Intelligent Temperature Control of Ceramic Kiln Based on Data Fusion Technology

Objective To investigate the clinical effectiveness of an intelligent shell temperature control system in deep tumor hyperthermia for controlling the temperature in the outer shell of chest and abdominal malignancies. Methods From 2015 to 2016, a randomized comparative study was carried out in the intelligent temperature control system (experimental group containing 37 patients with chest tumor and 26 patients with abdominal tumor) and conventional cooling by replacement of water bags (control group containing 37 patients with chest tumor and 25 patients with abdominal tumor) for chest and abdominal tumor hyperthermia. The effectiveness in controlling the outer shell temperature was compared between the two methods. Results In the experimental group, the first boot of the cooling system occurred at 24.1 and 21.8 minutes on average after the hyperthermia started and the average number of system boots was 2.5. In the control group, patients reminded to replace the water bag at 33.1 and 33.8 minutes on average after the hyperthermia started and the average number of replacements was 3.6. The highest outer shell temperature in patients with chest tumor and abdominal tumor averaged 38.5 and 39.1 ℃ at the first boot of the cooling system in the experimental group and 39.3 and 40.4 ℃ at the first replacement of the cooling water bag in the control group, respectively. The treatment with and without the intelligent temperature control system caused grade ≥Ⅱ empyrosis in 1 and 4 patients with chest tumor and in 0 and 3 patients with abdominal tumor, respectively. Conclusions In deep tumor hyperthermia, compared with the conventional cooling by replacement of water bags, the intelligent shell temperature control system can detect and cool the high-temperature zone in the outer shell at an earlier time, which effectively reduces the incidence of skin burn injury. Key words: Deep hyperthermia; Scalded skin; Temperature control

Due to the rapid development of precision manufacturing technology, much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness, thereby improving the efficiency and precision of manufacturing. In a multisensor system, each sensor independently measures certain parameters. Then, the system uses a relevant signal-processing algorithm to combine all of the independent measurements into a comprehensive set of measurement results. The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems. The architecture of multisensor measurement systems is reviewed, and some implementations in manufacturing systems are presented. In addition to the multisensor measurement system, related data fusion methods and algorithms are summarized. Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

In industrial production, temperature and humidity are common main manipulated parameters, especially in heat treatment industry, the temperature control is becoming more and more important. This paper from two aspects that hardware and software introduced single-chip microcomputer (SCM) C8051F intelligent temperature and humidity control system, and described schematic diagram of hardware and software. The design adds the integration of carbon dioxide concentration and light intensity detection and necessary communication function. It is a more humanized and more practical intelligent temperature and humidity measurement and control system.

With the widespread application of large and complicated network, network safety has become an important issue. In this paper, a security operation center (SOC) concept based on multi-sensor data fusion technology is presented from the viewpoint of the network security. A structure of a SOC system based on radial basis function neural (RBFN) network is proposed, and the detailed method of data fusion in SOC is discussed. A prototype of SOC system is developed according to this structure of the SOC, experimental results indicate that the SOC system based on RBFN network can increase greatly the correctness of detection intrusion and decrease the rate of false positive

Aiming at the problems of low degree of intelligence and insufficient remote interaction ability of the traditional electric control box temperature control system, this study designs and implements an intelligent temperature control system based on STM32 microcontroller for the Internet of Things (IoT). The system adopts a multi-channel sensor fusion scheme, through the fixed point of multiple DS18B20 temperature sensors to build a distributed temperature measurement network, combined with the sound to determine the equipment failure method to achieve the intelligence of the temperature control system. A dual-channel power driver module was designed at the hardware level, with cooperative control of the heat source power and the AC fan. At the software level, an IoT architecture based on MQTT protocol was developed to realize real-time uploading of data to the built cloud platform and remote interaction through mobile APP to realize temperature regulation and control in the electric control box. The fan speed and the power of the heat source are adjusted in time according to the change of temperature in the real-time temperature control cabinet to ensure that the temperature in the temperature control cabinet remains constant. The content of this study helps to provide assistance in designing temperature control systems in the Internet of Things.

Обоснован перечень задач самообучения интеллектуальных систем управления автономными роботами и мультиагентными робототехническими группировками. Обсуждаются основные проблемы создания интеллектуальных самообучающихся систем управления. Показано, что один из наиболее перспективных подходов к реализации процессов самообучения в интеллектуальных системах управления автономными роботами и многоагентными робототехническими группировками связан с использованием метода автоматического построения деревьев классификации. Приводятся результаты комплексного моделирования.

For the purpose of energy saving and energy efficiency, the LED intelligent dimming system making use of solar power supply is designed in this paper. The system is based on STC12C5A60S2 microcontroller with multiple pyroelectric infrared detectors and light intensity sensors, in which LED dimming will be achieved according to the analysis and computation results through Multi-sensor data fusion technology. PWM dimming is adopted in the system depending on presetting programmable counter array (PCA) module of microcontroller in pulse width modulation (PWM) operation mode. Following the discussion about principle, composition and achievement, the Multi-sensor data fusion analysis is stated in accordance with different space distribution of light intensity sensors. Experiments show that the system is in simple structure and stable and reliable with the features of energy saving and intelligent.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

In heat treatment and other industry detection region, we usually adopt artificial or conventional instrument to control heat treatment furnacepsilas temperature. But due to fluctuation amplitude of furnace temperature is larger, which usually lead to poor production quality appearance. Aiming to this problem, we design an intelligent heat treatment furnacepsilas temperature intelligent monitor and control system based on single chip microprocessor (SCM) AT89C52. Through cold segment concentration technology, and automatic control technology, etc, system can efficiently realize heat treatment furnacepsilas temperature distributed monitor, control and centralized management. Experiment shows that system can accurately control heat treatment furnacepsilas temperature in real time, and temperature monitor error reaches monitor requirement, as well as realizes sound-light alarming once furnacepsilas fact temperature is out of setting limit value.

For a long time, the protection and maintenance of complex equipment system mainly adopts periodic inspection, maintenance and fault diagnosis and emergency repair during service. However, it can't evaluate and forecast the overall quality and long-storage performance of the system, which has many inconveniences for regular inspection and maintenance, and may affect the reliability of mechanical equipment. According to the structure and use characteristics of complex equipment system, the application software engineering idea and object-oriented programming method, a complex system data fusion software and method were developed using data fusion technology. The method can analyze the data of the material layer, the component layer and the system layer, and respectively fuse the data of each layer to realize the corrosion analysis and prediction of various complex mechanical systems. Using the missile tank system as an example, the feasibility and accuracy of the data fusion method are verified.

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

The energy consumption of wireless sensor network has a direct impact on network lifetime and sensing efficiency, and data fusion technology can perfectly solve this problem. By using the spatial correlation of sensing data between adjacent nodes in the network, the redundant data are fused at the relay nodes, so as to reduce the energy consumption of the network. Considering the demand of data privacy preserving in wireless sensor networks, many data fusion methods with privacy preserving are proposed. This paper analyzes the existing typical privacy preserving data fusion methods, compares their advantages and disadvantages, and puts forward some views on the future research direction.

Designing a smart vegetable greenhouse temperature control system based on automatic computer controlled, elaborated on the temperature of the system acquisition, temperature control system, heater control circuits and other system hardware design ideas to improve the system’s control algorithm, using MATLAB for system simulation. The simulation curve of the system has better control and tracking performance, high precision temperature control, also composed of two computer-controlled systems and the host computer, to facilitate the centralized management of the production. Practice shows that the study design, vegetable greenhouses intelligent temperature control system for man–machine interface, easy operation, high degree of automation, low cost, with a good prospect and promotional value.KeywordsVegetable greenhousesTemperature controlFuzzy control

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

The paper designed a WLAN system with RFID systems joint locating algorithm based on Federated Kalman Filtering. For the problem in indoor mobile node single positioning system, such as insufficient positioning accuracy, longer positioning time and not ideal algorithm complexity, it was proposed that the federated filtering techniques was applied in the location system, and was combined with data from WLAN and RFID to realize all accuracy improved. In the system, sub-filter use unscented Kalman filter algorithm independent measurement updates and time update, and the subsystem information is fused with the main filter based on no feedback mode. It was proved from the result that the calculation improved was of feasibility, which reduced unscented Kalman filter calculation, decreased maximum deviation and minimum deviation and improved the positioning accuracy. Introduction With the increase of the number of mobile devices and improvement of the degree of intelligence, location technology [1] is highly valued by the researcher as the core support functions of various kinds of mobile applications. At present, common indoor positioning technology includes ultrasonic positioning technology, WLAN positioning technology, Zigbee positioning technology, infrared positioning technology and RFID positioning technology. One single positioning technology is difficult to achieve a satisfactory result in practice, so multi-sensor data fusion technology has become an important issue in the field of indoor mobile node localization. WLAN positioning technology has developed rapidly in recent years,which mainly includes Nibble system[2] based on acquisition and analysis of noise ratio or signal quality, WHAM! Positioning system[3] based on receiving the signal strength of AP, Ekahau positioning system[4] , Horus positioning systems and Rice systems.The RFID system,whose positioning time is shorter, the precision is higher, received much concern for its advantages of non-line and non contract ,although the positioning range is limited to the distance between the transmitter and the receiver .This paper is to effectively improve the positioning accuracy of the mobile node and real-time through information fusion of WLAN positioning system and RFID positioning system based on Federated Kalman Filtering, which has certain practical value. Theoretical basis Federated Kalman Filtering Model Traditional Kalman filtering technology for multi-sensor data fusion methods generally includes centralized Kalman filtering and the decentralized Kalman filter. Federated filter proposed by Carlson[5] achieves good effect in practical application because of its small amount of calculation, good fault-tolerant performance and flexible design. Fusion algorithm based on Federated Kalman Filtering includes algorithm both whose estimate for each sub filter is correlated and uncorrelated. Due to the premise that the various sub filters is uncorrelated can not be guaranteed in the actual use,this paper mainly study the fusion algorithm for the estimate of each sub filter,using the variance upper bound technique to transform the filtering algorithm,thus each sub filter will be estimated from the correlated state.Federated filter is a kind of two-stage filtering structure. It is assumed that the state estimation of each sub filter can be 4th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2016) © 2016. The authors Published by Atlantis Press 2001 expressed as: ˆ ˆ ˆ ci i bi X X X   =       . ˆ ci X is the estimate of the common state c X for each sub filter. ˆ bi X is the proprietary state estimate of filter i . Process design of Federated Kalman filter Under normal circumstances, federated filter process mainly includes four processes as follow : information distribution, information update, measurement update of information and information fusion. Step1 Distribution of information.Determine the proportionality coefficient of information distribution,between the main filters and sub filters as shown in formula (1). 1 k Q − and ( ) 1 | g k k P − refer to system process information.In formula (1), parameter i β is called an information distribution coefficient to meet the conditions of 0 i β > and distribution principle in formula (2).