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Accurate prediction of future observations based on past data is the key to near real-time disturbance detection using satellite image time series (SITS). To overcome the limitations of existing methods, we present an attention-based long-short-term memory (LSTM) encoder-decoder model in which the historical time series of a pixel is encoded with a bidirectional LSTM encoder while the future time series is produced by another LSTM decoder. An attention mechanism is integrated into the encoder-decoder model to align the input time series with the output time series and to dynamically choose the most relevant contextual information while forecasting. Based on the proposed model, we develop a framework for near real-time disturbance detection and verify its effectiveness in the case of burned area mapping. The prediction accuracy of the proposed model is evaluated using moderate resolution imaging spectroradiometer (MODIS) time series and compared with state-of-the-art models. Experimental results show that our model achieves the best results in terms of lower prediction error and higher model fitness. We also evaluate the disturbance detection ability of the proposed framework. The proposed approach improves the detection rate of disturbances while suppressing false alarms, and increases the temporal accuracy. We suggest that the proposed methods provide new tools for enhancing current early warning systems in real time.

The processing and application of public opinion are getting more and more attention all over the world with increasing technologies and systems (software) one after another, however, rare of them focus on the technology of reverse relation. In this paper, it takes the classic association algorithms Apriori and FP-Tree as its basis, presents a process design of reverse relation on public opinion, and realizes the acquisition and query of hot public opinions in social networks.

Due to the expansion of data scale, the data in Transportation Management System (TMS) of the State Grid has the features of distributed storage, more data noise and larger data dimension, making data analysis extremely difficult. Therefore, data sorting and feature selection should be carried out on the basis of the original data for subsequent learning tasks. The optimization method of feature selection for TMS classification learning is able to effectively select a large number of features, thus improving the accuracy of classification learning. In order to train a feature selection model suitable for TMS, this paper studies the method of applying random forest model integrated by multiple decision trees to TMS feature selection. By comparing the number of decision trees, the criteria of feature classification, the maximum number of features in candidate subset of feature classification, and the change of model accuracy after feature rearrangement with key algorithms, a feature selection method most suitable for TMS data is given and verified by experiments. Experimental results show that the proposed optimization method of feature selection lays a good foundation for future learning tasks.

The effectiveness evaluation of ship-borne communication equipment (SCE for short) is studied. The existing methods of equipment effectiveness evaluation are summarized. In view of the fuzziness, uncertainty and difficulty in quantifying the management factors of SCE by probability and statistics method, the overall effectiveness evaluation of SCE is carried out by using fuzzy comprehensive evaluation method. Considering the use characteristics of SCE, Availability Dependability and Capability (ADC for short) method is used to evaluate the service capability index, and the results are integrated into the overall evaluation of the fuzzy comprehensive evaluation method. Two case calculations and simulation results are given, and the corresponding decision-making reference is given according to the results.

InAs/GaAs quantum dot (QD) lasers monolithically grown on Ge or Si are a promising method to achieve low cost, large scale, and high yield optical sources for photonic integrated circuits [1]. This work shows that a free-running Ge-based QD laser can output periodic pulse oscillations with one, two and three periods, without incorporating saturable absorber or employing any external perturbations [2,3]. The QD laser under study was epitaxially grown on a 4-inch Ge-wafer by the gas-source molecular beam epitaxy, and the active region consists of five stacked dot-in-well layers [4]. The wafer was fabricated into a ridge-waveguide laser with a ridge width of 4.0 μm and a cavity length of 4.4 mm. The laser shows a lasing threshold of 60 mA at 20 °C, and the lasing peak is at 1213 nm. Figure 1(a) shows that the relative intensity noise (RIN) of the Ge-based laser pumped at 80 mA exhibits a common resonance with continuous-wave output. However, the resonance of the laser pumped at 160 mA does not become overdamped, but evolves into pulse oscillations with a high and sharp peak. Figure 1(b) shows the resonance frequency and the damping factor extracted from the RIN spectra. The resonance frequency increases with the pump current from 0.4 GHz at 80 mA up to 1.1 GHz at 170 mA, whereas the damping factor declines from 4.0 GHz down to 0.03 GHz. The damping reduction confirms the onset of peculiar pulsing dynamics for pump currents above 140 mA.

Under some occupational conditions, workers are inevitably exposed to high-intensity radiofrequency (RF) field, so the effects of long-term exposure to RF field on health have already become a public concern. In this study, we investigated the effects of one-month exposure to a 220 MHz RF field at two different average power densities (50 W/m2 and 100 W/m2) on the sperm quality in male adult Sprague-Dawley (SD) rats. Sperm quality was evaluated by measuring the number, the abnormality and the survival rate of sperm. The morphology of testis was examined by hematoxylin-eosin (HE) staining. Levels of secreting factors by Sertoli cells (including glial cell line-derived neurotrophic factor (GDNF), stem cell factor (SCF), transferrin (TRF) and androgen binding protein (ABP)) and factor by Leydig cells (testosterone, T) were determined by enzyme linked immunosorbent assay (ELISA). The expression levels of the apoptosis-related protein (Caspase 3, BAX and BCL 2) in testis were assessed by Western Blotting analysis. Compared with the sham group, the sperm quality in RF groups decreased significantly in a dose-dependent manner. Levels of GDNF, SCF, TRF and ABP in testis as well as the morphology of testis, showed on obvious changes after RF exposure. The level of serum T decreased significantly in the rats treated with 50 W/m2 RF exposure and increased significantly in the rats treated with 100 W/m2 RF exposure, compared with sham group. In addition, the protein levels of Caspase 3 and the BAX/BCL 2 ratio in testis increased obviously after RF exposure, compared with sham group. These data collectively suggested that under the present experimental conditions, 220 MHz RF exposure could affect sperm quality in rats, and the disruption of secreting function of Leydig cells and increased apoptosis of testis cells induced by RF field might both account for this damaging effect.

The foraging problem, which evolved from ants finding food and delivering them to the nest, is for a swarm of robots to transport objects from a source to a destination. To avoid collision of robots or congestions, object transportation is generally conducted in a pipelining manner. Existing solutions towards the foraging problem either have performance problems or cannot tolerate failures. We present a self-stabilizing solution for the swarm robotic system to form a pipeline structure to transport objects and achieve the foraging task. In this paper, we first define the stable state for the swarm, which includes two requirements: (1) the robots operate in non-overlapping regions, i.e., transport objects in a pipeline structure and (2) the transportation rate of the system is optimal. Then, we introduce our self-stabilizing algorithm for the foraging problem and prove its convergence and the correctness of its convergence properties. Due to the self-stabilization nature, our solution is decentralized and fault tolerant. The swarm can achieve the foraging task as long as there is at least one working robot. Due to the definition of the stable state, our swarm system, when converged, can achieve optimal performance. We conduct simulations to evaluate the effectiveness of our algorithm, and the experimental results show that from any state, our algorithm can converge very quickly to reach the stable state and provide optimal performance for object transportation.

Power line inspection is an essential but costly task while automated UAV (unmanned aerial vehicle) inspection can greatly reduce such costs. However, navigating along power lines is a challenging task due to the narrow width and limited features of power lines. Existing power line tracking methods have threshold selection problems and cannot work well for complex and changing backgrounds. We make use of power line specific knowledge to build a model to achieve predictive and continuous parameter selection so that the best thresholds are selected for changing scenarios. Experimental studies show that our solution is much better than existing ones. Specifically, we have built the first fully automated UAV that successfully tracks power lines in the real world.

Natural disasters or human activities, such as forest fire, flood, and deforestation, may lead to anomaly or disturbance of land cover. Continuous detection of anomalies is important for studying spatial-temporal processes of land cover changes. Although many time-series-analysis methods have been developed for change detection, to the best of our knowledge, few methods focus on continuously detecting anomalies in satellite image time series. This study proposes a method for continuous anomaly detection in satellite image time series based on Z-scores of Season-Trend model Residuals (ZSTR). The ability of ZSTR for continuous anomaly detection was validated with an experiment for detecting spatial-temporal anomaly regions caused by severe flooding.

Continuous anomaly detection in satellite image time series is important for studying spatial-temporal processes of land cover changes. In another study, we proposed a method based on Z-scores of Season-Trend model Residuals (Z-STR), which can continuously detects anomaly regions in image time series. However, Z-STR only detects anomalies with large shifts but cannot detect anomalies with small or gradual shifts. To improve the effectiveness of continuous anomaly detection, in this study, we propose a new method based on Exponentially Weighted Moving Average (EWMA) of Season-Trend model Residuals (EWMA-STR). Experiment for detecting spatial-temporal anomaly regions caused by severe flooding validated the better performance of EWMA-STR than Z-STR for time series anomaly detection. By using information of all historical time series data, EWMA-STR can detects anomalies with small or gradual shifts as well as anomalies with large or abrupt shifts.

In some agile applications, cyber and physical entities need to be rapidly composed to cope with dynamically arising situations and to meet high dependability requirements. Automated service composition reasoning techniques can be leveraged to compose cyber and physical services in a timely way. However, the modeling of the physical services poses some challenges. In this paper, we present novel models for the specification of the physical services and the state of the world to facilitate real-time service composition of cyber physical systems (CPS).

Based on the operator theory, the general solutions of the extended displacements and stresses in three-dimensional piezoelectric media are expressed in terms of two potential functions. By introducing the generalized Almansi's theorem and the Hankel transform, the obtained general solutions are further simplified and expressed by harmonic functions. Finally, the fundamental solutions of the extended displacements and stresses corresponding to unit concentrated extended displacement discontinuities are derived.

Service computing technologies have been widely applied to many application domains to facilitate rapid system integration for desired goals. However, existing service models need to be enhanced for cyber-physical systems (CPS) and internet-of-things (IoT). In this paper, we develop an ontology model for the specification of services in CPS/IoT. First, we discuss the major differences in modeling software services and services in CPS/IoT. Then, we propose a novel PT-SOA (PT stands for physical things) model, which is mainly extended from OWL-S, to enhance existing service models for CPS/IoT systems. Finally, we show a case study system to illustrate how our model can facilitate proper service selection and composition.

The physical world is becoming smarter and smarter due to the advances in smart devices and CPS/IoT technologies. In this paper, we investigate the roles various cutting-edge technologies, such as service computing, big data analytics, crowd sourcing, gaming technologies, etc., can play to significantly enhance the intelligence of our physical environment and subsequently benefit the society. We consider a smart physical world (SPW) consisting of physical entities, cyber entities, and human. Service technologies can be used to model the entities in SPW and specify their capabilities. Service discovery and composition techniques can be used to, based on the modeling, compose these capabilities to solve real-world problems. To enable higher level intelligence, we further discuss how various technologies, such as big data analytics and artificial intelligence, can be used in the smart world to reason from sensor inputs to derive the situation facts, and from the situation facts to derive the reactive actions. From the derived actions (tasks), the service technologies can be used to compose the capabilities of the entities to realize the task. However, current technologies and artificial intelligence may fall short in many situations. We further propose a gaming-based crowd sourcing platform to make use of human intelligence to enable successful completion of some challenging reasoning and control tasks.

As the carbon emission becomes a serious problem, a lot of research works now focus on how to monitor and manage carbon footprints. One promising approach is to create a “carbon footprint aware” world to expose people to the carbon footprints associated with the products they buy and the services they use. Carbon footprint labeling (CFL) of products enables the consumers to choose their products not only based on quality and cost, but also based on their carbon footprints. Similarly, carbon footprints of common activities and services can also be labeled to enable informed choices. CFL can impact the supply chain operations as well. With the carbon footprint information, the carbon-footprint-optimal supply chain can be identified to model the supply chains with least carbon emissions. Existing carbon footprint management systems mostly rely on databases to maintain carbon footprint data. But database alone is not sufficient for carbon footprint labeling. In this paper, we develop an ontology model, CFL-ontology, to specify how products are produced, the processes involved in activities and services, and the computation functions to derive the carbon footprints of the products, activities, and services, based on the associated descriptions. With the CFL-ontology, reasoning can be performed to automatically derive the carbon footprint labels for individual products and services.

The effective evaluation of the comprehensive performance of ecological residential need take consideration of the ecological technical level, the economic effects and the level of operational management. Gray clustering evaluation method based on the center point of the triangle whitening weight function can make the results of the evaluation to reflect the comprehensive level of ecological residential We build a set of strong operability ecological residential evaluation index system, and use the index system to evaluate the YX community. This conducted a strong role in guiding the ecological residential development.

As the green house gas emission becomes a serious problem, a lot of researches now focus on how to monitor and manage carbon footprint (CF) of a production process or transportation, especially in the supply chain. Usually, most of the carbon footprint management systems are based on databases. But database is not sufficient in describing the production and transportation processes and the facilities used in these processes. In this paper, we develop an SOA based model for the carbon footprint management and labeling (CFML), using ontology and OWL-S techniques. We use OWL-S to describe the processes and workflows for production and transportation and extend it to specify the methods for deriving CF of them. We use the existing energy conversion formula to derive the CF when the energy data can be collected separately. We also derive an approach to separate the CFs when data of different processes have to be collected together. In a supply chain, a production company may have different choices of suppliers to provide certain components. To balance the tradeoff between carbon dioxide emission and cost of the overall production process, we design a supplier selection algorithm to derive the optimal solution.

The complexity of resource allocation problems is usually NP-complete, which makes exact method inadequate to handle Resource allocation problems, and encourages heuristic techniques to this class of problems for obtaining approximate solutions in polynomial time. Different heuristic techniques have already been investigated for handling various Resource allocation problems. In this paper, we propose a novel hybrid search algorithm that combines the merit of genetic algorithm and tabu search optimization. In our implementation, both iteration number fixed algorithm and running time fixed algorithm are well designed for the resource allocation problem. Furthermore, Simulation results are reported and it shows that proposed algorithm indeed have admirable performance for tested problems.

Due to the working hazardous or other conditions, such as tele-medical, tele-embodiment, operations should be executed with a fully remote control and monitoring. So, tele-control and reality simulation are crucial in these environments. However, there exists a lack of an effective system architecture that integrates remote condition monitoring and control of automated equipment; that give much consideration about data transfer time delay via TCP/IP data package. This paper presented a novel prototype architecture for tele-control and reality simulation, which can guarantee the non-distortion-transfer of control information and reduce the action time difference between local simulated virtual equipment and remote real equipment, couple the remote control and virtual reality together. In order to demonstrate and validate the effectiveness of the novel architecture, a 3 DOF Fischertechnik industry robot remote operation and monitoring system have been developed. Experimental results are encouraging and demonstrate a promising application in any other relevant environment.

In so many combinatorial optimization problems, Job shop scheduling problems have earned a reputation for being difficult to solve. Genetic algorithm has demonstrated considerable success in providing efficient solutions to many non-polynomial-hard optimization problems. In the field of job shop scheduling, genetic algorithm has been intensively researched, and there are nine kinds of methods were proposed to encoding chromosome to represent a solution. In this paper, we proposed a novel genetic chromosome encoding approach, in this encoding method, the operation of crossover and mutation was done in three-dimensional coded space. Some big benchmark problems were tried with the proposed three-dimensional encoding genetic algorithm for validation and the results are encouraging.

In this paper, we consider the design issues in building robotic swarm systems as a service and develop a multi-level service-oriented robotic swarm (SORS) framework. First we consider how to support easy composition of services into workflows to accomplish user tasks. We identify a set of primitive virtual services for this purpose. Virtual services are not physical robotic swarm actions, but are high-level primitive services geared toward easy specification of the workflows of many tasks. To realize virtual services, SORS incorporates the layers of planning services and micro services to plan and control the swarm of robots. Two example micro services and the detailed algorithms in their implementation are given in the paper. Also, a case study system is developed to show the use of virtual services to specify the workflow for an example task and to illustrate the design of micro services to realize virtual services.

Context model is a fundamental issue when the intelligent interaction system is constructed based on context-aware in the Internet of Things. In the traditional interaction system of the user-centric and human-computer interaction oriented, the main context is users and their surrounding's information. However, in the Internet of Things of the thing-thing interaction, the main context will be changed to the perception nodes and their surrounding's information. But, there is not a unified context model aiming at this point. This paper presents a context model and analyzes the conception, classification and formalization of the context. Therefore, this work can have been an important instructing meaning and direct effect on constructing the intelligent interaction system in the Internet of Things.

An efficient K-shortest paths routing algorithm for computer networks is presented. The Algorithm is based on enhancements to currently used link-state routing algorithms such as OSPF, which is only focusing on finding the shortest path route by Dijkstra algorithm. The desire effect to achieve is mainly through the use of K-shortest paths algorithm, which has been implemented successfully in some fields like traffic engineering. The correctness of this Algorithm is discussed as long as the comparison with OSPF.

The structure of Switched Reluctance Motor is simple, high reliability and efficiency and excellent speed adjusting performance, thus paid widespread attention in recent years. But its unique double salient poles structure leads to serious torque ripple. Especially in Starting Process of the motor torque ripple is particularly evident, The thesis applies direct instantaneous torque control in Starting Process of SRM. And compare with the current chopping control. The results show that the direct instantaneous torque control to achieve minimum torque control.

Due to its adaptation to environmental change and market competition, the organizational form of dynamic alliance has been developing rapidly in recent years, but it also brings significant risk to enterprises. Taking into account the interaction and feedback relationships between criteria/indices, an index system for evaluating the risk of dynamic alliance is presented. Considering the vagueness and inaccuracy of information during the evaluation process, a risk evaluation model for dynamic alliance based on fuzzy analytic network process method is proposed. The local weights of indices are determined by fuzzy preference programming method. Meanwhile, an unweighted supermatrix based on its network structure is built for those interactional indicators, and the limit supermatrix is calculated. Matlab software is used to solve the problems. Finally, a numerical example of the risk evaluation of a dynamic alliance is illustrated by proposed method.

Vibration detecting about mechanical equipment is a effective way to judge the performance of machinery. According to failure of the vibrating screen's motor shaft fracture, plate fracture, exciter destroy and so on, this article introduces the hardware and software of ZigBee wireless network test node based on CC2430 and accomplishes acquisition and smoothing of vibrating screen's acceleration signal.

Vertical launching technology can effectively improve the performance of antiaircraft missile, which can reduce the preparative time for antiaircraft missile to attack the targets. The scheme of vertical launching control system is present. The autopilot of missile has used the angle of pitch, yaw and roll as the feedback signal. The missile can flight to any direction from the vertical launching state by controlling the Euler angle of the missile. The model of the antiaircraft missile which has used multiple controls of aerodynamic actuator and TVC (thrust vector control) is given, and then the flight control system has been designed by using linear quadratic regulator (LQR) theory. Finally it is proved by simulation that the performance of the vertical launching control system can meet the requirement and the scheme of the control system is feasible.

Applications of neural network were widely used in construct project cost estimate. Aim at handling weakness of poor convergence and insufficient forecast, an improved fuzzy neural network method based on SOFM (self-organizing feature map) and GA (genetic algorithm) was proposed to replace the fashionable T-S fuzzy neural network. The method illustrated how to apply SOFM and GA to improve the fault such as poor convergence and insufficient forecast. After optimizing of T-S fuzzy neural network model, construct project cost estimate model had been built up. Finally, the model was set up with the purpose of comparing generalization ability by 18 examples and 2 testing samples. Comparing the simulation, a positive result was found that genetic fuzzy neural network had a better performance in reducing the forecast error and iterating times than BP, BP optimized by GA, GA-BP, fuzzy neural work. Therefore, this model is fit for handling construct project cost estimate.

The main objective of this paper is to analyze models, techniques, algorithms and infrastructures needed to complete decentralized data fusion. It is often to use the Simple Tracking Fusion Algorithm in decentralized data fusion for Multi-sensor. But that algorithm is on the hypothesis that the output of each local filter is uncorrelated. If the fusion result is given back to each local filter, the output of the local filter will has correlation with each other. For that case, if still using the Simple Tracking Fusion Algorithm, the fusion data will lose consistency. But in the Covariance Intersection Algorithm, it is unnecessary to consider the correlation of each local filter. In this paper, a new decentralized data fusion algorithm with feedback framework based on the Covariance Intersection algorithm is proposed for Multi-sensor. The simulation results show the effectiveness and robustness of the proposed algorithm.

Incremental contour fusion plays a very important role in updating topographic database. The most popular fusion methods in current use depend on manual work basically, in which it results in large workload, low efficiency and it can't ensure the quality of the spatial data. Therefore, the automatization of contour fusion should be developed exactly. Topological relation between unchanged contour and changed one is an important topic for contour fusion. In this paper, a new approach based on whole object is pursued to compute the binary topological relationship between them, in which "FL Points" are introduced, intersection and difference operators are selected from set operators to distinguish the topological relations between neighboring spatial line objects; three types of topological invariants are used for the computational results of set operations: contents, dimension and connectivity-number. Then 14 fusion rules are concluded and a prototype system for automated fusion is implemented. The proposed approach is examined to be reasonable and practicable by real and simulated experimental data.

Group mobility in wireless sensor network (WSN) is of particular importance in many practical application scenarios. In this paper, the group mobility management in IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) based WSN is considered and the application of network mobility (NEMO) protocol to support group mobility in WSN is discussed. A new network architecture supporting the integration of NEMO and 6LoWPAN-based WSN is proposed, the group mobility management mechanism and the corresponding signaling flow are discussed. Simulation results demonstrate that comparing to MIPv6, the application of NEMO protocol in 6LoWPAN reduces both the handoff latency and the energy consumption of sensor node.

The hi-tech projects' investment is characterized with high risk and huge profit, so a scientific and accurate risk evaluation model is of importance to projects' investment. Based on the analysis of the indicators influencing the investment risk of hi-tech projects, some typical indicators were selected. Firstly, according to the quantitative assessment results from the experts, some samples were used to establish the projection pursuit model for comprehensive evaluating the investment risk of the hi-tech projects. Then, the model without samples was constructed to avoid the impact of training samples. The optimal projection vector was optimized by genetic algorithm, so the above problem was transformed into one dimension problem. Compared with the conventional assessment methods such as AHP (Analytic Hierarchy Processing) and ANN (artificial neural net), the model can effectively overcome the faults of the subjective feature from the AHP and unsatisfied generalization ability from ANN. Additionally, under no training samples, the model can also sort well, so the model has wide applicability.

Aimed at the faults of low level of geological exploration in Dingxiang district in south of Anbui Provence, an integrated finding mineral model was constructed after studying the mineralization regularity of copper-polymetallic deposits. According to the model, collected and analyzed the comprehensive information from geology, geophysics, geochemical and remote sensing. Then six key prediction variables were chosen based on spacial analysis function of GIS. Made the grid thematic layers of the variables, gave their corresponding weights according to the importance in metallogenic diagnosis. Some perspective zones were enclosed by view of the metallogenic favorability. Result indicates that combining the integrated finding mineral model with GIS can effectively make use of expert knowledge, and has the characteristics of simple calculation, reliable results. So it can provide decision-making basis for further exploration.

At present, the research works about sociality agent emphasis on what are social mental status, and use abstract social mental status to establish sociality agent model. These works lack study that mapping sociality agent model to particular architecture, analysis running process of architecture, and prove architecture reasonable. Aim at this problem, based on understanding about human being sociality, from application angle, this paper think that sociality means agent can take on roles, play roles, and locate in some agent organization at all time; give agent role form describe and role model, define a sociality agent architecture based on role (R_SA) using Π calculus. Based on our theoretical analysis about modules interaction in R_SA, the R_SA can maintain architecture stable, and can be used in application area.

Mobile agent is a software proxy that can migrate continuously between network computing devices and use services locally to implement computing tasks. This paper presents a method of dynamicly programming mobile agent workflow path based on social acquaintance network. We assume that every member in the social acquaintance network, according to the service contract, should provide the runtime environment, computing services and recommendation services based on social acquaintances that are in charge of recommending one or more suitable workplaces to mobile agent. Finally, we give an example to demonstrate that transitive acquaintance relations in the social acquaintance network can guarantee reachability of workflow path and service redundancy in this network can make agent have a good environmental adaptability.

Three UHV transformer substations have been on commercial run. UHV substation integrates a series of high-voltage and secondary electrical equipments which brings out electromagnetic environment deserving more attention. If the electromagnetic environment of substation is severe, it will affect the neighbouring radio stations' normal work and also the residents around and worker at the UHV substation. This paper discusses the electromagnetic environment of Chinese UHV AC substation, and put forward some measurements to electromagnetic environmental amendatory

Proactive handoff is expected to be one of the distinct feature of 4G heterogeneous wireless systems, which is obviously different from the passive handoff commonly existed in traditional cellular systems. Users with multi-network interface terminals can actively and easily handoff to access network for better QoS satisfaction at a minimum expense, on the other hand, service providers (SPs) will have to face more intense competition for attracting more users to maximize their profits. In this paper, the relationship between competitive SPs and users is modeled as a noncooperative game, and the Nash equilibrium solutions corresponding to the best response price offered by SP are obtained. Based on the optimal prices, a novel vertical handoff algorithm for maximizing user performance-price-ratio is proposed. Numerical results demonstrate that all SPs can attain maximal profits from the Nash equilibrium results, and both of the network utilization efficiency and user QoS can be guaranteed.

The lightning strike on transmission lines could have electromagnetic effects on the adjacent buried metallic pipelines. The resistance coupling in pipelines is the severest, because the most high-energy lightning current is directly injected into the earth by ground conductor of fault tower when lightning strike happens. Thus for 1000 kV UHV AC double-circuit transmission lines, the paper studies the electromagnetic coupling in the adjacent buried metallic pipelines when the top of tower is subject to lightning strike and results in failure. By the establishment of simplified calculation model for the transient electromagnetic field of lightning, the effects of various factors, such as the distance between transmission lines and pipelines, crossing angle, soil resistivity, and the amplitude of lightning current, on interference voltage imposed onto pipelines and their rules are calculated and analyzed.

Different kinds of wireless access systems will be integrated in next generation network. To support seamless mobility between these heterogeneous wireless access networks, efficient and reliable vertical handoff schemes are required. In this paper, a survey on vertical handoff decision algorithms is given and the algorithms are described, analyzed and compared. In the end, major existing problems and future research direction are discussed.

The appearance of cast cells in urine sediment is an essential sign of serious renal or urinary tract diseases. However, due to uneven illumination, low contrast against the background and complicated components of the microscopic urine sediment images, detection and recognition of cast cells in former study can not be considered sufficient. In this paper, an efficient approach for casts detecting and recognition in urine sediment images is proposed. It consists of three stages: Firstly, 4-direction variance mapping image is acquired from gray scale image. Secondly, we obtain binary image by applying an improved adaptive bi-threshold segmentation algorithm to the above mapping image. In the last stage, five texture and shape characteristics of casts are extracted from both gray scale image and binary image. Based on these characteristics, we develop an decision-tree classifier to distinguish casts from other particles in the image. Experimental results show that our method produces satisfactory segmentation, achieves an easy-implemented, time-saving classifier and has improved recognition performance.

The particles in urinary microscopic images are hard to classify because of noisy background and strong variability of objects in shape and texture. In order to overcome these difficulties, firstly, a new method of texture feature extraction using the distance mapping based on a set of local grayvalue invariants is introduced and the feature is robust to the shift and rotation. Secondly, we reduce the high dimensional feature into a lower dimensional space using PCA. Thirdly, a multiclass SVM is applied to classify 5 categories of particles after trained them reasonably. Finally the experiment results achieve an average of accuracy of 90.02% and a F 1 value of 90.44%.

This paper analyses the principle and development potential of Precision Agriculture, then provides the elementary scheme of Precision Agriculture in China based on applied mathematical model and information technology.

This paper studies how to calculate the difference between two intuitionistic fuzzy sets (IFSs). A new property of distance measure on IFSs is proposed at first. Some new distance measures on IFSs are proposed secondly. These new properties and new distances measure can be used in pattern recognition, and the new distances measure is more flexible as the decision-maker can set different weights according to different scenarios.

In the research of Agent theory and technology, BDI model is the most studied one with the broadest applications. But it lacks specific mapping relationship research between BDI model and particular Agent architecture. In this paper, we firstly establish abstract mapping between BDI model and Agent autonomy, then stimulate human being autonomy mechanism, establish specific mapping relationship between autonomy Agent BDI architecture and individual autonomy, map Agent autonomy belief to inside blackboard, autonomy desire to controller, autonomy intention to sensor, effector and data operator, and define this autonomy Agent BDI architecture using monadic Pi calculus. Based on our theoretical analysis, this architecture can achieve autonomy because of its closed loop feedback.

A common simplifying assumption that there exists, at most, a single fault in the system at any given time does not hold for complex systems with large number of components or systems with little opportunity for maintenance during operation. By employing concepts from rough sets theory, information theory and heuristic search approach, a novel sequential testing algorithm based on rough sets is presented for multiple fault diagnosis, which can efficiently improve the diagnostic precision, substantially reduce the expected testing cost and realize the real-time dynamic diagnosis independent of the priori probability of system.

Aiming at the prevalently concerned mining problem about constructing concept search in current Web search engine area, especially applying the Vector Space Model VSM to web search mining based on the association rules, this paper provides a highly efficient mining algorithm EARS. The EARS algorithm implements the association rules pruning based on VSM via constructing association library and computing similarity. The EARS stores the frequent itemsets via multi-dimensional linked lists and utilizes the recurrence relation among the frequent itemsets in order to effectively obtain the association rules. We verify the extended function of retrieving and querying on the web and the results of these experiments indicate that our method is effective and feasible.