PLC控制系统在中央空调上的应用介绍

本文简要介绍了PLC控制系统在地源热泵中央空调机组上的应用,控制系统的硬件组成及部分软件组成等。     

Effect of pressure on three catalytic partial oxidation reactions at millisecond contact times

The effects of pressure on reactant conversion and product selectivities in three catalytic oxidation systems have been examined at pressures between 1 and > 5 atm. Reaction was sustained autothermally near adiabatic operating conditions at temperatures of 1000°C with residence times over the noble metal catalysts between 10^–4 and 10^–2 s. The three systems investigated were (1) HCN synthesis over Pt-10% Rh gauze catalysts, (2) methane oxidation to synthesis gas (CO and H₂) over rhodium-coated monoliths, and (3) ethane conversion to ethylene over platinum-coated monoliths. We find that selectivities in all three reactions do not change dramatically with approximately a five-fold increase in pressure. This strongly suggests that free radical homogeneous chain reactions are not significant in these processes and that they can be operated reliably above atmospheric pressure. For the synthesis of HCN over Pt-10% Rh gauzes, the selectivity to HCN can be maintained above 0.75 at pressures up to 5.5 atm. Selectivities to synthesis gas (CO and H₂) from a methane-air mixture over a Rh-coated foam monolith at pressures up to 5.5 atm were maintained above 0.90. Over a Pt-coated foam monolith, the selectivity to ethylene from ethane-air and ethane-O₂ mixtures was independent of pressure up to 6.5 atm and conversion rose slightly although it was necessary to maintain constant velocity and residence time over the catalyst to avoid carbon formation.This research was supported by DOE under Grant No. DE-FG02-88ER13878.     

Phase Equilibrium studies in the system “FeO”–SiO2–Al2O3–CaO–MgO at 1200 °C and Po2 10-8atm

Iron oxides and silica are the major components of copper smelting slag. The oxides of aluminum, calcium and magnesium are also present in the slag that is introduced through copper concentrate, flux and refractories. Liquidus temperatures of the copper smelting slags are usually controlled by Fe/SiO₂. The concentrations of Al₂O₃, CaO and MgO, and FeO/Fe₂O₃ in the slag can also affect the liquidus temperatures where FeO/Fe₂O₃ is a function of oxygen partial pressure. High temperature equilibration under controlled oxygen partial pressure followed by quenching and electron probe microanalysis were used to determine the compositions of the liquid and solid phases at 1200 °C and Po₂ 10^-8 atm. The experimental results are presented in the forms of pseudo-ternary sections “FeO”-CaO-SiO₂ at fixed 2, 4 and 6 wt pct MgO, and 2 + 2, 4 + 4 and 6 + 6 wt pct MgO + Al₂O₃. Spinel and tridymite are the major primary phases in the composition range investigated. In addition, CaSiO₃, pyroxene, olivine, and melilite are also present. The isotherms in the spinel and tridymite primary phase fields move towards higher SiO₂ concentration directions with increasing CaO, Al₂O₃, and MgO concentrations. The experimentally determined results are compared with the FactSage calculations.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).     

基于深度学习的自然灾害遥感影像语义分割

自然灾害种类繁多, 通过遥感影像语义分割相对比较困难. 为了能够更好实现遥感影像分割, 本文提出一种基于生成对抗网络的3层遥感影像语义分割模型, 针对不同场景的解析, 基于全卷积神经网络FCN, 设计一种多层次的遥感语义分割框架. 有效对遥感图像语义分割进行处理, 从而提高了模型的分割精度. 实验表明利用这种模型是有效的, 特别是受损建筑的分割结果, mIoU为82.28%, 通过该模型与其他网络模型进行对比, 其性能评价指标明显优于其他网络模型. 最后, 通过对自然灾害各种场景影像进行分析, 为应急管理部门提供一份可靠的数据报告.     

Alternating pressure control system for hydraulic robots

Hydraulics is a promising technology for robots. However, traditional hydraulic infrastructures are often large and power-inefficient, with large power sources that hinder mobility. In contrast, electro-hydrostatic actuators are relatively power efficient, but their cost and weight can be excessive in systems with a higher number of degrees of freedom. In this paper, we propose a new alternating pressure control system for hydraulic systems with a higher number of degrees of freedom based on an alternating pressure source system. In this system, the valves open and close in synchronization with a pump with sensor feedback, allowing either pressure or position in each actuator to be controlled independently. With the proposed system, a centralized pump can be used with simplified tubing and simple on–off valves. Moreover, we developed a dynamic duty ratio system that improves performance and reduces pump utilization time. The experimental results confirmed that both the position and pressure of each actuator can be controlled in parallel on a multi-degree-of-freedom system.     

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).     

0.618法最优确定盘状摆动凸轮机构的最小基圆半径

在满足机构许用压力角条件下,采用０．６１８法最优确定盘状摆动凸轮机械的最小基圆半径。