The greenhouses are equipped with a sophisticated device of regulation. Many greenhouses are still controlled manually and require the intervention of the grower . But the Automated Greenhouse climate control system can be used in order to reduce disease infection and to influence plant development in quickly. A Tomato Crop growth in greenhouse is basically determined by the climate variables within the environment like Temperature of air, Humidity of air, Soil Moisture, CO2 Concentration, Light Intensity and pH scale.
Abstract Development of controlled environment in greenhouse is of prim importance for out of season production, increasing yield and enhancing the quality of produce. Due to high cost and impossibility of continuous human attendance in greenhouse, it is desirable to control the greenhouse environment by employing automatic control devices. In This study, the greenhouse conditions were controlled by using artificial neural network (ANN). First, an experimental greenhouse was built and equipped with control instruments. Then by using electronic sensors, some climatic parameter data (temperature, humidity, carbon dioxide and light index) were measured and saved during five minute periods. In the next stage, three types of ANN including feed forward neural networks with multiple delays in the input, two-layer neural network with a feedback from hidden layer and input delay and three-layer neural network with two feedbacks from hidden layers and input delay were trained by 66% of the recorded data, and were evaluated by using the remaining data. The three-layer neural network with two feedbacks from hidden layers and input delay was able to better predict humidity and light index of the greenhouse with MSE,s of 0.025 and 0.032, respectively. Temperature and infrared index were better predicted by using the feed forward neural networks with multiple delays in the input with MSE,s of 0.016 and 0.017, respectively. In all cases, training time was less than 14 minutes and simulation time being always less than 0.2 second, makes using neural network feasible for automatic control of greenhouse.
port manager” component handles the communication between the local server and the coordinator. When the local server receives a serial message, the “serial port manager” forwards the message to the “message dispatcher” component. The “message dispatcher” works for all the message exchange within the local server. It parses and redirects the messages to the correct component according to the received message type. The “web service synchronizer” component is implemented to synchronize messages between the wireless sensor network and the main server. Generally, the “web service synchronizer” forwards the sensor message from the local server to the main server. It also sends polling message to the main server to ask for pending control schedules. Once the control schedule is received, the “web service synchronizer” forwards the schedule to the “message dispatcher” for further process. The “control function” component is the central software module that manages all the control operations. It contains the logic to generate the command to control the wireless sensor network with different control algorithms. The “warning function” component checks the status of the wireless device and sends out warning message when emergency happens.
Ergonomics objectives include designing and optimizing human well-being and overall system performance, in order to design equipments, devices and processes that fit the human body and its cognitive abilities for offering safety, luxury, and physiological control to humans‘ daily life . The application of ergonomics principles and practices, have proven success in improving performance, productivity, competitiveness, safety and health in most occupational sectors . Ergonomics can contribute to the quality of education in the following three segments: preservation of the health of students, creation of a comfortable working environment and adjusting the process of education according to students' abilities . In current work, as a part of smart universities, a design of a smart educational environment assessing and control system using Fuzzy Logic algorithms is introduced, The system is designed such that, using specially designed wireless sensor modules, it continuously reads, calculated, monitors, and assesses the educational environment in terms of the environmental and climatic indices, particularly; the thermal comfort, the apparent temperature, and the temperature-humidity index with their related levels of comfort, dangers and experienced by students disorders. Based on measurements and referring to both regulations by WHO, parameters in human dwelling places and the best of human environment feeling, the system will take the best actions to maintain the optimal levels of these environmental variables and factors to ensure high quality of educational environment, enhancing learning efficiency for students, and finally maintain safety of students and equipment. This paper is organized as follow. Section 2, provides system Methodology and working principle/Algorithm. In section 3, the applied environmental and climatic variables, factors and working conditions, their definitions and limits are introduced. In section 4, the applied direct indices, limit values and experienced by human effects are presented. In section 5, system software and hardware design, prototyping, testing and evaluation including Fuzzy algorithm design are discussed. Finally conclusions and future work
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Circulating refrigerant vapor enters the compressor (located in the engine bay) and is compressed to a higher pressure, resulting in a higher temperature as well. The hot, compressed refrigerant vapor is now at a temperature and pressure at which it can be condensed and is routed through a condenser, usually located in front of the car's radiator. Here the refrigerant is cooled by air flowing across the condenser coils and condensed into a liquid. Thus, the circulating refrigerant rejects heat from the system and the heat is carried away by the air.
SDSP has been studied extensively, even though at this point it has never been carried out (The Royal Society, 2009). The proponents point to volcanic eruptions and the impact of, for example, the 1991 eruption of Mount Pinatubo, which injected 20 megatons of sulfur dioxide gas into the stratosphere. Mount Pinatubo’s eruption resulted in a peak global cooling of approximately 0.5 K (Soden, 2002). This global cooling led to several regional impacts including a strengthening of the North Atlantic Oscillation, which is an extremely important mode in climate variability (Stenchikov, 2002). SDSP is a geoengineering scheme that mimics the volcanic effect by releasing large quantities of sulfur, a precursor gas, into the lower tropical layer of the stratosphere. This particle or precursor gas will react with other gases and oxidize in the lower stratosphere, then will circulate and scatter from the winds globally, and in the process will disperse incoming solar radiation back into space. The lower layer of the stratosphere already encloses a naturally occurring deposit of sulphuric acid particles which reflect sunlight away. These particles come from the troposphere layer, and may be natural sulfur gases, particles from the eruption of volcanoes, or manmade sulfur particles from the burning of fossil fuels for industrial uses which are circulated by winds all over earth. The stratospheric layer is very stable, and therefore the particles tend to stay in the layer for years, however, as shown from the eruption of Mount Pinatubo, the cooling effect of the particles becomes miniscule after 1-‐2 years. For this reason geoengineering of the
CFCA increasingly provided a space for open discussions about ways of tackling climate change. The attention the climate camps got from the British public goes some way to demonstrating the inability of the established political order to provide spaces for those discussions. Indeed, the mass media and politicians gathered at the camp, because here exciting deliberations and political debate – mostly absent from parliament and media – actually took place. For many of its key organisers, however, the climate camps were meant to do more than simply help to refresh liberal democracy by creating a new political forum. The climate camp was not meant to rejuvenate the political status quo. Rather, it was supposed to prefigure the change needed to tackle climate change by building a radically different and better society. In order to do so, the camp needed to adopt an antagonistic position vis-a-vis the status quo. When the CFCA decided to discontinue the organisation of climate camps, this might well have been because few camp organisers were motivated to provide space for deliberations that no longer fundamentally questioned the political status quo. Protest camps become political significant when they claim to be better places, occupying territories outside the status quo. As I have indicated, protest camps need to stress this claim, and perform it above and beyond their relationships with the outside. From an anarchist perspective there is no use for a camp within the status quo.
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Abstract. To date, the transitivity of the global system has been analysed for late Quaternary (glacial, interglacial, and present-day) climate. Here, we extend this analysis to a warm, almost ice-free climate with a different configuration of continents. We use the Earth system model of the Max Planck Institute for Meteorology to analyse the stability of the climate system under early Eocene and pre-industrial conditions. We initialize the simulations by prescribing either dense forests or bare deserts on all continents. Starting with desert continents, an extended desert remains in central Asia in the early Eocene climate. Starting with dense forest cover- age, the Asian desert is much smaller, while coastal deserts develop in the Americas which appear to be larger than in the simulations with initially bare continents. These differences can be attributed to differences in the large-scale tropical cir- culation. With initially forested continents, a stronger dipole in the 200 hPa velocity potential develops than in the simu- lation with initially bare continents. This difference prevails when vegetation is allowed to adjust to and interact with cli- mate. Further simulations with initial surface conditions that differ in the region of the Asian desert only indicate that lo- cal feedback processes are less important in the development of multiple states. In the interglacial, pre-industrial climate, multiple states develop only in the Sahel region. There, local climate–vegetation interaction seems to dominate.
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information (usually within 20-60 s), the time of viewing the resulting data and the analysis of results; ensuring of durable monitoring with the update of processing results; the high reliability of monitoring; the automatic receipt of the results of multiple tests as an electronic protocol; the quick conducting of complex calculations with the presentation of results in digital or graphical form; easy to view the structure of the results and their dynamics. The automated electronic measuring structure, developed on the base of the use of modern electronic technologies and software, is offered for the first time and has significant advantages compared to existing methods of monitoring and control of the dynamics of the development of the joint mobility of locomotor system. The scientific potential of the technical equipment for testing in physical education allows to control and evaluate the indicators of test quality at a very high level. The main methodical result of the work is that the using of the offered system allows to intensify the testing process during physical training of students.
Earlier the desalting plant in petroleum refinery is controlled using Programmable Logic Controller (PLC). It uses programmable memory to store instructions and specific functions that include ON/OFF control, timing, counting, sequencing, arithmetic, and data handling. Since PLC is a centralized control system it has problems with flexibility, redundancy and reliability during desalting process control. Desalting facilities are often installed in crude oil production in order to minimize the occurrence of water and salt content in oil emulsions. The main objectives of installing DCS in desalting process are; maintaining production rate in a field, decreasing the flow of salt content to refinery distillation feed- stocks, reducing corrosion caused by inorganic salts. DCS is used because of its increased flexibility, redundancy and high performance capability in desalting process control than any other centralized systems. DCS ensures accurate process control condition and this in-turn means a better plant performance. The project work involves developing an interface card using CENTUM VP software, which is proprietary to Yokogawa. The need for DCS in desalting process is to increase the uptime, maintaining process safety, and reduction in process control operating costs. C. DCS control distribution in desalting process
criteria. The application of MPC in building energy control has been investigated for climate control [8–10] and appliance scheduling applications [11–14]. In  a randomized MPC approach based on weather and occupancy predictions is proposed to regulate comfort levels in buildings and to minimize the buildings energy consump- tion. Recently, a review on optimal control systems applied to energy management in smart buildings has been given by Shaikh et al. . In spite of the requirement of a model and additional computation burden, the work by Privara et al.  is an evidence of one of the early implementation for building heating systems. The major advantage of predictive control algorithms is that the energy controllers can adjust the control input in advance of future requirements based on prediction. In this way, the effects of slow thermal dynamic response of building systems can be counteracted by the predictive control algorithm. Lee et al.  and Cho et al.  studied control methods for ﬂoor heating in Korea. They concluded from their studies that predictive control methods performed bet- ter than on/off controllers with regard to energy consumption. Ma et al.  and Vieira et al.  shows in their studies that predictive control performs better than the proportional control in context of energy and cost saving. Chen [22,23] and Pyeongchan  have applied model base predictive control techniques in ﬂoor heat- ing applications. Different forms of objective functions have been explored by them leading to different formulations, such as based on minimizing the operating costs or accumulated heat supply ﬂux with indoor temperature target interval band.
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A bottom-up approach to defining national or sub-national ecological boundaries maintains the four-step logic of the original planetary boundaries framework but adapts the first and/or second steps to fit the scope of the analysis in question. Dearing et al.'s (2014) case study of two Chinese localities is a bottom-up analysis that defines ecological processes and control variables based wholly on local environmental conditions within the case study sites. A top-down approach, on the other hand, adheres strictly to the Earth-system processes and control variables defined at the planetary level, while attempting to disaggregate them to lower levels. Nykvist et al. (2013) use a top-down approach to attribute and compare national shares of four disaggregated planetary boundaries (climate change, freshwater use, land-system change, and nitrogen) across 61 countries. In a South African case study, Cole et al. (2014) apply an interesting mix of both top-down and bottom-up approaches, depending on whether the specific environmental dimension is characterized as a global boundary, a national limit or a local threshold.
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Climate control systems in buildings are becoming increasingly more complex. Their mathematical approach requires conceiving them as thermodynamic systems formed by open subsystems. In this paper, a component has been defined as an open system characterized by its inputs, outputs and a non-permeable surface through which heat can be transferred among other components. This way, the different subsystems of a climate control system can be considered as components or open systems. The different components exchange energy with other components by means of two very different mechanisms: (i) forced energy transport by means of a fluid associated to its input and output and (ii) the thermal exchange through the borders of each component. Blue directional arcs linking different components indicate the energy transport in a flowing fluid. Red non-directional arcs indicate the thermal exchange. This way, a graph is comprised of different components that take part in the system, together with different blue and red arcs.
A control system is the means by which any of interest in a machine, mechanism or equipment is maintained or altered in accordance Introduction of feedback into a control system has the advantages of of system performance to internal variations in system parameters, improving transient response and minimizing the effects of disturbance signals. However, feedback ncreases the number of components, increases complexity, reduces gain and introduces the A system is stable if its response to a bounded input vanishes as time t approaches ∞. An any control task. A stable system with low damping is also not desirable. Therefore, a stable system must also meet the specifications on relative stability which is a quantitative measure of how fast the in the system. The oots of system’s characteristic equation determines the stability of the system [1 The roots of the system’s characteristic equation are the same as the poles of the closed-
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which indicates that it is used to fit minimum data though sometimes it is used to fit maximum data as well (Castillo et al., 2005) . Hence, there may be a threshold wind velocity below which Weibull distribution can be considered suitable. However, for the analysis of the wind power density it is worthy to mention that the cut-in and cut-out wind velocities for most of the wind turbines are 5 and 25 m/s respectively (EI-Wakil, 2002) . Hence, for the correct prediction of wind power density, wind velocity probability distribution should be able to model wind velocities till 25 m/s whereas Weibull distribution has been found to be suitable to model velocities till the maximum range of 14 m/s (Sarkar et al., 2017). Apart from this, fatigue failure due to cross wind vibration is important to consider for the wind velocity with an annual exceedance probability of 1/50. Hence, the modelling of upper tail is also important for this case. In addition, the probability factor was specified in IS: 875 Part III(2015) by modelling maximum daily gust wind velocities. It would obviously be better if this factor can be specified by the modelling of the same hourly mean wind velocity data. However, based on existing plotting techniques for wind velocity distributions, it cannot be predicted if the extreme of wind velocity data follows a Weibull model. Therefore, in this work, the authors are interested in verifying the suitability of Weibull model for modeling extreme wind velocities using suitable plotting techniques. If Weibull model is inappropriate to describe the extremes, then the limiting velocity up to which it can be considered appropriate must be determined. In this case, it is also necessary to determine the best theoretical estimator to fit wind data beyond this threshold value. In the present work, the details of wind velocity data are given in section 2; various probability distributions are described in section 3; determination of parameters and wind climate modeling using Weibull distribution are performed in section 4; the threshold value of the wind
Abstract: - Internet of Things (IOT) is the backbone of the change in the today’s growing technological era. Basically, in the real world the things having sensor capability, sufficient power supply and connectivity to internet makes field like IOT possible. For such rapid growing technology, it is the necessity to have very light, inexpensive and minimum bandwidth protocol like Message Queuing Telemetry Transport (MQTT) Protocol. Such non-established protocol it is easy for the clients to publish or/and subscribe the desire topic through the host acting as server of the network also known to be the broker. The Wi-Fi enabled ESP8266 & Atmega328 board interfaces with LM35, LDR sensor, DHT11 sensor, Soil moisture sensor and Gas Sensor which monitor the temperature, ambient light inside green house respectively, humidity, and soil moisture. Collected data form sensor is in Analog data format and Atmega328 microcontroller unit will fetch the data and convert it and process it into the digital format. Therefore, collected data form specific node will send to IOT server where it can view and analyses by an expert. MQTT server like broker also provides the facility of monitoring through the dashboard. By analyzing the system will get the temperature, humidity and light intensity level with the respective update. According to the light intensity level the brightness of greenhouse & other parameters are controlled By Node. In this system we implement the idea for the smart Green house monitoring system.
distributed over a large area in agriculture for farming or animal gazing, the use of WSN finds the best option . The low power consumption makes it able to work on batteries or solar for stationary node. The actuator nodes are connected to personal area network (PAN) as they require high power and their numbers are not as much as sensor nodes. This overall system can be integrated into IoT based system using existing local area network (LAN) and internet . This hardware system is supported by cloud-based data analytic software which helps in remote monitoring and controlling of field nodes. So, IoT can play vital role in uplifting the role and functions of every component of agriculture and farming. From cultivation to livestock rising, IoT can integrate overall system into cloud-based system. Each animal and field area can be a node in IoT system and their status, performance parameter can be recorded into system. This information can be used for real time monitoring and control of various performance parameters like field moisture, nutrients, humidity and temperature of the field and crop. Furthermore, the tags on animals and livestock help to track their mobility pattern, feeding time table and health conditions. The resources in the field can be managed for maximum utilization of available resources. In developing nations, the main advantages of the IoT in agriculture and farming is the economical technology for development of agriculture process and system. The main advantages of integration of IoT in agriculture are listed:
“point” control targets are often incompatible. On the one hand, there is a strong coupling between indoor temperature and relative humidity; on the other hand, the operation of the same equipment usually affects both indoor temperature and relative humidity. Therefore, it is very difficult for all control objectives to achieve optimal states. Even if all the environmental factors can be controlled at certain points accurately, it is usually at the expense of high energy consumption, which can not meet the profit requirements of growers. From this point of view, the control of greenhouse microclimate is a very complex work with multiple conflicting objectives. Xu et al. proposed a framework of conflict multi-objective compatible control and studied it [34-36]. In conflict multi-objective compatible control algorithm, the control objective is sub optimal interval target instead of precise point target. As the control target is relaxed, there is a lot of room to coordinate the conflict between the control precision of each environmental factor and the energy consumption of the greenhouse. Yang et al. applied the weight trade-off method to the three control objectives of energy consumption, temper- ature and relative humidity to achieve the compatible and optimized control . Zhu et al. integrated the preference information of energy saving into genetic algorithm, and realized the energy saving control of indoor air temperature . Hu et al. applied multi-objective evolutionary algorithm and genetic algorithm to coordinate two PID controllers successively, and studied multi objective com- patible control of greenhouse system [39, 40]. Ramirez et al. studied the multi-objective conflict control problems, including maximizing profits, fruit quality and water use efficiency . The idea of conflict multi-objective compatible control is suitable for greenhouse microclimate control. However, it is only a control framework. In this framework, it is necessary to adopt appropriate control methods for each specific device or system composed of multiple devices. At present, there are not many related studies, and further studies are needed in order to really solve the control prob- lem of greenhouse microclimate.
The administrator is always concerned about their employee’s presence in his/her owns divisions as well as the other divisions, with the prior permission. The system automatically generates total time–stamp of the individual staff as well as the particular division. It also generates the time report i.e. when the particular task assigned to the concerned employee and when it is completed, based on this evaluation the administrator decides the further increment/promotion. The system generates the database of number of visitors visited with purpose to the organization and division with time–stamp
The CCS system was tested first in simulation, in the context of the Violin toolbox, then, incrementally, on the real physical system. During simulation, virtual devices (for sensors/actuators) were used and assertions (Raju and Shaw, 1994; Fortino and Nigro, 2000; Furfaro et al., 2006) were introduced for checking the functional/temporal behaviour of the system. The actual shape of assertion programming, is shown below. A simple assertion is reported which checks that the Controller is always able to receive the latest sampled data from the WindVelDirSensor machine. The assertion is triggered when the control engine is up to dispatch a rendezvous on the WindVDCh channel between Wind VelDirSensor and the Controller.
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