A Robot Hybrid Hierarchical Network for Sensing Environmental Variables of a Smart Grid.

With the rapid development of the social economy, high-voltage transmission lines as power supply infrastructure are increasing, subsequently presenting a new challenge to the effective monitoring of transmission lines. The dynamic sensor network integrated with robots can effectively solve the elastic monitoring of transmission lines, but the problems of real-time performance, energy consumption and economy of the network need to be solved. To solve this problem, a dynamic network deployment method based on the hybrid hierarchical network (HHN) is proposed to realize a low-cost, energy-saving and real-time dynamic sensing system for overhead high-voltage transmission lines. Through case analysis and simulation, combined with the vague set multi-attribute decision-making method (MADM) with scheme preference, the network deployment and optimization results under multi-parameter constraints are obtained.

Obstacle distance measurement based on binocular vision for high-voltage transmission lines using a cable inspection robot.

Obstacle distance measurement is one of the key technologies for cable inspection robots on high-voltage transmission lines. This article develops a novel method based on binocular vision for extracting the feature points of images and reconstructing 3D scenes. The proposed method seamlessly incorporates camera calibration, dense stereo matching, and 3D reconstruction. We apply a novel calibration method to acquire intrinsic and extrinsic parameters and use an improved Semi-Global Matching (SGM) algorithm based on the least squares fitting interpolation to refine the basic disparity map. Based on the depth information of the optimized disparity map and the principle of binocular vision measurement, a model is established to estimate the distance of an obstacle from the cable inspection robot. Extensive experiments show that the proposed method achieves an estimation accuracy of less than 5% from 0.5 m to 5.0 m, offering extremely high distance estimation accuracy and robustness. The study improves the autonomy and intelligence of inspection robots used in the power industry.

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids.

The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.

Multi-Robot Cyber Physical System for Sensing Environmental Variables of Transmission Line.

The normal operation of a power grid largely depends on the effective monitoring and maintenance of transmission lines, which is a process that has many challenges. The traditional method of the manual or remote inspection of transmission lines is time-consuming, laborious, and inefficient. To address this problem, a novel method has been proposed for the Multi-Robot Cyber Physical System (MRCPS) of a power grid based on inspection robots, a wireless sensor network (WSN), and multi-agent theory to achieve a low-cost, efficient, fault-tolerant, and remote monitoring of power grids. For the sake of an effective monitoring system for smart grids, the very research is conducted focusing on designing a methodology that will realize the efficient, fault-tolerant, and financial balance of a multi-robot team for monitoring transmission lines. Multiple testing scenarios are performed, in which various aspects are explored so as to determine the optimal parameters balancing team performance and financial cost. Furthermore, multi-robot team communication and navigation control in smart grid environments are introduced.

Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data.

Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from "layer" to "block" according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection.

A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data.

With the growth of the national economy, there is increasing demand for electricity, which forces transmission line corridors to become structurally complicated and extend to complex environments (e.g., mountains, forests). It is a great challenge to inspect transmission line in these regions. To address these difficulties, a novel method of autonomous inspection for transmission line is proposed based on cable inspection robot (CIR) LiDAR data, which mainly includes two steps: preliminary inspection and autonomous inspection. In preliminary inspection, the position and orientation system (POS) data is used for original point cloud dividing, ground point filtering, and structured partition. A hierarchical classification strategy is established to identify the classes and positions of the abnormal points. In autonomous inspection, CIR can autonomously reach the specified points through inspection planning. These inspection targets are imaged with PTZ (pan, tilt, zoom) cameras by coordinate transformation. The feasibility and effectiveness of the proposed method are verified by test site experiments and actual line experiments, respectively. The proposed method greatly reduces manpower and improves inspection accuracy, providing a theoretical basis for intelligent inspection of transmission lines in the future.

[Weighted TOPSIS method and weighted RSR method in the construction of drug supervision and supply networks in rural areas of Hunan].

OBJECTIVE: To evaluate the rationality and validity of weighted TOPSIS method and weighted RSR method to evaluate drug supervision and supply networks construction in rural areas of Hunan . METHODS: Data of drug network construction in rural Hunan in 2010 were used to establish a comprehensive evaluation model, and weighted TOPSIS and RSR method were applied to this model and the results of which were compared to that of synthetical scored method to examine the validity. RESULTS: A comprehensive evaluation model was established, comprising of 3 primary indices, 8 secondary indices and 56 tertiary ones. The result of weighted RSR method was highly correlated to that of synthetical scored method, yet the result of TOPSIS was less correlated to the formers. All correlations were significant (P<0.01). CONCLUSION: Both weighted RSR and TOPSIS are not perfect methods, but the application of the methods in drug network evaluation is scientific and effective.

GC-MS-based metabolic profiling reveals metabolic changes in anaphylaxis animal models.

Clinical definition and appropriate management of anaphylaxis is a clinical challenge because there is large variability in presenting clinical signs and symptoms. Monitoring of the metabolic status of anaphylaxis may be helpful in understanding its pathophysiological processes and diagnosis. The purpose of this study was to conduct GC-MS serum metabolic profiling of anaphylaxis animal models and search for potential biomarkers of anaphylaxis. Thirty-six guinea pigs were randomly divided into an ovalbumin group (n = 12), a cattle albumin group (n = 12), and a control group (n = 12). The IgE level in the serum of the guinea pigs was evaluated by use of ELISA kits and the major metabolic changes in serum were detected by gas chromatography-mass spectrometry. Typical clinical symptoms appeared after the animals had been challenged with ovalbumin or cattle albumin. The IgE levels in serum of both model groups were significantly higher than those of the control group. Clustering trend of the three groups based on variables was observed and nine out of 858 metabolomic features were found to be significantly different between control group and model groups. Among the nine features, six features were tentatively identified as metabolites related to energy metabolism and signal transduction in anaphylaxis. In conclusion, GC-MS-based metabolic profiling analysis might be an effective auxiliary tool for investigation of anaphylaxis.

Occurrence, fatality and time distribution of food poisoning in Hunan Province.

OBJECTIVE: To explore the incidence, mortality, trends and time distribution of food poisoning in Hunan Province. METHODS: The data on food poisoning was derived from the Information Office of Hunan Provincial Health Department. Using the trend-test and circular distribution methods, we have described the current situation of food poisoning and tested the central tendency of the peak time points and the peak time zone of food poisoning in Hunan from 2000 to 2009. RESULTS: On average, the incidence of food poisoning in Hunan from 2000 to 2009 was 0.072 per 100000 population. And the average number of people affected in these incidents was 1.937 per 100000 population. There were no apparent trends in either the number of incidents or people affected between 2000 and 2009 (u=-0.98, P>0.05; u=-1.34, P>0.05, respectively). The average mortality was 0.015 per 100000 population. The trend-test indicated that the average annual mortality decreased significantly from 2000 to 2009 (u=-1.72, P<0.05). Meanwhile the average annual fatality rate was 0.77%. The trend-test revealed statistically significant differences for the average annual fatality rate (u=-1.88, P<0.05). The circular distribution analysis showed that there was a central tendency of the distribution of food poisoning cases, with the average peak time at August 28th and the average peak time zone from June 7th to November 18th for food poisoning from 2000 to 2008. CONCLUSION: From 2000 to 2009, there is a significant tendency in the average annual mortality and fatality rate of food poisoning in Hunan. Summer and fall are the high seasons for food poisoning. We should pay attention to the peak time zone, especially the peak time point of food poisoning for food safety monitoring, and strengthen the prevention and control on food poisoning.