Support System Model Research Articles

Approach to diagnosing multiple abnormal events with single-event training data

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

Decision Support System For Admission Of New Employees With Weighting Method (Weight Product Model)

Firli Group is an e-commerce and digital marketing company. At every new employee recruitment, Firli Group is still manual or semi-computerized, making it difficult to determine prospective new employees. Therefore we need a system that can be petrified to facilitate decision making, namely by using the Weight Product Model (WPM). WPM is a model of Decision Support System (DSS), namely by ranking, this analysis is carried out with a sample of 5 people. Decision support system on the acceptance of new employees, is expected to help the company.

KAJIAN KARAKTERISTIK LAHAN UNTUK MENDUKUNG PENYUSUNAN MODEL DECISION SUPPORT SYSTEM (DSS) OPTIMALISASI LAHAN DI KABUPATEN PANGKEP


 The biophysical characteristics and environmental conditions of the land are required for the development of a Decision Support System (DSS) model for land optimization. The purpose of this study was to examine the characteristics of land cover, and the biophysical and environmental conditions of the land in Pangkep Regency. Suboptimal land identification and analysis of land and climate characteristics are carried out on the basis of secondary data obtained from satellite images, both land cover data in 2022, data in the form of three-dimensional DEM (Digital Elevation Model), and land characteristic data in the form of land system maps produced by the Geospatial Information Agency. The land cover in the Pangkep Regency area consists of fourteen types of land cover dominated by jungles, rice fields, and ponds. Flat topography with slopes of 0-2% and geological formations of alluvium and coastal deposits, Tonasa Formation, and Sekala Formation are dominant in Pangkep Regency. The dominant soil characteristic in this region is a lithosol soil type, soil depth more than 150 cm, soil pH is slightly acidic (5.6-6.5), clay texture, soil drainage criteria are inhibited and good, soil salinity in the range of 0.33%, C-Organic content with very low and low criteria, medium soil CEC, low total N value with a range of 0.1–0.21% and medium with 0.22–0.51%, Soil potassium content including very low criteria (<10 mg/100g) is in the range of 0.05 – 0.2 mg/100g and dominant 0.2 mg/100g, and rock abundance is 100+. The rainfall in this region is predominantly 2500 mm.th-1 and the average temperature is 23 - 32°C.

Investigation on the Rotordynamic Characteristics of Turbopumps with Angular Contact Ball Bearings

The support stiffness of the turbopump rotor system with angular contact ball bearing varies with the rotational speed, which leads to the inaccurate prediction of the dynamics of the turbopump rotor system. The model of the rotor bearing system was constructed based on the theoretical model of angular contact ball bearing stiffness, and the dynamics characteristics of the turbopump system were calculated. To verify the accuracy of the stiffness and the dynamics model, a test system of the turbopump rotor with angular contact ball bearings was designed. Since the bearing stiffness cannot be measured directly, a stiffness identification model was introduced, and an unbalanced response test was conducted to verify the dynamics model. It was found that the turbopump bearing stiffness increased dynamically with speed and reduced the unbalance response of the rotor. The results show that the angular contact ball bearing stiffness model and the dynamics model of the rotor support system are accurate and provide support for the dynamics design of the turbopump rotor system with angular contact ball bearings.

Digital twins and blockchain technology in the industrial Internet of Things (IIoT) using an extended decision support system model: Industry 4.0 barriers perspective

Blockchain technology (BT) can be considered a great help to the concept of digital twins (DT) as it can make sure of transparency, the immutability of data, decentralization of data storage, and peer-to-peer communication in industrial sectors. DT refers to the integrated multiphysics, multiscale, and probabilistic representation of a real-world physical component. The current study attempts to well envisage the ways blockchain can restructure and transform DTs to provide secure manufacturing, assuring compliance, traceability, quality, authenticity, and safety. However, to the adoption of DTs and BT in the industrial Internet of Things (IIoT), there are several barriers. To identify the key barriers, the present study discusses a survey approach through comprehensive literature and interviews with experts. To do so, this study introduced a novel approach using decision-making theory under the q-rung orthopair fuzzy set (q-ROFS) to analyze the identified barriers. This study developed a new methodology called q-ROF-subjective and objective weight integrated approach (SOWIA)-weighted aggregated sum product assessment (WASPAS). An empirical case study to assess the barriers to employing BT into DTs for IIoT implementation in the era of Industry 4.0 is taken. Also, comparative work and sensitivity analysis are discussed to demonstrate the advantage of the presented approach.

Development of an anti-vibration aircraft model support system with magnetorheological annular squeeze dampers for wind tunnel

Sting support is an important equipment in wind tunnel test and is also vulnerable to resonance under the influence of wind flow, which will reduce the accuracy of test data and even pose a hazard to the wind tunnel. To address the vibration problem, an innovative magnetorheological sting support (MRSS) was proposed in this paper. Specifically, the vibration reduction mechanism was explained by using a combination of Euler-Bernoulli beam and Kelvin-Voigt element. Furthermore, the relationship between the stiffness and damping of the magnetorheological damper (MRD) and the natural frequency (NF) and damping ratio (DR) characteristics of the MRSS was illustrated. To provide controllable damping and stiffness for the support system, an MRD with annular squeeze mode was designed, taking into account various factors such as low influence on wind flow, sting shape, and magnetic circuit requirements, and was also optimized by considering target magnetic field and low power consumption as the objectives. Tests on the manufactured MRD demonstrate the effectiveness of the structure design and optimization. Also, the response time characteristic satisfies the system vibration control. Subsequently, the controllability and superior fail-safe property of MRSS were verified through laboratory and wind tunnel tests. Finally, an on–off control logic was developed, and real-time vibration control tests were conducted in various impulse excitation, the resonance peak was significant attenuated by 27.1 dB. Additionally, the resonance peak was attenuated by 17.6 dB in the passive state. The experimental results showed that the MRSS was able to suppress the vibration with efficiency and reliability.

Group decision support system model to determine supervisor lecturers for student creativity programs

A group decision support system (GDSS) model was created in this study by implementing the weighted product (WP) method and the borda method to determine prospective assistant or supervisor lecturers for student creativity programs (PKMs) that are routinely carried out every year at the Sriwijaya State Polytechnic. This study applies 5 criteria, including i) education level, ii) academic position, iii) group tenure, iv) lecturer certification, and v) achievement in the field of three pillars of higher education. The decision-makers in this study consisted of the head of the department (DM–1), the secretary of the department (DM–2), and the head of the study program (DM–3) where they carried out the decision-making process in groups. The WP method is used to make preferences independently of the decision-makers to determine the best alternative based on predetermined criteria. The borda method is currently used to aggregate the decision-makers to obtain the final result in the form of an alternative ranking. The results of this study are sufficient to be used as a reference in determining the supervisor lecturer for PKM activities at the Department of Computer Engineering, State Polytechnic of Sriwijaya.

Group decision support system model to determine supervisor lecturers for student creativity programs

A group decision support system (GDSS) model was created in this study by implementing the weighted product (WP) method and the borda method to determine prospective assistant or supervisor lecturers for student creativity programs (PKMs) that are routinely carried out every year at the Sriwijaya State Polytechnic. This study applies 5 criteria, including i) education level, ii) academic position, iii) group tenure, iv) lecturer certification, and v) achievement in the field of three pillars of higher education. The decision-makers in this study consisted of the head of the department (DM–1), the secretary of the department (DM–2), and the head of the study program (DM–3) where they carried out the decision-making process in groups. The WP method is used to make preferences independently of the decision-makers to determine the best alternative based on predetermined criteria. The borda method is currently used to aggregate the decision-makers to obtain the final result in the form of an alternative ranking. The results of this study are sufficient to be used as a reference in determining the supervisor lecturer for PKM activities at the Department of Computer Engineering, State Polytechnic of Sriwijaya.

Development of a Problem-Based Learning Model Integrated with 21st Century Skills

This research aims to develop the PBL-4C Learning Model to promote mathematical problem-solving skills among students and to create a valid, practical, and effective PBL-4C learning model. It falls under the category of research and development (R&D). The model development process follows the Plomp development model, which comprises four phases: (1) initial investigation phase, involving student needs analysis, task analysis, concept analysis, and learning objective formulation; (2) design phase, encompassing the design of the PBL-4C learning model, including model syntax, social principles, reaction principles, instructional impact, facilitation, and support system; (3) implementation phase, which involves the complete construction of the PBL-4C model based on the previous design phase; and (4) testing, evaluation, and revision phase, which includes validation and pilot testing. The validation results of various validity instruments include the average score of the PBL-4C learning model book (3.67), student’s book (3.98), student’s worksheets (4.35), lesson plans (4.48), learning outcome tests (3.71), students’ response questionnaires on the implementation of the PBL-4C learning model (4.12), students’ response questionnaires on the student’s worksheets (3.73), students’ response questionnaires on the student’s book (3.75), teacher’s response questionnaires (3.65), students’ activity observation sheets (3.75), and learning implementation observation sheets (3.40). Each of these validity instruments falls under the highly valid category, indicating that the PBL-4C learning model book and its supporting instruments meet the validity criteria. The PBL-4C model product is effectively implemented in teaching mathematics, specifically the topic of systems of linear equations with two variables. The effectiveness of the PBL-4C learning model is evaluated based on data analysis of its effectiveness components: (a) the average percentage of student activities during the implementation of the PBL-4C model in pilot test I is 63.25%, which falls under the "good" category, and the average percentage of student activities in pilot test II is 79%, falling under the "good" category; (b) the average percentage of student responses in pilot test I is 76% with positive responses, and in pilot test II, it is 82% with positive responses; (c) the learning outcome test results indicate that in pilot test I, 80% of students achieved mastery, while in pilot test II, 92% of students achieved mastery, thus meeting the criteria for classical mastery.

Sistem Pendukung Keputusan Penentuan Barista Terbaik Menggunakan Rank Sum dan Additive Ratio Assessment (ARAS)

The selection of the best barista has become a crucial factor in guaranteeing a unique and satisfying coffee experience for connoisseurs. As an appreciation of the art of coffee making increases, barista selection no longer revolves around technical skills, but also on creativity, dedication, and interpersonal expertise. This study aims to apply the ARAS (Additive Ratio Assessment) method in determining the best barista using the Rank Sum weighting method, so that the results of the criteria weighting will be better in producing the best barista recommendations based on the decision support system model. The criteria used in selecting the best barista are barista skills, barista knowledge, experience, mastery of tools, and communication. Based on the ranking results using the ARAS method, it recommended rank 1 with a final value of 0.9483 obtained by Fandy, rank 2 with a final value of 0.9294 obtained by Abdur, and rank 3 with a final value of 0.8902 obtained by Delvin. The results of the test assessment using the blackbox testing model all the features of the best barista determination application are in accordance with their functions, the final results of application testing by 100% have worked according to the features made.

Building Information Modeling and Building Performance Simulation-Based Decision Support Systems for Improved Built Heritage Operation

Adapting outdated building stocks’ operations to meet current environmental and economic demands poses significant challenges that, to be faced, require a shift toward digitalization in the architecture, engineering, construction, and operation sectors. Digital tools capable of acquiring, structuring, sharing, processing, and visualizing built assets’ data in the form of knowledge need to be conceptualized and developed to inform asset managers in decision-making and strategic planning. This paper explores how building information modeling and building performance simulation technologies can be integrated into digital decision support systems (DSS) to make building data accessible and usable by non-digital expert operators through user-friendly services. The method followed to develop the digital DSS is illustrated and then demonstrated with a simulation-based application conducted on the heritage case study of the Faculty of Engineering in Bologna, Italy. The analysis allows insights into the building’s energy performance at the space and hour scale and explores its relationship with the planned occupancy through a data visualization approach. In addition, the conceptualization of the DSS within a digital twin vision lays the foundations for future extensions to other technologies and data, including, for example, live sensor measurements, occupant feedback, and forecasting algorithms.

Integrated urban regeneration for high-rise multi-family buildings by providing a multidimensional assessment model and decision support system

Current urban challenges on promoting an integrated urban regeneration demand new decision support systems to adjust and optimise renovation strategies in the housing stock. This research aims to provide a multidimensional decision support system, specifically focused on high-rise multi-family buildings, which entails an added complexity in the design and decision-making stages of building renovation. The need to promote assessment protocols of key parameters that promote a viable and efficient renovation in high-rise multi-family buildings constitutes a research gap to be fulfilled in this study. This research contributes with an assessment and weighting model based on 12 indicators, both during and after renovation works, under 4 multidisciplinary dimensions: Technical; Social; Economic; and Environmental, in a -5 to 5 drawback-benefit index scale. An application procedure has been tested in two representative neighbourhoods in Argentina and Spain, identified by GIS resources, and demonstrating its operation and usefulness for vulnerable neighbourhoods due to global inflation. The implications of the graphic output of results, weighted for Mild, Moderate, Intense and Deep action strategies, allows us to identify drawbacks and benefits of each strategy independently, for each of the 12 indicators, visualising the trend, performance and variations between dimensions and strategies in large-scale buildings. Conclusions generate key recommendations and insights on decision-making patterns to urban policymakers by ensuring feasible and satisfactory renovation strategies in high-rise multi-family buildings.

The effects of compliant support on the dynamics of a dielectric elastomer actuator: a parametric study

The resonant actuation of dielectric elastomer actuators (DEAs) can greatly amplify the power outputs and energy efficiencies and has facilitated numerous applications in soft robotics. In many circumstances, the DEAs are mounted on robotic bodies that are made of soft materials or compliant structures. Such compliant supports can demonstrate an equivalent stiffness and inertia comparable to the DEAs, thereby complicating the dynamics of the DEAs and threatening the performance and controllability of the soft robots by introducing additional degrees of freedom to the systems. Toward the goal of achieving reliable and controllable resonating actuation of the DEAs on soft robots, the effects of compliant supports on the dynamics of DEAs require dedicated investigations. By adopting a double cone DEA configuration, this work conducts a comprehensive study on the dynamics of a double cone DEA-compliant support system. A nonlinear dynamic model of the double cone DEA-compliant support system is developed. Together with experimental studies, the dynamics of the system in different support configurations are characterized and the influences of the key parameters in the system are clarified. The key findings of this work can potentially guide the designs of future high-performance DEA-driven soft robots.

Factors Hindering Solar Photovoltaic System Implementation in Buildings and Infrastructure Projects: Analysis through a Multiple Linear Regression Model and Rule-Based Decision Support System

Energy plays a predominant role in the development of society. With advancements in technology and the growth of society (buildings and infrastructures), the demand for energy is rapidly increasing. Developing countries typically rely on the import of fossil fuels and capital investments in infrastructure development to meet their energy needs. The execution of solar PV projects in developing countries is currently not being implemented promisingly. Therefore, the determination of the critical success factors hindering the implementation of solar PV projects is the need of the hour. The aim of this study is to determine the factors that hinder the implementation of solar PV projects through the use of a multiple linear regression model (MLRM) and a rule-based decision support system (RBDSS). Seven categories of factors were identified through a detailed literature review and interviews with energy experts. Four hundred and twenty-nine complete responses were collected in total through a questionnaire, and they were analyzed using relative importance indexing (RII) and MLRM and RBDSS approaches. A comparison was carried out against both methodologies to determine the most critical barriers to the implementation of solar PV projects. The findings regarding the MLRM approach showed that the top seven critical factors were economic conditions, encouraging policies, technological knowledge, organizational support, social awareness, market stability, and miscellaneous aspects. Similarly, the results for the RBDSS approach identified that the top seven critical factors were encouraging policies, economic conditions, organizational support, technological knowledge, market stability, social awareness, and miscellaneous aspects. The application of MLRM and RBDSS will help stakeholders in making timely decisions and corrections during the implementation phase, providing a systematic way to support the performance and execution of solar projects.

Issues of Development of the Information Support System of Innovative Enterprises Based on Modern Digital Platforms

The article is devoted to the issues of developing the information support system of innovative enterprises based on modern digital platforms. The relevance of effective development of the economy of the current era and its various fields with the application of digital innovations and high technologies has been shown. The importance of improving the information support system of innovation enterprises, its architectural-technological structure model based on Industry 4.0 technologies, and other modern platforms is substantiated. At the same time, directions for improving the information support of enterprise management activities based on the Open Group Architecture Framework, its main blocks, and elements are also given. The main content and elements of the enterprise architecture are defined. The directions for the formation of the concept of the effective architecture of the enterprise have been determined. A conceptual structural model of the information support system of innovative enterprises based on modern platforms has been proposed. The interconnected levels of the Open Group Architecture structure are shown. Based on the Information Management Technical Architecture Platform, the concept of Information Systems Architecture was developed and its advantages were determined. Taking into account the components of the Industry 4.0 platform, relevant proposals and recommendations for the modernization of the information support system of innovative enterprises have been developed.

Decision support system for power cable insulation vulcanization production line management

The paper proposes a structural model of a decision support system for controlling a cable insulation continuous vulcanization line. The proposed decision support system is based on a mathematical model of the cable insulation continuous vulcanization technological process, a database, and a mode correction algorithm. The DSS will make it possible to quickly develop a new regime in case of using new materials or structures, as well as to correct the current regime in case of unplanned changes during the production process. The core of the DSS is the proposed mathematical model, which is based on conservation laws and is presented as a system of differential equations closed by boundary conditions. The vulcanization process is described taking into account the temperature-time dependence of the kinetic parameters, which made it possible to take into account the uneven heating of the cable, both in the radial direction and along the length. The numerical implementation of the differential mathematical model made it possible to carry out a systematic analysis of the nature of the processes in the vulcanization pipe and to evaluate the influence of various technological, structural and material parameters on the cable insulation vulcanization completion degree. As a result of the analysis, significant parameters were identified that significantly affect the vulcanization process completion degree. Based on the obtained numerical results, technological parameters dependence technological surfaces on the geometry of the product and the properties of the materials used were constructed, a regression mathematical model was proposed that allows determining the values of the process control parameters without resorting to the use of a differential mathematical model. Based on the results of the analysis, an algorithm for correcting the technological regime is proposed, taking into account only significant process parameters. The proposed algorithm allows you to adjust the value of the isolation rate, depending on external influences and deviations from the specified parameters. The results of the study can be used in the production of cable and wire products with vulcanizable insulation, when it is necessary to quickly select the optimal technological mode, take into account changes in the cable design, properties of the insulation material, as well as possible pressure deviations inside the vulcanization pipe.

A New Method for Commercial-Scale Water Purification Selection Using Linguistic Neural Networks

A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination and interrelationship of neurons and nodes with each other facilitate the transmission of information. An ANN has a feed-forward neural network. The neurons are arranged in layers, and each layer performs a particular calculation on the incoming data. Up until the output layer, which generates the network’s ultimate output, is reached, each layer’s output is transmitted as an input to the subsequent layer. A feed-forward neural network (FFNN) is a method for finding the output of expert information. In this research, we expand upon the concept of fuzzy neural network systems and introduce feed-forward double-hierarchy linguistic neural network systems (FFDHLNNS) using Yager–Dombi aggregation operators. We also discuss the desirable properties of Yager–Dombi aggregation operators. Moreover, we describe double-hierarchy linguistic term sets (DHLTSs) and discuss the score function of DHLTSs and the distance between any two double-hierarchy linguistic term elements (DHLTEs). Here, we discuss different approaches to choosing a novel water purification technique on a commercial scale, as well as some variables influencing these approaches. We apply a feed-forward double-hierarchy linguistic neural network (FFDHLNN) to select the best method for water purification. Moreover, we use the extended version of the Technique for Order Preference by Similarity to Ideal Solution (extended TOPSIS) method and the grey relational analysis (GRA) method for the verification of our suggested approach. Remarkably, both approaches yield almost the same results as those obtained using our proposed method. The proposed models were compared with other existing models of decision support systems, and the comparison demonstrated that the proposed models are feasible and valid decision support systems. The proposed technique is more reliable and accurate for the selection of large-scale water purification methods.

Safety and Stability Analysis of Demolition and Reconstruction of Existing Railway Bridge Piers and Caps

The process of bridge reconstruction often involves the demolition and reconstruction of bridge piers and caps, while most of the construction methods used in the previous bridge reconstruction projects changed the bridge-bearing positions. In this paper, an in situ replacement scheme of bridge piers and caps is proposed, which can maintain the existing stress state of the bridge without changing the bearing position. In order to figure out the safety and stability of the in situ replacement scheme of existing railway bridges, a steel support system model for the removal and reconstruction of the bridge piers and caps is established by ABAQUS, according to a domestic railway bridge reconstruction project, and verified by field measurement test. Based on the model, the stress and deformation of the steel support system under a trainload are analyzed, as well as those of the bearing foundation and the superstructure. The results show that the steel support system and steel pipe piles located directly below the line carrying the trainload are subjected to the greatest stress and deformation. While under various load conditions, the stress and deformation of the main components in the steel support system of the in situ replacement scheme meet the design requirements, and the structure is safe under a trainload. In addition, guided by the numerical calculation results, the implementation scheme, existing problems, and solutions of the project are introduced in detail, which can provide a reference for similar projects.

Modeling of the intelligent support system for the internal audit of the business entity

The main aspects of the development of an intelligent internal audit support system (ISPSA) of a business entity are considered. A brief conceptual description of the algorithm for the development of internal audit support systems is given, with a statement of the main difficulties in the selection of components. The development of an intelligent internal audit support system is an important and necessary task, as internal audit plays an important role in ensuring effective management and control of a business entity. An intelligent system can help increase the efficiency of audit processes and provide more accurate results of automation and application of analytical tools. Over time, a business entity accumulates more and more data, and their analysis becomes an extremely difficult task. An intelligent system can quickly process large amounts of data, identify trends, anomalies, and risks that allow for more informed decisions; it can also identify unusual transactions or shortages that may indicate the possibility of fraud or error; it can provide auditors with valuable information and analytics to make informed decisions decisions on improving management processes and ensuring effective control mechanisms. This will help prevent the possibility of financial losses and preserve the reputation of the business entity. Consideration of some steps of the ISPVA algorithm is based on a brief comparative analysis of mathematical support methods.

Developing Risk Level Prediction Model and Clinical Decision Support System for Cardiovascular Diseases in Ethiopia

Cardiovascular diseases have become one of the severe health problems in both developing and developed countries. This research aimed to develop a risk level prediction model and clinical decision support system for CVD in Ethiopia using data mining techniques. A total of 4004 datasets were used to develop the model. Moreover, primary data was collected from the domain experts via interviews and questionnaires. The domain experts identified thirty-one risk factors, of which only eleven attributes were selected after experimentation to develop the model. Based on the result of experimentation, the model was developed by an unpruned J48 classifier algorithm which produced F-Measure 0.877, which is comparatively the best algorithm. The prototype system was developed by Visual C# studio tool. The developed prototype system helps health care providers to identify risk level CVD diseases. It was developed using a data mining technique, which can efficiently predict cardiovascular disease risk levels. However, developing the model by using more datasets and changing the default setting of WEKA, a data mining tool, will be the future work of this study.