Operation Support System Research Articles

Study on the restoration and landscaping contents caused by the system design and operational support system considering the characteristics of historic buildings

Study on the restoration and landscaping contents caused by the system design and operational support system considering the characteristics of historic buildings

DEVELOPMENT OF ONTOLOGICAL DECISION MAKING SYSTEM OF THE NEGOTIATION PROCESS ON COOPERATION PRODUCTION IN AIRCRAFT

Subject matter: means of information support for the negotiation process on the organization of cooperative production of aircraft. Goal: an outline of the process of developing effective means of information support for negotiations on cooperative production, which will provide financial savings, firstly, by reducing the negotiation time, and secondly, is reducing the likelihood of making irrational decisions that can lead to additional costs during project implementation. Tasks: Increase the efficiency of the negotiation process on the organization of cooperative production by creating a problem-oriented methodological basis, and on this basis - software tools for the development, deployment and operation of the appropriate decision support system. Methods: system analysis and set theory, for the formal presentation of relevant information about the subject area "Negotiations on cooperative aircraft production"; ontological engineering, in particular the IDEF5 standard, as well as elements of expert systems technology, for building an ontological decision support system. Results: an aggregate of methodological tools for the synthesis of ontological decision support systems was developed during negotiations on the organization of cooperative production of aircraft, a scenario example of organizing an ontological dialogue in the environment of an ontological decision support system for defining an aircraft model as an object of cooperative production was given. Conclusions: the need to supplement the mathematical support of traditional negotiation support systems with special methodological means is shown to increase the efficiency of information support for the negotiation process on organizing cooperative production in aircraft construction; it is substantiated that for the program implementation of the system of information support for negotiations on the organization, it is advisable to use the ontological approach; the architecture of the intellectual core of the ontological decision support system for organizing the negotiation process for organizing the cooperative production of aircraft in the form of a hierarchy of frames, with the organization of knowledge-based inference by means of attached procedures is proposed; a scenario example of the formation of a decision on the choice of an aircraft model for the organization of cooperative production in the environment of an ontological decision support system is given.

A reliable intelligent diagnostic assistant for nuclear power plants using explainable artificial intelligence of GRU-AE, LightGBM and SHAP

When abnormal operating conditions occur in nuclear power plants, operators must identify the occurrence cause and implement the necessary mitigation measures. Accordingly, the operator must rapidly and accurately analyze the symptom requirements of more than 200 abnormal scenarios from the trends of many variables to perform diagnostic tasks and implement mitigation actions rapidly. However, the probability of human error increases owing to the characteristics of the diagnostic tasks performed by the operator. Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted recently to reduce the likelihood of human errors; however, reliability issues due to the black box characteristics of AI have been pointed out. Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI diagnostic evidence for operators, is considered. In conclusion, the XAI to solve the reliability problem of AI is included in the AI-based diagnostic algorithm. A reliable intelligent diagnostic assistant based on a merged diagnostic algorithm, in the form of an operator support system, is developed, and includes an interface to efficiently inform operators.

Risk-based Decision-making System for Information Processing Systems

The article is dedicated to using the methodology of building a decision support system under threats and risks. This method has been developed by modifying the methods of targeted evaluation of options and is used for constructing a scheme of the decision support system. Decision support systems help to make correct and effective solution to shortage of time, incompleteness, uncertainty and unreliability of information, and taking into account the risks. When we are making decisions taking into account the risks, it is necessary to solve the following tasks: determination of quantitative characteristics of risk; determination of quantitative indicators for the effectiveness of decisions in the presence of risks; distribution of resources between means of countering threats, and means that are aimed at improving information security. The known methods for solving the first problem provide for the identification of risks (qualitative analysis), as well as the assessment of the probabilities and the extent of possible damage (quantitative analysis). However, at the same time, the task of assessing the effectiveness of decisions taking into account risks is not solved and remains at the discretion of the expert. The suggesting method of decision support under threats and risks has been developed by modifying the methods of targeted evaluation of options. The relative efficiency in supporting measures to develop measures has been calculated as a function of time given on a time interval. The main idea of the proposed approach to the analysis of the impact of threats and risks in decision-making is that events that cause threats or risks are considered as a part of the decision support system. Therefore, such models of threats or risks are included in the hierarchy of goals, their links with other system's parts and goals are established. The main functional modules that ensure the continuous and efficient operation of the decision support system are the following subsystems: subsystem for analysing problems, risks and threats; subsystem for the formation of goals and criteria; decision-making subsystem; subsystem of formation of the decisive rule and analysis of alternatives. Structural schemes of functioning are constructed for each subsystem. The given block diagram provides a full-fledged decision-making process.

Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks. This implies that the exchange of raw monitoring data should be minimized across the network by bringing the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying traffic patterns and channel conditions, making thereby the data collected in different points non independent and identically distributed (non-IID), which is challenging for FL. To sidestep this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This a priori entropy is calculated using a multi-dimensional spectral clustering scheme over both the features and the supervised output spaces, and is suitable for classification as well as regression tasks. The FL aggregation operations support system (OSS) server then uses the reported dataset entropies to devise 1) an entropy-based federated averaging scheme, and 2) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to show the superiority of these novel approaches.

Advanced Technologies for Supporting Operational Decision-Making in Civil Aviation

In civil aviation, many tasks require the use of modern technologies, in particular artificial intelligence technologies. The features of the crew and dispatcher decision-support tasks that can be solved based on deep neural networks are considered. The features of the intelligent crew interface for improving interaction with the operational decision support system are considered. Optimization of air traffic management by using genetic algorithms is illustrated by the example of two tasks. A special feature of the genetic algorithm for solving the problem of optimizing the sequence and landing times of arriving aircraft is a non-standard crossing operator. A special feature of the genetic algorithm for solving the problem of constructing conflict-free trajectories of a given length at low altitude in conditions of complex terrain is the method of forming the objective function. In conclusion, there is a list of problems that need to be solved for the wider use of artificial intelligence in civil aviation.

Application of automated integrated geotechnical monitoring system in support of rib pillars of large cross-section and length in the Iletsk rock salt field

The Iletsk rock salt field development for 200 years has been carried out is a disordered manner. Alongside open pits, a few underground mines were operated (Elizavetinskaya, Mariinskaya) but not for long and were flooded soon. The flooding of those mines and Mine No. 1 later on was explained by their occurrence inside the top jointed and karstic salt core. Mining safety and efficiency under conditions of flooded Mine 1, as well as actual mining operations on the second and third levels of Mine 2 is ensured by means of handling some geomechanical problems. In the course of the stress–strain assessment in a rib pillar of large cross-section and extension, it was recorded that the load on the rock bolting system in the pillar grew but the deformation studies in the pillar edges showed no straining. The obtained results make it possible to draw a conclusion that the existing rock bolting system performs the duty: it distributes load between rock bolts and holds small pieces of rock salt between the rock bolts in case of inrush. The former function is especially critical for the reliable operation of the whole support system in the conditions of plasticity. The average increment in the load applied to the rock bolting system is estimated as 120 kg per month, or 1.5 ton per year. The integrated geotechnical monitoring system allows recording and display of deformation and incremental loading of support in pillars. The linear patterns of the obtained data make it possible to predict the load increase and to adjust the mining process flows if necessary.

Knowledge acquisition and representation for intelligent operation support in offshore fields

Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems’ instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support.

Design of computerized operator support system for technical specification monitoring

A critical but difficult task performed by the operators of nuclear power plants (NPPs) is monitoring their operation in compliance with their technical specification (Tech Spec). The volume of the document, complexity of the contents, high workload, diversity of the interpretation, and time dependence are the representative challenges encountered by operators. To address these challenges, several researchers have attempted to develop operator support systems that can assist operators to perform Tech-Spec-related activities. However, complying with a Tech Spec requires various types of monitoring functions, and a rule-based system is not typically an efficient method. Therefore, this paper proposes a computerized operator support system for monitoring a Tech Spec. First, this study analyzes the structure and the surveillance requirements in a Tech Spec. Second, artificial intelligence techniques are reviewed. Third, the architecture of the system is proposed. Finally, this paper introduces a prototype for monitoring a Tech Spec.

A situational understanding enhancer based on augmented visual prompts for teleoperation using a multi-monitor system

This study proposes an operator support system that enhances understandability of multiple monitor for teleoperated disaster response robots. The autonomous environmental camera control system we previously developed could provide operators with various visual information suited to the situation. To enable operators to adequately recognize environmental situations, multiple camera images are projected on multiple monitors, but this may increase the cognitive load of operators. The proposed situational understanding enhancer thus supports selective visual attention, situational awareness, and action decision, based on augmented visual prompts (AVPs). To support both work facilitation and danger avoidance, the AVPs include (i) monitor frame, (ii) obscuration, (iii) end-point vertical arrow, and (iv) distance arrow, with variable color and flashing effect. The system adaptively draws visual attention by changing the types and parameters of the AVPs according to the situations. Debris-removal tasks were conducted using a virtual reality simulator to compare the systems with and without AVP support. The results showed that the AVP support reduced the frequent changes in point of gaze, provided a sense of depth, and drew attention to potential collisions.

TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System

Mechanization in agriculture involves all stages of cultivating and preparing innovations, from basic and essential hand devices and tools to more complex and mechanized implements. Fundamentally, such devices and tools enable agricultural activities to initiate different crop yields in many different neighborhoods in the world's ecological system. Due to the unstable nature of fuel prices, market demands, and damaging effects of tillage machinery vibrations on the operator, exploring efficient methods of preventing these situations has become indispensable. There are many existing methods related to tillage optimization, including fast, high quality, and cost-effective tilling. However, none of the existing methods perform real-time and dynamic recommendations due to the inability to provide real-time tillage data. Another issue is the unavailability of a tillage recommendation model that acts based on the changes of a fixed set of tillage parameters and provides the necessary guidance to the tractor driver during the tillage operation. This paper proposes a Tillage Operations Quality Optimization (TOQO) model that aims to improve the efficiency of tillage operations. The model provides real-time tillage data through integrating the Internet of Things (IoT) technology and a dynamic Decision Support System (DSS) for tillage machinery operation and process optimization. The proposed model has been successfully implemented, tested, and evaluated in real-world tillage operations. The results of this TOQO study clearly show improvements in the tillage operations. This is because the TOQO gives online recommendations in real-time based on dynamic measurements of six tillage performance evaluation parameters. The parameters are vibration, bulk density, slippage ratio, fuel consumption, real tillage depth, and field efficiency. The TOQO model successfully handles more than one tillage machinery tractor by using cloud computing services. Vibration increases when using the system on the X-axis at 46.63843 rms, Y-axis at 23.23612 rms, and the Z-axis at 51.47240 rms. Vibration fractures the soil, reducing soil cohesiveness when using an oscillating tillage tool and producing smaller soil aggregates than a non-oscillating one. When using the system, the value of the virtual density increases by 0.23821 g/cm3. Most of the bulk density values for the optimized phase are within the recommended or required growth range. There is a slight increase in depth by 0.4279 cm and a decrease in slippage ratio by 0.64364%. Tractor fuel consumption decreases by 1.2169 L per hour, indicating a reduction in tractor wheel slippage. Reduced fuel consumption and wheel slippage of tillage operation are essential parameters to increase field efficiency and reduce tillage operation costs.

Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

Usability assessment of compaction operator support systems using virtual prototyping

The successful adoption of Hot Mix Asphalt (HMA) compaction Operator Support Systems (OSSs), which use sensory information to help operators improve the safety and productivity of their operations, is heavily dependent on the usability of the systems. However, there is a major dilemma in the design and development of compaction OSSs. On one hand, providing raw sensory data (i.e., temperature and compaction count) to the operator in a descriptive manner, can cognitively overloading the operators, i.e., infobesity problem. On the other hand, the over-processed data, which can be presented to the operators as prescriptive compaction guidance, e.g., a compaction trajectory, may give operators the sense of losing control of the operation and de-professionalization of their trade. Therefore, there is an ongoing debate over the best strategy for the design and development of compaction OSSs. To put this usability aspect at the heart of the design and development strategy of compaction OSS, first, it is essential to systematically assess various OSS alternatives from the usability perspective. However, the conventional approach of usability testing, which relies on the use of physical prototypes, can be time-consuming and logistically difficult to execute. To address this issue, this research proposes and implements a Virtual Prototyping (VP) approach to analyze the usability of different compaction OSSs. In this approach, a Virtual Reality (VR) compaction simulator is developed and utilized to present 3 different alternatives for compaction OSS, which varies in the level of support they provide and receive feedback from the end-users. The results show that from the user perspective, semi-guidance compaction OSSs, which present the compaction priority, are preferred over both descriptive and prescriptive systems. The users tend to perceive this level of support as a middle ground solution that offers them a means for real-time strategy (re-)development without compromising their control of the process. It is also demonstrated that the VR simulator has the potential to serve as a powerful technology assessment platform that allows end-users to have an open and substantive dialogue about the system with researchers and machine designers.

Review of laws for the efficient operation of the economic support system for victims of crime: Focusing on the Crime Victim Protection Act

Review of laws for the efficient operation of the economic support system for victims of crime: Focusing on the Crime Victim Protection Act

Evaluation of an operation support system using the femoral anterior tangent line to determine intraoperative femoral component rotation in total knee arthroplasty

BackgroundThe femoral anterior tangent (FAT) line refers to a line parallel to the anterior surface of the distal femur in the axial plane. This study aimed to evaluate the effectiveness of a new operation support system which uses the FAT line to set the femoral component rotational alignment in total knee arthroplasty (TKA). MethodsA total of 170 consecutive knees in 139 patients undergoing primary TKA with the JIGEN (Jig Engaged Three-dimensional (3D) Pre-Operative Planning Software for TKA) operation support system was examined. The JIGEN system creates 3D models of bones using computed tomography data, allowing for surgical simulations such as positioning of implants while calculating positions of the intramedullary alignment rod (IM rod) and surgical jig. We retrospectively analyzed the FAT line angle relative to the surgical epicondylar axis (SEA) on the axis plane perpendicular to the IM rod and evaluated the accuracy of the femoral component alignment after TKA with the 3D measurement system. ResultsThe FAT line was 9.6° ± 3.7° (range, 1.4°–20.4°) internally rotated relative to the SEA. The average absolute error was 1.4° ± 1.1° in the coronal plane, 2.0° ± 1.5° in the sagittal plane, and 1.6° ± 1.3° in the axial plane. The femoral component outliers (i.e., >3° away from the goal alignment) were 7.7% in the coronal plane, 20.6% in the sagittal plane, and 10.3% in the axial plane. ConclusionsOur findings suggest that the FAT line is a reliable and reproducibly identifiable axis for the accurate determination of proper rotational alignment in TKA. An operation support system which uses the FAT line for determining intraoperative femoral component rotation can effectively achieve highly accurate positioning of the femoral component in TKA.

Development and Evaluation of ICT Operation Support System for Urban Flood Control Facilities

This study was conducted to develop and evaluate the prediction accuracy and effectiveness of “ICT operation support system for urban flood control facilities,” which is installed in Eba catchment area in Hiroshima city, Japan. This system consists of real-time facilities monitoring technology, rainfall data from eXtended RAdar Information Network (XRAIN), and Real-time flood prediction technology. High prediction accuracy is crucial for effective control facility management using ICT operation support system. In this study, ICT operation support system was installed for effective operation of stormwater pump. The prediction accuracy and effectiveness of this system were evaluated based on the past rainfall record from XRAIN. The system takes only three minutes to distribute the urban flood prediction information, and it has been operated stably during the operation. Flood risk reduction effect can be better expected in case of central concentrated rainfall pattern.

Using the unit software model to improve design solutions and optimize process management

Advanced design power units are distinguished by a high degree of digital transformation. Therefore, of particular interest are operator information (intelligent) support systems, which can reduce the workload on operating personnel as well as predict possible deviations long before they evolve into severe emergencies. The article analyzes the current standard process documentation that requires solutions to support the operator and determines the list of system functions that should be provided to improve the safety level of nuclear power plants. A brief overview of the world experience in implementing such solutions is also provided. As an example of the further development of operator support systems, the authors consider the operator information support system (OISS), which is being developed at the NvNPP pilot unit with the VVER-1200 reactor. The OISS functions will make it possible to fulfill the requirements of standard process documentation that are currently not implemented in the power unit design. The key features of the OISS under development are step-by-step interactive procedures and the unit software model. The authors provide a brief description of the power unit software model and consider several examples of its practical application as part of the OISS to improve design solutions and optimize automatic process control. In the years ahead, it is proposed to implement the OISS at power units under construction in order to reduce the information overload of operators and create conditions for a step-by-step increase in the automation level of the power unit control.

Roots-tracing of communication network alarm: A real-time processing framework

In the communication network, since the interconnection of a large number of components, mobile network operators run Operations Support Systems that generate vast amounts of alarm events. The harsh challenge for network operators is how to find the potential root causes from massive alarms in real time. In this paper, we propose a novel solution for the root causes analysis. The solution includes a silent gap based approach to resolve the asynchrony of alarms, an algorithm for constructing Bayesian network (BN) based on sequentiality between alarms, and Bayesian inference to identify the root causes. The silent gap-based approach reduces preprocessing time while taking into account the validity. Also, the proposed BN-based mechanism allows the identification of the root causes with a higher accuracy. Experiments conducted on a real alarm dataset are provided to support the proposed methods. In addition, we propose a new algorithm processing framework.

Software and Hardware Decision Support System for Operators of Electrical Power Systems

The transience and unpredictability of processes in electric power systems (EPS) determine complexity of its control. This problem is especially relevant for EPS with renewable energy sources and flexible alternating current transmission systems technologies. Automatic control systems simplify the solution of this problem. Despite this, many issues related to EPS state control are solved by power system operators. Decision support systems (DSS) are applying to increase efficiency of operators work. However, existing algorithms in practice are inoperable in some cases: unrealizable decisions are formed, or decision cannot be found at all, etc. Another important problem is impossibility to preliminarily verify the proposed solution. In this regard, operators can additionally use EPS simulators, which are usually limited either by the size of simulated scheme or by the details of equipment mathematical models. The limitations are a consequence of numerical methods using, which are badly applicable for large-scale power systems simulation. In this article, a novel DSS has been developed based on a hybrid approach to EPS modeling combining properties and capabilities of several simulation techniques: analog, physical and digital. The implicit integration of differential equations in analog way makes possible modeling faster than real time. Such opportunity allows operator to verify more decisions and select the most effective ones. The algorithm for DSS application has been developed and described in the article. The properties and capabilities of the developed DSS are confirmed by experimental studies and tests in a real EPS.

Effects of the COVID-19 Pandemic on Energy Systems and Electric Power Grids—A Review of the Challenges Ahead

The COVID-19 pandemic represents not just a global health crisis, but may signal the beginning of a new era of economic activity, the potential consequences of which we currently do not fully understand. In this context, the mid-to-long-range impacts of the pandemic on the energy sector have been studied extensively in the last few months. Despite these efforts, the pandemic still raises many open questions concerning the long-term operation and planning of power systems. For instance, how will the pandemic affect the integration of renewable energy sources? Should current power system expansion plans change in light of the COVID-19 pandemic? What new tools should be provided to support system operators during global health crises? It is the purpose of this paper to better understand the many aspects of these open questions by reviewing the relevant recent literature and by analyzing measured data. We point out the main challenges that the pandemic introduced by presenting patterns of electricity generation and demand, frequency deviations, and load forecasting. Moreover, we suggest directions for future research that may assist in coping with the mentioned challenges. We hope that this paper will trigger fruitful discussions and encourage further research on these important emerging topics.