Rule-based System Research Articles

What Matters in the Effectiveness of Airborne Collision Avoidance Systems? Monte Carlo Simulation of Uncertainties for TCAS II and ACAS Xa

TCAS II is a rule-based airborne collision avoidance system (ACAS) that is used in current commercial air transport operations, and ACAS Xa is a new optimization-based system. Operational validation studies have mainly used deterministic simulations of ACAS performance using various sets of encounters. Recently a new approach was developed, which employs Monte Carlo (MC) simulation of agent-based models to evaluate the impact of sensor errors and pilot response variability. This paper contrasts the results of both approaches in a comparison of TCAS II and ACAS Xa for various types of synthetic encounters. It was found that conventional estimates of near mid-air collision (NMAC) probabilities are often lower than the estimates achieved using MC simulation, and that the biases in the P(NMAC) estimates are consistently larger for ACAS Xa than for TCAS II. Contributions to unresolved risk are largest for pilot performance, then for encounter types, and finally for sensor errors. The contribution of non-responding pilots is much larger than the differences between TCAS II and ACAS Xa. It is concluded that the agent-based MC simulation overcomes the limitations in traditional evaluation of altimetry errors and pilot response, providing an independent means to effectively analyze the robustness of ACASs.

Automatic control of water content in a polyurethane foam packed bed used in biotrickling filters for polluted air treatment

Biotrickling filters (BTF) have been applied to treat waste air. Controlling the water content (WC) of BTFs is fundamental to ensure an adequate interfacial mass transfer and the support of the biofilm activity. A sensor system based on electrical conductance measurement was developed for the online assessment of the WC in a bench scale polyurethane foam (PUF) packed bed. ON/OFF and rule-based control algorithms were applied to keep the water content in a biofilm-free PUF-packed bed within the optimal range of 70–90% through the activation of a hydraulic pump. The applied control algorithms activated the pump at various times, which yielded different energy and water savings even under similar WC conditions suitable for biotrickling filtration of waste air. It was estimated that using the ON/OFF and rule-based system control schemes reduced the electrical power consumption by 98% and 99.8%, respectively, compared to systems under continuous water irrigation.

A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques

A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.

Light weight Rule-Based Examination System for Web of Things

Light weight Rule-Based Examination System for Web of Things

A comparative investigation between rule- and inverse model-based fault detection and diagnostics for HVAC control systems

Fault detection and diagnostics (FDD) tools provide valuable information regarding system faults and deviation from expected operation. Most existing FDD tools apply rule-based fault detection algorithms that generate an alarm when a rule is met; however, these tools cannot evaluate the overall performance of a system. Inverse-model-based FDD algorithms can be deployed to complement the fault alarms triggered by rule-based building energy management systems (BEMS). This paper examines the faults detected by rule- and inverse model-based algorithms used to detect faults in multiple zone variable air volume air handling unit systems. The capability of the rule- and inverse model-based algorithms in detecting and diagnosing faults is demonstrated through illustrative examples using data from three commercial buildings in New Brunswick, Canada. The results show that inverse model-based algorithms could diagnose faults that were not detected by the rule-based FDD algorithms implemented in a commercially available BEMS tool.

THE ROLE OF NATURAL LANGUAGE PROCESSING

Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-based systems to contemporary deep learning models. Natural Language Processing is a subfield of computer science and artificial intelligence that deals with the interactions between computers and humans using natural language. It focuses on the ability of computers to understand, interpret, and generate human language. Keywords Natural Language Processing, Text Analysis , Text Mining , Speech Recognition.

Multi-View Fuzzy Classification With Subspace Clustering and Information Granules

Multi-view learning becomes increasingly attractive and promising because multimodal or multi-view data are commonly encountered in real-world applications. In this study, we develop a novel multi-view Takagi-Sugeno-Kang (TSK) fuzzy system framework to handle classification problems for such data. We propose an anchor and graph subspace clustering strategy to discover and represent the actual latent data distribution for each view separately. In this way, the discriminate anchors (landmarks) are learned to capture the main structure of the multi-view data. This strategy also provides a computationally efficient clustering algorithm with respect to the number of instances. These resulting anchors are formed as the prototypes of information granules (IGs) for fuzzy modeling. Then we construct an information-granule-based multi-view TSK fuzzy classification model inherited from the natural interpretability of fuzzy rule-based systems. Concretely, the relationship between the multi-view input and label output spaces is depicted by IGs-oriented fuzzy rules. The experimental studies involve various commonly used benchmark datasets, which indicate that our proposed method achieves comparable or better performance compared to the state-of-the-art algorithms.

Big Data Confidentiality: An Approach Toward Corporate Compliance Using a Rule-Based System.

Organizations have been investing in analytics relying on internal and external data to gain a competitive advantage. However, the legal and regulatory acts imposed nationally and internationally have become a challenge, especially for highly regulated sectors such as health or finance/banking. Data handlers such as Facebook and Amazon have already sustained considerable fines or are under investigation due to violations of data governance. The era of big data has further intensified the challenges of minimizing the risk of data loss by introducing the dimensions of Volume, Velocity, and Variety into confidentiality. Although Volume and Velocity have been extensively researched, Variety, "the ugly duckling" of big data, is often neglected and difficult to solve, thus increasing the risk of data exposure and data loss. In mitigating the risk of data exposure and data loss in this article, a framework is proposed to utilize algorithmic classification and workflow capabilities to provide a consistent approach toward data evaluations across the organizations. A rule-based system, implementing the corporate data classification policy, will minimize the risk of exposure by facilitating users to identify the approved guidelines and enforce them quickly. The framework includes an exception handling process with appropriate approval for extenuating circumstances. The system was implemented in a proof of concept working prototype to showcase the capabilities and provide a hands-on experience. The information system was evaluated and accredited by a diverse audience of academics and senior business executives in the fields of security and data management. The audience had an average experience of ∼25 years and amasses a total experience of almost three centuries (294 years). The results confirmed that the 3Vs are of concern and that Variety, with a majority of 90% of the commentators, is the most troubling. In addition to that, with an approximate average of 60%, it was confirmed that appropriate policies, procedure, and prerequisites for classification are in place while implementation tools are lagging.

Experiment of Fuzzy Logic on Game System

Abstract: Applications of fuzzy logic in game creation have proven useful, especially when it comes to modelling the behavior of non-player characters (NPCs) in video games. NPC behavior in video games has historically been governed by strict rule-based systems or scripted sequences, which frequently led to restricted and predictable interactions. On the other hand, fuzzy logic adds a degree of flexibility and ambiguity to the decision-making processes of NPCs. By improving the naturalness, adaptability, and emotional resonance of NPC behavior, fuzzy logic may greatly enhance player experiences in games. It provides players with progressively more engaging and dynamic gaming experiences as it continues to be incorporated into game production, opening up new possibilities for interactive narrative, gaming, and immersive virtual worlds. Based on Utku Kose's article, "Developing a Fuzzy Logic Based Game System," I have investigated the use of fuzzy logic in gaming contexts for NPC decision-making in this study.

Hybrid-driven BRBCS-BOM with expert intervention and its application for abnormity recognition in electrolytic cell

Belief rule-based classification system (BRBCS) is a useful model to handle classification problems. In our previous work, a novel data-driven BRBCS with batch-by-batch observation, online learning and multi-weights (BRBCS-BOM) is proposed. It can powerfully obtain knowledge from data. However, in some applications of industrial engineering, it can be hard to obtain enough training samples. The knowledge contained in data is not enough to deal with the problem well. It is necessary to adopt expert-driven BRBCS as an important supplement (hybrid-driven). In this paper, a hybrid-driven BRBCS-BOM with expert intervention (HBRBCS-BOM/E2) is proposed. It adopts a modified inheriting-and-learning hybrid-driven mode. Generally, it inherits the belief rules generated from training samples and make a secondary optimization for these inherited belief rules based on learning from expert-driven BRBCS. Moreover, a novel mode of expert intervention is proposed, based on the reliability evaluation. It can diversely-and-precisely obtain some important online new training samples for enhancement of data-knowledge, making hybrid-driven model better. The related experiments on an industrial engineering classification problem, called abnormity recognition of synthetical balance of material and energy, have demonstrated that the proposed HBRBCS-BOM/E2 not only makes an effective improvement for data-driven BRBCS-BOM from above two ways, but also has a more advanced performance compared with other existing high-performance BRBCS.

Hardware design for blood glucose control based on the Sorensen diabetic patient model using a robust evolving cloud-based controller

Diabetes Mellitus (DM) is the most hazardous public health challenge requiring engineering study to prevent disease complications. In this paper, a Sorensen-based diabetic model is presented in which the insulin-glucose process of a Type 1 patient is maintained by considering other factors such as physical characteristics and changes in mental aspects of the diabetic patient. The purpose of the research is to include a non-linear model of a patient with diabetes who is affected by stress, meals, exercise, and Insulin Sensitivity (IS), and a suitable RECCo controller is designed as a notable recent innovation that implements the concept of ANYA fuzzy rule-based system, which is an online adaptive type of controller that is used in this research work with an uncertainty case of the condition, where the blood glucose must be regulated. To ensure the performance of the proposed controller, a simple insulin pump is designed in a practical case, and a hardware experiment is conducted. The result of the hardware is analyzed and shows the success of the implementation of the controller in blood glucose regulation, thereby preventing complications such as hypoglycemia and hyperglycemia. The comparison analysis of RECCo was performed with other types of controllers, such as MPC and MRAC. The accuracy of the model was validated using the N-BEATS algorithm with a data-set collected from the simulated model, which is around 98%.

Data-driven operator functional state classification in smart manufacturing

AbstractOne of the main challenges in the industry is having trained and efficient operators in manufacturing lines. Smart adaptive guidance systems are developed that offer assistance to the operator during assembly. Depending on the operator’s level of execution, the system should be able to serve a different guidance response. This paper investigates the assessment and classification of the operator’s functional state using observed task execution times. Five different classifiers are studied for operator functional state classification on task execution time series. The experiments are based on an industry case and the ground truth is provided by an expert rule-based system. Three classification scenarios are defined that segment the problem on the level of the task, the individual, or the team. Furthermore, the investigation includes the evaluation of four distinct window-size configurations. The examination of how these scenarios and window-sizes influence the studied dataset across diverse classifiers reveals that achieving enhanced accuracy necessitates a larger input dimension. In this context, Convolutional Neural Networks predominantly exhibit superior performance compared to alternative classifiers. Careful attention needs to be paid to performance over classes and skills, but results confirm the validity of the approach for data-driven operator functional state classification.

Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.

Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.

An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout

Given the shortage of intensive care units (ICUs) due to the coronavirus disease (COVID-19), a prediction model is essential in ensuring the availability of ICU beds. However, several challenges, such as the importance of distinguishing indicators, efficiency of ICU admission records, and the explainability and effectiveness of the prediction model, hinder the effective prediction of ICU admissions. To mitigate these challenges, an explainable decision model that uses the extended belief rule-based system is introduced to predict ICU admission. First, an indicator extraction model is proposed to measure the importance of the various indicators and obtain representative indicators. Second, a Charnes, Cooper, and Rhodes (CCR) model is constructed to measure the efficiencies of the belief rules to achieve the compact structure of an extended belief rule base. Third, a new extended belief rule-based model, optimized by parameter optimization and domain division-based rule reduction, is developed to predict ICU admission. These procedures enable the explainable decision model to adapt to big data situation, offer explanations and realize high efficiency. Finally, a case study of ICU admission during a COVID-19 outbreak is conducted to demonstrate the implementation and effectiveness of the proposed model in a comparative analysis.

Endpoint security reinforcement via integrated zero-trust systems: A collaborative approach

In the rapidly evolving field of information technology, network security, especially enterprise endpoint security, has emerged as a major challenge. This paper presents a comprehensive and interactive method for network access and user authentication utilizing a zero-trust framework. This method integrates key elements, including the Network Access (NA) Agent, Identity and Access Management (IAM) Agent, Policy Enforcement Point (PEP) Agent, and Situational Awareness (SA) Agent, to mitigate security risks associated with critical business information exposure and unauthorized network access. Leveraging a zero-trust approach, the method dynamically controls user permissions, thereby enhancing endpoint security. It also introduces an efficient solution that coexists with legacy infrastructures, balancing security necessities with user accessibility, and offering a unified solution for both internal corporate networks and the Internet. We present a thorough analysis of potential risks associated with this method and propose preventative measures to minimize these threats. We conclude that our method provides a more secure and efficient approach to enterprise network security compared to traditional static rule-based systems, offering a promising direction for future research and implementation.

Revolving around political connections: the negative effect of government venture capital backing on IPO valuation

Abstract We examine how government venture capital (GVC)—a specific type of political connection—affected initial public offering (IPO) valuation. Contrary to the well-recognized benefits of political connections in channeling access to financial resources in China, our analysis of 959 IPOs between 2008 and 2014 suggests that GVC backing lowers IPO valuation. This baseline effect is moderated by other sources of political connection (e.g. government ownership, state sector experience of top management team members, private sector partner status, and institutional environments). We argue that it is the negative signaling mechanism revolving around political connections that accounts for this observable pattern. This research enriches the signaling theory by uncovering signal emergence and analyzing the interactions between several signaling sources of political connection. Specifically, it contributes to a better understanding of political connections by specifying an undesirable consequence of state-led financialization, which has timely practical relevance as China’s capital market is steering toward a rule-based system.

Extrovert or Introvert? GAN-Based Humanoid Upper-Body Gesture Generation for Different Impressions

AbstractGestures, a form of body language, significantly influence how users perceive humanoid robots. Recent data-driven methods for co-speech gestures have successfully enhanced the naturalness of the generated gestures. Moreover, compared to rule-based systems, these methods are more generalizable for unseen speech input. However, many of these methods cannot directly influence people’s perceptions of robots. The primary challenge lies in the intricacy of constructing a dataset with varied impression labels to develop a conditional generation model. In our prior work ([22]) Controlling the impression of robots via gan-based gesture generation. In:Proceedings of the international conference on intelligent robots and systems. IEEE, pp 9288-9295), we introduced a heuristic approach for automatic labeling, training a deep learning model to control robot impressions. We demonstrated the model’s effectiveness on both a virtual agent and a humanoid robot. In this study, we refined the motion retargeting algorithm for the humanoid robot and conducted a user study using four questions representing different aspects of extroversion. Our results show an improved capability in controlling the perceived degree of extroversion in the humanoid robot compared to previous methods. Furthermore, we discovered that different aspects of extroversion interact uniquely with motion statistics

Ensemble extended belief rule-based systems with different similarity measures for classification problems

The extended belief rule-based (EBRB) system is shown to have the potential to handle both quantitative and qualitative information under uncertainty, and it has been used as an effective tool for decision support and classification problems. However, despite these advances, several drawbacks have emerged recently, and the most significant one is caused by its similarity measure using Euclidean distance, which could lead to counterintuitive individual matching degrees, while other widely used similarity measures have not been studied for their application in the EBRB system. To this end, seventeen similarity measures are investigated and applied in the EBRB system in this paper, and based on the analysis, an ensemble method for EBRB system with different similarity measures is proposed. Firstly, the problem of the similarity measure of the conventional EBRB system is investigated. Then, a variety of similarity measures are analyzed and their application in the EBRB system is studied. Next, the ensemble method for EBRB systems with different similarity measures is proposed, which consists of two parts, the adaptive weight learning method for determining the weight of each EBRB system with different similarity measures, and the evidential reasoning (ER)-based combination method for combining the inferential results of different methods. Finally, 25 classification datasets are studied to test the performance of EBRB systems with different similarity measures as well as the proposed method, and the results are compared with existing works. The comparison results show that the proposed method could not only achieve better results than any other EBRB systems with different similarity measures, but also outperform other conventional classifiers on some classification datasets, especially small-scale datasets.

A hybrid framework based on knowledge distillation for explainable disease diagnosis

Deep learning (DL) techniques provide highly accurate disease predictions by extracting implicit higher-order correlations between features. However, DL opinions are not advisable for direct clinical practice because they lack interpretability in their reasoning logic. Fuzzy rule-based systems (FRBSs) can provide transparent and effective diagnostic results, but the rule explosion problem and the long-tail distribution of rules weaken their performance. Therefore, DL and FRBS complement each other. This paper proposes a hybrid framework based on DL models and fuzzy rules for explainable disease diagnosis. This framework has three components: interpretable component, uninterpretable component, and knowledge distillation component. In the interpretable component, we propose a strong rule extraction method to avoid the rule explosion problem and the long-tail distribution of rules by discarding weak rules. In the uninterpretable component, we use DL models to capture the implicit higher-order interactions between features to improve the model's performance. The knowledge distillation component embeds the hidden knowledge extracted from DL models into SFRBSs to guide the parameter tuning. This framework can predict new instances and provide interpretable prediction results by extracting explicit knowledge (fuzzy rules) and feature weights. Experimental results based on three real-world medical datasets show that the framework achieves the highest AUC and accuracy, which is more accurate and interpretable than other diagnostic models.

Fuzzy inference system model for the spectrum characteristic periods of the Türkiye Building Earthquake Code (2007)

Seismic design codes define response spectra with crisp numerical classifications of seismic parameters, which mainly affect the spectrum's shape and determination of seismic design loads. The efficiency of structural safety and construction costs depends on the optimum design and accurately determined seismic forces. As presented in the seismic design codes, several parameters are utilized to calculate the seismic design forces with response spectra. This study proposes a rule-based fuzzy inference system (FIS) model with fuzzy set numbers to determine the relevant parameters. By defining the soil profile thickness and shear wave velocity as inputs, the model generates the spectrum characteristic periods specified in the Türkiye Building Earthquake Code (TBEC 2007). The response spectra of twenty different samples with the FIS and crisp models were generated and compared to assess the model's superiority. Unlike crisp seismic code classifications, the proposed FIS model accounts for imperfections in soil group selection and topmost soil layer thickness, offering a more realistic representation of uncertainties and proving to be an effective tool for addressing linguistic vagueness in seismic response spectra analysis. The comparison between fuzzy and crisp output seismic parameters revealed significant differences in response spectra shape and spectrum intensity values. The FIS model-generated spectra were more conservative in certain building locations, while in others, they provided similar or lower values, suggesting potential cost savings in design. The FIS model demonstrates its efficacy in producing more accurate and robust designs by considering the uncertainties inherent in the problem. Furthermore, this approach has the potential to be extended to study seismic parameters of other design codes, although further research is required to comprehensively explore its capabilities and limitations.