Rule-based Research Articles

Exploration of Practical Teaching Mode of Electrical Control and Programmable Logic Controller Application Based on Project Teaching Method

In today’s prosperous industrial era, training electrical control personnel to meet the needs of the industry remains one of the important tasks of colleges and universities. How to effectively cultivate electrical control personnel has become an unavoidable issue for industry and colleges and universities. Aiming at the problems existing in the current teaching mode of electrical control and programmable logic controller (PLC) application, this paper proposes a new teaching mode based on the project teaching method. The methodology aims to increase student engagement, deepen understanding of theoretical knowledge, and reinforce the development of practical skills. Through the implementation of the project teaching method, it is expected to effectively cultivate electrical control talents to meet the needs of the industrial control industry and inject new vigor into the development of modern industry.

IMPLEMENTATION OF A BASE OF RULES FOR DIFFERENTIAL DIAGNOSIS OF CLINICAL AND HEMATOLOGICAL SYNDROMES BASED ON MORPHOLOGICAL CLASSIFICATION ALGORITHM

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

Usability Evaluation of Iraqi News Applications

The research examines and evaluates the usability of Iraqi news applications, (Alrabiaa) and (Alsumaria), using regular observation and the heuristics evaluation method based on a set of criteria that were derived from the basic rules of ease of use. The research is descriptive research, the survey methodology steps were followed to achieve the objectives. The researchers reached a number of conclusions, including that the applications considered the general rules and principles of usability, despite the existence of errors in terms of format and content, which can be addressed to obtain a better user experience, such as font colors, text alignment, options to change the type of view and user interface, and the appearance of ads in published news. The researchers also noted a significant correlation between the applications and their parent media institutions, in addition to the superiority of (Alsumaria) application over (Alrabiaa) application, in terms of its media services that were characterized by speed, efficiency and ease of access.

Single-zinc vacancy unlocks high-rate H2O2 electrosynthesis from mixed dioxygen beyond Le Chatelier principle.

Le Chatelier's principle is a basic rule in textbook defining the correlations of reaction activities and specific system parameters (like concentrations), serving as the guideline for regulating chemical/catalytic systems. Here we report a model system breaking this constraint in O2 electroreduction in mixed dioxygen. We unravel the central role of creating single-zinc vacancies in a crystal structure that leads to enzyme-like binding of the catalyst with enhanced selectivity to O2, shifting the reaction pathway from Langmuir-Hinshelwood to an upgraded triple-phase Eley-Rideal mechanism. The model system shows minute activity alteration of H2O2 yields (25.89~24.99 mol gcat-1 h-1) and Faradaic efficiencies (92.5%~89.3%) in the O2 levels of 100%~21% at the current density of 50~300 mA cm-2, which apparently violate macroscopic Le Chatelier's reaction kinetics. A standalone prototype device is built for high-rate H2O2 production from atmospheric air, achieving the highest Faradaic efficiencies of 87.8% at 320 mA cm-2, overtaking the state-of-the-art catalysts and approaching the theoretical limit for direct air electrolysis (~345.8 mA cm-2). Further techno-economics analyses display the use of atmospheric air feedstock affording 21.7% better economics as comparison to high-purity O2, achieving the lowest H2O2 capital cost of 0.3 $ Kg-1. Given the recent surge of demonstrations on tailoring chemical/catalytic systems based on the Le Chatelier's principle, the present finding would have general implications, allowing for leveraging systems "beyond" this classical rule.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

A grid-connected eleven-level power conversion interface

ABSTRACT This paper proposes a grid-connected eleven-level power conversion interface (GCELPCI) for renewable power systems. The proposed GCELPCI is composed of a four-port DC-DC power converter (FPDDPC) and an eleven-level inverter (ELI). The FPDDPC combines a boost power converter and a transformer with a turn ratio of 2:1 to generate multiple combinations of the output voltages for the DC bus voltage of the ELI. The ELI integrates an asymmetric voltage technique on a T-type inverter to further convert the DC output voltages of FPDDPC into eleven-level AC voltage. A sinusoidal current is generated by the proposed GCELPCI through the filter inductor and injected into the power grid. Only nine power electronic switches are used in the ELI to generate an eleven-level AC voltage, the number of power electronic switches is reduced to simplify the power circuit. In addition, the capacity of the filter inductor is reduced. A digital signal processor (DSP) and a complex programmable logic device (CPLD) chip are used as the digital controller to complete a hardware prototype to verify the performance of the proposed GCELI.

A deep belief rule base-based fault diagnosis method for complex systems

Complex systems are prone to faults due to their intricate structures, potentially impacting system stability. Therefore, fault diagnosis has become crucial for maintaining stable operation. In the field of complex systems, the combinatorial explosion problem in belief rule base (BRB) has attracted significant attention. The interdependence among system components leads to numerous variables and the need for rules, heightening model complexity. Regarding the combinatorial explosion problem, an improved belief rule network structure called deep BRB (DBRB) is proposed. First, the extreme gradient boosting (XGBoost) feature selection method is employed to choose the relatively important feature subset. Next, driven by the importance of features, different levels of features are input into the model, forming a complete and progressive network structure. Finally, the model undergoes the reasoning and optimization process. The effectiveness of the model is confirmed with a bearing fault dataset. After a comprehensive evaluation of multiple indicators, this method demonstrates a consistent improvement in classification performance as the depth increased. Moreover, compared to the traditional BRB model, this method notably reduces the number of parameters, improving its efficiency of processing complex data. In short, this method effectively tackles combinatorial explosion while ensuring model performance. The selection and assignment of feature subsets enhance the logic and readability of the model. Through the network structure, various fault features are captured well. This fault diagnosis method, rooted in the DBRB, offers a novel perspective on diagnosing complex system faults.

Program logic of a service collaboration to support parents with intellectual disability.

This article describes the Steps to Confident Parenting (SCP) program, developed by an Australian family service consortium. The SCP integrates home-based and case-management services to enhance the skills of parents with a diagnosed or suspected intellectual disability/cognitive impairment and to prevent child protection interventions. 'Program explication' methodology documented the components/activities, and underpinning evidence for this practitioner designed service through interviews with nine agency staff. A literature review evaluated evidence for the implicit program benefit theory. The SCP comprised five logically consistent components-Targeted Referral, Assessments, Initial Consultation, Program Delivery, Closure and Follow-up. Components generally had 'some' supportive evidence, however there was a 'lack of' evidence for Closure and Follow-up. In the context of a partnership seeking to build the evidence for the SCP, it was recommended that a protocol for a randomised trial evaluation with longer term follow-up be drafted by the consortia.

Desain Human Machine Interface (HMI) Pada Sistem Pencetak Genteng Otomatis

Roof Tile Printing Machines are important equipment in the industry for printing roof tiles and managing the roof tile production process. This tile printing machine includes a press machine and conveyor that can be adjusted so that the system can operate optimally during production. Operating a tile printing machine requires a program that can control various machine functions, such as conveyor motors and pneumatic cylinders. These programs are generally implemented using a Programmable Logic Controller (PLC). Therefore, an automatic tile printing system based on PLC and HMI with pressure and current parameters was created. This system can control and monitor the tile printing system while operating via the HMI screen. This method is used to collect data related to working methods, diagrams, cable connections, PLC programs, and data management in the HMI. One method used involves components such as limit switches, pressure sensors, current sensors, and pneumatics. The data generated from these processes is processed in the PLC and displayed on the HMI screen. The results obtained from this system are capable of monitoring the tile printing process in real time via the HMI screen.

XAI Human-Machine collaboration applied to network security

Cyber attacking is easier than cyber defending—attackers only need to find one breach, while the defenders must successfully repel all attacks. This research demonstrates how cyber defenders can increase their capabilities by joining forces with eXplainable-AI (XAI) utilizing interactive human-machine collaboration. With a global shortfall of cyber defenders there is a need to amplify their skills using AI. Cyber asymmetries make propositional machine learning techniques impractical. Human reasoning and skill is a key ingredient in defense and must be embedded in the AI framework. For Human-Machine collaboration to work requires that the AI is an ultra-strong machine learner and can explain its models. Unlike Deep Learning, Inductive Logic Programming can communicate what it learns to a human. An empirical study was undertaken using six months of eavesdropped network traffic from an organization generating up-to 562K network events daily. Easier-to-defend devices were identified using a form of the Good-Turing Frequency estimator which is a promising form of volatility measure. A behavioral cloning grammar in explicit symbolic form was then produced from a single device's network activity using the compression algorithm SEQUITUR. A novel visualization was generated to allow defenders to identify network sequences they wish to explain. Interactive Inductive Logic Programming (the XAI) is supplied the network traffic meta data, sophisticated pre-existing cyber security background knowledge, and one recurring sequence of events from a single device to explain. A co-inductive process between the human cyber defender and the XAI where the human is able to understand, then refute and shape the XAI's developing model, to produce a model that conforms with the data as well as the original device designers programming. The acceptable model is in a form that can be deployed as an ongoing active cyber defense.

ОЦІНКА ОБЧИСЛЮВАЛЬНОЇ СКЛАДНОСТІ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given. A mathematical apparatus for estimating the asymptotic complexity of a genetic algorithm is presented, which takes into account the costs of forming the initial population and performing evolution. Evolution occurs through iterations, during which generations of individuals are subjected to certain operations in order to find an optimal solution (crossing, mutation, chromosome decoding, etc.). GA, as a global search algorithm, is considered to find the optimal path without getting stuck in local minima. To assess the computational complexity of GA, we consider solving the traveling salesman problem (TSP) for 28 cities of Ukraine using a modified TSPLIB library and the DEAP platform created in the Python programming language. A block diagram of the GA is presented, the main elements of which are the tournament selection operator, the ordered crossover operator, and the inversion mutation operator. The influence of the population size and the number of generations on the asymptotic complexity of the genetic algorithm in solving the TSP problem is studied. The study considered changing the size of the GA population from 50 to 500 with a step of 50, while for each such value four sets of the number of generations were modelled: from 50 to 200 with a step of 50. Based on the obtained results, we show a linear dependence of the GA execution time on the size of the considered input data. It is shown that the smallest time complexity of the presented GA for the given TSP problem is 0.33848 seconds with a population size of 50 and a similar number of generations, while the largest value is 3.752734 seconds with a population size of 500 and a number of generations of 200. The obtained results can be used to optimise the performance of a GA in the TSP problem.

Cybersecurity Measures Safeguarding Digital Assets and Mitigating Risks in an Increasingly Interconnected World

As interconnectivity and reliance on digital technologies continue to rise exponentially, so too do cybersecurity threats and risks. Malicious cyber actors are employing increasingly sophisticated techniques to target valuable data and disrupt critical systems and infrastructure. The impacts of successful cyber-attacks can be financially devastating and undermine an organization's reputation and customer trust. This paper examines how firms may utilize strong cybersecurity to protect digital assets and reduce risks in a connected society. As technology and communications improve, cyber threats do too. Problems keep popping up. Malware, phishing scams, and data breaches still harm businesses. Hackers can also get in simpler with more linked devices and the Internet of Things. This makes protecting digital assets and systems, networks, and critical infrastructure from sophisticated cyberattacks even more crucial. Cybercriminals can steal data, intellectual property, money, and customer information in many ways. Advanced persistent threats are used by state-backed parties for eavesdropping and geopolitics. Therefore, organizations in all professions and industries must prioritize safety and risk reduction. Businesses may increase their safety with technical, process-based, and strategic cybersecurity solutions, according to the report. This requires strong access restrictions, networks, encryption, software security, and audits. Human aspects like cyber awareness training and a comprehensive plan that matches an organization's risks and resources are considered. A thorough security plan discusses international conventions, following the rules, and using modern technologies. Two case studies demonstrate real-world operations. The first describes how a large retailer stopped a complex APT with numerous defenses. Learn how crucial segmentation, detection tools, incident reaction, and public-private partnerships are. The second one examines a major hotel chain data breach and offers simple measures like strong passwords and vendor tracking to reduce similar dangers. Even if digital adversaries are getting smarter, basic cybersecurity "hygiene." can block many attempts, the report concludes. No organization is totally safe, but following basic rules, laws, and best practices can help protect people, organizations, and governments' assets in today's linked globe. Researchers may investigate how blockchain and AI might lessen hacking risks.

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints.

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect.

Development of structural schemes for collecting monitoring and diagnostic data of signaling points of the railway run based on programmable logic controllers

Development of structural schemes for collecting monitoring and diagnostic data of signaling points of the railway run based on programmable logic controllers

A logic device based on memristor-diode crossbar and CMOS periphery as spike router for hardware neural network

A programmable logic device based on a memristor-diode crossbar and CMOS logic has been developed. The crossbar implements NAND logic gates using memristor ratioed logic and CMOS inverters. The digitally controlled peripheral circuit provides digital signals transmission and allows modification and evaluation memristor states in the crossbar. The proposed logic device circuit requires fewer transistors than known analogues and less area on the chip.The maximum size of the crossbar in a logic device is estimated by numerical simulation at the level of electrical circuits. The limited size is caused by the degradation of the logic levels voltages in the memristor-diode crossbar. The operability of peripheral circuits as part of a complete electrical circuit of a logic device is demonstrated during the simulation of the execution of logical operations, the processes of modification and evaluation states of individual memristors.

Analisis Penerapan Sanksi Pidana Pelaku Residivis Tindak Pidana Narkotika

Abstract In positive law, narcotics crimes are qualified as extraordinary crimes, because the impact of these crimes has a dangerous situation that is widespread. Law No. 35 of 2009 is the basic rule for law enforcement officers to carry out law enforcement for perpetrators. In Article 54 of the Law it is explained that judges are obliged to consider conducting medical rehabilitation for drug users. In Decision No. 745/Pid. Sus/2018/PN Srg, the defendant is a drug case recidivist who had previously been rehabilitated. By using a normative juridical research method that uses the approach of legislation and legal principles, this study aims to examine and determine the application of punishment to recidives in Decision No. 745/Pid. Sus/2018/PN Srg by using the theory of judges' considerations and the purpose of sentencing. The results of this study are the application of sanctions against narcotics crime recidivists must use the purpose of punishment which results in a deterrent effect for the perpetrator, so that the perpetrator does not repeat the action.

Strategic Decision-Making in Swiss-System Chess Tournaments

This study delves into the decision-making strategies of chess players in the final round of Swiss-system chess tournaments. This paper first introduces the basic rules and characteristics of Swiss-system tournaments, as well as the objectives of players in the competition. Subsequently, this paper analyzes the strategies adopted by players in the final round under different circumstances, including scenarios without tied players, with tied players but leading or trailing in rankings, and situations where players are tied and have similar rankings. Through theoretical analysis and validation with empirical data, this paper derives optimal strategies for players in different situations to ensure achieving the highest possible ranking in the final round. Starting with a simple four-player championship model, the study provides important insights and guidance for player decision-making in Swiss-system chess tournaments, while also offering valuable exploration into understanding the decision-making process of players. The strategies are well discussed in Section 3.

The generalised distribution semantics and projective families of distributions

We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic programming as such to encompass frameworks from probabilistic databases, probabilistic finite model theory and discrete lifted Bayesian networks. To demonstrate the usefulness of such a general approach, we completely characterise the projective families of distributions representable in the generalised distribution semantics and we demonstrate both that large classes of interesting projective families cannot be represented in a generalised distribution semantics and that already a very limited fragment of logic programming (acyclic determinate logic programs) in the deterministic part suffices to represent all those projective families that are representable in the generalised distribution semantics at all.

Research on the optimization of belief rule bases using the Naive Bayes theory

The belief rule base is crucial in expert systems for intelligent diagnosis of equipment. However, in the belief rule base for fault diagnosis, multiple antecedent attributes are often initially determined by domain experts. Multiple fault symptoms related to multiple antecedent attributes are different when an actual fault occurs. This leads to multiple antecedent attributes matching with multiple fault symptoms non-simultaneously, thereby resulting in a fault diagnosis lacking timeliness and accuracy. To address this issue, this paper proposes a method for belief rule-based optimization based on Naive Bayes theory. First, a fault sample is taken in a long enough window and divided into several interval samples, making the analysis samples approximate the overall samples. Second, using Gaussian mixture clustering and Naive Bayes optimization, iteration is performed over the threshold and limit values of fault symptoms in the belief rule base based on the requirements of the timeliness and accuracy of fault diagnosis results. Finally, the belief rule base is optimized. Using fault samples from high-pressure heaters and condensers, the validation results show that there is a there is a significant improvement in the timeliness and accuracy of fault diagnosis with the optimal belief rule base.

A semantic reasoning-based emergency rescue assistant decision method for maritime accidents involving chemicals

Maritime accidents involving chemicals (MAICs) constitute a unique category of marine emergency characterized by low frequency, high risk, and complex disposal challenges. These accidents often entail fires, explosions, and pollutant leakage, demanding effective emergency responses. The success of MAICs rescue operations relies on multiple factors, encompassing the structural features of the involved vessel, personnel, cargo, marine environment, and the availability of resources and response procedures. Proficiency in various professional fields, including ship transportation, chemistry, firefighting, and environmental science, is essential for informed decision-making. This paper addresses the need for a standardized description of knowledge in the MAICs emergency rescue decision-making process, aiming to enhance practical application frequency and decision-making efficiency. Employing ontology theory, the approach involves structurally modeling the knowledge of MAICs emergency rescue, constructing an ontology model, and establishing a decision rule base. Validating the proposed method, the emergency response to the “Longqing 1″ accident is examined as a case study, illustrating its potential for supporting MAICs emergency rescue decision-making. The application of ontology theory in this context holds both theoretical and practical significance. It facilitates the sharing, reuse, and expansion of knowledge in the emergency rescue field, providing crucial technical support for intelligent decision-making in MAICs emergencies.