Decision Support System For Control Research Articles

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Opportunity of the NEGFRY Decision Support System for the Sustainable Control of Potato Late Blight in A Limia (NW of Spain)

The NEGFRY system is presented in this research work as a novelty strategy for the control of potato late blight in A Limia (NW Spain). The aim was to analyse the number of fungicide applications established by NEGFRY (Decision Support System, DSS) with respect to the routine calendars of this potato production area. This approach is in accordance with the requirements imposed by European policies, participating in more profitable and environmentally friendly agriculture. For this purpose, the relationships between the pathogen and the environmental conditions and epidemiological parameters were analysed in different plots compared to late blight infection in both Routine and DSS strategies. In addition, economic and environmental impacts of fungicide sprays were also analysed to check the adaptability of the decision support system to late blight in the area. Results confirmed that it is possible to reduce sprays following the NEGFRY model by more than 50% compared with Routine applications. Therefore, NEGFRY is a promising strategy to achieve lower footprint potato yields through a reduction in applied pesticides and water, lower gas emissions and less soil compaction due to a reduction in the number of tractor passes over the soil. The commitment to this strategy contributes to improving the economy of farmers, the health of the biodiversity, the environment and consumers, as well as to comply with demands of global economies in terms of more sustainable and resilient agriculture.

Optimal Power Flow in Hybrid AC/DC Microgrid using ANN for Cost Minimization

Currently, this work lays the ground work for sophisticated control methods and decision support systems in hybrid microgrid operations by providing insightful information about integrating artificial intelligence for improved microgrid control. In this work, a neural network (NN) method is proposed for power flow analysis in an IEEE 12-bus-based Hybrid AC/DC Microgrid. The study optimizes power dispatch, minimizes expenses, and minimizes losses in both AC and DC components. Simulation is carried using MATLAB software and the results are presented and analysed. The accuracy of the NN’s predictions of active power flows is demonstrated by training it on historical data and validating it on real-time observations. Regression plots comparing anticipated and real values demonstrate the effectiveness of NN-based analysis in reaching the ideal power distribution.

Designing a Website-Based Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) for Parental Control of Children's Media

The massive amount of children's television programming poses a challenge for parents seeking to mediate suitable viewing habits. This research puts forward a website-based Decision Support System (DSS) leveraging Analytic Hierarchy Process to recommend appropriate shows per personalized criteria. The DSS allows parents to set priorities across factors like educational value, empathy, and social norms reflected in shows. Quantitative AHP methodology ranks options by synthesizing comparative judgments. A prototype is implemented on PHP/MySQL with an intuitive interface explaining decision logic. Testing with parent focus groups indicates significantly increased perceived utility over traditional controls. Recommendation accuracy metrics averaged 0.81 against test cases. Outcomes demonstrate feasibility and benefits of applying multi-criteria DSS techniques to aid value-based media selection for impressionable minds. The system aims to aid busy caregivers overwhelmed by the exponential growth in media choices, promoting discerning consumption. Further enhancements incorporating explanatory facilities and predictive analytics promises wider applicability inside content distribution ecosystems. This research highlights the importance of DSS-AHP integration for child development in the modern information age.

PHENTHAUproc – An early warning and decision support system for hazard assessment and control of oak processionary moth (Thaumetopoea processionea)

The incidence of oak processionary moth (OPM), Thaumetopoea processionea L. (Lep., Notodontidae), in Central Europe has increased since the 1990s. Given potential climatic and land-use changes, increased abundance and range expansion of OPM may occur in the future. These changes could lead to more severe defoliation of oaks and higher threats to humans and animals, caused by the urticating hairs (setae) released by OPM larvae. These negative impacts on oak trees, people and animals urgently require effective hazard mitigation strategies.For OPM hazard assessment and decision support on OPM monitoring and control, the online early warning system “PHENTHAUproc – phenology modelling of Thaumetopoeaprocessionea” was created and validated. PHENTHAUproc implements daily updated temperature-based procedures to calculate the phenological development of OPM and to model the host trees’ phenology, i.e. bud swelling and leaf unfolding of Quercus robur L.The hatching date of the first instar larvae (L1) is calculated using three different already existing models. To determine the onset of L1 feeding, a bud swelling model for Q. robur was generated for the first time. The phenological development of OPM larvae and pupae is predicted by a model which was first created based on laboratory rearing data for the distinct development stages.Moreover, an existing leaf unfolding model for Q. robur was included into PHENTHAUproc. The predicted date of leaf unfolding can be complemented by in situ observations of leaf unfolding by the German Meteorological Service (DWD). The predicted dates of leaf unfolding and OPM forth instar development are used to define the optimal timespan for control measures such as the prophylactic spraying of biological insecticides. In addition, modelling of larval and pupal development can be used to estimate the seasonally increasing hazard resulting from setae dissemination, as well as to calculate the onset of monitoring of flying moths with pheromone traps.PHENTHAUproc provides phenological forecasts on a local and regional level, i.e. for weather station locations and raster maps countrywide. The models can be used anywhere in Europe as soon as air temperature data and information on potential adaptations of OPM to local climate and host tree phenology are available. This enables phenological forecasts for timely and effective protection of oak tree vitality as well as human and animal health against OPM-related hazards.

A decision support system for verifying the quality of application software code

The proposed architecture of the decision support system for quality control of software code of applied codebases. The proposed approach is modular and applicable to most software development projects. The proposed system allows deriving empirical rules for long-term software development and maintenance.

Towards pest outbreak predictions: Are models supported by field monitoring the new hope?

Physiologically-based models are the core of Decision Support Systems (DSS) for insect pest and disease control in cultivated fields. However, the large-scale use of DSS remains scarce and limited, despite the continuous update and formulation of new models by the literature. The main reason behind this lack of real-world use relates to the purely descriptive approach of these models, which are usually validated a posteriori. The major limiting factors that preclude the use of these tools for prediction purposes are their dependence on time zero and initial abundance to start the simulations. In this study, we present a theoretical framework that includes field monitoring data as an active part of a pest population density model simulation, which helps to overcome these obstacles. More specifically, we propose the application of an estimator scheme in the form of an Extended Kalman Filter (EKF) to a revised physiologically-based model from the literature. In the paper, we carry out a preliminary test of the theoretical framework applied to the case of Drosophila suzukii. This case study shows that the dependence of the simulations on the initial conditions and time zero is strongly reduced by using the EKF. Overall, the outcome of this research indicates that an estimator scheme is a necessary step to move from description to prediction in the pest population modelling field.

Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

Real-time operating decision support system for reservoir operation using weather forecast and a hydrological model

This paper presents the application of a real-time decision support system (DSS) for power generation and flood control at the Thac Xang hydropower plant in Lang Son province, Vietnam. The DSS consists of three key components: real-time operation and visualization, forecast, and optimized decision modules. The real-time operation and visualization module provides operators with a graphical interface that displays real-time data on the plant's status, such as water levels, reservoir levels, and power output. The weather forecasting module provides information about future precipitation. This information is used as input data for the inflow forecasting model. The real-time computing module takes into account current conditions, such as the water level in the reservoir to compute relevant variables such as current inflow rate or outflow rates from the reservoir. The simulation module is used to test different operating scenarios taking current and future inflow rates into account. This helps the plant operators to understand the impact of their decisions on the plant's performance. The simulation module can also be used to identify potential problems and develop contingency plans. Therefore, a mathematical model is developed. Particularly, this model uses real-time information of dams. The main objective is to maximize economic value over the time horizon by producing electricity when it is most valuable. An approach of a simulated annealing algorithm is used to solve this model. We also present a decision support software tool for small hydropower systems that provide hydropower equipment operators with the information required to optimize the dam's performance in terms of power efficiency and effectiveness.

Deterioration Control Decision Support System for the Retailer during Availability of Trade Credit and Shortages

The deterioration rate is a significant aspect of perishable goods. Since perishable items will always deteriorate, there are effective methods for reducing the rate of deterioration. Furthermore, in the existing inventory control literature, the deterioration rate is often viewed as an exogenous component. Keeping this problem in mind, this article develops the perishable inventory control system from the retailer’s perspective in which: (i) the deterioration rate is a controllable factor and suggests a new fresh quality technology (FQT) indicator, (ii) demand is determined by the perishable product’s quality, that is controlled by its rate of deterioration, (iii) the credit duration is predefined, and (iv) shortages are expected. The key goal is to demonstrate that there is an ideal quantity of the order that minimizes the retailer’s overall cost in terms of cycle time and deterioration rate. Finally, theoretical results are validated by solving two numerical illustrations and conducting a sensitivity analysis of the main factors resulting from the following managerial implications: (i) if the range of deterioration is between zero and one then the retailer should invest in the preservation factor to preserve the perishable product and if greater than one the retailer should not invest in the preservation factor, (ii) credit period significantly reduces the total cost. Hence, this trade credit strategy is more beneficial to the model.

Development of Decision Support System (DSS) for Greenhouse Ventilation and Cooling Control

The confined heat created by the sunlight and ambient temperature is one of the obstacles to greenhouses in the tropics. A conservatory in tropical regions thus requires a proper ventilation and cooling system to provide an ideal greenhouse temperature to support crop production. The main objective of this study is to develop a Decision Support System (DSS) for optimum greenhouse ventilation and cooling control. An equation model to predict the temperature inside was developed by considering heat generation and heat loss inside the greenhouse. The accuracy of the equation model was validated with secondary data from the on-field greenhouse by statistical evaluation metrics. The statistical evaluation proved that the equation model is good with 2.11°C of Root Mean Square Error (RMSE), 6.22% of Percent BIAS (PBIAS) and 0.94 of Nash-Sutcliffe efficiency (NSE) value. A DSS using Microsoft Excel with Visual Basic for Applications (VBA) is developed based on the equation model to provide the ventilation and cooling system information. Input required for the DSS is a climatic condition, dimension and covering greenhouse material. The output of the DSS is the temperature inside the greenhouse, natural ventilation design, forced ventilation, active cooling system design using an evaporative pad and fan, and fogging system. The DSS can be used as an approach for ventilation and cooling design recommendation for the optimum temperature inside the greenhouse.

Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves

In marine operations, the performance of model-based automatic control design and decision support systems highly relies on the accuracy of the representative mathematical models. Model fidelity can be crucial for safe voyages and offshore operations. This paper proposes a data-driven parametric model identification of a ship with 6 degrees of freedom (6DOF) exposed to waves using sparse regression according to the vessel motion measurements. The features of the complex ship dynamics are extracted and expressed as a linear combination of several functions. Thruster inputs and environmental loads are considered. The hydrodynamic coefficients and wave-induced loads are simultaneously estimated. Unlike earlier studies using a limited number of unknown functions, a library of abundant candidate functions is applied to fully consider the coupling effects among all DOFs. The benefit of the proposed method is that it does not require the exact construction of the library functions. Based on the estimated model, short-term motion prediction is achievable. The algorithm is verified through experiments. The method can be extended to other types of floating structures.

Developing a prescriptive decision support system for shop floor control

PurposeThe reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time control of manufacturing operations. The work puts forward a framework for designing prescriptive decision support system for a multi-machine manufacturing environment.Design/methodology/approachThe schema of the decision support system design begins with the development of a simulation model for a manufacturing shop floor. The developed model facilitates prediction followed by prescription. As a connecting link between prediction and prescription mechanism, heuristics for intervention have been proposed. Sequential design and simulation-based demonstration of activities that span from development of a multi-machine shop floor model; a prediction mechanism and a scheme of intervention that ultimately leads to prescription generation are the highlights of the current work.FindingsThe study reveals that the effect of intervention on the observed predictors varies from one another. For a machine under observation, subject to same intervention scheme, while two of the predictive measures namely penalty and desirability stabilize after a certain point, a third measure, i.e. complexity, shows either an increase or decrease in percent change. The work objectively establishes that intervention plans have to be evaluated for every machine as well as for every environmental variable and emphasizes the need for dynamic evaluation and control mechanism.Originality/valueThe proposed prescriptive control mechanism has been demonstrated through a case of a high pressure die casting (HPDC) manufacturer.

Development of a web-based geospatial intelligent decision support system for control of herders and farmers crisis in Benue State Nigeria

The biggest challenge in Nigeria today is herder-farmer conflict particularly in Benue State Nigeria. This study developed a web-based geospatial decision support system for control and management of herders/farmers crisis in Benue State. Landsat 5 TM (1989), Landsat 7 ETM+ (2004) and Landsat 8 (2019) were pre-processed and classified. The study revealed that forested areas had depleted by -4.17% between 1989 and 2004 and -2.20% between 2004 and 2019. Farmland had an increase of 34.01% between 1989 and 2004 and 17.92% between 2004 and 2019 and grassland had -7.12% between 1989 and 2004 and -3.89% between 2004 and 2019. The study also revealed five conflict zones with Agatu in Very high conflict zone with 88 -230 clashes, the high conflict zone included Guma, Gwer West and Logo with 41 – 87 clashes, the moderate conflict zone include Gwer East, Ohimini Markudi and Okpokwu with 16 – 40 clashes, the low conflict zone include Otukpo with 3 – 15 clashes, the very low conflict zone include Apa, Ado, Obi, Oju, Kwande, Ukum, Katsina Ala, Gboko, Buruku, Tarka, Ushongo, Konshisha, Ogbadibo, and Vandeikya with 0 -2 clashes. The potential sites for grazing reserves were determined using the Analytical Hierarchy Process (AHP). The results revealed a total area of 8361.46hectares (0.27%) of the total land mass as potential grazing reserves, covering 18 local government areas. A database of the potential areas for grazing reserves was implemented in QGIS software. The study recommended potential areas for establishment of grazing reserves in Benue State.

Development of a web-based geospatial intelligent decision support system for control of herders and farmers crisis in Benue State Nigeria

Development of a web-based geospatial intelligent decision support system for control of herders and farmers crisis in Benue State Nigeria

HYBRID MULTI-AGENT METHOD FOR OPTIMIZATION OF FUZZY COMPUTER SYSTEMS

Recently, intelligent computer systems based on fuzzy logic and soft computing are used quite effectively to solve a wide range of complex applied problems in various fields of human activity. One of the promising areas of modern research in the field of artificial intelligence is the creation and testing of bioinspired multi-agent and evolutionary methods for the synthesis and optimization of fuzzy automatic control systems (ACS) and decision support systems (DSS). This paper is devoted to the development and research of a multi-agent method of parametric optimization of fuzzy systems (FS) based on hybrid improved grey wolf algorithms. The proposed method allows optimizing the parameters of fuzzy computer systems more efficiently, compared to the basic and improved grey wolf methods. This method uses group hunting and dimensional learning-based hunting strategies, as well as local search strategies based on algorithms of gradient descent and extended Kalman filter, which significantly reduces computational costs and increases the rate of convergence to optimal solutions when optimizing parameters of complex fuzzy systems. To study the effectiveness of the developed hybrid multi-agent method in this work, the synthesis and parametric optimization of the fuzzy flight control system for the unmanned aerial vehicle (UAV) is carried out. In particular, the optimization of the vectors of normalizing coefficients, adjustable parameters of linguistic terms (LT), and weight coefficients of the rule's consequences of the rule base (RB) for the altitude controller of the fuzzy ACS for the UAV is performed. The obtained results of the comparative analysis confirm the significant advantages of the developed hybrid multi-agent method of parametric optimization, as well as the feasibility of its application for the synthesis of different types of ACSs and DSSs based on fuzzy logic.

Integrated EDM and 4D BIM-Based Decision Support System for Construction Projects Control

Project schedule monitoring and controlling are critical challenges of construction project management that are not adequately implemented, likely due to the predominance of earned value management and the lack of utilizing technology, such as BIM tools. Unlike earned value, earned duration management (EDM) was developed, which includes several indices to track schedule progress and measure the performance of a schedule. The goal of this research was to establish a decision support system to track and monitor construction project activities during construction, with better performance and accuracy. A survey was conducted and distributed among ten site engineers, selected from different construction sites. The survey asked the site engineers about the possible durations of certain activities; based on their answers, the authors started the proposed system. In this study, we aimed to develop a decision support system (DSS), which combines BIM with EDM to help calculate probabilistic total project duration, visually detecting critical activities, monitoring visually risky activities subjected to delay and visually categorizing the accuracy of estimated duration for delayed activities.

Intelligent decision support system based on video recognition of the blast furnace tuyeres

An approach to creation of an intelligent system for predicting the state of a technological process in real time is presented. The approach is based on the analysis of a video sequence of images obtained as a result of streaming video by cameras installed on the tuyeres of a blast furnace. Algorithms for recognizing video images of tuyere foci, as well as scenario forecasting of the evolution of technological situations are proposed. A historical background regarding the development of methods for automatic control of the blast-furnace process, in particular, the use of artificial intelligence, is presented. The study is aimed at the ability of rapid analysis of the production situation (PS) and prediction of the PS evolution in the course of functioning of the blast furnace process, which will provide the possibility of timely decisions on adjusting control in an automatic or automated mode. Using the developed algorithm for analysis and prediction of the process dynamics and proceeding from the revealed regularities of the change in video data, a method for early detection of a tendency to the occurrence of certain events on tuyeres, including those leading to the destabilization of the blast furnace process, is proposed. The novelty of the presented approach lies in the fact that not only the state of the process at the next moment of time, but also the most probable chain of several subsequent states is predicted. Real-time forecasting algorithms are based on the construction and replenishment of the base of inductive knowledge — regularities revealed through the intellectual analysis of the revealed information — in the course of real functioning. Methods of studying Markov chains, machine learning and wavelet analysis are used for the associative search for patterns. The algorithms developed by the authors can be used in decision support systems for blast-furnace control. The results of practical research, confirming the effectiveness and viability of the proposed approach, are presented.

Decision Support System for Adaptive Restoration Control of Distribution System

Aiming at the high-dimensional uncertainties of restoration process, an optimization model for distribution system restoration control is proposed considering expected restoration benefits, expected restoration costs, and security risks of the overall restoration scheme. In the proposed model, the effect of security control on restoration process is actively analyzed considering the security control costs of preventive, emergency, and correction controls. A two-layer decision support framework for distribution system restoration decision support system (DRDSS) is also designed. The upper layer of the proposed framework generates the pre-adjustment schemes of operation mode for energized power grid by load transfer and selects the optimal pre-adjustment scheme and the corresponding partitioning scheme based on the partition adjustment results of each pre-adjustment scheme. In addition, it optimizes the spatial-temporal decision-making of the inter-partition connectivity. For each partition, the lower layer of the proposed framework pre-selects the units and loads to be restored according to the pre-evaluated restoration income, generates the table of alternative restoration scheme for coping with uncertain events through simulation and deduction, and evaluates the risk and benefit of each scheme. For the uncertain events in the actual restoration process, the current restoration scheme can be adaptively switched to a sub-optimal scheme or re-optimized if necessary. Meanwhile, the proposed framework provides an information interaction interface for collaborative restoration with the related transmission system. A 123-node test system is built to evaluate the effectiveness and adaptability of the proposed model and framework.

Nursing Workloads in Clinical Settings and the Needs of Decision Support System For A Tight Glycemic Control: A Glucoprotm Study

Nursing Workloads in Clinical Settings and the Needs of Decision Support System For A Tight Glycemic Control: A Glucoprotm Study