Rule-based Research Articles

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.

Self-organizing interval type-2 function-link fuzzy neural network control for uncertain manipulators under saturation: A predefined-time sliding-mode approach

Robotic manipulators have extensive applications in academic and industrial fields such as grabbing, welding, and machining. Nowadays, better tracking performance and faster transient response have been the fundamental demands in the field of manipulator control. However, robotic manipulators suffer from uncertainties due to various factors in real-world environments. Therefore, achieving satisfactory tracking performance for the manipulator in the presence of uncertainties is a pressing problem. Moreover, most existing control schemes struggle to determine the convergence time of the whole system and rely on specifically designed parameters via experience and prior knowledge, which leads to poor transferability and generalization. To address the abovementioned issues, a predefined-time controller based on a self-organizing interval type-2 function-link fuzzy neural network (IT2FLFNN) is proposed in this paper. Different from most existing fuzzy neural network (FNN) based control schemes, the proposed network starts from an empty rule base and it is constructed by a self-organizing mechanism during the online control process, which provides greater flexibility in terms of network structure and input partition. Also, a function-link network (FLN) is integrated into IT2FLFNN to enhance its convergence speed and rejection of uncertainties. Furthermore, to ensure the predefined-time convergence, a predefined-time sliding-mode control part is merged into the control framework and online learning laws for IT2FLFNN are established via the Lyapunov stability analysis. Finally, a comparative simulation and an experiment demonstrate the superiority of the proposed IT2FLFNN control scheme.

Implementation of the Baduy Tribe's Inheritance System in Kanekes Village, Leuwidamar District, Lebak Regency, Banten Province

The Muslim Baduy community basically adheres to the customs and customs they adopted from their ancestors. The inheritance system used in Baduy customary inheritance uses a bilateral system, namely from father to mother who are equally powerful. The problem that will be discussed is how to implement inheritance in the Baduy Dalam tribe community. The type of research used in this research is normative juridical legal research, namely library research which only focuses on written statutory regulations, so this research is very closely related to libraries because it requires secondary data. The fundamental difference between the inheritance law of the Baduy community and Islamic inheritance law lies in Pikukuh which is the basis for the unwritten inheritance distribution rules which in Islamic inheritance law are clearly based on the Al-Quran and As-Sunnah. Apart from that, there are similarities between Baduy inheritance law and Islamic inheritance law in that the heirs are the descendants of the person who died and the inheritance can only be distributed after the death of the testator. However, the Baduy community does not distribute inheritance to the lineage and above.

Saving resources without sacrificing results: an empirical investigation of the dishwashing reality of British consumers in an international comparison

Abstract Routine household tasks, such as laundry or dishwashing, are complex socio-technical systems in which a variety of factors and actors interact, including manufacturers, technologies, regulators, consumer practices, cultural norms and infrastructures. A deep understanding of these socio-technical systems is necessary to find the right recommendations on how to optimise this system in order to achieve relevant savings in resource consumption without requiring a complete renewal of the infrastructure installed. In our case, we focus on automatic dishwashing in the United Kingdom and try to find out what an optimised consumer use of the dishwashers installed can look like and how many savings can be achieved. Accordingly, we modelled the base of dishwashers installed and conducted a consumer survey on dishwasher usage behaviour in sufficient detail to understand the interplay between the various factors. By simulating a change in usage behaviour, we find a potential energy saving of 21.3 % simply by following basic rules for selecting the appropriate programmes without the risk of a loss of performance.

Carnap and Wittgenstein: Tolerance, Arbitrariness, and Truth

This article discusses the relationship between Ludwig Wittgenstein’s and Rudolf Carnap’s philosophies of logic during the time of Wittgenstein’s interactions with the Vienna Circle and up to 1934 when the German edition of Carnap’s The Logical Syntax of Language was published. Whilst Section 1 focuses on the relationship between Carnap and Wittgenstein’s Tractatus, including Wittgenstein’s accusation of plagiarism against Carnap in 1932, Section 2 discusses the relationship between Carnap’s principle of tolerance and Wittgenstein’s similar principle of the arbitrariness of grammar. I argue that, although Carnap’s claim in Logical Syntax to ‘go beyond’ Wittgenstein has certain justification in relation to the Tractatus, so does Wittgenstein’s priority claim. The relationship between Carnap’s philosophy of logic and the Tractatus is thus more complicated than is often recognized. If the reference point is Wittgenstein in the early 1930s, however, Carnap cannot be described as going beyond him, and by 1934, Wittgenstein had advanced further than Carnap would ever venture. Despite evidence that Carnap knew about Wittgenstein’s principle of the arbitrariness of syntax well before his first articulations of his principle of tolerance, the extent of the influence of Wittgenstein’s principle on Carnap remains unclear. What can be established with certainty is that Wittgenstein’s principle predates Carnap’s and that Carnap resisted acknowledging him despite being urged to do so. Arguably, Wittgenstein’s account of syntax as both arbitrary and non-arbitrary is also superior in clarity to Carnap’s misleading claim about a ‘complete freedom’ implied by the principle of tolerance, because such a freedom only exists for idle syntactical systems that are not put to work. In Section 3, I discuss the relationship between Carnap’s notion of expediency and Wittgenstein’s account of the correctness or truth of logical accounts. As my discussion of Wittgenstein’s account brings out, Carnap’s rejection of truth in logic for expediency as the goal of logical clarifications does not follow from the principle of tolerance and is not justified by it. It remains unclear what justifies Carnap’s rejection of truth as the goal of logical clarification. Again, Wittgenstein’s account seems preferable, given the vacuity of the claim that expediency constitutes the basis of choice between different logical languages and clarifications.

CHILDREN'S HOMES IN THE SYSTEM OF STATE INSTITUTIONS FOR ORPHANS AND CHILDREN WITHOUT PARENTAL CARE IN THE BSSR: TYPES, FUNCTIONS, THEIR ROLE IN THE SOCIALIZATION OF CHILDREN (1944–1956)

The article examines the activities of the People's Commissariat of Education of the BSSR to solve the problems of child homelessness and neglect in the post-war period. The author examines the features of the development of the system of institutions for orphaned children, analyzes the main regulatory documents that determined and regulated the functioning of orphanages, the basic rules for accepting the enrollment of pupils, and the problems of material support for orphanages.

A health assessment method with attribute importance modeling for complex systems using belief rule base

In the health assessment for complex systems, system attributes refer to the indicators or components having an impact on the health state of the system. When assessing the health state of complex systems, the relative importance of system attributes should be accurately modeled, otherwise incorrect results may occur. Especially for some complex systems with small samples, data-driven methods are prone to overfitting. In this article, a new method using belief rule base (BRB) is proposed to model the relative importance of system attributes while assessing the health state. As an interpretable modeling tool, BRB constructs nonlinear mapping relationships between system attributes and health state. A data-knowledge hybrid-driven ensemble feature selection (DKH-EFS) method is developed to calculate the relative importance of the system attributes, namely attribute importance (AM). A random sensitivity analysis (RSA) method of the model output to the input of BRB is proposed to calculate the feature importance (FI) of BRB. A new parameter optimization model considering the consistency between the AM and FI is introduced to improve the modeling ability of BRB for the AM. A case study on the health assessment of the fiber optic gyroscope (FOG) validates the proposed method.

Syntactic analysis of traditional houses in urban kampung

This research investigates a syntactical study and comparative analysis of the statistical and spatial characteristics of traditional houses in an urban kampung settlement, focusing on a selected case study built by a participatory Arabic community in Palembang, South Sumatra, Indonesia. This research aims to determine a formal rule basis for spatial configurations to reveal identical sociospatial structures based on syntactical analysis. This experiment measures spatial layout variation through space syntax analysis to provide a better understand of how the correlation between spatial configuration and sociospatial structures in traditional houses can be deconstructed. This syntactical analysis applies four distinctive procedures: a selected case study, data collection, statistical and graphical analysis, and graph analysis. The results indicate that the spatiality of all traditional houses in this kampung settlement highlights the spatial hierarchy order as a formal rule-based system, and approximately an average of 10% of this community is concerned with designing intelligible layouts. Rumah Batu and other dwellings have a similarity and closeness. The main dwelling’s function involves more steps to separate public and private functional rooms, but a functional transformation from a dwelling into a public facility creates a short distance for easy access by users. Additionally, this separation affects occupants, especially in terms of spatial distribution activities, movement flows, and other social phenomena. This approach provides practical and tangible benefits for preservation values related to buildings; this strategy may also change how buildings are perceived in other built environments.

Design of Automatic Flood Control System in Kulon Progo, Special Region of Yogyakarta, Indonesia

The construction of Yogyakarta International Airport has resulted in land use changes and increased the topography in the area by up to 9 m, which has led to potential flooding. A spatial analysis was conducted on the flow direction after the airport's construction, specifically in the West Carik watershed. It was discovered that the flow direction was uncertain and trapped upstream of the airport. A polder system with automation is proposed as a solution to the drainage problem. An automation system is designed for pump operation with the objective of improving efficiency. The system is expected to reduce the volume of water in the polder by reducing human involvement and providing ease in monitoring. The results of hydrological analysis with a 50-year return period flood resulted in a design flood discharge of 11.73 m3/s. The polder has a storage capacity of 63,312 m3, a depth of 4 m, and a surface area of 1.58 ha. It requires a total pump capacity of 8.4 m3/s, consisting of three pump units, with one unit having a capacity of 1.8 m3/s and two units with 3.3 m3/s. One additional pump with a capacity of 3.3 m3/s is also planned as a backup pump. The pump automation system simulation employs an Arduino UNO as the controller, water level sensor, and relay. This system successfully fulfills the desired operational system for the pump. In the actual implementation, a Programmable Logic Controller (PLC) is required as the controller, with water level sensors, relays, and contactors as inputs and outputs.

Use of the Alice Visual Programming Environment in Programming Education : A Descriptive Case Study

The process of learning a programming language is seen as complicated, especially for those who first start to learn programming language. For this reason, visual programming tools have begun to be used in educational environments. Visual programming tools purpose to concretize abstract programming language concepts. Alice program is one of the visual programming tools. The aim of this study is to determine the effects of the Alice program on programming learning in the education environment. The study group was 18 freshman students enrolled in the Object Based Programming I course at the vocational school computer technology department. The study lasted 8 weeks, with 4 hours each week. The teaching was realized on Gagne’s nine events of instruction model. At the end of the teaching process, two focus group interviews were conducted with the students to determine the obstructing and facilitating factors in using the Alice program. This study was utilized a descriptive case study. The data were analyzed by using the content analysis. When the results were examined; It was seen that the Alice program makes learning the basic code concepts easy and understanding programming logic, and increases the desire to learn programming. However, it was stated that the practicality of writing code could not be improved because code could not be written in the Alice program. It is thought that employing the Alice program is beneficial for enhancing comprehension of programming logic.

Multi-objective optimization of virtual machine migration among cloud data centers

AbstractWorkload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.

On the continuous probability distribution attribute weight of belief rule base model

On the continuous probability distribution attribute weight of belief rule base model

A Student Performance Prediction Model Based on Hierarchical Belief Rule Base with Interpretability

Predicting student performance in the future is a crucial behavior prediction problem in education. By predicting student performance, educational experts can provide individualized instruction, optimize the allocation of resources, and develop educational strategies. If the prediction results are unreliable, it is difficult to earn the trust of educational experts. Therefore, prediction methods need to satisfy the requirement of interpretability. For this reason, the prediction model is constructed in this paper using belief rule base (BRB). BRB not only combines expert knowledge, but also has good interpretability. There are two problems in applying BRB to student performance prediction: first, in the modeling process, the system is too complex due to the large number of indicators involved. Secondly, the interpretability of the model can be compromised during the optimization process. To overcome these challenges, this paper introduces a hierarchical belief rule base with interpretability (HBRB-I) for student performance prediction. First, it analyzes how the HBRB-I model achieves interpretability. Then, an attribute grouping method is proposed to construct a hierarchical structure by reasonably organizing the indicators, so as to effectively reduce the complexity of the model. Finally, an objective function considering interpretability is designed and the projected covariance matrix adaptive evolution strategy (P-CMA-ES) optimization algorithm is improved. The aim is to ensure that the model remains interpretable after optimization. By conducting experiments on the student performance dataset, it is demonstrated that the proposed model performs well in terms of both accuracy and interpretability.

Industrial Sustainable Development: The Development Trend of Programmable Logic Controller Technology

Programmable Logic Controllers (PLCs) are indispensable for current and future industrial development, especially in smart factories, smart home technology, automated production lines, and machinery manufacturing. This study presents the trends in PLC software and hardware development through a technology roadmap and offers relevant suggestions to help industries achieve sustainable development, enhance market competitiveness, and provide references for research. Through expert interviews and fuzzy Delphi analysis, this study points out that future PLC development needs to focus on editing interfaces, syntax, Central Processing Units, Memory Units, and Communication Modules. Specific recommendations include visualizing regional/global label settings and connection settings, adding Python, JAVA, LabVIEW, and Scratch syntax, improving instruction execution speed, expanding program and expansion capacities, and adopting dual-channel Ethernet and connections to external networks and wireless networks. Fuzzy hierarchical analysis shows that Communication Modules are the most important component, followed by Central Processing Units and syntax expansion, and, finally, program and expansion capacity enhancements. These suggestions aim to promote product innovation and social environment demand evaluation, enhance product competitiveness, and achieve sustainable development goals.

Network Security Prediction of Industrial Control Based on Projection Equalization Optimization Algorithm.

This paper predicts the network security posture of an ICS, focusing on the reliability of Industrial Control Systems (ICSs). Evidence reasoning (ER) and belief rule base (BRB) techniques are employed to establish an ICS network security posture prediction model, ensuring the secure operation and prediction of the ICS. This model first integrates various information from the ICS to determine its network security posture value. Subsequently, through ER iteration, information fusion occurs and serves as an input for the BRB prediction model, which necessitates initial parameter setting by relevant experts. External factors may influence the experts' predictions; therefore, this paper proposes the Projection Equalization Optimization (P-EO) algorithm. This optimization algorithm updates the initial parameters to enhance the prediction of the ICS network security posture through the model. Finally, industrial datasets are used as experimental data to improve the credibility of the prediction experiments and validate the model's predictive performance in the ICS. Compared with other methods, this paper's prediction model demonstrates a superior prediction accuracy. By further comparing with other algorithms, this paper has a certain advantage when using less historical data to make predictions.

Development of a new social prescribing intervention for families of children with cerebral palsy.

To co-design a social prescribing intervention (the EPIC-CP programme: Equitable Pathways and Integrated Care in Cerebral Palsy) with children with cerebral palsy (CP), their families, and clinicians to address unmet social needs. The study was conducted (August 2021 to March 2023) at the paediatric rehabilitation departments of the three tertiary paediatric hospitals in New South Wales, Australia. Eligible participants attended or worked at one of the departments, including children with CP, parents/caregivers, and clinicians. Mixed-methods co-design was used in intervention co-production and prototyping. The project was overseen by research advisors with lived experience of CP. More than 200 participants contributed to the co-design research. Families experienced a substantial burden of unmet social needs. Co-designed interventions involved systematic identification of unmet social needs with (1) targeted community resources and (2) engagement with a 'community linker' who supported children/young people and their families to access health, education, and social services that matched their identified needs and preferences. Research participants co-developed the programme logic model and prototype. This was piloted in research action cycles and iteratively refined until consensus was achieved. We co-designed a social prescribing programme responsive to the needs of its end-users and purposefully developed to be embedded in the Australian health setting. A pilot randomized controlled trial will further evaluate this intervention.