Rule-based Research Articles

Closed-loop Speed Control for a Three-Phase Alternating-Current Motor using a Modbus Network

This paper presents a closed-loop controller for speed regulation of a three-phase induction motor by means of a Modbus network, which is compound only by industrial-purpose hardware, namely two programmable logic controllers, a human-machine interface and a variable-frequency drive. This represents a step beyond the state of the art because the results in similar works reported in the literature were obtained using personal computers, academic-purpose hardware or a combination of both academic-industrial technologies. The speed regulation was achieved through a proportionalintegral controller executed in a programmable logic controller, whose feedback was supplied by another similar device completely dedicated to read and decode an industrial optical encoder; furthermore, the control signals were commanded to the alternating current motor using a variable-frequency drive. The proposed control network was tested through diverse regulation experiments, under different conditions, in order to assess its effectiveness and robustness. The obtained results were successful in terms of accuracy of the speed set-point tracking in comparison to the most similar works of the state of the art, namely zero steady-state error was achieved, 12 seconds earlier for 75% larger references and 22% less RMSE than other works, which are the best results reported so far. Such successful achievements encourage the later implementation of more advanced control techniques on three-phase-motor based industrial machinery using only industrial-purpose hardware.

Integrating deep learning, MATLAB, and advanced CAD for predictive root cause analysis in PLC systems: A multi-tool approach to enhancing industrial automation and reliability

The integration of Deep Learning (DL), MATLAB, and Advanced Computer- Aided Design (CAD) in the root cause analysis of prognostic errors in Programmable Logic Controller (PLC) systems represents a significant advancement in industrial automation and reliability. This research explores the synergistic application of these technologies to diagnose, predict, and mitigate failures in PLC systems, which are critical for controlling automated processes in various industries. By employing DL algorithms, the study enhances predictive maintenance capabilities, allowing for early detection of anomalies and reducing downtime. MATLAB is utilized as the central platform for data processing, algorithm development, and simulation, providing a versatile environment for integrating DL models with real-time data from PLCs. Advanced CAD tools are employed to model and visualize the physical systems controlled by the PLCs, offering a comprehensive view that bridges the gap between digital analysis and physical implementation. The research methodology includes data collection from PLC systems, DL model training and validation, MATLAB-based simulations, and CAD modelling. The findings demonstrate improved accuracy in identifying the root causes of PLC prognostic errors, leading to more efficient maintenance strategies and enhanced system reliability. This paper concludes that the integration of DL, MATLAB, and CAD provides a powerful approach for advancing predictive maintenance in industrial settings, ultimately contributing to greater operational efficiency and cost savings.

Optimal Energy Management System in Thailand Convenience Stores

The purpose of this research is to study and investigate the energy consumption and reduction of convenience stores. There is one retail building that is increasing rapidly in Thailand every year, and almost all of them operate 24 hours per day. There is a significant amount of energy consumption. This research is to present an updated energy portion of convenience stores through sampling at 13 stores. These stores have a sales area of 153 to 311 m2 and operating hours of 16 to 24 hours per day. We found that energy consumption recorded from the Digital Energy Meter shows that all of them have cooling systems that use a huge portion of energy (refrigeration and air conditioning systems, 48% and 24%, respectively). Moreover, this research aims to develop a Retail Energy Management System (REMS) to increase the energy efficiency of air conditioning systems at convenience stores in Thailand while still maintaining comfort for occupants by keeping the required temperature at 26 degrees Celsius. We selected 3 of 13 stores that have the same sales area of 280 m2 but different operating hours and numbers of air conditioners. We optimized the operating air conditioning system by data-driven with the Internet of Things system concept, which manages and controls the air conditioning system through the Programmable Logic Control (PLC) program. REMS can reduce energy consumption by 24 and 15 kWh/day, or 14.5% and 8.3%, at operating hours of 24 and 16 hours per day, respectively. There is an average return on investment (ROI) of 3-5 years, depending on the operating hours of the store. In the future, regarding the infrastructure of energy management systems, that can extend to finding some opportunity to save energy consumption in another system, for example, the refrigeration system of their convenience stores.

Rational Design and Facile Preparation of Palladium-based Electrocatalysts for Small Molecules Oxidation.

Direct liquid fuel cells (DLFCs) can convert the chemical energy of small organic molecules directly into electrical energy, which is a promising technique and always calls for electrocatalysts with high activity, stability and selectivity. Palladium (Pd)-based catalysts for DLFCs have been widely studied with the pursuit of ultra-high performance, however, most of the preparation routes require complex agents, multi-operation steps, even extreme experimental conditions, which are high-cost, energy-consuming, and not conducive to the scalable and sustainable production of catalysts. In this review, the recent progresses on not only the rational design strategies, but also the facile preparation methods of Pd-based electrocatalysts for small molecules oxidation reaction (SMOR) are comprehensively summarized. Based on the principles of green chemistry in material synthesis, the basic rules of "facile method" have been restricted, and the fabrication processes, perks and drawbacks, as well as practical applications of the "real" facile methods have been highlighted. The landscape of this review is to facilitate the mild preparation of efficient Pd-based electrocatalysts for SMOR, that is, to achieve a balance between "facile preparation" and "outstanding performance", thereby to stimulate the huge potential of sustainable nano-electrocatalysts in various research and industrial fields.

Numerical study of gas crossover effect on hydrogen-oxygen proton exchange membrane fuel cell

Hydrogen-oxygen proton exchange membrane fuel cell (H2/O2 PEMFC) is a typical fuel cell for the applications of air-free environments, such as aerospace and underwater. Gas crossover has an adverse effect on the cell performance especially on open-circuit voltage (OCV), and this phenomenon would be severe for H2/O2 PEMFC. In this study, we develop a H2/O2 PEMFC model considering hydrogen and oxygen crossover, and investigate the gas crossover effect on H2/O2 PEMFC, and effects of temperature, relative humidity, inlet pressure and membrane thickness. The numerical results show that the gas crossover effect on the cell performance is obvious in the low current density region, and this effect becomes weak with current density. Although the crossover flux rate of hydrogen is generally greater than that of oxygen, the crossover current density of oxygen could be greater than that of hydrogen. Increasing temperature and relative humidity will decrease the OCV, and increase hydrogen and oxygen crossover current density. The temperature effect on OCV is more obvious at high relative humidity conditions. Increasing inlet pressure only slightly increases the OCV with considering the gas crossover effect. The OCV increases with increasing membrane thickness, and this positive effect becomes weak when the membrane thickness is greater than 75 μm in the investigated range. The work illustrates the basic influence rules and some quantified results of the critical parameters on the gas crossover effect and OCV of H2/O2 PEMFC.

Personal Finance Management Application

The paper presents a personal finance management mobile application developed for the Android operating system. The application is in the process of trials and test deployment among selected customers. It provides opportunities for managing a personal budget, retrieval of a financial status report for a certain period, working with expenses and income, report generation and visualization through charts, and barcode scanning. The methodology for designing and implementing the developed prototype includes pre-testing and preliminary interviews with potential customers who need convenient and easy access to manage their personal finances. Extensive research has been done on the availability of similar desktop and mobile applications. Appropriate technologies have been selected for the implementation of the program logic, software part and user interface. As a result, a mobile application has been developed that provides full integration with the web-based personal finance management system, previously created by the authors. The app is open to future improvements.

The Meaning of Causality and the Premise and Root of Its Existence

This article aims to understand the most fundamental operating principles of the phenomenal world and figure out how phenomena within the phenomenal world are interconnected. Causality is the most basic connection, rule, logic and fact of the phenomenal world, since everything in the phenomenal world is originally in the interconnection of three elements: cause, condition and effect. Finding the causal connection between things and utilising it to achieve the goal of avoiding harm and ultimately attaining happiness is the fundamental motivation for all human intellectual activities, including scientific research, philosophical discernment, and religious beliefs. The article gives a universal definition of causation as the primordial, underlying, transcendental, inherent, intrinsic, ultimate, essential, fundamental, inevitable, invariable, timeless, universal, complex, multidimensional, dynamic connection, law, logic, and fact among the three elements of cause, condition and result in the phenomenal world. The article explains in detail why the foregoing definition is universal from both ontological and epistemological perspectives.

Milton Friedman and nominal income targeting

AbstractFor much of his career, Milton Friedman advocated a constant monetary growth rule to limit central bank discretion and to achieve long‐term price stability. By contrast, Friedman wrote little on nominal income stabilization as an alternative goal for monetary policy. Nevertheless, a careful examination of Friedman's work shows that in his view the desirability of a constant monetary growth rule depended on its ability to stabilize nominal income. Friedman's preference for simple rules, requiring minimal knowledge on the part of central banks, would have given him strong reason to support nominal income targeting over central banks' standard practice of inflation targeting. Several strategies for achieving a nominal income target are considered, including a modified Taylor rule and several monetary base rules.

PLCBlox: Using blockchain-based audit trails to generate secure PLC commands

This research paper discusses security issues with current deployments of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition Systems (SCADA) in the industry and proposes a solution that enables PLC devices to query a blockchain infrastructure for commands and setpoints. The blockchain assumes a dual role in this context: serving as an immutable audit trail database as well as a trusted source for critical commands and setpoints. In contrast to the conventional paradigm, this novel approach does not require write access at the PLC level, thus minimizes its attack surface and helping to protect against known and zero-day vulnerabilities often used in cyberwarfare, such as in the case of the notorious Stuxnet worm. Applications that enforce the logging of user operations for Good Manufacturing Practices (GMP) or compliance purposes use the blockchain network as an audit trail database for user actions. Any attempt to maliciously circumvent the logging operation would not affect the operation of a critical process. Additionally, a prototype implementation developed as part of this research finds that modern PLC devices are more than capable of interacting with private Ethereum blockchain nodes. The required libraries and user code consume a small percentage of available resources, while the duration of a complete request-response cycle measured around 22msec. The authors anticipate that PLCBlox can be used as a drop-in replacement for applications requiring higher security standards and logging enforcement, such as nuclear power plants or other critical infrastructure.

Decentralized System Synchronization among Collaborative Robots via 5G Technology.

In this article, we propose a distributed synchronization solution to achieve decentralized coordination in a system of collaborative robots. This is done by leveraging cloud-based computing and 5G technology to exchange causal ordering messages between the robots, eliminating the need for centralized control entities or programmable logic controllers in the system. The proposed solution is described, mathematically formulated, implemented in software, and validated over realistic network conditions. Further, the performance of the decentralized solution via 5G technology is compared to that achieved with traditional coordinated/uncoordinated cabled control systems. The results indicate that the proposed decentralized solution leveraging cloud-based 5G wireless is scalable to systems of up to 10 collaborative robots with comparable efficiency to that from standard cabled systems. The proposed solution has direct application in the control of producer-consumer and automated assembly line robotic applications.

A soft actor-critic reinforcement learning framework for optimal energy management in electric vehicles with hybrid storage

The efficient energy management of electric vehicles (EVs) equipped with hybrid energy storage systems (HESS) poses a significant challenge due to its vast search space, numerous control variables, and intricate driving conditions. In response to this challenge, this paper introduces a novel deep reinforcement learning (DRL) algorithm, specifically the soft actor-critic (SAC), tailored for optimizing energy distribution within EVs equipped with battery-supercapacitor (SC) HESS. The proposed SAC-based energy management system (EMS) is designed to address inherent limitations present in most existing DRL algorithms, such as slower convergence rates, discretization errors, unstable training dynamics, and suboptimal optimization effects. The SAC-based EMS undergoes training in a continuous action space through self-play, utilizing a complex driving cycle and a novel reward function. The algorithm demonstrates its efficiency by rapidly maximizing cumulative rewards and refining its decision-making policies. Afterward, extensive experiments showcase the superiority of the proposed SAC-based EMS over rule-based (RB) techniques, deep deterministic policy gradient (DDPG), and battery-only configurations across various driving cycles. Notably, the approach effectively allocates high-power surges and braking energy to the SC, reducing the frequency of charge/discharge cycles and thereby extending the battery's lifespan. Additionally, the learned SAC-based EMS policy achieves substantial reductions in electricity consumption by 39.6 %, 47.87 %, and 45 % compared to battery-only configuration, and 42.56 %, 47.87 %, and 45.83 % compared to RB technique under the NYCC, LA92, and FTP cycles, respectively. In contrast, the DDPG algorithm tends to rely heavily on the SC to deliver the total power required, even under normal driving conditions.

Simulation and Mitigation of False Data Injection Cyberattacks on a Space Nuclear Reactor Power System

This paper investigates the response of the DynMo-CBC space nuclear reactor power system to simulated cybersecurity attacks during a startup transient and demonstrates the effectiveness of the mitigation measures. The system nominally generates 134 kW(electric) continuously for 12 years and does not have a single-point failure in reactor cooling and energy conversion. The reactor core is divided into three hydraulically independent sectors, each having a separate loop with a single shaft, closed Brayton cycle (CBC) turbomachinery unit. A He-Xe gas mixture with a molecular weight of 40 g/mol cools the reactor core sectors and is the CBC unit’s working fluid. This paper examines the effects of simulated false data injection attacks (FDIAs) on the operation parameters of the power system. The simulated FDIAs decrease or increase the external reactivity insertion beyond nominal to cause spikes in the reactor’s power and temperatures. The results demonstrate the effectiveness of the programmable logic controller regulating the control drums’ drive motors. It mitigates the effects of the simulated FDIAs on the transient operation of the power system and shortens the recovery time after the termination of the simulated cyberattacks.

Design and Validation of a PLC-Controlled Morbidostat for Investigating Bacterial Drug Resistance.

During adaptive laboratory evolution experiments, any unexpected interruption in data monitoring or control could lead to the loss of valuable experimental data and compromise the integrity of the entire experiment. Most homemade mini-bioreactors are built employing microcontrollers such as Arduino. Although affordable, these platforms lack the robustness of the programmable logic controller (PLC), which enhances the safety and robustness of the control process. Here, we describe the design and validation of a PLC-controlled morbidostat, an innovative automated continuous-culture mini-bioreactor specifically created to study the evolutionary pathways to drug resistance in microorganisms. This morbidostat includes several improvements, both at the hardware and software level, for better online monitoring and a more robust operation. The device was validated employing Escherichia coli, exploring its adaptive evolution in the presence of didecyldimethylammonium chloride (DDAC), a quaternary ammonium compound widely used for its antimicrobial properties. E. coli was subjected to increasing concentrations of DDAC over 3 days. Our results demonstrated a significant increase in DDAC susceptibility, with evolved populations exhibiting substantial changes in their growth after exposure.

Transient hydraulic analysis of the water delivery pipeline in mountainous areas for cascade pressurized pump stations

ABSTRACT Aiming at the problem that cascade pressurized pump stations in mountainous areas are more prone to pump shutdown accidents compared to single-stage pressurized pump stations. Various pump shutdown accidents that may occur in the pressurized water supply system of the terrace pumping station were analyzed in a targeted manner. Additionally, it was also explored that in addition to water hammer protection measures, remote-control measures for water pumps based on PLC (Programmable Logic Controller) may be used to reduce the severity of accidents. A single-factor experimental method was used to numerically analyze various accident conditions of a simplified model of a three-stage series pressurized water delivery system consisting of a water intake pump station, a midway pump station, and a final-stage pump station, without water hammer protection devices. The impact of different control variables on the transient hydraulic calculations of pumping stations at all levels was evaluated. The results showed that optimizing the precedence of shutdowns, and the sequential shutdown times of each stage pump station could be assisted to some extent in water hammer protection by the PLC. This provides technical support and detailed optimization for the scientific control of cascade booster pumping stations in mountainous areas.

CHARM 2.0: Composing Heterogeneous Accelerators for Deep Learning on Versal ACAP Architecture

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogeneous architectures featuring both FPGA and dedicated ASIC accelerators have emerged as promising platforms. For example, the AMD/Xilinx Versal ACAP architecture combines general-purpose CPU cores and programmable logic with AI Engine processors optimized for AI/ML. An array of 400 AI Engine processors executing at 1 GHz can provide up to 6.4 TFLOPS performance for 32-bit floating-point (FP32) data. However, machine learning models often contain both large and small MM operations. While large MM operations can be parallelized efficiently across many cores, small MM operations typically cannot. We observe that executing some small MM layers from the BERT natural language processing model on a large, monolithic MM accelerator in Versal ACAP achieved less than 5% of the theoretical peak performance. Therefore, one key question arises: How can we design accelerators to fully use the abundant computation resources under limited communication bandwidth for end-to-end applications with multiple MM layers of diverse sizes? We identify the biggest system throughput bottleneck resulting from the mismatch between massive computation resources of one monolithic accelerator and the various MM layers of small sizes in the application. To resolve this problem, we propose the CHARM framework to compose multiple diverse MM accelerator architectures working concurrently on different layers within one application. CHARM includes analytical models which guide design space exploration to determine accelerator partitions and layer scheduling. To facilitate system designs, CHARM automatically generates code, enabling thorough onboard design verification. We deploy the CHARM framework on four different deep learning applications in FP32, INT16, and INT8 data types, including BERT, ViT, NCF, and MLP, on the AMD/Xilinx Versal ACAP VCK190 evaluation board. Our experiments show that we achieve 1.46 TFLOPS, 1.61 TFLOPS, 1.74 TFLOPS, and 2.94 TFLOPS inference throughput for BERT, ViT, NCF, and MLP in FP32 data type, respectively, which obtain 5.29 \(\times\) , 32.51 \(\times\) , 1.00 \(\times\) , and 1.00 \(\times\) throughput gains compared to one monolithic accelerator. CHARM achieves the maximum throughput of 1.91 TOPS, 1.18 TOPS, 4.06 TOPS, and 5.81 TOPS in the INT16 data type for the four applications. The maximum throughput achieved by CHARM in the INT8 data type is 3.65 TOPS, 1.28 TOPS, 10.19 TOPS, and 21.58 TOPS, respectively. We have open-sourced our tools, including detailed step-by-step guides to reproduce all the results presented in this paper and to enable other users to learn and leverage CHARM framework and tools in their end-to-end systems: https://github.com/arc-research-lab/CHARM .

A New Student Performance Prediction Method Based on Belief Rule Base with Automated Construction

Student performance prediction (SPP) is a pivotal task in educational analytics, enabling proactive interventions and optimized resource allocation by educators. Traditional SPP models are often hindered by their complexity and lack of interpretability. This study introduces a novel SPP framework, the Belief Rule Base with automated construction (Auto–BRB), designed to address these issues. Firstly, reference values are derived through data mining techniques. The model employs an IF–THEN rule-based system integrated with evidential reasoning to ensure both transparency and interpretability. Secondly, parameter optimization is achieved using the Projected Covariance Matrix Adaptive Evolution Strategy (P–CMA–ES), significantly enhancing model accuracy. Moreover, the Akaike Information Criterion (AIC) is then applied to fine-tune the balance between model accuracy and complexity. Finally, case studies on SPP have shown that the Auto–BRB model has an advantage over traditional models in terms of accuracy, while maintaining good interpretability. Therefore, Auto–BRB has excellent application effects in educational data analysis.

An extended belief rule-based system with hybrid sampling strategy for imbalanced rule base

Imbalanced dataset is an important focus for classification. As the mainstream of addressing imbalanced dataset, data-level methods are trapped in facing difficultly-determined subjective parameters, and the inconsistency between new minority samples and original minority samples simultaneously. To address it, this paper develops an extended belief-rule-based (EBRB) system with hybrid sampling strategy, which is a white-box classifier. The hybrid sampling strategy is composed of an under-sampling process and an oversampling process, in which subjective parameters are not involved. The under-sampling is to identify and remove overlapping majority rules by iteratively determining an appropriate objective threshold for calculating the inconsistency degree of rule base, and to determine and remove redundant non-overlapping majority rules by using the density of non-overlapping rules in clustered groups. The oversampling is to design a differential evolution based iterative process to generate new minority rules in groups by minimizing the inconsistency of rule base. The distribution of original dataset is maintained extremely by balancing rules in clustered majority and minority groups, respectively. This EBRB system is used for the auxiliary diagnosis of thyroid nodules, and its superior performance is highlighted by the comparison with existing EBRB systems, representative data-level methods, and algorithm-level methods.

Design of an efficient Transformer-XL model for enhanced pseudo code to Python code conversion

The landscape of programming has long been challenged by the task of transforming pseudo code into executable Python code, a process traditionally marred by its labor-intensive nature and the necessity for a deep understanding of both logical frameworks and programming languages. Existing methodologies often grapple with limitations in handling variable-length sequences and maintaining context over extended textual data. Addressing these challenges, this study introduces an innovative approach utilizing the Transformer-XL model, a significant advancement in the domain of deep learning. The Transformer-XL architecture, an evolution of the standard Transformer, adeptly processes variable-length sequences and captures extensive contextual dependencies, thereby surpassing its predecessors in handling natural language processing (NLP) and code synthesis tasks. The proposed model employs a comprehensive process involving data preprocessing, model input encoding, a self-attention mechanism, contextual encoding, language modeling, and a meticulous decoding process, followed by post-processing. The implications of this work are far-reaching, offering a substantial leap in the automation of code conversion. As the field of NLP and deep learning continues to evolve, the Transformer-XL based model is poised to become an indispensable tool in the realm of programming, setting a new benchmark for automated code synthesis.

A Critical Review of Inductive Logic Programming Techniques for Explainable AI.

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

Rule-Ranking-Based Approximate Knowledge Interpolation With Directional Monotonicity.

Fuzzy rule interpolation (FRI) empowers fuzzy rule-based systems (FRBSs) with the ability to infer, even when presented with a sparse rule base where no direct rules are applicable to a given observation. The core principle lies in creating an intermediate fuzzy rule-either interpolated or extrapolated-derived from rules neighboring the observation. Conventionally, the selection of these rules hinges upon distance metrics. While this approach is easy to grasp and has been instrumental in the evolution of various FRI methods, it is burdened by the necessity of extensive distance calculations. This becomes particularly cumbersome when swift responses are imperative or when dealing with large datasets. This article introduces a groundbreaking rule-ranking-based FRI method, termed RT-FRI, which overcomes the constraints of the longstanding distance-centric FRI approach. Instead of relying on distances, RT-FRI harnesses ranking scores for rules and unmatched observation. These scores are produced by amalgamating the antecedent attributes using aggregation functions, thus streamlining the rule selection procedure. Recognizing the rigid monotonicity demands of aggregation functions, a variant-DMRT-FRI-has been introduced to ensure directional monotonicity. Experimental results indicate that RT-FRI emerges as a highly efficient technique, with DMRT-FRI exemplifying a notable balance of accuracy and efficiency.