Model Of Intelligent System Research Articles

Technical requirements of age-friendly smart home technologies in high-rise residential buildings: A system intelligence analytical approach

Smart home technology (SHT) has been identified as a promising means of helping seniors to remain independent and maintain their quality of life (QoL) while containing spiralling care costs for older people. Despite official pilot schemes in many countries to promote SHT in seniors housing, there is limited understanding of the forms that such SHT interventions should take. This study builds on the analytical model of intelligent building control systems developed by the author; the aim is to provide a systematic approach to understanding the key intelligent attributes of smart-home devices. A qualitative participatory evaluation approach involving focus groups was adopted to investigate the needs of seniors and their SHT preferences. Fourteen features of the SHT technical requirements of four key intelligent attribute types were identified. This study’s insights will help to shape the way SHT is designed and used.

Planning for tourism routes using social networks

Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present the PlanTour, a system that creates personalized tourist plans using the human-generated information gathered from the minube11www.minube.com traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest from minube, groups these points with clustering techniques to split the problem into per-day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, the PlanTour planner is able to organize relevant points of interest taking into account user’s expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combining human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems.

Some objectives for constructing a model of intelligent control systems of dynamic objects

Some objectives for constructing a model of intelligent control systems of dynamic objects

A model developed for teaching an adaptive system of recognising cyberattacks among non-uniform queries in information systems

The study presents results aimed at further development of models for intelligent and self-educational systems of recognising abnormalities and cyberattacks in mission-critical information systems (MCIS). It has been proven that the existing systems of cyberdefence still significantly rely on using models and algorithms of recognising cyberattacks, which allow taking into account information about the structure of incoming streams or the attackers’ change of the intensity of queries, the speed of the attack, and the duration of the impulse. A mathematical model has been suggested for the system module of intelligent identification of cyberattacks in heterogeneous flows of queries and network forms of cyberattacks. The model recognises heterogeneous incoming flows of queries and any possible change in the query intensity and other parameters of a targeted cyberattack aimed at a MCIS. Simulation models, which had been created in MATLAB and Simulink, were used to research the dynamics of changes in the states of the subsystem of blocking queries in the process of detecting cyberattacks in a MCIS. The probability of solving the problem of recognising cyberattacks in heterogeneous flows of queries and network forms of cyberattacks is 85–98 %, depending on the type of the cyberattack. The results of the modelling allow selection of ways to counter and neutralize the effects of the impact of such targeted attacks and help analyse more sophisticated cyberattacks. The suggested model of recognising complex cyberattacks if attackers use non-uniform flows of queries is more accurate, by 5–7 %, than the other existing models. The developed simulation models enable a 25–30 % decrease in the setup time for projects of cyberdefence systems, including SIRCA for CIS or MCIS.

Hybrid cloud entropy systems based on Wiener process

Purpose The paper outlines a self-contained scheme for multiple networking agents at a location. It proposes a mathematical model for intelligent cloud entropy management systems. The purpose of this paper is to minimize the cost of system functionality by proposing the substantial use of a cloud-based system. Design/methodology/approach The paper proposes a hybrid cloud system, based on a fractional calculus of hybrid integral systems. Its discrete dynamics are suggested by using the fractional entropy type known as Tsallis entropy. This approach is based on the Wiener process (i.e. diffusion processes). This involves the net movement of information or data from a state of high meditation to a state of low observation. This property is a basic characteristic of hybrid cloud computing systems. Findings The paper offers a number of solutions to minimize the costs of cloud systems. The method is a proficient technique for presenting various types of fractional differential solutions. Research limitations/implications Researchers are encouraged to test and modify the proposed method. Practical implications The paper includes suggestions for the expansion of a powerful method for managing and integrating cloud systems stably. Originality/value This paper addresses an acknowledged need to study how the cost function of cloud systems can be achieved.

Design and Implementation of Intelligent Energy Efficient Conveyor System Model Based on Variable Speed Drive Control and Physical Modeling

Design and Implementation of Intelligent Energy Efficient Conveyor System Model Based on Variable Speed Drive Control and Physical Modeling

A Performance Evaluation of a Multi-Agent Mobile Learning System

Mobile devices have emerged as our daily companion whose applicability evolves as the day unfolds. One of such applications is in the area of learning, called mobile learning (M-learning). However, as with all new technologies, M-learning is faced with the issues of standard, content packing, and deployment. And like other distributed applications on ubiquitous networks, M-learning is challenged with performance issues. This work shows the implementation and evaluation of a model for intelligent mobile learning system (IMLS) using a multi-agent system (MAS), such as the Java Agent Development Environment (JADE) framework. Two M-learning applications (agent-based and non-agent) were developed, deployed and tested. The agent-based application is deployed using an HTTP-based Message Transfer Protocol (MTP). The results suggest that agents can increase the performance of an M-learning application up to eight times.

Improved modeling of intelligent tutoring systems using ant colony optimization

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems.

Theoretical model and implementation of a real time intelligent bin status monitoring system using rule based decision algorithms

Due to the rising trend of urbanization along with overconsumption of non-recyclable resources, the volume of municipal solid waste is increasing every day. An efficient, cost effective and environment friendly solution for real time bin status monitoring, collection and transportation of municipal solid waste is still a major challenge to the local municipal authorities. This research proposes a novel model, architecture and intelligent sensing algorithm for real time solid waste bin monitoring system that would contribute to the solid waste collection optimization. The monitoring application is based on decision algorithms for sensing solid waste data in a wireless sensor network. The system is built on a three level architecture like smart bin, gateway and control station. The elementary concept is that, smart bins collect their status when any changes occur and transmit the status data to a server via an intermediate coordinator. A set of applications in server presents the updated bin status on real time. The field test performances show that the system can efficiently monitor real time bin status that makes it feasible to decide, which bin should collect and which should not. Thus the proposed system has achieved its goal to provide real time bin status information to the solid waste management operator. Later, this information can be used for collection route optimization to reduce collection costs and carbon emissions which in turn contribute to build green society.

Management of Underground Infrastructure

India has announced a project of making 100 smart cities. For making a smart city one needs to consider many parameters such as smart water, smart electricity, smart transportation etc. There will be a need of smart underground infrastructure which includes underground water pipelines, communication cables, gas pipelines, electric flow, etc. As most of the cities in India have adopted underground drainage system, it is very important that this system should work in proper manner to keep the city clean, safe and healthy. If they fail to maintain the drainage system the pure water may get contaminated with drainage water and can spread infectious diseases. So different kind of work has been done to detect, maintain and manage these underground systems. Also Leaks and bursts are an unavoidable aspect of water distribution systems management, and can account for significant water loss within a distribution network if left undetected for long periods. This paper presents the implementation and design functions for monitoring and managing underground drainage system with different approaches. It also gives description about Waterwise system and detection method to detect leakage defects in sewer pipeline. Also some part of condition rating model for underground Infrastructure Sustainable Water Mains and Intelligent system for underground pipeline assessment, rehabilitation and management are explained.

Study on the Students Intelligent Food Card System Based on SaaS

The intelligent food card system has become an essential part of the digital society. Recently the solutions based on SaaS in China have become popular for the advantages of less upfront investment, good service quality and convenience maintainability. In this study, we discussed the solutions for three challenges: personalized demand, system integration and information utilization. Firstly, it was suggested that different users should select the private cloud or public cloud mode. Next, the students intelligent food card system model based on SaaS was proposed. The model with layered architecture has good configurability, flexibility and integration metrics. Then, several methods were put forward to solve the integration between the card systems with other information systems.

Efficiency prediction of control room operators based on human reliability analysis and dynamic decision-making style in the process industry

Process industries have a potential for the occurrence of major accidents. These accidents can have severe adverse effects on human health and the environment and they can cause extensive damage to equipment and buildings. During major process upsets, central control rooms are among the most stressful workplaces in the world. Therefore, Human Reliability Analysis and Dynamic Decision-Making Styles (DDMSs) play an important role in safety management in these industries. This study employs the intelligent Adaptive Neuro Fuzzy Inference System model associated with two questionnaires along with Cognitive Reliability Error Analysis Method to analyze the Human Reliability Influencing Factors (HRIFs) and DDMSs of the control room operators and to determine the efficiency of operators as well as their dominant and efficient decision-making styles. Nine influencing factors on human reliability and five DDMSs are evaluated and the correlation between the HRIFs is investigated. Efficiency of the operators, according to the HRIFs, is determined and they are ranked. Next, the most dominant and efficient of the DDMSs among the operators was identified. Finally, an intelligent algorithm for determining the efficiency of a control room's operators is developed. © 2015 American Institute of Chemical Engineers Process Saf Prog, 2015

Automated generation of multiphysics simulation models to support multidisciplinary design optimization

To ensure a consistent design representation for serving multidisciplinary analysis, this research study proposes an intelligent modeling system to automatically generate multiphysics simulation models to support multidisciplinary design optimization processes by using a knowledge based engineering approach. A key element of this system is a multiphysics information model (MIM), which integrates the design and simulation knowledge from multiple engineering domains. The intelligent modeling system defines classes with attributes to represent various aspects of physical entities. Moreover, it uses functions to capture the non-physical information, such as control architecture, simulation test maneuvers and simulation procedures. The challenge of system coupling and the interactions among the disciplines are taken into account during the process of knowledge acquisition. Depending on the domain requirements, the intelligent modeling system extracts the required knowledge from the MIM and uses this first to instantiate submodels and second to construct the multiphysics simulation model by combining all submodels. The objective of this research is to reduce the time and effort for modeling complex systems and to provide a consistent and concurrent design environment to support multidisciplinary design optimization. The development of an unstable and unmanned aerial vehicle, a multirotor UAV, is selected as test case. The intelligent modeling system is demonstrated by modeling thirty-thousand multirotor UAV designs with different topologies and by ensuring the automatic development of a consistent control system dedicated for each individual design. Moreover, the resulting multiphysics simulation model of the multirotor UAV is validated by comparing with the flight data of an actual quadrotor UAV. The results show that the multiphysics simulation model matches test data well and indicate that high fidelity models can be generated with the automatic model generation process.

Improving Battery Lifetime of Gridable Vehicles and System Reliability in the Smart Grid

The intermittent nature of renewable energy sources (RESs) and unpredictable load demands are two major challenges in providing uninterrupted power supply from a smart grid. One way to address these challenges is to use storage devices that can store surplus energy from RESs and discharge the energy back to the grid when needed. Researchers have recently introduced the idea to use electric vehicles with vehicle-to-grid capability, which are called “gridable vehicles” (GVs), as storage devices in the smart grid. Using GVs as loads is well accepted, but as sources, they disrupt system reliability if insufficient GVs are available for discharging when needed. An availability planning model is thus required to address this issue. GV owners' concern over battery lifetime reductions is another issue that impedes the required participation rates for GVs in vehicle-to-grid discharge programs. In this paper, we present an intelligent smart grid system model, which mitigates real-time unavailability of GV sources via an availability planning model. We also propose a GV selection model that prevents GV batteries from premature expiry due to their vehicle-to-grid operations. Simulation results confirm that our proposed models maintain better overall reliability and increase average battery lifetime by up to 3.5 years compared with existing vehicle-to-grid models.

A framework with reasoning capabilities for crisis response decision–support systems

This paper reviews the methods in decision–support systems for crisis management. While much research has been conducted in this field, little emphasis has been placed on the uncertainty representation, reasoning, learning and real time decision-making capabilities of system. The purpose of this paper is to explore the basic assumptions of constructing an intelligent decision–support system for crisis response management. A novel framework for crisis response decision-making system under the assumption of openness to various kinds of uncertainties, reasoning and learning with real-time response is proposed. We applied the Non-Axiomatic Logic in representing and reasoning the uncertainty knowledge in the framework and demonstrated the reasoning and learning mechanisms of the framework through an application in a case study in the field of urban firefighting. The results show that the framework provides a suitable model for intelligent crisis response decision support systems.

A new intelligent hardware implementation based on field programmable gate array for chaotic systems

In the present study, a new intelligent hardware implementation was developed for chaotic systems by using field programmable gate array (FPGA). The success and superior properties of this new intelligent hardware implementation was shown by applying the Modified Van der Pol–Duffing Oscillator Circuit (MVPDOC). The validation of intelligent system model was tested with both software and hardware. For this purpose, initially the intelligent system model of MVPDOC was obtained by using the wavelet decompositions and Artificial Neural Network (ANN). Then, the intelligent system model obtained has been written in Very High Speed Integrated Circuit Hardware Description Language (VHDL). In the next step, these configurations have been simulated and tested under ModelSim Xilinx software. And finally the best configuration has been implemented under the Xilinx Virtex-II Pro FPGA (XC2V1000). Furthermore, the High Personal Simulation Program with Integrated Circuit Emphasis (HSPICE) simulation of MVPDOC has been carried out under ModelSim Xilinx software for comparison with proposed intelligent system. The results obtained show that the proposed intelligent system simulation has much higher speed in comparison with HSPICE simulation.

Bayesian Networks in Educational Assessment: The State of the Field.

Bayesian networks (BN) provide a convenient and intuitive framework for specifying complex joint probability distributions and are thus well suited for modeling content domains of educational assessments at a diagnostic level. BN have been used extensively in the artificial intelligence community as student models for intelligent tutoring systems (ITS) but have received less attention among psychometricians. This critical review outlines the existing research on BN in educational assessment, providing an introduction to the ITS literature for the psychometric community, and points out several promising research paths. The online appendix lists 40 assessment systems that serve as empirical examples of the use of BN for educational assessment in a variety of domains.

A Fuzzy Logic-Based Prediction Model for Kerf Width in Laser Beam Machining

In laser beam machining, the main concern is the machining quality as kerf width of the end product. It is essential for industrial applications to cut the workpiece with minimum kerf width. However, it is difficult to develop a precise functional relationship between input and output variables in laser machining. Therefore, an effort has been conducted to build up an intelligent fuzzy expert system (FES) model to predict the kerf width in CO2 laser cutting. The employed input parameters were assisting gas pressure, laser power, cutting speed, and standoff distance. The fuzzy logic was performed on fuzzy toolbox in MATLAB R2009b by employing Mamdani technique. In total, 81 experiments were carried out and experimental results were used for training and testing of the developed fuzzy model. Relative error and goodness of fit were used to investigate the accuracy of the prediction ability and the values of 3.852% and 0.994, respectively, were found to be satisfactory. This paper will extend knowledge about the prediction of kerf width by using FES model.

On accurate determination of PVT properties in crude oil systems: Committee machine intelligent system modeling approach

This study is focused on accurate determination of PVT properties of reservoir oil using an ensemble approach referred to as committee machine intelligent system. PVT properties of interest are oil formation volume factor (Bo) and bubble point pressure (Pb). Committee machine intelligent system model developed in this work combine multi-layer perceptron network, radial basis function network, and least squares support vector machine via a modified weighted averaging method, while optimizing these weights using genetic algorithm. Developed committee machine intelligent system model was found to accord excellently with experimental data yielding correlation coefficients (R2) of 0.980 and 0.976 for bubble point pressure (Pb), and oil formation volume factor (Bo), respectively. Comprehensive comparisons were also carried out between a variety of PVT prediction models and committee machine intelligent system model developed in this study. At the end, leverage value statistics method was applied to detect and identify some probable outlying points from the gathered databases.

Transient Absorption Spectroscopy of Anatase and Rutile: The Impact of Morphology and Phase on Photocatalytic Activity

We employed transient absorption spectroscopy (TAS) to investigate the kinetic dependences of photocatalysis in anatase and rutile TiO2 films of varying morphology. In mesoporous films, anatase was ∼30 times more efficient than rutile in the photocatalytic degradation of an intelligent ink model system. Independent of phase, up to 100 lower levels of photocatalysis were found in dense films. Charge carrier lifetimes were probed by TAS on the microsecond to second time scale. For both rutile and anatase, recombination was independent of morphology. Rutile exhibited up to 10 times slower recombination kinetics than anatase. Efficient, irreversible hole scavenging by alcohols was present in mesoporous anatase alone, resulting in the generation of long-lived electrons (τ ≈ 0.7 s) which, upon the addition of the dye reduction target resazurin, enabled efficient electron transfer (τ ≈ 3 ms). Hole scavenging by alcohols on mesoporous rutile was substantially less efficient and more reversible than anatase, resulting in only a marginal increase in electron lifetime. The lower activity of rutile was not due to differences in recombination but rather to the deficiency of rutile holes to drive efficient and irreversible alcohol oxidation.