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To apply artificial intelligence technology to the sensitivity analysis of power transmission project cost factors, this paper uses BIM calculation system to realize the visualization of project calculation. Firstly, we analyze the information process of BIM during the process of power transmission and transformation project cost, and establish a power transmission and transformation project library that meets the calculation specification of power engineering quantity base on IFC. Then, through the comparison and selection of pricing schemes, the engineering quantity statistics is developed to read the quantities exported from the Revit model by pricing software. Finally, we adopt a real project to test the performance of our scheme and the experimental results show that the actual investment and cost estimation are within the scope of engineering practice, which basically meet the expected requirements.

Both line width roughness (LWR) and queue time (Q-time) have attracted attention in the high-volume manufacturing (HVM). In this paper, we will present the results for the effect of HBr cure process on LWR in pitch doubling process with fluorine-based and chlorine-based chemistries using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM). Optimization of HBr cure process with Cl-based chemistry has significantly improved LWR by 60% and space imbalance by 60%. Converting from the fluorine-based etch chemistry to Cl-based chemistry leads to elimination of Q-time from resist etch to atomic layer deposition (ALD) steps, which significantly maximizes HVM production throughput by 11% and improves product quality.

Aerosol Optical Depth (AOD) is an important physical quantity of atmospheric turbidity and a key factor for the atmospheric correction of optical remote sensing image. Several approaches have been developed to retrieve AOD from remote sensing data, and at ground level the sunphotometer is usually used to measure AOD. However, the sunphotometer is expensive and not portable for field campaign, which make it difficult to synchronously obtain AOD data along with other relevant measurement. In order to overcome this problem, this study proposed a new way to estimate AOD using Ground-measured Spectral Skylight Ratio method (GSSR) on the basis that skylight ratio is highly determined by such parameter. In the GSSR method, a look-up table (LUT) containing spectral skylight ratio in 400-1000nm was first established from the MODTRAN code under various AOD levels, solar zenith angles (SZA), and atmospheric types. Based on the LUT and actual skylight ratio measured from a ground spectrometer, the AOD was then determined using a shortest distance between the measurement and the spectral values in the LUT. Finally, the AOD derived from the GSSR method was validated using the AOD data from the CE318 sunphotometer data, and it was found that AOD error was about 0.0185, which demonstrated that the GSSR method can be used to estimate AOD accurately, and can be regarded as an alternative method to estimate AOD in the field work if no sunphotometer is available.

The application of the immersive virtual reality (VR) has injected new vitality into educational innovation, but there are also some voices of doubt on its practical learning effects. Considering two aspects of media and instructional methods, the study investigated the effect of immersive virtual reality and planning strategy on simulation-based learning by a $2\times 2$ experimental cross - panel design. The results showed that both of them had a significant main effect, indicating that the immersive VR and planning led to better behavioral transfer performance and immersive VR increased sense of presence and self-efficacy as well. No interaction effect between media and method was found.

Heavy haze results in severe image degradation and thus hampers the performance of visual perception, object detection, etc. On the assumption that dehazed binocular images are superior to the hazy ones for stereo vision tasks such as 3D object detection and according to the fact that image haze is a function of depth, this paper proposes a Binocular image dehazing Network (BidNet) aiming at dehazing both the left and right images of binocular images within the deep learning framework. Existing binocular dehazing methods rely on simultaneously dehazing and estimating disparity, whereas BidNet does not need to explicitly perform time-consuming and well-known challenging disparity estimation. Note that a small error in disparity gives rise to a large variation in depth and in estimation of haze-free image. The relationship and correlation between binocular images are explored and encoded by the proposed Stereo Transformation Module (STM). Jointly dehazing binocular image pairs is mutually beneficial, which is better than only dehazing left images. We extend the Foggy Cityscapes dataset to a Stereo Foggy Cityscapes dataset with binocular foggy image pairs. Experimental results demonstrate that BidNet significantly outperforms state-of-the-art dehazing methods in both subjective and objective assessments.\ud

Adding post enhancing filter after traditional image decoder to improve reconstruction quality is nowadays a very common method [1],[2],[3],[5]. Researchers use a large network filter or repeatedly stack single/multiple types of relative simple filters together. They all achieved better results. On the other hand the training materials and training time increases exponentially for a larger network scale, and the performance improvement becomes less and little. We learn this experience from the CLIC2019 low-rate track, where we proposed the VimicroABCnet and VimicroSpeed[4], with 2 post filters of different scale. The later one(5 time larger than the small one) achieved the final test's PSNR by improvement of only 0.02db@0.15bpp. In this paper, we propose a method to variate an existing post network filter(base filter). The base filter is altered into different ones, alternation only happens to weights. The key of the method is to divide the training data into different groups. Based on the pre-trained base filter, different altered filters are individually fine-trained with different group of training data. There are different ways to divide the training data, and we use a relative simple one. Sort by compression rate(with traditional codec) and bin the training images in to 4/8 group of training data subsets. With the new filters plus the base one, we now have 5/9 filters candidates in encoding phase and choose the best. The CLIC2019 test data show that PSNR increases 0.04db@0.15bpp and 0.06db@0.15bpp than the one filter VimicroSpeed method. This method requires the same training data and perfectly suitable for multi-GPU training scheme, and retraining the altered filters is much easier and consuming less time than training a relative large network filter. Also the result is better(5 filters scheme@0.04db vs VimicroABCnet@0.02db).

Electromagnetic radiation characteristics within an electronic system are closely related to the characteristics of multi-radiation source (equipments) and their spatial layout. In this paper, two methods are proposed for multi-radiation prediction and equivalence. One is according to the theory of spherical wave expansion and the spatial layout of the equipments, the other is the spherical equivalent dipole array method (SEDAM) proposed by the authors, which is based on the theoretical relationship between three components of magnetic fields and magnetic dipole moments in the spherical coordinate system. The feasibility of the spherical wave spectrum method is verified by a simple dipole antenna model, but the accuracy of the method is not very high for complex models. On the other hand, the SEDAM has high equivalent accuracy for complex models, such as the MIMO Ring antenna. By comparing the results of the far-field magnetic (electric) field and far-field direction pattern with simulation software, the equivalent error of two MIMO Ring equivalent model is within 2.3 dB. Finally, the feasibility of the SEDAM is verified by the actual measurement of the system with two intercoms. The results show that the SEDAM is more accurate under the same measurement conditions.

This work demonstrates a flexible piezoelectric pulse sensing system combined with the mode energy ratio (MER) analysis in order to detect human pulses and reveal pregnant conditions. According to the pulse diagnosis principle of traditional Chinese medicine (TCM), the flexible piezoelectric sensor is used to realize digital and intelligent pulse acquisition. And the algorithm of MER based on empirical mode decomposition (EMD) is proposed to describe energy distribution of pulses in different pregnancy status. The experiment results of 83 female volunteers' MER analysis indicate that the identification and warning of pregnancy status and physical health indicators are realized.

© 2019 IEEE. The key to dynamic pressure calibration is the generation of sinusoidal pressure signals. This paper uses a liquid-gas two-phase piston sinusoidal pressure generator for generating higher frequency pressure signals. The design of the contraction section has a large effect on the sinusoidal pressure generator. The flow field characteristics of the contraction section were simulated by Fluent simulation software. We analyze the frequency response, exit velocity distribution and axial pressure distribution of different contraction curves, then select the appropriate contraction curve and verify it by experiment. The simulation work in this paper provides a reference for the design of liquid-gas two-phase high-frequency sinusoidal pressure generator.

© 2019 IEEE. Falls happen frequently to older people in their daily life. Heel and toe clearances are regarded as significant indicators for the risk of falling. In this study, we developed a foot-worn inertial system to capture kinematic gait data. An estimation method of continuous heel and toe clearances during walking was proposed. Based on heel strike and toe off events detection, a foot movement model was presented to calculate the continuous heel and toe clearances. To compensate the sensor drift, an algorithm combined Zero Velocity Update, Zero Angular Rate Update, Zero Vertical Displacement Update plus a liner drift correction model was implemented. To verify the effectiveness of the drift compensation algorithm, the calculated IMU clearances before and after the implementation of the algorithm were compared and the walking tests results supported its feasibility. Furthermore, the continuous heel and toe clearances were estimated successfully using the foot movement model.

We propose a single-stage detection framework that jointly tackles the problem of multi-scale object detection and class imbalance. Rather than designing deeper networks, we introduce a simple yet effective feature enrichment scheme to produce multi-scale contextual features. We further introduce a cascaded refinement scheme which first instills multi-scale contextual features into the prediction layers of the single-stage detector in order to enrich their discriminative power for multi-scale detection. Second, the cascaded refinement scheme counters the class imbalance problem by refining the anchors and enriched features to improve classification and regression. Experiments are performed on two benchmarks: PASCAL VOC and MS COCO. For a 320×320 input on the MS COCO test-dev, our detector achieves state-of-the-art single-stage detection accuracy with a COCO AP of 33.2 in the case of single-scale inference, while operating at 21 milliseconds on a Titan XP GPU. For a 512×512 input on the MS COCO test-dev, our approach obtains an absolute gain of 1.6% in terms of COCO AP, compared to the best reported single-stage results[5]. Source code and models are available at: https://github.com/Ranchentx/EFGRNet.

© 2019 IEEE. The three-axis MEMS magnetometer is widely applied to assist the inertial sensor to estimate the attitude (especially heading) in strapdown inertial navigation system. It is necessary that the system errors of the magnetometer are analyzed and compensated, just like the inertial sensor. The system errors that contain biases, scale factors, nonorthogonal angles and iron effects can be quantified by the calibration. In general, error items can be derived based on the geomagnetic field vector observation. In most magnetometer calibration algorithm, the data from the sensor is processed directly by ellipsoid fitting without any pretreatment. In order to guarantee the reliability of the data, this paper proposes a new method for testing the rationality and gross errors of the calibration data based on geometric parameters of the ellipsoid. The experimental results show that the unreasonable data is detected, so that the calibration accuracy is improved to some extent.

In the past few years, deep convolutional neural networks (CNNs) have benefited great progress in salient object detection due to the strong ability of feature representation. As different level of features can be reflected in different layers, traditional CNNs based methods usually integrate features from multiple layers to obtain appealing results. However, the fine details of salient objects tend to be lost because of multiple subsampling operations such as convolution and pooling. Meanwhile, the non-salient regions reflected in the low level features would be easily integrated into the final results incorrectly. In order to address these issues, we propose a novel deep saliency detection network by recurrently aggregating and refining cross-layer residual features (RARCRF). Instead of directly refining the intermediate saliency map as many previous plain network based methods do, we design a residual learning module and embed it into RARCRF to learn residual features. Then, the residual features learned from multiple layers are aggregated and refined step by step in a cross-layer manner. By this way, the complementary information in deep features extracted from different layers can be fully captured for detecting salient objects with different scales, i.e., the features integrated from low-level layers can serve to refine the details of detected salient objects while the features integrated from high-level layers with semantic information can benefit the locating of salient objects. Finally, different saliency detection results from different layers are fused to generate the final saliency map. Experimental results with ablation analysis on five benchmark datasets demonstrate that our proposed RARCRF outperforms other 14 state-of-the-art competitors.

Urban area is the main and important space of human activities with a large number of population. Compared with rural and other natural areas, the dense buildings and high-intensity land use are the most different features of urban areas. Therefore, the urban area has obvious texture in remote sensing images. Effective and accurate identification of urban area can play an important role in urban study, urban planning and other urban-related fields. In this paper, a new method based on urban and non-urban scene classification using Convolutional Neural Network (CNN) technique is developed to identify the boundary of urban areas and is applied in Beijing as an example. An acceptable result of the urban area identification was obtained, indicating a great potential of deep learning method in urban related studies.

This paper first introduces the number of servers faced by higher vocational colleges in the process of informatization construction, the low resource utilization rate, the incompatibility of management systems, and the low degree of management and maintenance automation. The virtualization based on Huawei FusionSphere architecture is proposed. The integrated implementation method performs unified logical division and management of virtual machine resources, shields the physical platform details, and improves the degree of automated management and maintenance of the server.

In order to increase the sensitivity and enhance the recognition feature during the measurement of the dew point based on the active temperature-controlled quartz crystal microbalance (QCM) dew point sensor, a new hydrophilic QCM dew point sensor and dew point recognition method were proposed in this paper. In the sensor, the QCM coating with a reduced graphene oxide (rGO) thin film was used as the sensitive unit. Dew point recognition method based on calculating the second derivative of the frequency variation curve was proposed to get the accurate dew point values. Experiments based on the designed sensor was carried out under different dew point conditions and the dew points were recognized by using the designed method. The results showed that the designed sensor had obvious dew point recognition feature and could achieve acceptable measurement error as less than 0.31°C compared with the precise dew point meter MICHELL 84000.

This work demonstrates a novel bionics-based heartbeat sensor combined with approximate entropy (ApEn) algorithm for health warning. The design of the proposed sensor was inspired by pulse-taking therapy in traditional Chinese medicine (TCM). A flexible piezoelectric film was adhered to a wooden cylinder to simulate the structure of the finger to achieve pulse-taking. The simulation of piezoelectric film under different force area and bending degree was realized by ANSYS. The results showed that the sensor structure proposed in this paper can not only simulate the characteristics of the finger to achieve the TCM pulse-taking, but also improve the sensitivity of piezoelectric film. The sensor was used to measure the pulse signal of human under different states of motion and different health conditions. Then the sample data were analyzed using the ApEn. The results showed that the ApEn value is 0.1 can be used as a threshold for judging and predicting human health status. The experiment proved that this method can avoid the requirement of the same force in the traditional pulse-taking. It not only can obtain the low distortion pulse signal, but also can obtain the heart rate accurately. Using this method can quickly achieve rapid detection and early warning of human health.

Abundant characteristics information of equipment or systems could be obtained from electromagnetic emission data. In this paper, those characteristics of electromagnetic emission are analyzed via the adaptive boosting (Adaboost) algorithm. Based on the “basic emission waveform theory”, four types of basic fundamental elements, characteristics-harmonic, narrowband and envelope-of complex emission in frequency domain, could be extracted. For taking weights combination patterns to effectively improve the classification performance of a single classifier, high classification accuracy could be achieved by Adaboost algorithm. In our study, 100% precision classification accuracy of three types of characteristics could be obtained using Adaboost with 13 decision tree weak-classifiers. Compared with other classification methods, the Adaboost algorithm used in this paper is the most accurate. The outcomes of this research can be used as a guide in the electromagnetic compatibility design and rectification.

In a Futures-Exchange, such as the Chicago Mercantile Exchange, traders buy and sell contractual promises (futures) to acquire or deliver, at some future pre-specified date, assets ranging from wheat to crude oil and from bacon to cash in a desired currency. The interactions between economic and security properties and the exchange's essentially non-monotonic security behavior; a valid trader's valid action can invalidate other traders' previously valid positions, are a challenge for security research. We show the security properties that guarantee an Exchange's economic viability (availability of trading information, liquidity, confidentiality of positions, absence of price discrimination, risk-management) and an attack when traders' anonymity is broken. We describe all key operations for a secure, fully distributed Futures-Exchange, hereafter referred to as simply the ""Exchange"". Our distributed, asynchronous protocol simulates the centralized functionality under the assumptions of anonymity of the physical layer and availability of a distributed ledger. We consider security with abort (in absence of honest majority) and extend it to penalties. Our proof of concept implementation and its optimization (based on zk-SNARKs and SPDZ) demonstrate that the computation of actual trading days (along Thomson-Reuters Tick History DB) is feasible for low-frequency markets; however, more research is needed for high-frequency ones.

For active temperature controlled quartz resonant dew point sensors, the combination of Peltier and quartz crystal resonators (QCR) is a key factor in the design of sensor structures. A structure in which the Peltier and the QCR are surface to surface combined with single-sided cooling has unstable vibration when environmental parameters continuously change. In order to achieve continuous and accurate dew point measurement in a dynamically changing atmosphere, this paper presents a new structure based on double cooling and double sensitive surfaces. The Peltier is flexibly connected to the QCR non-electrode region using elastic thermal materials. The designed sensor has a dew point measurement test in an environment where there is a continuous change of humidity and temperature. The results show that the relative error is less than 0.06°C when the cooling rate is 0.02°C/s, compared to MICHELL S4000.

Aiming at the fast and accurate measurement of the output frequency of quartz resonant dew point sensor, there is developed a 1-bit fourth-order bandpass (BP) sigma-delta (ΣΔ) modulator to be applied in it. The automatic design and optimization have been both accomplished in the noise transfer function (NTF) and the architecture of 1-bit fourth-order bandpass (BP) sigma-delta (ΣΔ) modulator. It improved the design efficiency, variability of center frequency and signal-to-noise ratio (SNR) without stability problems compared to the traditional method. The variability of the center frequency is fully applicable to the measurement of quartz crystal resonators (QCR) with different base frequencies. Compare the general 14-bit modulator and the designed BP ΣΔ modulator in the frequency extraction, results show that the proposed method is more effective for fast frequency extraction of high frequency signals from QCR dew point sensor.

Dynamic Partial Reconfigurable Systems can significantly improve the utilization of hardware by performing multiple tasks on the reconfigurable resources in run-time, whereas the limited reconfigurable resources constraint the number of tasks. To achieve efficient operation of systems, it is crucial to increase the utilization of resources while allocating tasks. This paper proposes a novel MER-based heuristic based on 3D-Adjacency heuristic, trying to reduce the area fragmentation, to improve the utilization rate and to deal with the problem that more than one task may arrive at the same time. It is a new attempt to combine MER technique with adjacency heuristic. Meanwhile, this paper also introduces an innovative task placement algorithm which taking into account the types of resources (like CLBs, RAMs and so on) and their relative positions that tasks contain. Simulation experiment results indicate that our methods have higher resource utilization, a higher task acceptance ratio, and a lower fragmentation ratio compared with some existing methods. Our algorithm can improve 17% to 26% in terms of task acceptance ratio compared to conventional algorithms.

We present a new mechanism to realize broadband transmission (BT) at near infrared in a dual-layer metallic-silver-grating structure with a silicon nitride dielectric spacer. The physical mechanism is ascribed to the partial degeneracy between the first-order magnetic polariton (MP1) mode supported in the thin dielectric spacer and the Fabry-Pérot-like resonant modes supported in the gratings' slits. With parameters optimized, high transmittance up to 85% with full-width-at-half-maximum bandwidth of 64% near 1.9 μm can be realized for TM-polarized light. Moreover, the BT can be maintained in a large range of oblique incident angles from 0° to 30°. In another word, the BT is capable of not only collimated beams but also noncollimated incident lights. Moreover, the high-transmission window can be tuned in the IR spectrum region by modulating the metallic gratings' thicknesses.

The issue of information security is hard to ignore in the process of informatization construction in higher vocational colleges. This paper focuses on the issues of domestic universities and colleges' informatization process, analyzes the problems of the information security management, and tries to provide reasonable solutions and measures for universities and colleges' information security management.

This paper focuses on the communication effectiveness (CE) of VHF frequency-hopping (FH) radio when threatened by the barrage jamming (BJ) in actual environment. First, a FH radio simulation model is built on the Simulink platform based on VHF FH radio's operating principle. Then, the jamming signals are quantified classified by their threat level and simulations of jamming signal from typical paths are made. Finally, emulated studies are made on effects of CE for wideband BJ and comb BJ with different threat level on different FH rate and different FH bandwidth. Also, a hardware-in-the-loop system is also built to verify the simulation results. Corresponding results are drawn.

Development of computer technology based on cloud, and the popularity of mobile devices, provide a new method for the innovation of learning style, this approach can use mobile learning to sum up, mobile learning has overseas situation of the important characteristics of continuous learning, this paper analyzes the existing mode of study limitations and the advantages of mobile learning model, gives the design of mobile learning platform based on cloud computing, and describes in detail the platform includes overall design the system architecture, composition of common interface module, such as the design of the content.

The paper defines the blind source separation (BSS) problem, demonstrates the mathematic model and conditions for its solving, and explains the uncertainty of its solving. The paper analyses the algorithm of independent component analysis (ICA), proving EFFICA more suitable for engineering data processing. The paper illustrates the application of EFFCIA to the gravity and magnetic data processing and simulated experiments. The results show the advantage of ICA application in denoising and extraction of weak signal over the traditional gravity and magnetic data processing, worth further research.

Based on the current problems such as inadequate server's processing and storage capacity, inconvenient operation and deficient interactivity in the information management system for academic administration in vocational colleges, we develop a cloud computing-based mobile teaching management and evaluation system in vocational colleges, which solves the problems in the present teaching management as well as provides an economic and effective idea for information construction in vocational colleges.

Server Virtualisation (SV) can improve the application efficiency of network sever resources, reduce the investment of hardware and less the cost of maintenance. This paper analyses that five key factors that are hardware cost, energy cost, software cost, maintenance cost and outage (or downtime) recovery cost in the Server Virtualisation. There a sample case is an university or a company what its application cost of the Server Virtualisation is examined and evaluated by contextual analysis. In addition, it will associate with the result of the case study to set out some critical suggestion in the concluding section.

Aiming at solving the problem of high-humidity measurement, an original humidity sensor is described. It is based on the resonant sensing principle. The sensor is composed of a quartz crystal and a Peltier module. Proactive approach is taken to produce condensation in the surface of the quartz crystal. The electrical parameters of the quartz crystal is changed due to dew condensation and then the resonant circuit stops oscillating. The time required until the circuit stops the oscillation is the dew condensation generated time and it is considered as a parameter evaluating humidity. The results show that relative humidity is detected as function of the time required until the oscillation stops and a good linear relationship in the range of 60-95% RH. It proved that the sensor is highly stable, sensitive, fast recovery and low-cost, and the response time and the dewetting time all less than 10s.

According to the problem of atmospheric humidity measurements, the dew point measurement method was proposed based on the resonant and an isolated dew point sensing device was designed. The device has a sensor module to accommodate the quartz crystal sensor and a cooling system. When water condensation produced by the Peltier cooling appears on the quartz crystal, the quartz crystal resonant frequency change suddenly, which tells the beginning moment of condensation, i.e. dew point. The temperature of the quartz surface can be obtained effectively by measuring the temperature of the cold surface of Peltier element and using temperature compensation curves. The measurement results with the proposed device were compared with the reference dew points, though some measurement error was yielded, it is proved that the proposed device is useful for measuring the dew points.

Based on extensive use of cloud computing, combined with various backup strategies, achieve a backup client running on the user's desktop. The users who used cloud backup confront with the diverse cloud storage interface, directories and large files backup trouble. Based on the SNIA cloud storage model, compared with diverse interface from several cloud storage vendor, a cloud interface layer which shields the difference cloud storage interface is designed. Mapping the file system directory hierarchies and cloud storage data model, using hierarchical transmission implements directories backup. Though extending the file metadata information to achieve full backup, incremental backup, differential backup. Using breakpoint transmission to realize big files backup. Finally. the backup client performance test results show that the technology achieve a good performance.

The purpose of the present study was to examine the effects of astragaloside IV, a saponin isolated from Astragalus membranaceus (Fisch) Bge, on insulin resistance in L6 myotubes induced by high glucose and insulin (high Glc/Ins). Chronic treatment of high Glc/Ins resulted in impaired insulin-stimulated glucose transport in differentiated L6 myotubes, which was paralleled by reduced tyrosine phosphorylation of insulin receptor substrate-1 (IRS-1), phosphatidylinositol 3-kinase activity and Akt phosphorylation. Pretreatment of insulin-resistant cells with astragaloside IV exhibited increases of insulin-mediated glucose transporter 4 (GLUT4) translocation to the plasma membrane and glucose uptake. This potentiation correlated positively with the enhancement of insulin signaling pathway by increasing tyrosine-phosphorylated IRS-1, PI 3-kinase activity and Akt phosphorylation at Thr308. The results indicate that astragaloside IV may improve insulin resistance in L6 myotubes through IRS-1/PI 3-kinase/Akt signaling pathway.

In order to evaluate the stability of the process of aluminum alloy pulsed Metal Inert Gas(MIG) Arc welding, an approximate entropy analysis has been done on voltage signal in the process of aluminum alloy pulsed MIG welding. The calculation and comparison of the approximate entropy on voltage signal under different welding parameters, such as welding speeds, wire feed speeds, duty cycle and so on, shows that the approximate entropy of voltage can reflect the stability of the process of aluminum alloy pulsed MIG welding. The results show that the smaller the average value of approximate entropy, the more stable the welding process will be. Otherwise, the welding process is unstable while the average value is bigger.

The cascaded step-changed LPGs transmission spectra are calculated and simulated and the results demonstrate that the transmission spectral profile can be tailored into a double rejection band filter, with the parameters of the LPGs being adjusted, which can constitute the multi-channel filter used in coarse wavelength division multiplexing.

In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity. In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-the-art: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account.

The algorithms of grid resource scheduling is one of the key factor in promoting grid performance, and a smart algorithms would substantially promote the effective utilization of grid resources, as well as significantly reduce the implementation time. A lot of algorithms have been applied into resource scheduling, in which the evolutionary algorithms are more prominent. The shortages of traditional differential evolution algorithm were discussed in this paper, which was easily trapping in the local value and lead to premature convergence. In order to solve these problems, an improved differential evolution algorithm was proposed to control the scaling factor with the utilization of mutation operator. Experiment showed that, the improved differential evolution algorithm had a significant performance in the grid resource scheduling.