Intelligent Systems and Photovoltaic Cells Empowered Topologically by Sudoku Networks

A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and chemistry.In this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical compounds.These networks are generally utilized in disorder,fractals,recursive groupings,and complex frameworks.Our outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced forms.The study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in them.The calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.

Multi-Sensor Intelligent System for On-Line and Real-Time Moneitoring Tool Cutting State in FMS

The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.

An intelligent system for calculating the scale of rational,enlarged production of an underground non-ferrous metal mine

The enlarged production scale of underground non-ferrous metal mines is affected by many uncertain factors difficult to describe mathematically with any level of accuracy.The problem can be solved by a synthesis of artificial intelligence.Based on the analysis of the major factors affecting the scale of enlarged production,we first interpreted in detail the design principles and structure of the intelligent system.Secondly,we introduced an ANN subsystem.In order to ensure technological and scale efficien- cies of the training samples for ANN,we filtrated the samples with a DEA method.Finally,we trained the intelligent system,which was proved to be very efficient.

3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

An Intelligent System for Analysing Manas Prakrti in Human Resource Management

Human resource management is said to be the importance of spiritual, ethical, and human values that condition human behaviour. The immediate problem that it poses for a full understanding of human functioning is that the inner subjective experiences of consciousness based on human resource management. Ayurveda occupies the heights of human psychological accomplishment and could usefully call upon the insights of any of these sources to aid in addressing the problematic nature of modern-day businesses and have significant bearing on human behaviour. Manas prakrti in Ayuverda contributes to the study of personality. Tamas-Rajas-Sattva temperamental groups give rise to the framework of Space-Time-Causation when evolution starts in association with Consciousness Principle in manas prakrti. In this paper I present a methodology to analyze Temperamental groups that are found in manas prakrti by using an intelligent system. This will guide understand, instrumental values, operating values, and weak values of employees in human resource management.

NEURAL NETWORK INTELLIGENT SYSTEM FOR THE ON-LINE OPTIMIZATION IN CHEMICAL PLANTS

A strategy of developing on-line optimization intelligent systems based on combiningflowsheeting simulation and optimization package with artificial neural networks(ANN)is presented inthis paper.A number of optimization cases for a certain chemical plant are obtained off-line byusing PROCESS-Ⅱ or other flowsheeting programming with optimization.Then,taking these cases astraining examples,we establish a neural network systems which can be used on-line as an optimizer toobtain setpoints from input data sampled from distributed control system through gross error detectionand data reconciliation procedures.Such an on-line optimizer possesses two advantages over nonlinearprogramming package:first of all,there is no convergence problem for the trained ANN to be usedonline;secondly,the frequency for setpoints updating is not limited because only algebraic calculationrather than optimization is required to be carried out on-line.Here two key problems ofimplementing ANN approaches to the on-line optimization

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

The Design and Management of Intelligent Systems in Mechanical Domestic Water Meters

The design and implementation of mechanical domestic water meters in current industrial organization and intellectual properties have been registered as an invention to solve the problems of current meters. The device operation includes inquiry of printing, subscription connection and disconnection in an emergency. This system includes a software and hardware parts on the users and the control center connecting with two-way mobile phone. Central control software sends the message through the wireless telecommunication lines to the user’s software, requesting the desired information and also provides the commands needed to be sent through the same. The same information can also be submitted to the control center. Through the same way, some of advantages of this method are as follows: installing on existing meters, cheap cost of inquiry call meter, the possibility of declaring illegal manipulation to the control center, the exchange of information using information encoding, and manipulating digital meters applying minor changes.

Study on the Intelligent System of Gear Pitch Deviation Measurement and Analysis

The intelligent controlling and data process of pitch error measurement is proposed.The whole system takes C++ as the development tool and takes advantages of object-oriented and visualization,having the advantages of easy operation and hu- manistic interface.In the meanwhile,the system can be complemented and improved according to the demand of user,having cer- tain independence.The research of the system provides an efficient and reliable way to measure and analyze the gear pitch,which can be referenced in the future research.

Application of artificial intelligent systems for real power transfer allocation

The application of various artificial intelligent(AI) techniques,namely artificial neural network(ANN),adaptive neuro fuzzy interface system(ANFIS),genetic algorithm optimized least square support vector machine(GA-LSSVM) and multivariable regression(MVR) models was presented to identify the real power transfer between generators and loads.These AI techniques adopt supervised learning,which first uses modified nodal equation(MNE) method to determine real power contribution from each generator to loads.Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques.The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of various AI methods compared to that of the MNE method.

Rough Mereology as a Tool for Knowledge Discovery and Reasoning in Intelligent Systems: A Survey

In this work, we present an account of our recent results on applications of rough mereology to problems of 1) knowledge granulation;2) granular preprocessing in knowledge discovery by means of decision rules;3) spatial reasoning in multi-agent systems in exemplary case of intelligent mobile robotics.

Contribution to Enterprise Intelligent Systems Architecture: Assumptions, Expectations and a Proposal

Currently, enterprise intelligent systems are built without expressed assumptions likely to enable harmonizing the field and correctly attributing the intelligence label to enterprise systems within the way they are built. In the present paper we propose three base assumptions for an enterprise intelligent system architecture as related to 1) Cognitive Enterprise, 2) Embodied Cognition and 3) Agent Paradigm. The aim is to open up possibility to deal with intelligence at the early stages of enterprise architecture and related disciplines such as system engineering and software development. In addi-tion, we suggest possible expectations from Enterprise Intelligent Systems Architecture and propose an architectural frame based on the cognitive architecture CogAff. Compared with similar works, we noted differences in the fact that our work takes into consideration the cognitive aspect of the firm and the general aspect of intelligence.

An Intelligent System for a Resource Allocation Problem Based on Fuzzy Reasoning

The development of the assistive abilities regarding the decision-making process o fan Intelligent Control System （ICS） like a fuzzy expert system implies the development of its functionality and its ability of specification. Fuzzy expert systems can model fuzzy controllers, i.e., the knowledge representation and the abilities of making decisions corresponding to fuzzy expert systems are much more complicated that in the case of standard fuzzy controllers. The expert system acts also as a supervisor, creating meta-level reasoning on a set of fuzzy controllers, in order to choose the best one for the management of the process. Knowledge Management Systems （KMSs） is a new development paradigm of Intelligent Systems which has resulted from a synergy between fuzzy sets, artificial neural networks, evolutionary computation, machine learning, etc., broadening computer science, physics, economics, engineering, mathematics. This paper presents, after a synergic new paradigm of intelligent systems, as a practical case study the fuzzy and temporal properties of knowledge formalism embedded in an ICS. We are not dealing high with level reasoning methods, because we think that real-time problems can only be solved by rather low-level reasoning. Solving the match-time predictability problem would allow us to build much more powerful reasoning techniques.

Health Seek: A Deep Learning-Based Intelligent System to Aid Medical Diagnosis

Medical diagnosis is one of the most tedious and complex processes that healthcare personnel face in their day-to-day life. To establish an adequate treatment, it is essential to carry out a correct and early evaluation of each patient. Occasionally, given the number of tests that need to be performed, this evaluation process can require a significant amount of time, and can negatively affect the patient’s recovery. The objective of this work is the development of a new software that, using Artificial Intelligence (AI), offers the healthcare professional support in the process of diagnosing the patient, as well as preventing the probability of suffering a certain disease, based on test information analytics and demographic information available. The system allows storing multiple models based on Deep Learning (DL), previously trained for the diagnosis of different diseases. These models allow predictions to be made based on available medical information. As a use case, one of these models has been successfully tested in diagnosing stroke events.

Intelligent System for Parallel Fault-Tolerant Diagnostic Tests Construction

This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.

Integrated Business Intelligent System for E-Health: A Case for Dermatology Diseases

In the current era, information technology has boosted every field of life either business industry or healthcare to integrate the internal processes of it. Due to the demand of managing huge data related to these fields numerous information systems play different roles in making the organizational processes robust and up to date. This paper discusses the integrated business intelligence implication specifically for healthcare to provide the fast and precise information on time. Therefore, this paper discusses the idea of building intelligent system based on Enterprise Resource Planning (ERP) databases using exclusively for dermatology diseases by applying data mining techniques. Firstly, classification mining has been applied for categorization data based on patient’s record. Then rules and patterns generated from the categorized data related to dermatology diseases, symptoms and treatments. The proposed system will retrieve the corresponding information related to the given symptoms along with medication and complete treatment. This research aims to integrate different ERP processes with centralized ERP database to provide business intelligence effectively for the dermatologists. The paper has provided with the comprehensive conceptual framework and each step has been discussed in detail.

Design and Implementation of an Intelligent Monitoring and Early Warning System for Kitchen Garbage Treatment

With a population of 1.4 billion in China and a huge daily output of kitchen waste,intelligent treatment of kitchen waste is imperative.This article elaborates on the design and implementation of an intelligent monitoring and early warning system from five aspects:system architecture design,hardware equipment selection and configuration,data collection and processing flow,early warning algorithm and model development,and system integration and testing verification.It also points out the advantages of the intelligent monitoring and early warning system in kitchen waste treatment.

Vision based intelligent traffic light management system using Faster R‐CNN

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider(AUG)is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications,in which path planning is an essential problem.Due to the complexity and variability of the ocean,accurate environment modeling and flexible path planning algorithms are pivotal challenges.The traditional models mainly utilize mathematical functions,which are not complete and reliable.Most existing path planning algorithms depend on the environment and lack flexibility.To overcome these challenges,we propose a path planning system for underwater intelligent internet vehicles.It applies digital twins and sensor data to map the real ocean environment to a virtual digital space,which provides a comprehensive and reliable environment for path simulation.We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters.The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing.The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility.The task-related reward function promotes the rapid convergence of the training.The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Channel Estimation for Reconfigurable Intelligent Surface Aided Multiuser Millimeter-Wave/THz Systems

It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.