Process Modeling Method Research Articles

Modeling process for full forming sports underwear

Abstract Most of the sports underwear available is sewn using cutting and sewing techniques. During the exercise process, it is easy to cause discomfort to the skin due to repeated friction at the seam, and at the same time, it is also a waste of manpower and raw material costs. In order to address these issues present in existing sports underwear, during its design and development, combined with fully forming knitting technology, construction process model, so as to realize the integrated knitting of sports underwear. First of all, through the characteristics of the local style of sports underwear, the key points of its prototype were probed. Second, on this basis, its process model was established, including the forming methods and knitting rules of the body, shoulder collar, and bottom circumference. Finally, the feasibility of the technological model is proved by the braiding of a fully formed sports underwear. The change in the body size can be achieved by using the two-side braiding transfer method. The curve contour of the shoulder and neck can be obtained by using the triangular polyline segmentation method. The bottom part is divided into a cylinder part and a supporting part. The local braiding method is used to achieve the knitting. This study puts forward a process model and application method of fully formed sports underwear, which provides a certain method and reference for the design and production of fully formed sports underwear.

Quasi-hydrophobic deep eutectic solvents for simultaneous automated determination of polar and non-polar dyes in food products

In this work, quasi-hydrophobic deep eutectic solvents were used for the first time for the efficient extraction of both polar (Sunset Yellow FCF (E 110), Azorubine (E122), Ponceau 4R (E124)) and non-polar (Sudan I, Sudan II, Sudan III) dyes from food products at the same time. These solvents were made from polar and nonpolar components, which made it possible to extract analytes of various natures. To automate the technique, a flow analysis method was used, which increases the speed and reliability of determining analytes in food samples. For a detailed study of extraction mechanisms, various process modeling methods were used. The proposed DES-based method addresses the limitations of traditional extraction methods, offering a more robust and efficient solution for food analysis. Validation of the method has demonstrated high precision, accuracy and extraction efficiency, making it a promising tool for regulatory compliance and consumer safety in the food industry. The following analytical characteristics were achieved: the limits of detection were 0.03–0.13 mg L−1, limits of quantification were 0.09–0.44 mg L−1, an extraction recovery exceeding 85 %, a linear range was from 1 to 100 mg L−1, and a relative standard deviation ranging from 2 % to 10 %. The experiments were confirmed by both Conductor Like Screening Model-Segment Activity Coefficient (COSMO-SAC) and the Non-Covalent Interaction using the Reduced Density Gradient of the DES-dye system in aqueous media.

The Effect of Pollutantmigration on the Environmental Situation in the Zone of Chemical Weaponinfluence

The article is devoted to the issues of monitoring the environment state in the influence zone of chemically hazardous objects on the example of the environmental monitoring system of a chemical weapons storage and destruction facility (CWDF) in the city of Kambarka, Udmurt Republic. Among the components there are: production control systems and site monitoring, environmental monitoring and health monitoring, however, it is environment in the zone of the chemical waste disposal facility influence which hasthe largest area and is the most difficult to control. Here, due to large areas, the process of migration of pollutants over considerable distances is observedclearly. The importance of the system for arranging sampling points on the topographic map of the waste disposal facility is shown in order to take into account the process of pollutantmigration. Vertical and horizontal migration of pollutants can lead to the emergence of unobvious local areas of pollutantaccumulation both within and outside the influence zone of the chemical waste disposal facility. A solution to the problem of migration process modeling on a conditionally selected area of the territory within the facility influence zone is presented to show the effect pollutantmigration on the dynamics of the environmental situation within the chemical waste disposal area. It has been proven that within the area under consideration there will be a significant amount of pollutant, which, subject to further evaporation of rainwater runoff, will remain in this area. This indicates the inevitability of the appearance of local areas of pollutantaccumulation in the influence zone of chemically-disposable substances in excess of permissible standards for their concentration, which requires a revision of the arranging sampling pointsystem, taking into account the application of migration processmodeling methods. The need for dynamic observations in individual sites of the area, selected with respect to the analysis results of the pollutantmigration routes within the entire influence zone of the chemically-disposable waste disposal facility, in order to assess the degree of impact of the non-toxic waste disposal facility on the environment, is substantiated.

Pemodelan Proses Bisnis Input Nilai Berbasis OBE pada Sistem Informasi Akademik UIN Sunan Kalijaga Yogyakarta

The Academic Information System of UIN Sunan Kalijaga is a system where all information related to academic issues is contained in it. Business modeling in the academic information system of UIN Sunan Kalijaga uses the Business Process Model and Notation method and is assisted by Visual Paradigm tools. This modeling focuses on the value input features performed by the lecturer. The research method used was observation of the value input guide document and direct interviews with the lecturer concerned. The results obtained, the parties who play a role in this process are study programs whose role is to fill in the CP and CPMK. Lecturers play the role of making assessments, relating assessments to CPMK and Bloom's Taxonomy, and entering scores in each assessment. The last party who plays a role in this process is the student, whose role is to see the output or final result of the value of each course.

Forecasting the cutting force in end milling

The object of this study is the process of end milling, taking into account the discontinuity of the process, simultaneous cutting with several flutes arranged in a spiral, tool runout, and feedback in the elastic machining system, in particular, for the depth of cutting. The subject of the study is the cutting force and identification of its empirical model. During identification, the cutting force coefficient is automatically determined when matching the theoretical and experimental oscillograms of the cutting force component. The reported results related to forecasting the cutting force at end milling are based on a mechanistic approach and involve the process modeling method for forecasting. The simulation uses an algorithm for representing the interaction of the cutter flutes workpiece engagement, based on the scan of the cutter according to the rotation angle coordinate. The algorithm makes it possible to identify empirical coefficients and exponents of the cutting force model based on experimental oscillograms of cutting force components. The built model is implemented in an application program and owing to the representation of the machining system in the form of a closed structural diagram, it allows predicting the elastic displacement, which will determine the actual cutting depth. The developed program under an interactive mode using digital files of experimental cutting force components makes it possible to perform model identification and predict cutting force components with an error of 4.6 %. The adequacy of the algorithms was confirmed by measuring the profile of the machined surface in the places where the cutting mode changed with the feed stopped. The developed simulation algorithm makes it possible to take into account the simultaneous cutting by several flutes arranged in a spiral, the runout of the tool, and the feedback in the elastic machining system, in particular, the depth of cutting

A novel interval type-2 fuzzy consensus reaching process model and group decision-making method for renewable energy investment

Renewable Energy Investment (REI) is a critical challenge for both governments and investors, often complicated by conflicting criteria and uncertainties that must be managed to obtain reliable and useful decisions. To address the REI problems, a novel group decision-making approach under uncertainty is proposed. Our approach introduces the Interval Type-2 Fuzzy Sets (IT2FS) to construct linguistic terms that reflect their uncertainty and personalized semantics. Subsequently, we develop a novel automatic interval type-2 fuzzy Consensus Reaching Process (CRP) model to detect disagreements among experts and generate personalized recommendations to improve the consensus level. The proposed CRP model conducts interval analysis to facilitate consensus adjustments, thereby enhancing its capacity to describe uncertainty. Once the CRP is finished, the prospect theory is extended to the IT2FS to calculate the overall values for each alternative, which can capture the decision makers’ attitudes towards risk and uncertainty. Given that prospect values are characterized by interval values, we propose a two-stage interval-valued ranking approach to compare and rank them. Finally, an illustrative case study is provided to demonstrate the feasibility of our approach, along with comparative analysis to highlight its advantages and sensitivity analysis to show the robustness of parameters on decision results.

A Deep Learning Method for Dynamic Process Modeling of Real Landslides Based on Fourier Neural Operator

AbstractThe conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods.

DEVELOPMENT OF ANDROID-BASED AUGMENTED REALITY LEARNING MEDIA ON THE INTRODUCTION OF GEOMETRY MATERIAL FOR KINDERGARTEN STUDENTS

This research aims to develop an application that can help kindergarten students in understanding space buildings in the introduction of Geometry subject. Research and development of the application used Research and Development (R&D) method that aim to produce certain products and test the effectiveness of these products. The product was developed using the ADDIE Process model method. The ADDIE software development model has five stages: analysis, design, development, implementation, and evaluation. This geometry learning media application using Unity 3D was tested through the Black Box Testing method and questionnaire testing. This learning application can help teachers in teaching the material of geometry through media applications to contribute a more interactive learning process.

E-Farm Livestock Platform Requirements Engineering Using Loucopoulos and Karakostas Iterative Process Model

Global human population growth has forced farms to evolve in order to produce more livestock products more efficiently while also paying attention to public health, environmental sustainability, and animal welfare. However, problems arise when some diseases appear to affect farm animals and large companies providing livestock products dominate the market. It is necessary to develop a platform or application that can be used to solve these two problems, especially for breeders who have farms on a small scale. This study aims to outline the process of understanding engineering requirements by utilizing the Loucopoulos and Karakostas Requirements Engineering Process Model method, which consists of elicitation of requirements, specification of requirements, as well as validation and verification of requirements. The development process is carried out by hiring breeders and potential customers to determine the priority needs of the platform. The results showed that of the 25 defined functional needs, there were 22 final functional needs that were validated with values above 50%. The E Farm platform should be further developed based on the defined demands since a total of 22 validated needs have been determined to be able to represent 88% of the needs required by users.

A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China

Accurate soil type maps provide an important basis for agricultural decision-making and land degradation control. In soil classification studies, the various environmental covariates are often selected based on the soil-forming framework. Since the mapping area and available observation data are limited, meteorological and vegetation cover factors have not been fully developed, and their role in soil classification needs to be evaluated. In addition, whether deep learning has out-performance in soil classification remains to be tested. The aim of this paper is to evaluate the accuracy of deep learning modelling techniques in classifying soil type using different combinations of input variables, and evaluate the importance of soil-forming variables in soil type classification. Therefore, we collected commonly used environmental covariates in Northeast China (NEC), including multiple meteorological factors and adopted a satellite-based biophysical model (Boreal Ecosystem Productivity Simulator, BEPS) to enrich vegetation cover factors. Next, four modeling strategies were developed: the soil-forming factors of soil and relief were considered as traditional environmental covariates (T), as well as combined with meteorologic variables (T + C), vegetation cover variables (T + V) and all available environmental covariates (T + C + V). Then, the effectiveness of different modeling strategies for soil classification was explored with convolutional neural network (CNN) model and multi-layer random forest (MRF) model based on soil separability. Finally, a 30 m resolution soil type map was established. The results demonstrated that both MRF and CNN can achieve high accuracy soil classification, while the CNN model performs better. The descending order of classification accuracy based on different modeling strategies of the CNN model is shown as T + C + V: 91.08%, T + V: 88.84%, T + C: 86.82%, and T: 83.96%. Meanwhile, the separability of different soil-forming factors for soil classification is soil properties, vegetation cover, temporal variation, meteorologic and relief in descending order. For Castanozems and Brown soils, MRF has higher classification accuracy, while CNN has better performance in Meadow soils and Fluvo-aquic soils. The methodology proposed in this paper aims to achieve high accuracy soil classification, provide an approach to understand the importance of soil-forming factors for the region, as well as for different soil types, and provide references for facilitating the interpretation of misclassified areas. Our results are accurate in the core areas, and therefore, this work facilitates researchers to be able to focus more on areas where different soil types intersect, thus significantly improves efficiency and saves resources, and promises to be a useful tool for future soil surveys.

AHP-based quantitative evaluation of radiation safety in nuclear technology utilization units

In order to protect human health from the hazards of nuclear radiation, it is necessary to standardize the management of irradiation equipment. According to the principle of “man-machine-management-environment” intrinsic safety, analytic hierarchy process (AHP) and cloud model method were adopted. A quantitative safety evaluation index system was established from four aspects: safety management, safety environment, irradiation equipment and experimental team. Based on the actual situation of an experimental institution, the safety grade of this experimental institution was systematically analyzed. The safety grade of this experimental organization was excellent, but it still needs to improve certain aspects. Corresponding management recommendations were put forward for nuclear technology utilization units of different safety levels.

Numerical modeling of the working process in an oxygen-methane combustion chamber operating on gaseous fuel

A method for three-dimensional numerical modeling of the working process in the oxygen-methane combustion chamber of a heat engine operating on gaseous fuel is presented. The advantages and disadvantages of the method based on the gross reaction equation written for the stoichiometric ratio are considered. The vortex dissipation model is used as a combustion model, and the ideal gas model is used for the working body. A justification is given for the effects in the combustion chamber obtained as a result of modeling. The results of three-dimensional numerical modeling and thermodynamic calculations are compared. Recommendations are given for improving the design of the combustion chamber and increasing its effectiveness. Keywords heat engine, combustion chamber, vortex dissipation model, combustion, numerical modeling, thermodynamic calculation, gross reaction

Uncertainty-Aware Capacity Allocation in Flow-Based Market Coupling

The effective allocation of cross-border trading capacities is one of the central challenges for the implementation of a pan-European internal energy market. In contrast to traditional power flow-ignorant methods like net transfer capacities (NTC), flow-based market coupling (FBMC) has been shown to increase price convergence between market areas, while improving congestion management effectiveness. However, explicitly analysing FBMC for a future power system with a very high share of intermittent renewable generation is often overlooked in the current literature. This paper provides a comprehensive summary on the technical specification of the FBMC process and FBMC modeling methods. It discusses implications of policy considerations and explicitly discusses the impact of high-shares of intermittent generation on FBMC performance. Further, we propose probabilistic security margins compatible with the current FBMC implementation to better account for renewable uncertainty in FBMC modeling. We conduct numerical experiments on the IEEE 118 bus test system to showcase the proposed model formulations and our data and implementation is published open source.

Modeling Method for Aerobic Zone of A2O Based on KPCA-PSO-SCN

Sewage treatment plants face significant problems as a result of the annual growth in urban sewage discharge. Substandard sewage discharge can also be caused by rising sewage treatment expenses and unpredictable procedures. The most widely used sewage treatment process in urban areas is the Anaerobic–Anoxic–Oxic (A2O) sewage treatment process. Therefore, modeling the sewage treatment process and predicting the effluent quality are of great significance. A process modeling method based on Kernel Principal Component Analysis–Particle Swarm Optimization–Stochastic Configuration Network (KPCA-PSO-SCN) is proposed for the A2O aerobic wastewater treatment process. Firstly, eight auxiliary variables were determined through mechanism analysis, including Chemical Oxygen Demand (COD) and ammonia nitrogen (NH4+) and nitrate nitrogen (NO3−) of influent water, pH, temperature (T), Mixed Liquor Suspended Solid (MLSS), Dissolved Oxygen (DO) and hydraulic residence time (HRT) in the aerobic zone. Dimensionality reduction was carried out using the kernel principal component analysis method based on the Gaussian function, and the eight-dimensional data were changed to five-dimensional data, which improved the running speed and efficiency of subsequent models. Then, according to the advantages of the particle swarm optimization algorithm, such as low calculation cost and fast convergence, combined with the advantages of stochastic configuration network general approximation performance, the PSO-SCN model was established to predict the three water quality indexes of effluent COD, NH4+, and NO3− for the aerobic zone. The experimental results proved the effectiveness of the model. Compared with classic water quality prediction algorithm models such as SCN, PSO-BP, RBF, PSO-RBF, etc., the superiority of the PSO-SCN algorithm model was demonstrated.

SBR-Extended Kalman Filter model-based fault diagnosis and signal reconstruction for the papermaking wastewater treatment process

In the study, for an existing paper mill, to realize the fault diagnosis and further fault signal reconstruction for the papermaking SBR wastewater treatment process, a novel model-based SBR-EKF (Extended Kalman Filter) fault diagnosis model has been proposed. Combining the SBR process model and EKF method, using the field 120 sets of normal data of dissolved oxygen (DO) and level (L) from a paper mill, the model-based SBR-EKF fault diagnosis model for the papermaking SBR wastewater treatment process was established, and the weighted sum of squared residuals thresholds (WSSR0) was determined off-line. Subsequently, based on the normal data, four common types of faults, fixed bias, drift bias, total failure, and precision degradation, were generated and applied to the developed SBR-EKF model for on-line monitoring. The fault diagnostic results show that, by comparing the calculated WSSR with the obtained WSSR0, the developed model-based SBR-EKF model demonstrated acceptable fault detection rates for DO and L. Moreover, using the filtered value of the SBR-EKF model, the effective signal reconstructions for L and DO were realized. These investigation results reveal the effectiveness of the proposed SBR-EKF fault diagnosis model, achieving fault diagnosis with acceptable precision and reconstructed signal.

Perancangan E-Commerce Pada Victory Collection Berbasis Web

Along with developments in technology, companies are increasingly required to use advanced technology as a tool or media to survive and win the competition. The impact on the aspect of competition is the formation of an increasingly strong competition. The purpose of this research is to know how to design sales system at Victory Collection using web system; And to know how to optimize the sales system more effectively and efficiently at Victory Collection; As well as to find out how the solution to increase sales using a manual into a web-based system at Victory Collection. By using Waterfall Process Model method, all the system details that have been running and the system that will be proposed will be arranged and implemented to optimize the promotion and sales of existing products at Victory Collection. The Waterfall Process approach or method is a systematic and sequential systematic development model that starts at the level and progress of the system in all stages such as analysis, design, code, testing, and maintenance. This model covers activities such as engineering and modeling of information systems, needs analysis, design, coding, maintenance, and testing. The design of this information system is limited only to the food and beverage sales system. The results of this system is expected to minimize damage and loss of sales data and ordering goods at Victory Collection; And this system is expected to help facilitate the search and presentation of goods data at Victory Collection; And this system is expected to increase the sales ratio at Victory Collection.

A comprehensive approach for the evaluation of the impact of blockchain on photovoltaic supply chain using hybrid data analytic method

Blockchain technology has emerged as a disruptive and transformative innovation, drawing significant interest from organizations for its potential advantages. This study employs an exploratory research approach, utilizing a hybrid data analytics method, to investigate the impact of blockchain on the photovoltaic supply chain. Our research employs a comprehensive methodology, combining both quantitative and qualitative techniques. The research journey begins with the identification of crucial indicators, followed by the collection of data through a carefully designed questionnaire and the utilization of the Delphi method for expert insights. Subsequently, we employ advanced data analytics techniques, specifically the fuzzy Analytic Network Process (ANP) and fuzzy Interpretive Structural Modeling (ISM) methods, to prioritize these indicators and unveil their intricate relationships. Our findings underscore the paramount importance of several key criteria among the twelve identified indicators. These include data security, effective data management, data quality assurance, robust system documentation, and streamlined access protocols. Notably, customer satisfaction emerges as a pivotal factor, exerting substantial influence within our model. Moreover, our analysis underscores that data security stands as the foundational element in this multifaceted framework. This study offers valuable insights for stakeholders in the photovoltaic supply chain, aiding in the strategic adoption of blockchain technology to enhance transparency, efficiency, and overall performance. It serves as a robust framework for organizations seeking to navigate the evolving landscape of sustainable energy supply chains while harnessing the potential of blockchain innovation.

Advanced error modeling and Bayesian uncertainty quantification in mechanistic liquid chromatography modeling

Current mechanistic chromatography process modeling methods lack the ability to account for the impact of experimental errors beyond detector noise (e.g. pump delays and variable feed composition) on the uncertainty in calibrated model parameters and the resulting model-predicted chromatograms. This paper presents an uncertainty quantification method that addresses this limitation by determining the probability distribution of parameters in calibrated models, taking into consideration multiple realistic sources of experimental error. The method, which is based on Bayes’ theorem and utilizes Markov chain Monte Carlo with an ensemble sampler, is demonstrated to be robust and extensible using synthetic and industrial data. The corresponding software is freely available as open-source code at https://github.com/modsim/CADET-Match.

Spatio-temporal bidirectional redundancy removal for quality prediction of dynamic processes: A novel dynamic feature extraction strategy

Data-driven modeling has been widely recognized as an effective and essential approach for predicting key quality variables in industrial processes. However, due to the long production process and the dynamic fluctuation of operating conditions, it is necessary to integrate data from various spatial and temporal points to comprehensively describe the production process. Furthermore, data is characterized by massive redundancies and high dimensionalities, which pose challenges for modeling dynamic processes. To address these issues, this paper proposes a strategy of spatio-temporal bidirectional redundancy removal to extract dynamic features and applies it to quality prediction. Firstly, it constructs input samples with the spatio-temporal two-dimensional structure. Then, information redundancy is removed by implementing feature extraction in both the spatial and temporal directions, respectively. Additionally, a spatio-temporal weight matrix is used in this process to capture dynamic information which is more related to the output quality variable. Its effectiveness was verified by applying it to the debutanizer column process and the leaching process in zinc smelting. Results show that the proposed strategy generates efficient dynamic features with no information redundancy and low dimensionalities. Moreover, it outperforms other common types of dynamic process modeling methods in terms of quality prediction performance.

Ontological Method for the Modeling and Management of Building Component Construction Process Information

Knowledge of the construction process plays a decisive role in guiding construction and thus affects the quality of buildings. However, the integration and relevance of information in the traditional process file are poor, and it is easy to ignore the process changes caused by the differences when applying information directly. Therefore, this paper introduces an ontological method for the information modeling and management of the construction process. The proposed method uses machine readable language to integrate process knowledge in a structured way, thereby improving the relevance of information and promoting the reuse, sharing and retrieval of knowledge. At the same time, the semantic web rule language (SWRL) rules are used to model the relevant laws and regulations with reference to the construction product quality acceptance regulations and to correlate the construction process information so as to ensure the compliance of building products and the accuracy of process information. Finally, the feasibility of the method is verified via specific use cases on an ontology implementation platform.