Search results for: RSM Model

Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

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A Functional Model to mimic the functional correctness of a Neural Network Compute Accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of gcc compilers to BF-16 datatype, which we addressed with a native BF-16 generator integrated to our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex Neural Network Accelerator design by proposing a Functional Model-based scoreboard or Software model using SystemC. The proposed Functional Model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT bringing up micro-steps of execution.

Keywords: ISA (instruction set architecture), NN (neural network), TLM (transaction-level modeling), GEMM (general matrix multiplication)

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Authors: Li Chen, Alex Skvortsov, Chris Norwood

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Gas releasing into water can be found in for many industrial situations. This process results in the formation of bubbles and acoustic emission which depends upon the bubble characteristics. If the bubble creation rates (bubble volume flow rate) are of interest, an inverse method has to be used based on the measurement of acoustic emission. However, there will be sound attenuation through the bubbly plume which will influence the measurement and should be taken into consideration in the model. The sound transmission through the bubbly plume depends on the characteristics of the bubbly plume, such as the shape and the bubble distributions. In this study, the bubbly plume shape is modelled using a self-similarity model, which has been normally applied for a single phase buoyant plume. The prediction is compared with the experimental data. It has been found the model can be applied to a buoyant plume of gas-liquid mixture. The influence of the gas flow rate and discharge nozzle size is studied.

Keywords: bubbly plume, buoyant plume, bubble acoustics, self-similarity model

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

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Authors: Jin Gang Lee, Hyun-Soo Lee, Moonseo Park

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The utilization of BIM and IFC allows project participants to collaborate across different areas by consistently sharing interoperable product information represented in a model. Comments or markups generated during the coordination process can be categorized as communication information, which can be shared in less standardized manner. It can be difficult to manage and reuse such information compared to the product information in a model. The present study proposes an interoperable coordination method using BCF (the BIM Collaboration Format) for managing and sharing the communication information during BIM based coordination process. A management function for coordination in the BIM collaboration system is developed to assess its ability to share the communication information in BIM collaboration projects. This approach systematically links communication information during the coordination process to the building model and serves as a type of storage system for retrieving knowledge created during BIM collaboration projects.

Keywords: design coordination, building information model, BIM collaboration format, industry foundation classes

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Authors: Allé Dieng, Mamadou Bousso, Latif Dramani

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The purpose of this work is to show the complexity behind economical interrelations in a country and provide a linear dynamic model of economical dependency evolution in a country. The model is based on National Transfer Account which is one of the most robust methodology developed in order to measure a level of demographic dividend captured in a country. It is built upon three major factors: demography, economical dependency and migration. The established mathematical model has been simulated using Netlogo software. The innovation of this study is in describing economical dependency as a complex system and simulating using mathematical equation the evolution of the two populations: the economical dependent and the non-economical dependent as defined in the National Transfer Account methodology. It also allows us to see the interactions and behaviors of both populations. The model can track individual characteristics and look at the effect of birth and death rates on the evolution of these two populations. The developed model is useful to understand how demographic and economic phenomenon are related

Keywords: ABM, demographic dividend, National Transfer Accounts (NTA), ODE

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Authors: Christopher Mkirema Maghanga, Maurice Mghendi Mwamburi

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Parametric modeling provides a means to deeper understand the properties of materials. Drude, Brendel, Lorentz and OJL incorporated in SCOUT® software are some of the models used to study dielectric films. In our work, we utilized Brendel and Drude models to extract the optical constants from spectroscopic data of fabricated undoped and niobium doped titanium oxide thin films. The individual contributions by the two models were studied to establish how they influence the dielectric function. The effect of dopants on their influences was also analyzed. For the undoped films, results indicate minimal contribution from the Drude term due to the dielectric nature of the films. However as doping levels increase, the rise in the concentration of free electrons favors the use of Drude model. Brendel model was confirmed to work well with dielectric films - the undoped titanium Oxide films in our case.

Keywords: modeling, Brendel model, optical constants, titanium oxide, Drude Model

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Authors: Basil Manos, Parthena Chatzinikolaou, Fedra Kiomourtzi

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This paper presents a Multicriteria Mathematical Programming model for farm planning and sustainable optimization of agricultural production. The model can be used as a tool for the analysis and simulation of agricultural production plans, as well as for the study of impacts of various measures of Common Agriculture Policy in the member states of European Union. The model can achieve the optimum production plan of a farm or an agricultural region combining in one utility function different conflicting criteria as the maximization of gross margin and the minimization of fertilizers used, under a set of constraints for land, labor, available capital, Common Agricultural Policy etc. The proposed model was applied to the region of Larisa in central Greece. The optimum production plan achieves a greater gross return, a less fertilizers use, and a less irrigated water use than the existent production plan.

Keywords: sustainable optimization, multicriteria analysis, agricultural production, farm planning

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Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

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Many quality models have been used to measure e-government portals quality. However, the absence of an international consensus for e-government portals quality models results in many differences in terms of quality attributes and measures. The aim of this paper is to compare and analyze the existing e-government quality models proposed in literature (those that are based on ISO standards and those that are not) in order to propose guidelines to build a good and useful e-government portals quality model. Our findings show that, there is no e-government portal quality model based on the new international standard ISO 25010. Besides that, the quality models are not based on a best practice model to allow agencies to both; measure e-government portals quality and identify missing best practices for those portals.

Keywords: e-government, portal, best practices, quality model, ISO, standard, ISO 25010, ISO 9126

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Ozden Saygili, Eser Cakti

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The masonry building stock in Istanbul and in other cities of Turkey are exposed to significant earthquake hazard. Determination of the safety of masonry structures against earthquakes is a complex challenge. This study deals with experimental tests and non-linear dynamic analysis of masonry structures modeled through discrete element method. The 1:10 scale model of Mustafa Paşa Mosque was constructed and the data were obtained from the sensors on it during its testing on the shake table. The results were used in the calibration/validation of the numerical model created on the basis of the 1:10 scale model built for shake table testing. 3D distinct element model was developed that represents the linear and nonlinear behavior of the shake table model as closely as possible during experimental tests. Results of numerical analyses with those from the experimental program were compared and discussed.

Keywords: dynamic analysis, non-linear modeling, shake table tests, masonry

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Authors: Jung-Min Yang

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A method of tolerating input faults for input/state asynchronous sequential machines is proposed. A corrective controller is placed in front of the considered asynchronous machine to realize model matching with a reference model. The value of the external input transmitted to the closed-loop system may change by fault. We address the existence condition for the controller that can counteract adverse effects of any input fault while maintaining the objective of model matching. A design procedure for constructing the controller is outlined. The proposed reachability condition for the controller design is validated in an illustrative example.

Keywords: asynchronous sequential machines, corrective control, fault tolerance, input faults, model matching

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Authors: Gürkan Şakar, Fevzi Çakmak Bolat

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In this study free vibration analysis of aluminum honeycomb sandwich structures were carried out experimentally and numerically. The natural frequencies and mode shapes of sandwich structures fabricated with different configurations for clamped-free boundary condition were determined. The effects of lower and upper face sheet thickness, the core material thickness, cell diameter, cell angle and foil thickness on the vibration characteristics were examined. The numerical studies were performed with ANSYS package. While the sandwich structures were modeled in ANSYS the continuum model was used. Later, the numerical results were compared with the experimental findings.

Keywords: sandwich structure, free vibration, numeric analysis, 3D model, continuum model

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

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In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, triazoles, novel heterocycles

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Authors: N. Jinesh, K. Shankar

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This article presents a method of using the one dimensional piezo-electric patch on beam model for structural identification. A hybrid element constituted of one dimensional beam element and a PZT sensor is used with reduced material properties. This model is convenient and simple for identification of beams. Accuracy of this element is first verified against a corresponding 3D finite element model (FEM). The structural identification is carried out as an inverse problem whereby parameters are identified by minimizing the deviation between the predicted and measured voltage response of the patch, when subjected to excitation. A non-classical optimization algorithm Particle Swarm Optimization is used to minimize this objective function. The signals are polluted with 5% Gaussian noise to simulate experimental noise. The proposed method is applied on beam structure and identified parameters are stiffness and damping. The model is also validated experimentally.

Keywords: inverse problem, particle swarm optimization, PZT patches, structural identification

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Authors: Hidir S. Nogay

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In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

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Authors: Y. D. Lim, K. S. Yap, K. T. Ooi

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Cross Vane Expander Compressor (CVEC) is a newly invented expander-compressor combined unit, where it is introduced to replace the compressor and the expansion valve in traditional refrigeration system. The mathematical model of CVEC has been developed to examine its performance, and it was found that the energy consumption of a conventional refrigeration system was reduced by as much as 18%. It is believed that energy consumption can be further reduced by optimizing the device. In this study, the coefficient of performance (COP) of CVEC has been optimized under predetermined operational parameters and constrained main design parameters. Several main design parameters of CVEC were selected to be the variables, and the optimization was done with theoretical model in a simulation program. The theoretical model consists of geometrical model, dynamic model, heat transfer model and valve dynamics model. Complex optimization method, which is a constrained, direct search and multi-variables method was used in the study. As a result, the optimization study suggested that with an appropriate combination of design parameters, a 58% COP improvement in CVEC R134a refrigeration system is possible.

Keywords: COP, cross vane expander-compressor, CVEC, design, simulation, refrigeration system, air-conditioning, R134a, multi variables

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Authors: M. R. Dahmardeh Ghaleno, M. Nohtani, S. Khaledi

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Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km², and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.

Keywords: watershed simulation, WetSpa, stream flow, flood prediction

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Authors: Adam Beaunoyer, Cory Beaunoyer, Mohammed Elmorsy, Hanan Saleh

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This research aims to create a reinforcement learning agent capable of racing in challenging simulated environments with a low collision count. We present a reinforcement learning agent that can navigate challenging tracks using both a Deep Q-Network (DQN) and a Soft Actor-Critic (SAC) method. A challenging track includes curves, jumps, and varying road widths throughout. Using open-source code on Github, the environment used in this research is based on the 1995 racing game WipeOut. The proposed reinforcement learning agent can navigate challenging tracks rapidly while maintaining low racing completion time and collision count. The results show that the SAC model outperforms the DQN model by a large margin. We also propose an alternative multiple-car model that can navigate the track without colliding with other vehicles on the track. The SAC model is the basis for the multiple-car model, where it can complete the laps quicker than the single-car model but has a higher collision rate with the track wall.

Keywords: reinforcement learning, soft actor-critic, deep q-network, self-driving cars, artificial intelligence, gaming

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Authors: Franziska Mais, Till Gramberg

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The paper presents the development of a sustainable business model for a platform-based application tailored for sawing companies in small and medium-sized enterprises (SMEs). The focus is on the integration of sustainability principles into the design of the business model to ensure a technologically advanced, legally sound, and economically efficient solution. Easy2IoT is a research project that aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements, and potential solutions for smart services are derived. The structuring of the business ecosystem within the application plays a central role, whereby the roles of the partners, the management of the IT infrastructure and services, as well as the design of a sustainable operator model are considered. The business model is developed using the value proposition canvas, whereby a detailed analysis of the requirements for the business model is carried out, taking sustainability into account. This includes coordination with the business model patterns, according to Gassmann, and integration into a business model canvas for the Easy2IoT product. Potential obstacles and problems are identified and evaluated in order to formulate a comprehensive and sustainable business model. In addition, sustainable payment models and distribution channels are developed. In summary, the article offers a well-founded insight into the systematic development of a sustainable business model for platform-based applications in SME sawmills, with a particular focus on the synergy of ecological responsibility and economic efficiency.

Keywords: business model, sustainable business model, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Lei Zhao, Zhe Yuan, Wenyue Kuang

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This paper studies two-warehouse inventory models for a deteriorating item considering that the demand is influenced by inventory levels. The problem mainly focuses on the optimal order policy and the optimal order cycle with inventory-level-dependent demand in two-warehouse system for retailers. It considers the different deterioration rates and the inventory holding costs in owned warehouse (OW) and rented warehouse (RW), and the conditions of transportation cost, allowed shortage and partial backlogging. Two inventory models are formulated: last-in first-out (LIFO) model and first-in-first-out (FIFO) model based on the policy choices of LIFO and FIFO, and a comparative analysis of LIFO model and FIFO model is made. The study finds that the FIFO policy is more in line with realistic operating conditions. Especially when the inventory holding cost of OW is high, and there is no difference or big difference between deterioration rates of OW and RW, the FIFO policy has better applicability. Meanwhile, this paper considers the differences between the effects of warehouse and shelf inventory levels on demand, and then builds retailers’ inventory decision model and studies the factors of the optimal order quantity, the optimal order cycle and the average inventory cost per unit time. To minimize the average total cost, the optimal dispatching policies are provided for retailers’ decisions.

Keywords: FIFO model, inventory-level-dependent, LIFO model, two-warehouse inventory

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Authors: A. El Amri, M. El Yakhloufi Haddou, A. Khamlichi

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The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as fracture. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. The influences of thermal and mechanical loads cause damage. The temperature and strain rate dependent materials’ properties and their modelling are discussed. A Johnson-Cook Model of damage has been selected for the numerical simulations. Virtual software called the ABAQUS 6.11 is used for finite element analysis. This model was introduced in order to give information concerning crack initiation during thermal and mechanical loads.

Keywords: thermo-mechanical fatigue, failure, numerical simulation, fracture, damage

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Authors: Cau Tran, Tu Nguyen, Tien Pham

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Model-based design (MBD) is a mathematical and visual technique for addressing design issues in the fields of communications, signal processing, and complicated control systems. It is utilized in several automotive, aerospace, industrial, and motion control applications. Virtual models are at the center of the software development process with model based design. A method used in the creation of embedded software is model-based design. In this study, the LAT motor is modeled in a simulation environment, and the LAT motor control is designed with a cascade structure, a speed and current control loop, and a controller that is used in the next part. A PID structure serves as this controller. Based on techniques and motor parameters that match the design goals, the PID controller is created for the model using traditional design principles. The MBD approach will be used to build embedded software for motor control. The paper will be divided into three distinct sections. The first section will introduce the design process and the benefits and drawbacks of the MBD technique. The design of control software for LAT motors will be the main topic of the next section. The experiment's results are the subject of the last section.

Keywords: model based design, limited angle torque, intellectual property core, hardware description language, controller area network, user datagram protocol

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Authors: Kholil, Laksanto Utomo

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The food needs of the Indonesian people will continue increase year to year due to the increase of population growth. For ensuring food securityand and resilience, the government has developed a program food self-resilience village since 2006. Food resilience is a complex system, consisting of subsystem availability, distribution and consumption of the sufficiency of food consumed both in quantity and quality. Low access, and limited assets to food sources is the dominant factor vulnerable of food. Women have a major role in supporting the productive activities of the family to meet food sufficiency and resilience. The purpose of this paper is to discuss the model of food self-resilience village wich gender responsive by using a dynamic system model. Model will be developed into 3 level: family, vilage, and regency in accordance with the concept of village food resilience model wich has been developed by ministry of agriculture. Model development based on the results of experts discussion and field study. By some scenarios and simulation models we will able to develop appropriate policy strategies for family food resilience. The result of study show that food resilience was influenced by many factors: goverment policies, technology, human resource, and in the same time it will be a feed back for goverment policies and number of poor family.

Keywords: food availability, food sufficiency, gender, model dynamic, law enfrocement

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Authors: Reza Davoodi, Hamid Abbasi, Heidar Norouzi, Gholamabbas Alipourian

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A dynamic model shaping a process of knowledge management (KM) culture change is suggested. It is aimed at providing effective KM of employees for obtaining desired results in an organization. The essential requirements for obtaining KM culture change are determined. The proposed model realizes these requirements. Dynamics of the model are expressed by a change of its parameters. It is adjusted to the dynamic process of KM culture change. Building the model includes elaboration and integration of interconnected components. The “Result” is a central component of the model. This component determines a desired organizational goal and possible directions of its attainment. The “Confront” component engenders constructive confrontation in an organization. For this reason, the employees are prompted toward KM culture change with the purpose of attaining the desired result. The “Assess” component realizes complex assessments of employee proposals by management and peers. The proposals are directed towards attaining the desired result in an organization. The “Reward” component sets the order of assigning rewards to employees based on the assessments of their proposals.

Keywords: knowledge management, organizational culture change, employee, result

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Anusha P. Wijesundara, Dulap I. Rathnayake, Nihal D. Perera

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Bathymetric information is fundamental importance to coastal and marine planning and management, nautical navigation, and scientific studies of marine environments. Satellite-derived bathymetry data provide detailed information in areas where conventional sounding data is lacking and conventional surveys are inaccessible. The two empirical approaches of log-linear bathymetric inversion model and non-linear bathymetric inversion model are applied for deriving bathymetry from high-resolution multispectral satellite imagery. This study compares these two approaches by means of geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. Based on the Levenberg-Marquardt method calibrated the parameters of non-linear inversion model and the multiple-linear regression model was applied to calibrate the log-linear inversion model. In order to calibrate both models, Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Residuals were calculated as the difference between the derived depth values and the validation echo sounder bathymetry data and the geographical distribution of model residuals was mapped. The spatial autocorrelation was calculated by comparing the performance of the bathymetric models and the results showing the geographic errors for both models. A spatial error model was constructed from the initial bathymetry estimates and the estimates of autocorrelation. This spatial error model is used to generate more reliable estimates of bathymetry by quantifying autocorrelation of model error and incorporating this into an improved regression model. Log-linear model (R²=0.846) performs better than the non- linear model (R²=0.692). Finally, the spatial error models improved bathymetric estimates derived from linear and non-linear models up to R²=0.854 and R²=0.704 respectively. The Root Mean Square Error (RMSE) was calculated for all reference points in various depth ranges. The magnitude of the prediction error increases with depth for both the log-linear and the non-linear inversion models. Overall RMSE for log-linear and the non-linear inversion models were ±1.532 m and ±2.089 m, respectively.

Keywords: log-linear model, multi spectral, residuals, spatial error model

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Authors: Nadish Anand, Richard D. Gould

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A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

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Authors: Kolesnik Nadezda A.

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The research aims to elaborate inter-organizational network relationships management model to maximize value co-creation. We propose a network management framework that requires evaluation of network partners with respect to their position and role in network; and elaboration of appropriate relationship development strategy with partners in network. Empirical research and approval is based on the case study method, including structured in-depth interviews with the companies from b2b market.

Keywords: inter-organizational networks, value co-creation, model, B2B market

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Authors: Phanida Phukoetphim, Asaad Y. Shamseldin

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In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand.

Keywords: multi-model combination, rainfall-runoff modeling, stochastic gradient boosting, bioinformatics

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Authors: Michele Catalano, Jacopo Di Domenico, Luca Riccetti, Andrea Teglio

Abstract:

We develop a data-driven agent based model (ABM) for the Italian economy. We calibrate the model for the initial condition and parameters. As a preliminary step, we replicate the Monte-Carlo simulation for the Austrian economy. Then, we evaluate the dynamic properties of the model: the long-run equilibrium and the allocative efficiency in terms of disequilibrium patterns arising in the search and matching process for final goods, capital, intermediate goods, and credit markets. In this perspective, we use a randomized initial condition approach. We perform a robustness analysis perturbing the system for different parameter setups. We explore the empirical properties of the model using a rolling window forecast exercise from 2010 to 2022 to observe the model’s forecasting ability in the wake of the COVID-19 pandemic. We perform an analysis of the properties of the model with a different number of agents, that is, with different scales of the model compared to the real economy. The model generally displays transient dynamics that properly fit macroeconomic data regarding forecasting ability. We stress the model with a large set of shocks, namely interest policy, fiscal policy, and exogenous factors, such as external foreign demand for export. In this way, we can explore the most exposed sectors of the economy. Finally, we modify the technology mix of the various sectors and, consequently, the underlying input-output sectoral interdependence to stress the economy and observe the long-run projections. In this way, we can include in the model the generation of endogenous crisis due to the implied structural change, technological unemployment, and potential lack of aggregate demand creating the condition for cyclical endogenous crises reproduced in this artificial economy.

Keywords: agent-based models, behavioral macro, macroeconomic forecasting, micro data

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