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The purpose is to control the growth state of crops remotely, design intelligent detection application of farmland information and improve the efficiency of agricultural information management. Wireless sensor technology is applied to remote control device. First, through the integration of the system functions that need to be completed in the farmland remote monitoring, the module requirements of the device are further determined. Finally, the overall design is determined. From the aspects of hardware, such as the selection of sensors, RF transceivers and software (such as data processing center module), remote control device is designed, and the detection and collection of farmland real-time information is completed. The results show that the communication distance of wireless sensor nodes can reach 215 m under the test condition of 0.5 m in open space. The research content can help people improve the understanding of crop growth environment and growth situation, and then help people improve work efficiency, and finally contribute to the process of agricultural informatization.
The intake of the perishable fruits and vegetables (FVs) in the human diet can contribute to reduce the risk of some chronic diseases. But unfortunately, FVs loss rate is high among all the food produced annually and occurs at storage stage of post-harvest life cycle. One of the key factors contributing to this high loss rate is inability to gauge vital ambient environmental parameters in cold storage. The existing monitoring solutions about cold storage are limited to only gauge temperature, relative humidity and ignore other vital ambient environmental parameters such as luminosity and concentration of gases. This is a critical issue that needs to be addressed to overcome the loss rate of FVs. This paper presents a real-time intelligent monitoring and notification system (RT-IMNS) banked on an Internet of Things (IoT)-enabled approach for real-time monitoring of temperature, relative humidity, luminosity and concentration of gas in cold storage and notifies the personnel on exceeding of dangerous limits of these parameters. Moreover, decision support is implemented in the RT-IMNS using Artificial Neural Network (ANN) with forward propagation to classify the status of commodity into one of three classes i.e. good, unsatisfactory or alarming. The proposed prediction model outperforms Compress Sending (CS), Adaptive Naïve Bayes (ANB), Extreme Gradient Boosting (XGBoost) and Data Mining (DM) with respect to forecasting accuracy. We achieved 99% accuracy using forward propagation neural network model while existing models such as CS, ANB, XGBoost, DM achieved 95.60%, 87.50%, 93.59%, 90% accuracy respectively. Moreover, proposed approach achieved 100% precision, 100% recall, 100% F1-score for good class is achieved, for unsatisfactory class precision is 98%, recall is 99%, F1-score is 98% and for alarming class precision is 100%, recall is 98% and F1-score is 99%.
An ordered array of 1D ZnO nanorods obtained by colloidal templating is shown to dramatically enhance the sensing response of NO
x
at room temperature by confining light and creating periodic structures. The sensitivity is measured for a concentration varying from 2 to 10 ppm (response 53% at 10 ppm) at room temperature under white light illumination with ≈225 nm hole diameter. In contrast, structures with ≈450 nm hole size show better sensing under (response 98% at 10 ppm) elevated temperatures in dark conditions, which is attributed to the increased surface chemical interactions with NO
x
molecules due to the porous nature and enhanced accessible surface area of ZnO nanorods. Further, the decoration of ZnO Nanorods with gold nanoparticles shows enhanced sensor performance (response 130% at 10 ppm) due to localized surface plasmon resonance under white light illumination. The findings may lead to new opportunities in the visible light-activated room-temperature NO
x
sensors for healthcare applications.
Power cycling (PC) test is one of the important test methods to assess the reliability performance of power device modules related to packaging technology, in respect to temperature stress. In this paper, an advanced power cycler with a real-time VCE_ON and VF measurement circuit for the IGBT and diode, which for the wear-out condition monitoring are presented. This advanced power cycler allows to perform power cycling test cost-effectively under conditions close to real power converter applications. In addition, an intelligent monitoring strategy for the separation of package-related wear-out failure mechanisms has been proposed. By means of the proposed method, the wear-out failure mechanisms of an IGBT module can be separated without any additional efforts during the power cycling tests. The validity and effectiveness of the proposed monitoring strategy are also verified by experiments.
The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.
This paper presents an intelligent system named Magic-wall, which enables visualization of the effect of room decoration automatically. Concretely, given an image of the indoor scene and a preferred color, the Magic-wall can automatically locate the wall regions in the image and smoothly replace the existing wall with the required one. The key idea of the proposed Magic-wall is to leverage visual semantics to guide the entire process of color substitution, including wall segmentation and replacement. To strengthen the reality of visualization, we make the following contributions. First, we propose an edge-aware fully convolutional neural network (Edge-aware-FCN) for indoor semantic scene parsing, in which a novel edge-prior branch is introduced to identify the boundary of different semantic regions better. To further polish the details between the wall and other semantic regions, we leverage the output of Edge-aware-FCN as the prior knowledge, concatenating with the image to form a new input for the Enhanced-Net. In such a case, the Enhanced-Net is able to capture more semantic-aware information from the input and polish some ambiguous regions. Finally, to naturally replace the color of the original walls, a simple yet effective color space conversion method is proposed for replacement with brightness reserved. We build a new indoor scene dataset upon ADE20K for training and testing, which includes six semantic labels. Extensive experimental evaluations and visualizations well demonstrate that the proposed Magic-wall is effective and can automatically generate a set of visually pleasing results.
Online process condition monitoring is an essential component of closed-loop process-level automation of machining operations. This paper describes the development of an intelligent monitoring system for turning processes, which consists of three units: a tool wear predictor, a chatter detector and a tool chipping detector. Features are extracted from the signals of multiple low-cost and low-intrusive sensors, and then normalized using a novel scheme to eliminate their dependence on cutting conditions, workpiece materials and cutting tools. A systematic feature selection procedure, coupled with automated signal preprocessing parameter selection, is presented to select the optimal feature set for each unit. The tool wear unit is built with type-2 fuzzy basis function networks to predict tool wear with uncertainty bounds, while the chatter unit and tool chipping unit are built with support vector machines to maximize the classification fidelity. Experimental results show that the monitoring system achieved high accuracy, generalized applicability and satisfactory robustness for all the three process conditions, by using two affordable sensors: a power meter and an accelerometer. The three monitoring schemes are integrated into a monitoring software so that they can be implemented in different environments with minimal calibration efforts.
A room temperature formaldehyde sensor with high sensitivity is fabricated using vertical graphene (VG) networks decorated with tin dioxide (SnO2) nanoparticles. VG was directly grown on the sensor electrodes using a microwave plasma-enhanced chemical vapor deposition (MW-PECVD) method. SnO2 nanoparticles were decorated on the VG side planes and edges sparsely and evenly with an electrochemical deposition method. The VG/SnO2 sensor exhibited high responses to low-concentration formaldehyde under room temperature. The low limit of detection (LOD) of VG/SnO2 sensor for formaldehyde sensing is 0.02ppm. The response time and recovery time were 46s and 95s for 5ppm formaldehyde, respectively. Moreover, the sensor showed excellent selectivity and stability. The mechanism responsible for good sensing performance was analyzed based on the special structure of VG and the synergistic effect between VG and the SnO2 nanoparticles.
In light of current climate change projections in recent years, there has been an increasing interest in the assessment of indoor overheating in domestic environments in previously heating-dominated climates. This paper presents a monitoring study of overheating in 122 London dwellings during the summers of 2009 and 2010. Dry Bulb Temperature and Relative Humidity in the main living and sleeping area were monitored at 10 minute intervals. The ASHRAE Standard 55 adaptive thermal comfort method was applied, which uses outdoor temperature to derive the optimum indoor comfort temperature. It was found that 29% of all living rooms and 31% of all bedrooms monitored during 2009 had more than 1% of summertime occupied hours outside the comfort zone recommended by the standard to achieve 90% acceptability. In 2010, 37% of monitored living rooms and 49% of monitored bedrooms had more than 1% of summertime occupied hours outside this comfort zone. The findings of this study indicate that London dwellings face a significant risk of overheating under the current climate. Occupant exposure to excess indoor temperatures is likely to be exacerbated in the future if climate change adaptation strategies are not incorporated in Building Regulations, building design and retrofit.