Intelligent Decision System Research Articles

Research on intelligent perception and decision system for special welding mechanical arm of float plate in crude oil storage tank

Abstract In the rapid development of the petrochemical industry in the context of the increasing number of large tanks, large tanks in the production and installation process encountered the first problem the pontoon plate welding deformation and weld seam is too large and other defects, through the construction of the ship plate welding special robotic arm of the intelligent perception and decision-making system to solve such defects. Summarize the Structure of crude oil storage tanks and welding process characteristics based on the ultrasonic distance measurement in the distance sensor to obtain the characteristic data set, combined with the distinct data and computer programming language to build the intelligent perception and decision-making system of the unique robotic arm for ship plate welding. They set the relevant parameter values based on simulation experiments on the welding of a particular robot arm perception and decision-making system for the application of example analysis. Compared to the traditional LSTM algorithm, the YOLO-v5 network-driven weld target detection algorithm has a 5.5% higher accuracy and a 9.1% higher success rate in time robustness evaluation. In addition, the welding decision model based on the DDPG algorithm possesses excellent computational accuracy, and the average error of each parameter in the welding decision of the ship plate is controlled within 5%. This study greatly improves the welding quality of the pontoon plate of crude oil storage tanks and provides solid technical support for the successful completion of the project.

Disentangling Stochastic PDE Dynamics for Unsupervised Video Prediction.

Unsupervised video prediction aims to predict future outcomes based on the observed video frames, thus removing the need for supervisory annotations. This research task has been argued as a key component of intelligent decision-making systems, as it presents the potential capacities of modeling the underlying patterns of videos. Essentially, the challenge of video prediction is to effectively model the complex spatiotemporal and often uncertain dynamics of high-dimensional video data. In this context, an appealing way of modeling spatiotemporal dynamics is to explore prior physical knowledge, such as partial differential equations (PDEs). In this article, considering real-world video data as a partly observed stochastic environment, we introduce a new stochastic PDE predictor (SPDE-predictor), which models the spatiotemporal dynamics by approximating a generalized form of PDEs while dealing with the stochasticity. A second contribution is that we disentangle the high-dimensional video prediction into low-level dimensional factors of variations: time-varying stochastic PDE dynamics and time-invariant content factors. Extensive experiments on four various video datasets show that SPDE video prediction model (SPDE-VP) outperforms both deterministic and stochastic state-of-the-art methods. Ablation studies highlight our superiority driven by both PDE dynamics modeling and disentangled representation learning and their relevance in long-term video prediction.

Text processing using LLM for automatic creation of agricultural crops knowledge bases

The complexity of subject areas in which intelligent information systems operate is steadily increasing. Tasks assigned to smart agriculture systems are increasingly focused on automating and robotizing areas of human activity. Solving such tasks requires adaptive and flexible methods capable of accommodating dynamic changes in the environment in real-time. The mivar approach to creating intelligent decision-making systems enables working with adaptive discrete structures and provides methods for managerial decision-making based on adaptive active logical inference from the mivar rule knowledge base. The mivar logical inference machine forms the core of expert systems based on the mivar approach. As a result of the development of the mivar approach across various subject areas, different versions of mivar logical inference machines with their algorithms for rule traversal in the knowledge base have been created. Recent advancements in artificial intelligence and machine learning have opened new opportunities for enhancing the mivar approach. The integration of large language models for automating text processing in mivar systems significantly enhances the accuracy and efficiency of decision-making processes based on expert systems for sustainable agriculture. This paper demonstrates the feasibility of using automated text processing, intended for human training, through large language models, and its subsequent application in action planning tasks within technical systems. The proposed methodology is aimed at creating extensive knowledge bases based on textual information for real-time monitoring and decision-making in smart agriculture systems.

Intelligent design of cold storage room system based on low temperature process

Abstract In this study, we address the intelligent design requirements of a cold storage system by developing a monitoring module for operational oversight, setting up the requisite hardware and software operating environment, and establishing a three-dimensional digital twin-based intelligent decision-making system with the digital twin model serving as the core framework. In order to seek the best path for the operation of the refrigeration system and achieve the lowest energy consumption in the refrigeration system, the dynamic planning algorithm is used to configure the refrigeration system power-consuming equipment and analyze the energy consumption of the system unit under different θW values. For the control algorithm of the electronic expansion valve in the application of a low-temperature process in cold storage, the PID algorithm is introduced and optimized using a variable domain fuzzy control algorithm to explore the degree of system energy saving and control accuracy under the two algorithmic control methods. Under the same working conditions, the fluctuation range of the PID control effect is ± 0.8 ℃, the fluctuation range of the fuzzy PID control effect is ± 0.4 ℃, the fluctuation range of fuzzy PID control superheats is smaller, the system superheat maintenance is more stable, which is in line with the design of an intelligent system for cold storage room.

Reasons for using parallel activation of logical rules in solving management tasks

The complexity of the subject areas in which intelligent information systems operate is steadily increasing. The tasks set for such systems are increasingly aimed at automating and robotizing spheres of human activity. The solution of such problems requires adaptive and flexible methods capable of taking into account dynamic changes in the environment in real time. The mivar approach to creating intelligent decision-making systems allows working with adaptive discrete structures and provides methods for making management decisions based on adaptive active logical inference based on the knowledge base of mivar rules. The mivar machine of logical inference is the core of expert systems based on the mivar approach. As a result of the historical development of the mivar approach when working on different subject areas, various versions of mivar machines of logical inference with their algorithms for bypassing rules in the knowledge base were obtained. This paper discusses the reasons for the emergence of such options and demonstrates the need to use a parallel algorithm for activating rules in an adaptive network of logical rules when solving problems of action planning in technical systems.

Explainability and Evaluation of Vision Transformers: An In-Depth Experimental Study

In the era of artificial intelligence (AI), the deployment of intelligent systems for autonomous decision making has surged across diverse fields. However, the widespread adoption of AI technology is hindered by the risks associated with ceding control to autonomous systems, particularly in critical domains. Explainable artificial intelligence (XAI) has emerged as a critical subdomain fostering human understanding and trust. It addresses the opacity of complex models such as vision transformers (ViTs), which have gained prominence lately. With the expanding landscape of XAI methods, selecting the most effective method remains an open question, due to the lack of a ground-truth label for explainability. To avoid subjective human judgment, numerous metrics have been developed, with each aiming to fulfill certain properties required for a valid explanation. This study conducts a detailed evaluation of various XAI methods applied to the ViT architecture, thereby exploring metrics criteria like faithfulness, coherence, robustness, and complexity. We especially study the metric convergence, correlation, discriminative power, and inference time of both XAI methods and metrics. Contrary to expectations, the metrics of each criterion reveal minimal convergence and correlation. This study not only challenges the conventional practice of metric-based ranking of XAI methods but also underscores the dependence of explanations on the experimental environment, thereby presenting crucial considerations for the future development and adoption of XAI methods in real-world applications.

Development of a complex model for processing various data

The object of the research is intelligent decision-making support systems. The scientific problem that is solved in the research is the development of a comprehensive model for processing various types of data in intelligent decision-making support systems (DMSS). The relevance of the research lies in the fact that in intelligent DMSS circulate different in origin and units of measurement data obtained from various technical devices of obtaining information. The essence of the integrated approach in modeling is that two partial models are proposed: a model for processing different types of data in intelligent decision-making support systems and a model for processing homogeneous data in intelligent decision-making support systems. Analysis of the intelligent DMSS model for processing different types of data allows to draw such a conclusion. While solving the problem of processing different types of data, a model of intelligent DMSS is proposed, in contrast to traditional, even for the process of solving partial problems incorrectly started by experts with the help of self-organization, expressed in the coordination of partial tasks of the decision maker, striving for an ideal solution to the problem of processing different types of data, which increases the efficiency of finding an acceptable result for processing different types of data. The homogeneous data processing model is based on the idea that the same processing of homogeneous data in intelligent DMSS can be solved in parallel by different functional elements. Element integration relationships arise as internal non-verbal images in the user's memory, which can compare the dynamics of modeling a task for processing different types of data in intelligent DMSS from different points of view, which allows to see what modeling does not give using one model. The direction of further research should be considered the improvement of information processing methods in intelligent decision-making support systems.

Intelligent decision-making system for multiple marine autonomous surface ships based on deep reinforcement learning

This study addresses the issue of multi-ship collision avoidance and the requirements for autonomous navigation of Marine Autonomous Surface Ships. To address this challenge, we propose a collision avoidance decision-making system for multi-ship encounters that integrates the Proximal Policy Optimization (PPO) algorithm with the ship domain while considering ship manoeuvring characteristics. Moreover, the reward function designed for the PPO algorithm can effectively ensure the trained collision avoidance decision-making model adheres to COLREGs and to prioritizes the utilization of small angle steering whenever possible. Additionally, a neural network based on Bidirectional Gated Circulation Unit (BiGRU) is established, incorporating Layer Normalization to handle high-dimensional observation data and enhancing the algorithm's efficiency. Through simulation experiments, the results demonstrate the flexibility and safety of the proposed decision-making method in multi-ship collision avoidance and surface obstacle avoidance scenarios.

A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding

A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding

Enhancing Security Frameworks with Artificial Intelligence in Cybersecurity

Cybersecurity, in the digital era we live in today, has become a major concern that demands innovation. Data Science and Artificial Intelligence (AI) have played a central role in changing the way we understand and address cyber threats. This research will review the important role of innovation in this technology in improving an organization's ability to detect, prevent, and respond to cyber attacks. Identifying patterns and gaining insights from security events in cyber data, while developing appropriate data-based models, is a key element in realizing automated and intelligent security systems. This research reviews needs in the cyber security domain that can be addressed through Artificial Intelligence (AI) techniques. In this study, we employed quantitative methods to assess the impact of artificial intelligence on enhancing cybersecurity by distributing questionnaires to 85 respondents, which included companies operating in the banking and IT sectors. In addition, this research will explore how data-based intelligent decision-making systems are able to protect systems from known and unknown cyber attacks. This research will conclude by considering the future potential of Artificial Intelligence and cybersecurity.

An analysis of knowledge representation methods in intelligent decision-making support systems

The scientific task, which is solved in the research, is the analysis of knowledge representation methods in intelligent decision-making support systems. The problem is explained by the fact that the form of knowledge representation significantly affects the characteristics and properties of the system. In order to operate all kinds of knowledge from the real world with the help of a computer, it is necessary to carry out their simulation. In such cases, it is necessary to distinguish knowledge intended for processing by computational devices from knowledge used by humans. In addition, with a large amount of knowledge, it is desirable to simplify the sequential management of individual elements of knowledge. A homogeneous representation leads to a simplification of the logic management mechanism and a simplification of knowledge management. The research is aimed at the analysis of knowledge representation methods in intelligent decision-making support systems. Currently, many models of knowledge representation have been developed. The main models include: logical models; frame model; network models (or semantic networks); production models. Therefore, the object of research is the intelligent decision-making support system. The subject of research is an intelligent decision-making support system. The following is set: – the methods (models, approaches) presented in the research for presenting knowledge in intelligent decision-making support systems in a canonical form are not advisable to use for a number of objective reasons given in subsection 3.1 of the research; – it is necessary to develop new (improvement of existing) representations of knowledge in intelligent decision-making support systems, which will have the advantages of these approaches without their disadvantages. Further improvement of these approaches to reduce the number of shortcomings and limitations of their application should be considered as the direction of further research.

Strategies for improving crop comprehensive benefits via a decision-making system based on machine learning in the rice-rape, rice-wheat and rice-garlic rotation systems in Southwest China

Rice-rape, rice-wheat and rice-garlic rotations are common cropping systems in Southwest China, and they have played a significant role in ensuring ecological and economic benefits (EB) and addressing the challenges of China's food security in the region. However, the crop yields in these rotation systems are 1.25-14.73% lower in this region than the national averages. Intelligent decision-making with machine learning can analyze the key factors for obtaining better benefits, but it has rarely been used to enhance the probability of obtaining such benefits from rotations in Southwest China. Thus, we used a data-intensive approach to construct an intelligent decision-making system with machine learning to provide strategies for improving the benefits of rice–rape, rice–wheat, and rice–garlic rotations in Southwest China. The results show that raising the yield and partial fertilizer productivity (PFP) by increasing seed input under high fertilizer application provided the optimal benefits with a 10% probability in the rice–garlic system. Obtaining high yields and greenhouse gas (GHG) emissions by increasing the N application and reducing the K application provided suboptimal benefits with an 8% probability in the rice–rape system. Reducing N and P to enhance PFP and yield provided optimal benefits with the lowest probability (8%) in the rice-wheat system. Based on the predictive analysis of a random forest model, the optimal benefits were obtained with fertilization regimes by reducing N by 25% and increasing P and K by 8 and 74%, respectively, in the rice–garlic system, reducing N and K by 54 and by 36%, respectively, and increasing P by 38% in rice–rape system, and reducing N by 4% and increasing P and K by 65 and 23% in rice–wheat system. These strategies could be further optimized by 17-34% for different benefits, and all of these measures can improve the effectiveness of the crop rotation systems to varying degrees. Overall, these findings provide insights into optimal agricultural inputs for higher benefits through an intelligent decision-making system with machine learning analysis in the rice–rape, rice-wheat, and rice–garlic systems.

Intelligent decision-making support system for the commissioning of a small hydroelectric power plant in the Republic of Tyva

This paper investigates the cutting forces arising when using a single abrasive grain. Analytical studies were carried out using a model of a single abrasive grain in the form of a rod with a radiused apex acting on the workpiece material. The slip-line method (method of characteristics) was used to calculate the deformation intensity of a plastically edged workpiece material under the action of a single grain. Mathematical models were developed for the following factors: plastic deformation of the material, edging of the stagnated zone and its friction against the grain surface when moved upwards in the form of chippings, grain friction against the plastically deformed material, and the action of the dynamic component of plastic deformation. The significance of the dynamic component in the overall balance of forces related to plastic deformation was established by determining the ratio of dynamic stress on the break line and shear yield point. This dependence calculated for D16T and 30HGSA materials showed the feasibility of accounting for the dynamic component system based on a small hydropower station located in the Todzhinsky district of the Republic of Tyva. For an adequate assessment of the operational reliability of hydropower units, a logical and probabilistic method based on the kinetic theory of fault tree was used. The method allows the failures of the used equipment, as well as unplanned shutdowns of units due to a shortage of water resources in the Great Yenisey (low water, overdrying, and frozen frost), to be taken into account. The development of a generation system based on a small hydroelectric power plant for the settlements in the Todzhinsky district of the Republic of Tyva offers a load of up to 2,500 kW, which helps to reduce the cost of purchasing, delivering, and storing diesel fuel, while diesel generators can be used as backup power sources. 3 scenarios of structuring a small hydroelectric power plant were considered that involved various numbers and total capacity of hydrogenating units: 5x500 kW, 4x630 kW, and 3x800 kW. Therefore, by using the multi-criteria optimization method, the optimal structure of the generating system based on a small hydroelectric power plant having three hydroelectric units (each characterized by a capacity of 800 kW) was selected from the three proposed options, taking into account the reliability and uncertainty of the initial information.

A Semantic Network Method for the Identification of Ship’s Illegal Behaviors Using Knowledge Graphs: A Case Study on Fake Ship License Plates

With the advancement of intelligent shipping, current traffic management systems have become inadequate to meet the requirements of intelligent supervision. In particular, with regard to ship violations, on-site boarding is still necessary for inspection. This paper presents a novel approach for enhancing ships’ management and service capabilities through scientific knowledge graph technology to develop a ship knowledge graph. The proposed approach extracts key characteristics of ship violations from the ship knowledge graph, such as monitoring ships, expired ship certificates, multiple ship tracks, inconsistent ship tracks with port reports, and ships not reported to the port for a long time. Combining the characteristics of ship violations, the approach uses reasoning and identification techniques to detect specific instances of falsely licensed ships and other violations. The development of the ship knowledge graph analysis system enables the identification and verification of illegal ships using fake license plates, while also improving the effective utilization of maritime data and enhancing the ability to make informed decisions related to ship safety. By leveraging cognitive approaches and knowledge graphs, this study offers the potential to develop an intelligent decision-making system for maritime traffic management.

Optimization study of intelligent decision-making system for coal processing plant based on big data analysis

Abstract Optimizing the intelligent decision-making system of coal processing plants is better to improve the economic efficiency of coal processing plants and realize the high-quality development of coal processing plants. In this paper, an integrated intelligent decision-making platform for a coal processing plant is constructed based on big data technology, and the intelligent data analysis techniques of the platform are optimized by using an improved whale optimization algorithm and BP neural network. Examples analyze the optimized crude coal slurry and flotation systems’ processes, and the economic benefits are analyzed. From the optimization of the crude coal slurry sorting system, the ash content in the 0.25mm particle size region was reduced from 55.37% to 13.12%, and the ash content in the −0.125mm particle size region was reduced from 42.68% to 15.96%. From the flotation system optimization, when the flotation time increases from 120s to 180s, the ash content increases from 16.27% to 17.19%, and then to 240s, the ash content increases to 19.44%. Using the integrated intelligent decision-making platform can achieve a net increase in revenue of 4,276,800 yuan for the crude coal slurry sorting system and a net increase of 11,274,200 yuan for the flotation system. This shows that the integrated intelligent decision-making platform can improve the coal processing plant’s quality and efficiency and promote intelligent production.

Research on the Three-Machines Perception System and Information Fusion Technology for Intelligent Work Faces.

The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of information isolation and non-flow, independence between equipment, and weak cooperation of three-machines due to an insufficient fusion of perception data, a fusion method of the equipment's state perception system on the intelligent working surface was proposed. Firstly, an intelligent perception system for the state of the three-machines in the working face was established based on fiber optic sensing technology and inertial navigation technology. Then, the datum coordinate system is created on the working surface to uniformly describe the status of the three-machines and the spatial position relationship between the three-machines is established using a scraper conveyor as a bridge so that the three-machines become a mutually restricted and collaborative equipment system. Finally, an indoor test was carried out to verify the relational model of the spatial position of the three-machines. The results indicate that the intelligent working face three-machines perception system based on fiber optic sensing technology and inertial navigation technology can achieve the fusion of monitoring data and unified expression of equipment status. The research results provide an important reference for building an intelligent perception, intelligent decision-making, and automatic execution system for coal mines.

Intelligent Decision-Making System for Integrated Geological and Engineering of Deep Coalbed Methane Development

Intelligent Decision-Making System for Integrated Geological and Engineering of Deep Coalbed Methane Development

An Artificial Intelligence based automated case-based reasoning (CBR) system for severity investigation and root-cause analysis of road accidents – Comparative analysis with the predictions of ChatGPT

Road accidents have been progressively causing havoc in our society and certain preventive measures must be taken to reduce or possibly eliminate road accidents. The derivative of a road accident ranges from a mild injury to casualty. This research work mainly focusses on developing a novel case-based reasoning system to investigate and troubleshoot the cause of road accidents on a war-foot basis. First, the dominant attributes contributing to the cause of road accidents are identified and finalized as 28. A unique road accident dataset is developed which comprises of 1028 data collected from web resources, popular news magazines and extended further to large scale database of one-million cases by biased random number simulation. Each attribute is given a severity weightage of 1,2 and 3 for computing the net weighted score for a case in the database. Also, non-weighted scores are computed by introduction of a primary number dataset to maintain the uniqueness of the score which is further used for similarity analytics. Now, an accident news is randomly selected, and Rapid automatic keyword extraction (RAKE) schema is used as Natural language processor (NLP) for extracting the dominant keywords from the news articles. The extracted keywords are compared and further mapped into a factor-matrix comprising 28 attributes causing road accidents. Further, similarity analytics is performed to evaluate the severity scores and comparison of new cases. The system demonstrated high retrieval accuracy with all road accident cases collected from real world scenarios. This research has great prospects on troubleshooting road accident cases effectively and provides instant promising troubleshooting measures to prevent such accidents in the future. Also, the proposed framework might be useful for intelligent decision-making systems and automated driving systems. Based on the final outlook, a comprehensive framework for national road safety could be developed and passed as a valid law for implementation. Finally, the forecasted results of the proposed algorithm are compared with the predictions of Chat GPT program.

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the “eye–brain–hand” harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the “eye–brain–hand” picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production.

Becalm: Intelligent Monitoring of Respiratory Patients.

The Becalm project is an open and low-cost solution for the remote monitoring of respiratory support therapies like the ones used in COVID-19 patients. Becalm combines a decision-making system based on Case-Based Reasoning with a low-cost, non-invasive mask that enables the remote monitoring, detection, and explanation of risk situations for respiratory patients. This paper first describes the mask and the sensors that allow remote monitoring. Then, it describes the intelligent decision-making system that detects anomalies and raises early warnings. This detection is based on the comparison of cases that represent patients using a set of static variables plus the dynamic vector of the patient time series from sensors. Finally, personalized visual reports are created to explain the causes of the warning, data patterns, and patient context to the healthcare professional. To evaluate the case-based early-warning system, we use a synthetic data generator that simulates patients' clinical evolution from the physiological features and factors described in healthcare literature. This generation process has been verified with a real dataset and allows the validation of the reasoning system with noisy and incomplete data, threshold values, and life/death situations. The evaluation demonstrates promising results and good accuracy (0.91) for the proposed low-cost solution to monitor respiratory patients.