Design of ACO based PID controller for zeta converter using reduced order methodology

DC-DC conversion playing major role in many applications of power electronics and widely used in power circuit. Enormous methodology's of PID based converters are created in recent decades. The zeta converter is basically a DC – DC converter and its control the output voltage from an input voltage step up and step down the output voltage. The main scope of this paper is to design of ACO(Ant colony Optimization)based PID controller for zeta converter using model order technique with minimum of error, the zeta converter is basically fourth order system, design of PID controller for fourth order system is quite complex so model order reduction is used for controller design of zeta Converter, Therefore, the higher order system is reduced to second order using three different reduction techniques, then the ACO based PID controller is designed for reduced order system and is matched with the zeta converter, the results shows that designed controller for zeta converter gives quite good response for both the transfer function and is attached with zeta converter circuit, controller gives good performance indices is made on the basis of ISE, IAE and ITAE with minimum value of errors.     

Design of composite data acquisition gateway based on 5G network

The integrated data collection system hasn't changed much over the years. Their growth is dominated by digital filtering, and they get a new avionics bus. Time division multiplexing is still used to communicate with the system's data collector. These schemes make use of commands and data buses. While this method works, it has many drawbacks. These shortcomings are overwhelming the strict system architecture, system bandwidth limits, and expertise to only obtain an avionics bus, otherwise system bandwidth and one-way flow of data and control. This lead for high-end video recorders. To quietly monitor our actions and valuable information provides important warnings. However, it is usually completed by a device with this small processing power, which tracks the device, stores the data of the user's data, and is limited in processing. Therefore, it is important to store and process user data in the cloud to track 5 G network activity. This article proposes a simple 5G network gateway solution for custom user monitoring equipment. Collects charge data on Network 5G and Charging-aware multi-mode based routing protocol (CMRP). The CMRP routing mechanism is not yet stable, but the changes depend on the state of the energy charge sensor. This cannot be complemented by energy-efficient sensors and routing protocols that consume less energy.     

Application of iot medical image detection and prenatal genetic testing in obstetric clinic

The use of new techniques and methods can be significantly improved during prenatal examination and screening of fetal genetic abnormalities. Expanding the number of vectors in screening and screening for single-cell gene diseases in fetal DNA will increase the probability of success of the whole screening test.Recently, sub chromosomal abnormalities have been introduced into prenatal care. The lack of suitable tools makes it very difficult to obtain information about collection, management, and prenatal genetic testing. The purpose of this study is to improve the accessibility of nutritional information by using the advantages of advanced medical image detection and integrating the IoT (Internet of Things) and prenatal genetic testing platforms in obstetric outpatient clinics. Records are kept that the platform will allow IoT to interact with sensor practitioners and provide immediate access to medical images prior to delivery. This proposed system provides an IoT application for managing obstetric outpatient information and prenatal genetic testing requirements. The requirement for establishment is the realization of the development of an IoT platform for complete medical image detection information management with IoT input required for integration with existing medical information systems. In response, the purpose of the study is to use integrated IoT applications to help nutrition professionals, researchers, patients, and especially mothers, to enhance their collection of medical imaging and prenatal genetic testing IoT sensor based information transfer.     

Extraction of melody from polyphonic music using modified morlet wavelet

In this paper, the extraction of melody for polyphonic music is discussed. The method proposes a simple but effective modification in the Morlet wavelet as a modified Morlet wavelet (MMW). The pitch detection concept is applied to the transformed wavelet music signal for primary melody estimation and extraction. The proposed method is then compared with three other methods reported in the literature (the method focused on Short Time Fourier Transform, multi-resolution Fast Fourier Transform, and Morlet Wavelet). The percentage gross pitch error (GPE) parameter is considered for purposes of comparison. In comparison, it is observed that the proposed approach has the lowest GPE. The simulation results also present a lower percentage error in the predominant pitch frequency estimation for the proposed method.     

Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model

Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

Modeling polypharmacy effects with heterogeneous signed graph convolutional networks

Applied Intelligence - Pharmaceutical drug combinations can effectively treat various medical conditions. However, some combinations can cause serious adverse drug reactions (ADR). Therefore,...     

Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

Distributed and Parallel Databases - The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form...     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...