baghdad Science Journal

Machine learning models called artificial neural networks (ANNs) are widely used in many fields and real-world applications. The parameter vector that forms the basis of these models needs to be evaluated computationally. We calculated the ground-level binding energy of 146 nuclei with an odd mass number using three different models:the integrated nuclear model,the liquid drop model and the experimental model. The results of these models were compared with our theoretical results calculated by the artificial intelligence network. The mean squared error of the target and output values and how close they are to zero were calculated, and the degree of …