There are many kinds of big data in peer to peer communication network, traditional big data integration algorithms ignore the classification of big data state. The location correlation between big data cannot be accurately obtained, which leads to the time-consuming process of big data integration and the low acquisition accuracy of big data location correlation. A fuzzy integration algorithm for big data in peer-to-peer communication network based on deep learning is proposed. According to the big data integration conditions of peer-to-peer communication network, the benchmark model of big data integration is constructed by combining semi-supervised deep learning. The marked sample updating model of the prior single classification fuzzy integrated data model is used to process the corresponding sample data. Combining the steady-state and dynamic residuals of the datum model, multiple information grains are obtained. Based on this, kalman filter is adopted to fuzzy fusion of big data to obtain state parameters, and multi-scale analysis is carried out to filter out big data noise. Using the obtained information grains to constrain the characteristic correlation degree of big data, the position correlation of big data is completed. Implement fuzzy integration algorithm design of big data in peer-to-peer communication network based on deep learning. In order to verify the effectiveness of the proposed algorithm, a simulation experiment is designed. Experimental results show that compared with many traditional methods, the proposed algorithm takes less time and the computational complexity of the proposed algorithm is significantly reduced. And the big data integration is more accurate and has better application effect.

Fuzzy Integration Algorithm of Big Data in Peer-to-Peer Communication Network Based on Deep Learning

The urban ecological network system is the basic guarantee for maintaining the stability of the overall green ecological space of the city and an important carrier for the city to practice the community of landscape, fields, forests, and lakes. The paper analyzed the separate year’s land use types map in 2007, 2011, 2015, and 2019 in Zhengzhou City by using the morphological spatial pattern analysis (MSPA) and landscape connectivity method and combining the graphic theory, quantitative analysis, and evaluation of the temporal and spatial pattern of the ecological network of Zhengzhou and even the changes in network connectivity. The results show that from 2007 to 2019, the total area of the urban ecological network in Zhengzhou is increasing gradually, which is the result of a series of “Forest City” strategic projects in Zhengzhou. The “Edge” area has developed and the transitional green belt between urban lands has improved; the “Bridge” area, “Branch” area, and “Loop” area increase first and then decrease, the degree of urban landscape fragmentation increases, and the landscape connectivity decreases; besides, the degree of fragmentation of urban landscape increases while the landscape connectivity decreases. The analysis of ecological network based on the landscape connectivity index showed that the overall and possible connectivity levels of Zhengzhou ecological network have increased. The level of connectivity of ecological network’s study area was still relatively low; the research results can improve the system of Zhengzhou ecological network, meanwhile provide effective information for the protection and restoration of ecological environment, and offer reference for the planning and construction of ecological network in central plains cities.

The evolution of spatial and temporal patterns of Zhengzhou ecological network based on MSPA

Unique Carbon monoxide (CO) is a gas made when carbon-based powers and synthetics are scorched deficiently. The slow development of this destructive gas can create cerebral pains, queasiness, heaving, dizziness, and disarray, which are all hazardous to travelers' wellbeing and the climate. The objective of this examination is to make an incorporated framework for a vehicle that can detect and distinguish synthetics like carbon monoxide (CO). Moreover, it persistently takes a look at the degree of gases and shows their focus. It likewise creates a programmed caution when CO levels outperform the ordinary reach, permitting the ventilation framework to be initiated rapidly. An instant message (SMS) is shipped off the approved client through GSM to naturally enact the power windows to dispose of carbon monoxide.

Microcontroller based Ingrained Monitoring of Covellar Carbon Monoxide Identification and Air Revitalizing in Air Skilled Automotives

In this research, a control mechanism for fuzzy fractional-order proportional integral derivatives was suggested (FFOPID). The fractional calculus application has been used in different fields of engineering and science and showed to be improved in the past few years. However, there are few studies on the implementation of the fuzzy fractional-order controller for control in real time. Therefore, for an experimental pressure control model, a fractional order PID controller with intelligent fuzzy tuning was constructed and its results were calculated through simulation. To highlight proposed control scheme advantages, the performances of the controller were inspected under load disturbances and variations in set-point conditions. Furthermore, with classical PID control schemes and fractional order proportional integral derivative (FOPID), a comparative study was made. It is revealed from the results that the suggested control scheme outclasses other categories of the control schemes.

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Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID

Facilitating higher photovoltaic penetration in residential distribution networks using demand side management and active voltage control

In today’s scenario all the technologies are operated in automatic condition from anywhere around the world. Based on our government system first 100 unit of energy is not calculated by energy meter, which is used to calculate the electricity consumed unit in the consumer node. Generally the IoT’s are used for home automation application such as fan and lamp was controlled by mobile phones based on cloud platform. The existing microcontroller is used to control only one data at a time for cloud based process. However to control the data but cannot provide continues energy to the consumer node. This cloud platform is only stores the dates and control to the controller parts with use of ThinkSpeak website. But in this paper deals with monitoring the energy from the solar and energy meter with help of Arduino controller and home automation is done by IoT with Esp8266 Wifi module. This module is used for control both the data’s and monitoring the energy level of the consumer side. The data from solar and energy meter is sent to the cloud through the Wi-fi module and these data’s are dumped into the arudino controller. The controller controls both the data’s parallely with help of IoT and data’s are displayed in LCD. Thus the results of IoT energy from solar and energy meter are provides continues energy to the consumer for home appliances.

IoT Based Energy Management for Smart Home

Neural network based hybrid power scheme with constant output using three DC source

Carbon monoxide (CO) is a dry, unscented gas created as a side-effect of deficient ignition of hydrocarbons. CO ties quickly to Hemoglobin (Hb), prompting the development of carboxyhemoglobin (COHb), so the oxygen conveying limit of the blood will be diminished and causing tissue hypoxia. Starting indications of breathing in CO is discombobulation, sleepiness, disarray, windedness and trouble relaxing. Incidental carbon monoxide harming in vehicles can happen due to spilling debilitates and lacking ventilation. This is because of the distribution of air inside shut vehicles for long time. A model is made to screen carbon monoxide focus level in vehicles produced from climate control system and robotized air ventilation framework is fused in it. On the off chance that the degree of CO fixation increments above limit esteem (50 sections for each million (ppm) in air) the air ventilation framework begins naturally. Moreover, grouping of Carbon monoxide (CO) and air ventilation frameworks are effectively observed utilizing a versatile application. The fundamental target of this examination work is to dispense with the gamble of human existence by checking Carbon monoxide level in vehicles and programmed air ventilation executes when CO focus reaches out past edge.

Embedded Monitoring of Covellar Carbon Monoxide Detection and Air freshening in Air Trained Automotives

Poultry farms in India are mostly monitored and controlled manually. The most important and basic factors that need to be monitored and controlled in poultry farms are hotness, moisture, air value point, illumination, freshening, door magnetic sensor, light-dependent resistor, egg count, and food feeding. The point is to work on the creation of solid chickens and decrease the death pace of chickens to further develop efficiency via computerizing the course of observed and control utilizing the Internet of Things (IoT). When the above parameters exceed the edge esteems, the cycles are started naturally that can assist with decreasing the death pace of chickens on the ranch. The framework likewise sends a programmed ready warning to the client through message, E-mail, and online message App. A Web point of interaction is likewise made to screen and show these boundaries.

U. Ramani

Papers

IoT Based Energy Management for Smart Home

Neural network based hybrid power scheme with constant output using three DC source

Embedded Monitoring of Covellar Carbon Monoxide Detection and Air freshening in Air Trained Automotives

Smart Monitoring Instrumentation Design for Poultry Farmhouse

Microcontroller based Ingrained Monitoring of Covellar Carbon Monoxide Identification and Air Revitalizing in Air Skilled Automotives