Logic Algorithm Research Articles

A K-Additive Fuzzy Logic Approach for Optimizing FCS Sizing and Enhanced User Satisfaction

Traditional electric vehicle (EV) charging methods can lead to extended waiting times for users, resulting in decreased travel efficiency and user satisfaction, therefore impacting overall convenience. Moreover, a limited number of charging stations can lead to congestion, exacerbating waiting times, while an excessive number of stations incurs inefficient costs and reduces utilization. While prior research has primarily focused on sizing and allocating charging stations to enhance user performance, there has been comparatively less emphasis on optimizing waiting times and determining the optimal number of charging stations, which is crucial from the EV user’s perspective. This study introduces a K-additive fuzzy logic algorithm to predict the average waiting time and the optimal number of charging stations. The K-additive fuzzy inference system (K-FIS) defines membership functions, expert rules, and a formulation for achieving the optimal solution. The proposed approach integrates uncertain and independent input parameters into weighted control variables, addressing the objective function to optimize EV waiting times and costs represented by the number of charging stations. The scheme utilizes both Type 1 and Type 2 membership functions, offering a detailed comparison. To validate its efficiency, the proposed scheme undergoes a comparative study against related state-of-the-art approaches.

“It's Not Just About the Outfits”: Fashioning Gender, Race, and Class on #RushTok

This manuscript analyzes outfit of the day (OOTD) content on TikTok to consider how the style-fashion-dress and social media practices in the digital sociality known as “RushTok” (re)produce gender, race, and class norms in Panhellenic sororities. I used feminist virtual ethnography to explore 97 OOTD videos produced during the 2022 Panhellenic sorority recruitment cycle. I followed the concept of the sorority girl around this data to map her emergence and effects in the RushTok sociality. I find that RushTok OOTDs remediate practices from traditional fashion media, and that the style-fashion-dress practices in this sociality emphasize luxury branding, highly feminine but conservative styles, and bodily discipline. These are (re)shaped by spatial and algorithmic logics, which privilege enactments of the sorority girl that (re)produce the White, wealthy, cisfeminine ideal. The logics of Panhellenic sororities intra-act with the TikTok algorithm to make this idealized sorority girl more visible, further (re)producing hierarchies of difference.

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run in real-time with EMG muscle sensors and position sensors.

The Past and Future of High Technology

Abstract This interview was given in 2008 by Arkady Zakrevsky (1928–2014), Corresponding Member of the National Academy of Sciences of Belarus (1972), Doctor of Technical Sciences (1967), and Professor (1969). He stood at the origins of the birth of cybernetics in the Soviet Union. He proposed the programming language for logical tasks LYaPAS, on the basis of which a series of computer-aided design systems for discrete devices was created, and methods for implementing parallel algorithms for the logical control of interacting processes. Some monographs: LYaPAS: A Programming Language for Logic and Coding Algorithms (N.-Y., L.: Academic Press, 1969; with M. A. Gavrilov); Boolesche Gleichungen: Theorie, Anwendung, Algorithmen (Berlin: VEB Verlag Technik, 1984; with Dieter Bochmann and Christian Posthoff); Combinatorial Algorithms of Discrete Mathematics (Tallinn: TUT Press, 2008; with Yu. Pottosin, L. Cheremisinova); Optimization in Boolean Space (Tallinn: TUT Press, 2009; with Yu. Pottosin, L. Cheremisinova); Design of Logical Control Devices (Tallinn: TUT Press, 2009; with Yu. Pottosin, L. Cheremisinova); Combinatorial Calculations in Many-Dimensional Boolean Space (Tallinn: TUT Press, 2012); Solving Large Systems Logical Equations (Tallinn: TUT Press, 2013).

Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

Метод совершенствования планирования и управления технологическими процессами воздухообеспечения угольных шахт

Introduction. Management methods in complex technical systems of the mining and processing complex are currently considered as the main tool for improving the planning and management of technological processes in coal mines. In this regard, there is a need to develop an apparatus that allows research and design of methods on a unified basis. Methodology and research methods. During theoretical studies, methods of mathematical statistics and graph theory were used. The apparatus of mathematical logic, set theory and Gomori algorithms and their modifications were used. Discussion of research results. The article presents the results of research to develop a method for improving the planning and control of technological processes of air supply in coal mines. It has been established that the assessment and analysis of methods for improving the planning and control of technological processes of air supply (ATS) in coal mines are becoming increasingly relevant in connection with the modernization of mining production in the Russian Federation. Conclusion. Based on the research carried out, theoretical and practical problems were set and solved, an analysis of the patterns of TPV was carried out to improve the safety and prevent emergency situations of TPV in coal mines. Methods for calculating and analyzing safety for the variable structure of the system and its multifunctional states have been improved, allowing for the selection of optimal TPV parameters and performing calculations to ensure trouble-free operation of the TPV. The research results can be used to set the structure and control technological processes of air supply in coal mines. Conclusions on the article. The article presents the results of TPV studies conducted in coal mines. A method has been developed for analyzing and assessing components for controlling the air supply system of hot water supply, where its use allows for a comprehensive analysis and assessment of emergency situations of hot water supply. The research results can be useful when carrying out mining operations at all enterprises where air supply is necessary.

Soft Computing Models for Accurate COVID-19 Prediction:A Comparative Study

The purpose of this research is to explore the effectiveness of soft computing models for accurate COVID-19 prediction by conducting a complete comparison investigation. The implementation of soft computing approaches, such as neural networks, fuzzy logic, and genetic algorithms, is something that we are investigating as a response to the worldwide requirement for accurate forecasting in the continuing epidemic. The study of the relevant literature draws attention to the shortcomings of traditional modelling techniques, so laying the groundwork for the implications and possibilities of soft computing in the field of illness prediction. The selection of a wide variety of soft computing models, the diligent collecting of data, the use of preprocessing methods, and the establishment of a methodical framework for comparative analysis are all components of our approach. The findings and comparison analysis shed light on the unique advantages and disadvantages of each model, providing insights into the overall performance of the models as well as the variables that influence the accuracy of their predictions. According to the results of this research, significant insights have been contributed to the ever-changing environment of COVID-19 prediction, which has ramifications for the process of making informed decisions in the field of public health.

Hybrid Model of Fuzzy Logic and Genetic Algorithm for Product Assembly Sequence Optimization

Hybrid Model of Fuzzy Logic and Genetic Algorithm for Product Assembly Sequence Optimization

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.

IMPLEMENTATION IN SСAS KIDYM OF SOFTWARE DETERMINATION OF DIMENSIONS OF UNKNOWNS IN PROBLEMS OF MECHANICS BASED ON ENERGY RELATIONS

The paper presents the results of the study of an algorithm for processing measurement units of physical and geometric quantities in the problems of mechanics implemented in the specialized computer algebra system (SCAS) KiDyM. The KiDyM software complex is used for solving problems of mechanics of engineering and scientific orientation of any complexity. For the completeness of the initial data preparation process for such tasks, the use of measurement units in the initial and resulting data is essential. The output data of KiDyM tasks have a rather slow appearance due to their ana- lytical form. To provide assistance to the user, KiDyM programs have a special diagnostic block of output data. Therefore, the implementation of the dimensions used in KiDyM data pursues the additional goal of increasing the diagnostic capabilities of the system. Dimensional formulas are imple- mented here as ordinary analytical expressions, which enables the built-in computer algebra system to form expressions of data measurement units during their computer analytical transformations in the process of solving problems of dynamics, statics and kinematics. Thus, working out the meas- urement units in the SCAS KiDyM includes reading the values of the variables with the dimensions specified in formula form the initial data, diagnos- ing the correctness of dimensionising and compliance of the dimensions with the data, storing them in instances of the "variable" and "element" classes, calculating the dimensions according to data conversion formulas to obtain calculation results. The paper demonstrates how to efficiently es- tablish dimensions of geometrical and physical quantities using energy relations between coordinates and characteristics of elements of the mechanical model. For the demonstration of the logic of the implemented algorithm, simple compact examples from educational problems of kinematics, statics, and dynamics of flat and spatial systems are provided. The article shows how KiDyM constructs the necessary equations for solving tasks, and how units of measurement for equation components and their solutions can be obtained. Since dimensions are represented as formulas here, their simplifica- tion – reduction and substitution with derivative units – can be easily implemented. Moreover, since the variables involved in dimension formulas and the variables of the task itself, within KiDyM, are situated in different data spaces, they can have the same names without any confusion.

Integrating Fuzzy Logic and Brute Force Algorithm in Optimizing Energy Management Systems for Battery Electric Vehicles

The limited driving range of BEVs is the main challenge in developing zero-emission Battery Electric Vehicles (BEVs) to replace traditional fuel-based vehicles. This limitation necessitates an increase in battery energy while balancing the power supply and consumption requirements for the vehicle’s motor and auxiliaries, such as the Heating, Ventilation, and Air Conditioning (HVAC) system. This research proposes a solution to achieve more efficient control of HVAC consumption by integrating fuzzy logic techniques with brute-force algorithms to optimize the Energy Management System (EMS) in BEVs. The model was based on actual parameters, implemented using MATLAB-Simulink and ADVISOR software, and configured using a backward-facing design incorporating the technical specifications of a Malaysian electric car, the PROTON IRIZ. An optimal solution was proposed based on the Satisfaction Ratio (SR) and State of Charge (SoC) metrics to achieve the best system optimization. The results demonstrate that the optimized fuzzy EMS improved power consumption by 23.2% to 26.6% compared to a basic fuzzy EMS. The proposed solution significantly improves the driving range of BEVs.

Basin scale monitoring of microplastics and phthalates in sediments from the Persian Gulf and the Gulf of Makran using GIS-based algorithms: Insights towards spatial variation and potential risk assessment

Information on sedimentary microplastics and phthalates has been restricted to the coastal regions of the Persian Gulf and the Gulf of Makran. Our basin-wide study monitored their levels, spatial behaviors, and potential risks using GIS-based techniques. Microplastics and phthalates ranged from 5 to 75 particles/kg d.w and 0.004–1.219 μg g−1 d.w, respectively. Microplastics were in the size category of 100 μm to 3 mm, and black microfibers (< 1 mm) and high-density polymers were dominant. The total number of microplastics was between 356.333 × 1012 and 469.075 × 1012 particles in the surface sediments of the studied regions (confidence interval = 99 %). Diethylhexyl phthalate (DEHP) and Di-isobutyl phthalate contributed 88 % of detected phthalates. Significant correlations among microplastic abundance, total phthalates, and DEHP were distinguished (p < 0.05). Overall, the findings reiterated the widespread presence of microplastics and a potential link between phthalates and microplastics. Semi-variogram, cluster Voronoi polygons, and Trend analysis identified spatial outliers and major deposition sites of microplastics and phthalates and consequently outlined the localities where upcoming studies should be concentrated. A hotspot of potential risks was marked using Fuzzy logic and GIS-based algorithms in the Sea of Makran, covering an area equal to 342. 99 km2.

Energy Management for Hybrid Energy Storage System in Electric Based on Deep Deterministic Policy Gradient

In this paper, an intelligent control system design scheme based on deep deterministic policy gradient (DDPG) algorithm is proposed for the complex continuous action space problem in the hybrid energy storage system of electric vehicles. Firstly, the basic principle and internal logic of DDPG algorithm are introduced, including key elements such as Actor-Critic architecture, experience playback, target network, reward signal, policy gradient and value function update. Then, how to apply the DDPG algorithm to the industrial control system is described in detail. The Actor network learns the optimal strategy, the Critic network evaluates the value of the state-action pair, and uses the experience playback and the target network to improve the system stability and performance. Finally, the effect of the intelligent control system based on DDPG algorithm in complex environment is verified by simulation experiments. The results show that the system can effectively optimize the control strategy, improve the response speed and stability of the system, and has a good engineering application prospect.

TOAST stroke subtype classification in clinical practice: implications for the Get With The Guidelines-Stroke nationwide registry.

The TOAST (Trial of ORG 10172 in Acute Stroke Treatment) is the most commonly used ischemic stroke subtype classification system worldwide and a required field in the US National Get With The Guidelines-Stroke (GWTG-Stroke) registry. However, stroke diagnostics have advanced substantially since the TOAST classification was designed 30 years ago, potentially making it difficult to apply reliably. In this prospective diagnostic accuracy study, we analyzed consecutive ischemic stroke patients admitted to a Comprehensive Stroke Center between July-October 2021. Clinical practice TOAST classification diagnoses rendered by the stroke team in the electronic medical record (EMR) at discharge were retrieved from GWTG-Stroke registry and compared to a reference ("gold") standard diagnosis derived from agreement between two expert raters after review of the EMR and patient imaging. Among 49 patients; age was 72.3 years (±12.1), 53% female, and presenting NIHSS median 3 (IQR 1-11). Work-up included: brain imaging in 100%; cardiac rhythm assessment in 100%; cervical/cerebral vessel imaging in 98%; TTE ± TEE in 92%; and TCD emboli evaluation in 51%. Reference standard diagnoses were: LAA-6%, SVD-14%, CE-39%, OTH-10%, UND-M (more than one cause)-20%, and UND-C (cryptogenic)-10%. GWTG-Stroke TOAST diagnoses agreed with reference standard diagnoses in 30/49 (61%). Among the 6 subtype diagnoses, specificity was generally high (84.8%-97.7%), but sensitivity suboptimal for LAA (33%), OTH (60%), UND-M (10%), and UND-C (20%). Positive predictive value was suboptimal for 5 of the 6 subtypes: LAA (13%), SVD (58%), OTH (75%), UND-M (50%), and UND-C (50%). Clinical practice TOAST classification subtype diagnoses entered into the GWTG-Stroke registry were accurate in only 61% of patients, a performance rate that, if similarly present at other centers, would hamper the ability of the national registry to provide dependable insights into subtype-related care. Development of an updated ischemic stroke subtype classification system, with algorithmic logic embedded in electronic medical records, is desirable.

Soft Computing Technique for Harmonic Mitigation of Enhancing Grid-Integrated Photovoltaic (PV) System Power Quality

This abstract explores the use of soft computing techniques for harmonic mitigation in renewable energy integration, particularly for photovoltaic (PV) systems in power grids. Traditional methods often fail to effectively mitigate harmonics, leading to system performance compromise and grid instability. Soft computing's adaptability and ability to handle complex systems offer promising solutions. The study explores the use of soft computing techniques like artificial neural networks, fuzzy logic, and genetic algorithms to mitigate harmonics in PV systems. ANNs learn complex patterns, fuzzy logic handles uncertain data, and genetic algorithms optimize mitigation strategies for varying grid conditions and system dynamics. The study demonstrates the effectiveness of soft computing-based harmonic mitigation techniques in improving power quality indices like total harmonic distortion and voltage stability. It emphasizes the importance of integrating advanced computational approaches in PV systems for seamless grid integration and promoting sustainable renewable energy integration. Key Word: Renewable Energy sources, PV systems, harmonics, fuzzy logic controller, ANNs

SISTEM EVALUASI PELANGGARAN LALU LINTAS NTMC POLRI DENGAN ALGORITMA FUZZY LOGIC BERBASIS PYTHON

This research aims to build a traffic violation system using a fuzzy logic algorithm based on the Python language. By utilizing input components to receive traffic violation data and output components to produce traffic understanding level values. This method is used to process data based on traffic violations, system test results show the system's accuracy. To confirm the system's capabilities in traffic violations provides a baseline assessment of the level of accurate understanding of police law. The implications of this system show its potential as an effective detection tool for measuring existing legal violations from various levels of a person's understanding, especially in the legal field. Taking this research into account could provide a strong foundation for the development of similar systems in a variety of future comprehension evaluation contexts

SISTEM EVALUASI PELANGGARAN LALU LINTAS NTMC POLRI DENGAN ALGORITMA FUZZY LOGIC BERBASIS PYTHON

This research aims to build a traffic violation system using a fuzzy logic algorithm based on the Python language. By utilizing input components to receive traffic violation data and output components to produce traffic understanding level values. This method is used to process data based on traffic violations, system test results show the system's accuracy. To confirm the system's capabilities in traffic violations provides a baseline assessment of the level of accurate understanding of police law. The implications of this system show its potential as an effective detection tool for measuring existing legal violations from various levels of a person's understanding, especially in the legal field. Taking this research into account could provide a strong foundation for the development of similar systems in a variety of future comprehension evaluation contexts

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA’s efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics.

Sad Girls on TikTok: Musical and Multimodal Participatory Practices as Affective Negotiations of Ordinary Feelings and Knowledges in Online Music Cultures

ABSTRACT This article examines musical participatory practices on TikTok as mediations of ordinary feelings and knowledges. It conducts a multimodal analysis of user-generated videos that recirculated the song “Complex” by Scottish singer-songwriter Katie Gregson-MacLeod, which became widely successful on the app in 2022. Through its features and sociotechnical affordances, TikTok fosters feminine-coded articulations of intimate affects and negotiations of ordinary feelings and knowledges that have been marginalized in popular music cultures. Its intersecting algorithmic logics and human practices also create new rules about what affective sensibilities are foregrounded and how they are circulated through multimodal and memetic participatory practices.

Investigating the possibility of redesigning Oncology patients’ care based on datafrom Social Security administrative sources

Given the absence of a Greek neoplasm registry, this study aims to develop a logical systematic algorithm for the identification of newly diagnosed cancer patients (incidence) within a specified timeframe. Specifically, it focuses on four distinct cancer types, namely breast, lung, colon, and prostate cancer, for the year 2017. This alternative methodological approach leverages administrative datasets, particularly healthcare service records pertaining to cancer patients as documented by Greek Social Security Funds’s perspective.