Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Empirically, symbolic regression tries to identify, through genetic programming and within the sphere of mathematical expressions, a model which best explains the relationship between variables in a given set of data, in terms of precision and simplicity. Virtual teaching and learning environments focused on evaluation have been previously investigated, as they offer teachers an effective teaching and learning tool and the student the possibility of computer-assisted evaluation and customized learning. Within this context, the present paper introduces an alternative approach to automatic evaluation in virtual teaching and learning environments, which offers the following improvements when compared to other methods: a) superior accuracy when compared with the linear regression method; b) simplicity of implementation; c) possible deduction of final student grades; and d) context adaptive. To this extent, a case study was applied to the LabSQL environment, with the purpose of clarifying the benefits of symbolic regression via genetic programming, while emphasizing its efficiency and simplicity of implementation.

The paper presents a noninvasive scoliosis and other spine disorder automatic diagnostic solution implemented using low-cost commodity sensors, but still achieving adequate precision. The cost of similar commercial solutions is prohibitive to have them acquired by many healthcare institutions in less-developed countries, thus we have developed a low-cost one. The proposed solution can easily be used by healthcare institutions with small investment for obtaining otherwise expensive equipment. The precision and usefulness of designed spine disorder diagnostic solution is thoroughly tested during students’ annual health checkups.

This study designs an intelligent soilless culture device that overcomes the disadvantages of the traditional soilless culture device, such as complex structure, low automation, and management inconvenience. The said device has an embedded controller S3C6410 as its core and realizes the intelligent supplemental lighting of the soilless culture device. It is also capable of the automatic heating, automatic circulation, and automatic ion concentration balance of the nutrient solution by detecting the illumination, ion concentration of the nutrient solution, temperature, and other parameters through sensors. The soilless culture device also communicates with a mobile phone in real time through the CDMA module. The users can query various parameters of the device any time and send the control command in real time through the mobile phone. After testing, the device not only realizes on-demand and subband intelligent supplemental lighting and the automatic concentration balance, automatic heating, and automatic circulation of the nutrient solution, but also achieves remote wireless monitoring through the mobile phone. The culture contrast experiment of the leaf lettuce shows that the device developed in this study is superior to other traditional soilless cultivation devices. It has the significant characteristics of strong practicability, good scalability, stable and reliable work, and intuitive and simple manipulation.

The near-infrared (NIR) camera is extensively used in the optical tracking system for surgical navigation because it can effectively restrain the interference from environmental light in the imaging process. The accuracy of the optical tracking system is determined by camera calibration. However, the existing calibration methods are intended for visible-light cameras and are inapplicable to NIR cameras because the latter has no capacity to capture the calibration pattern. In the study a calibration pattern composed of near-infrared surface-mounted diodes is designed, and the corresponding intelligent algorithm based on geometric information that can be used to calibrate the NIR camera is proposed. Using this method requires the implementation of automatic decision of angular points via triangular gridding. The experimental results show that our proposed method is accurate and effective in meeting the application requirements of surgical navigation.

The typical teaching–learning-based optimization (TLBO) algorithm tends to devolve into local optimization and suffers from the rapid loss of population diversity. In this study, an improved TLBO algorithm with group learning (GTLBO) is established to solve these problems. In the proposed algorithm, a class is divided into several groups. The individual with the highest level is selected as the teacher for each group. Then, the teacher implements the TLBO algorithm in each group. This strategy of group learning can maximize the time before the students reach the teacher’s level and effectively ensure population diversity. Given an effectively diverse population, the idea of reversing the beginning and ending is introduced to boost the convergence rate of the algorithm. Moreover, a matrix displacement method is provided to solve the premature termination phenomenon of the algorithm. Finally, the performance of the GTLBO is investigated across six complex high-dimensional benchmark functions. Results obtained through experiments show that the GTLBO conduces enhanced performance in solving problems of multimodal function optimization. The convergence speeds and solution accuracy of the proposed algorithm are significantly improved compared with those of the typical TLBO algorithm.

The intelligent identification and positioning for melons and other fruits are important links for the melon and fruit picking robot to naturalize smooth picking, thus directly affecting the picking efficiency and success rate. Considering that the picking robot has low identification rate and low positioning accuracy for melons and fruits, this paper designed an intelligent watermelon identification and positioning method in a natural scene. This method included the following steps: first, the natural watermelon images shot by the left and right cameras were captured to increase the proportion of the watermelon region in the image; second, erosion, adaptive noise cancellation and filling, as well as other techniques were used for the captured watermelon image to identify the watermelon region and calculate its values of barycentric coordinates; third, the squint binocular positioning algorithm was designed based on the values of watermelon barycentric coordinates in two images to obtain the actual watermelon three-dimensional space coordinates, using the left camera as the origin of coordinates. The experiment verified that the relative positioning errors of this method were within±15% for the watermelon three-dimensional space coordinates in a natural scene, thereby providing a key intelligent identification and positioning method for the watermelon picking robot.

This paper applies the error-eliminating theory to create a new method for fuzzy multiple attribute decision-making problems whose attribute values contain interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers. First, the concepts of error, loss, and extreme loss are discussed in the context of fuzzy multiple attribute decision-making problems combined with error-eliminating theory. Second, the error function is constructed, and the extreme loss value is calculated based on decision makers’ psychological threshold interval and error types. Third, the attribute loss value is calculated through error values and extreme loss values based on the decision makers’ psychological characteristics of loss aversion. And then, the overall loss value of alternatives is obtained by the attribute loss value. Finally, the ranking and selection of alternatives are conducted based on the overall loss value. Using the practical example of the location of agricultural products’ logistics center, the rationality and scientificity of this study are illustrated by comparing the decision-making method proposed in this paper with three other methods.

Diagnosis of rice planthopper pests based on imaging technology is an efficient means to develop intelligent agriculture. Effective contour automation extraction is an important pretreatment technology at the early stage for identifying and classifying rice planthoppers. The traditional graph cut method-based active contour (GCBAC) requires human–computer interaction during segmentation. In addition, GCBAC is prone to shrinking bias phenomenon, thereby providing short-boundary segmentation results. This study proposed a novel approach to overcome these two problems. First, rice planthopper initial segmentation was completed through discrete cosine transform to weaken the interference of background, and this segmentation was used as the initial contour of GCBAC to avoid artificial contour initialization. Then, dilation direction of contour line on both sides was changed to a one-way lateral dilation to avoid boundary shrinking bias. Results show that the proposed method can accurately locate pest region and clearly segment the contour of rice planthoppers.

To solve the shortcomings of traditional guided image filtering (GIF) in edge preservation and denoising performance, this study describes a novel generalized guided image filtering method, which integrates an artificial swarm optimization algorithm. A locally adaptive weighting based on monogenic phase congruency and chaotic swarm optimization is used to produce a more robust method. Since the fixed regularization parameter cannot adapt to the grayscale difference between flat and edge patches, the box filter radius and regularization parameter of guided image filtering have significant influences on image-denoising effects. The chaotic swarm optimization algorithm, which is an improved optimization algorithm with a self-adapting search space, is adopted to find their optimal values for the best denoising effects. Compared with traditional guided image filtering for image denoising and other state-of-the-art methods with image quality as a performance metric, experimental results showed that the proposed denoising algorithm can not only remove noise efficiently and reduce halo artifacts, but can also preserve the edge texture well.

Identifying emerging technological topics is of great interest to decision makers, but identifying such technologies usually has two problems. First, an overwhelming amount of information is given to researchers on many subjects. Second, the effect of the topics is usually ignored. This paper describes a new overlap method for locating emerging topics in a specific technology domain. Two models of technological patents, namely, those based on the direct citation and genetic approaches, were combined to identify new and persistent clusters. The method was then applied to the technological domain of solar photovoltaic. Fifteen emerging topics were identified for the period 1980–2010, and these topics were evaluated by the history of innovation and development of solar cells. These topics were characterized in various ways to understand the motive forces behind their emergence. Results indicated that the methodology could be a useful tool for identifying emerging technological topics.

Performance evaluation of port supply chain involves various factors. These factors are characterized by fuzziness and incompatibility. It is a complicated multi-layered fuzzy evaluation issue. Based on the theoretical framework of balanced score card (BSC), this paper established a performance evaluation index system for port supply chain involving finance, customers, internal operation, learning and development. An evaluation model was constructed using fuzzy-matter-element analysis to comprehensively evaluate port supply chain performance. Results showed that the proposed evaluation method considered the fuzziness of designed indexes and combined with matter-element characteristics. The method was simple, effective and feasible.

This study proposed an intelligent exam management system based on the Browser/Server (B/S) structure for Hebei University. The system was integrated with three levels: user interface, business logical, and data access. These levels manage the test arrangements and performance of students through intelligent data processing. Three function modules based on C#, ADO.NET, and ASP.NET technology and business demand were developed; the modules were user management, examination arrangement management, and performance management. Moreover, 10 sub-function modules, including the exam arrangement module and the score registration module, were realized. The practical operation results show that the established intelligent exam management system can efficiently achieve education informatization and plays a significant role in educational administration and management system. Consequently, the efficiency of examination work and the safety of the exam management system can be enhanced.

With the development of the internet and the arrival of large volumes of data, the analysis of transactional data is becoming important in the field of data mining. Clustering algorithms for transactional trade datasets are becoming a hot topic. Among them, clustering with slope algorithm (CLOPE) is widely used as a result of its superior performance, lower memory use, and better quality than other clustering algorithms. However, the quality of the CLOPE algorithm is related to the sequence in which the data is input; different result will be clustered by different input sequences of the same dataset. This can even result in poor clustering. In order to solve the problem, this paper analyzes the CLOPE algorithm deeply and proves that records with more items ahead will improve the quality of the result greatly in theory. A procedure to preprocess the dataset according to item similarity is proposed. The experiment results show that the algorithm has obviously better quality result when the proposed method is used, and it is 10% faster than the traditional procedure. This algorithm is a valid algorithm that produces high quality results for transaction data sets.

The autoregressive integrated moving average (ARIMA)–backpropagation (BP) integrated intelligent algorithm for consumer price index (CPI) forecasting was designed based on the ARIMA intelligent forecasting method and the BP intelligent neural network algorithm. The irregular variations in CPI time series data were divided into linear and nonlinear variations. The linear variation was fitted by the ARIMA intelligent forecasting method, and the nonlinear variation was fitted by the BP intelligent neural network. The sum of the fitted linear and nonlinear variations was the CPI forecasted by the ARIMA-BP integrated intelligent algorithm. Results demonstrated that the ARIMA-BP integrated intelligent algorithm could achieve high-precision fitting of the historical CPI data of China. The proposed algorithm showed a forecast error that was smaller than that of the single ARIMA model. Owing to the complexity of the CPI and the combined influence of various factors, achieving accurate CPI forecast is difficult. Such a new integrated intelligent algorithm provides a referent scientific method to forecast the CPI of China in the future. The results can provide government departments reference information of timely price control.

The construction of a regional logistics industrial ecosystem elicits increasing attention because of the growing awareness of environment protection, and its health evaluation by considering environmental impacts is one of the kernel problems that must be addressed to promote the development of regional logistics. In this study, a comprehensive methodology based on fuzzy mathematics and matter-element analysis theory was presented to accurately evaluate the health status of the regional logistics industrial ecosystem in China. First, the forming mechanism and influencing factors of regional logistics industrial ecosystem were analyzed. Second, a fuzzy matter-element model was constructed to evaluate the regional logistics industrial ecosystem, and the weights of characteristic values of evaluation indexes for fuzzy matter-element were obtained using entropy decision method. Finally, this proposed method was applied to evaluate the logistics industrial ecosystem health of eight provinces in China. In this case, these evaluation indexes include energy consumption, logistics cost, and CO₂ emissions. The results show that this method is feasible and effective, and demonstrate the promising application of the proposed model in evaluating the health status of a regional logistics industrial ecosystem and in supplying reliable data for the environmental protection of regional logistics activities.

Business Intelligence System (BIS) has become an important tool for enterprises to make decision timely and effectively. However there are many differences in the quality and performance of the BIS on the market, it is necessary for enterprise managers to evaluate the BIS before buying, so that they could choose the right BIS. This study provided a fuzzy comprehensive evaluation method based on multi-attribute group decision making for selecting BIS. Eight evaluation criteria about BIS were firstly determined through literature review with three judgment methods being presented to score these criteria. Then the ordered pairwise comparison method was used to determine the weight of criteria, and the entropy measure method of interval-valued intuitionistic fuzzy sets was applied to determine the weights of experts. A fuzzy comprehensive evaluation algorithm based on multi-attribute group decision making was proposed, which we used to select suitable supplier of BIS. Finally an illustrative supplier selection problem was described to demonstrate the practicality and effectiveness of the proposed method.

Intelligent transport systems (ITS) have powerful technologies that work together to improve transportation performance. The use of ITS provide effective solutions for intelligent decision making in traffic engineering, reducing traffic congestion, and increasing traffic safety. This study explains the working mechanisms of how ITS improve the operating efficiency of urban traffic and describes the development of a scientific evaluation model that can calculate the net contribution from, and assess the operational performance of, ITS and, in addition, remove interference from other factors. From correlation analysis and Granger causality tests, four key indicators are presented. In addition, a combination of difference-in-differences and matching methods solves the problem of sample-selection deviation and endogenousness perfectly, and are useful in calculating the net contribution of ITS. According to the results, the contribution rate of ITS to the urban traffic operating efficiency in Guangzhou, China has been 6.32%. This value quantitatively demonstrates that ITS have a positive effect on urban traffic operating efficiency, and proves the effectiveness of ITS contributions to intelligent decision-making in traffic engineering.

In the past decade, the public–private partnership (PPP) has been more widely used in worldwide infrastructure construction. Traditional PPP project evaluation methodologies have been incapable of effectively solving the two-sided matching problem between government and enterprises. In this study, we first constructed a bilateral matching satisfaction index system for the PPP project at both the government and enterprise levels, and established the matching satisfaction judgment matrices of the two sides via intuitionistic fuzzy numbers. Considering the influence of multiple attribute decision-making relevance on the attributes of decision, we used the Choquet integral to match the satisfaction evaluation vectors of the two sides, and then obtained the final matching results by constructing a multi-objective decision model. Finally, a case study was implemented to illustrate the practicality and maneuverability of this method. The research results show that the intuitionistic fuzzy Choquet integral data fusion method, which uses a two-sided matching decision-making method for PPP projects, can effectively solve the problem of correlation among attributes in the decision-making process. Consequently, this method can provide a more scientific evaluation of the bilateral matching decision problem between the government and enterprises in PPP projects.

To solve the issues during grouting, such as low accuracy in grouting pressure detection, lack of stability, and tedious manual control work for pressure, a new adaptive control system for grouting pressure stability was designed based on single-chip technology, sensor technology, and automatic control technology for online detection and stability control of grouting parameters. The grouting simulating test bed was designed based on the actual grouting pipeline system in the project. The experiment tests were conducted by adjusting the following parameters: flow characteristics of the grouting pump, instantaneous flow pulsation variation law, and P-Q (pressure–quantity of flow) performance. Subsequently, the damping characteristics of valve under different pressures and pipeline hydraulic power balance control equations were proposed. Based on the experimental tests, whereby the opening of perturbation valve V01 was regulated and changed to create pressure fluctuations, changes at different pressure values are obtained. On the basis of fuzzy control algorithm tuning the parameters K _p , K _i , and K _d of Proportion, Integration, Differentiation (PID), the PID controller that varied the regulating signal and maintained a given pressure in grouting system was afforded. Finally, the reliability of the adaptive control system for grouting pressure stability was demonstrated by the application in rock mass curtain grouting project. The accuracy of automatic pressure control was maintained within 5% of the set value, and the control response time was 2 s to 3 s, thereby showing the high accuracy, fast response, and good stability of the designed system.

The development of private colleges and universities affects structural reform and changes the trend of higher education. To study the running level of private colleges and universities, an empirical study must be conducted. First, statistical information from 23 Shandong Province private colleges and universities was collected, and an index system of education level with six primary indicators and 13 secondary indicators was constructed. The index data were standardized through a statistical analysis software. Analytical hierarchy process was adopted to determine the weight of the evaluation index and test the consistency of the judgment matrix. A fuzzy comprehensive evaluation model was established, and the building evaluation set was improved. After confirming the membership matrix of the fuzzy relation, multi-index comprehensive evaluation and comprehensive ranking of private higher education level in Shandong Province were carried out. The improved traditional fuzzy comprehensive evaluation method can be used in evaluating the performance of private colleges and analyze scientific rankings. Results of this work have high theoretical significance and application value.

Storage software and products design/architecture from different storage vendors are often incompatible. This incompatibility of the heterogeneous storage makes the remote disaster recovery infeasible, which results in a lot of wasted storage resources and excessive duplication of investment. To solve the problem of incompatibility for remote disaster recovery of heterogeneous storage, this paper proposed a solution. The feasibility of the solution has been testified by the implementation of a practical case. Accordingly, this scheme can reach six disaster levels and meets the requirement of recoverability, reliability and real-time performance indicators.

Wireless sensor networks (WSNs) are envisioned for a number of application scenarios. However, the few focus on the features of a specific system, and rarely report about the characteristics of the target environment. This article presents an unequal clustering and cross-layer routing protocol for environmental monitoring based on WSNs under coal mine laneway. Considering the long and narrow structure of the road tunnels, a chain-type topology is developed to achieve an application-aware system. In order to improve the performance and prolong the lifetime of the network, an energy balancing strategy is deployed in the cluster head nodes. The mechanism of unbalanced energy exhaustion among cluster heads in the whole network is theoretically analyzed, and the effective density function (ρ (x)) is calculated, which is used for cluster head election in order to make the distribution density of cluster heads correspond to. In addition, a cross-layer routing algorithm is presented, which considers routing and MAC layers information to reduce the congestion, increase the packet delivery ratio, and minimize the energy usage. Simulations show that the proposed scheme outperforms other algorithms in terms of energy balance of average node, prolonging the network lifetime and better network throughput.

An effective method for reverse engineering message format specification is proposed, which is a necessary step to extract the protocol’s state machine. First, separators are scanned and compared to accomplish protocol fields partitioned hierarchically, so as to determine the field boundaries recursively and achieve the basic field; second, the protocol candidate tokens are extracted by frequency statistics; finally, the logic feature selector is designed for filtering the logic feature keywords. The experimental valuation demonstrates the validation of partitioning fields and extracting logic feature keyword.

Web Service diffusion is becoming an important way of Web information share. Because of the existence of large malicious nodes and Web services in online social network, the service diffusion in social network has got great trust crisis. It hinders the sharing and reusing of services in online social network. In order to improve the credibility and the speed of service diffusion in social network, we divide the nodes into common nodes and optimizational nodes by utilizing the trustworthy relationship among social network nodes. The algorithm is designed to find optimizational trustworthy nodes which have better trust values and better diffusion characteristics. Based on the optimizational trustworthy nodes, in the case of the trust, the diffusion speed and other factors, the trusted service diffusion is achieved by the algorithm. Therefore, the trustworthy services will be quickly diffused. The results of the experiments demonstrate that the diffusion model and our algorithm are correct and effective.

With the development of metro, the construction of subway tunnel and station have to cross the crowded building group. For different crossing existing structures, such as undercrosing, upcrossing and side-crossing engineering, the adjecent existing structures would be influenced unavoidably. It is important for adjecnt existing structure that the deformations and stabilities of them are controlled during the metro construction. So, according to one side-crossing engineering project, based on numerical simulation and model test method, the deformation of support structures of metro station are studied, and the impact of station side-crossing on adjecent existing undergroud structures are discussed in different construction methods. Through those researches, it can be founded that the deformation law and stability of support structures are revealled and the the main influencing factors are expounded. At the same time, the internal force characteristics of support structures and incline deformation law are argumented and the contributing factors are analysed for different construction method, such as open excavation construction and cover excavation reverse construction.

In order to reduce micromouse dashing time in complex maze, and improve micromouse’s stability in high speed dashing, diagonal dashing method was proposed. Considering the actual dashing trajectory of micromouse in diagonal path, the path was decomposed into three different trajectories; Fully consider turning in and turning out of micromouse dashing action in diagonal, leading and passing of the every turning was used to realize micromouse posture adjustment, with the help of accelerometer sensor ADXL202, rotation angle error compensation was done and the micromouse realized its precise position correction; For the diagonal dashing, front sensor S1,S6 and accelerometer sensor ADXL202 were used to ensure micromouse dashing posture. Principle of new diagonal dashing method is verified by micromouse based on STM32F103. Experiments of micromouse dashing show that diagonal dashing method can greatly improve its stability, and also can reduce its dashing time in complex maze.

Scientific and objective evaluation of emergency risk management capability is of vital importance to the improvement of emergency management system. A three-perspective evaluation system of emergency risk management capability is proposed, which includes the risk identification, risk reduction, and risk treatment. Considering the hesitation of people’s judgment about complex situations, a new fuzzy comprehensive evaluation model based on hesitant fuzzy Einstein hybrid geometric operator is developed. Finally, a practical example is given to verify the developed approaches and to demonstrate its practicality and effectiveness.

The research aims to design and build a set of intelligent control and precision sowing simulation system for wheat. the researchers attempt to drive feed shaft by DC motor replacing pure mechanical wheel-driven seeding mode, to make up the leakage of seeds and random seeding quantity during sowing. The variation of the motor speed is achieved by adjusting the duty ratio of PWM (pulse width modulation), and the PWM is controlled by stc89c52 MCU timer interrupt method. All the information will be processed in the host control system. After processing the information is real-time displayed by liquid crystal and sent out to the slave control system used to drive the feed shaft by PWM dynamically, to realize intelligent control and achieve drill operation speed change, seeding quantity and spacing in the rows stable. The DC motor used to drive feed shaft is with rated voltage of 12V, and the power is taken from the battery of the tractor. The seed-metering device is designed by the researchers. Simulation tests show that the application of the wheat intelligent control and precision seeding system can meet the requirements of precision sowing.

This paper investigates the enforcement of regulator using an appropriate penalty, in the framework of game theory, to force a polluting firm to act in a socially optimal way. In most related literature, the penalty is considered to be only related to the degree of pollution. In such a case, a stable equilibrium fully complying with environmental regulations is unreachable. Hence, a completely restraining penalty (CRP) is proposed in this paper to reduce the probability of the firm’s violating behaviors under the penalty mechanism with suspension of production (SOP). This statement is proved by introducing the concept of ɛ-Nash equilibrium. A set of simulation studies is conducted based on System Dynamics to verify the model. Conclusions derived from the simulation study give us further insights into the relationship between penalty and dynamic game.

Currently, Multiple Classifier System (MCS) attracts more and more attentions and has become one of the research hotspots in the pattern recognition field. Classifier selection is a commonly used strategy for MCS to achieve the final decision. A classifier selection method based on clustering and weighted mean is proposed in this paper. In the method, multiple clusters are selected according to the distances between cluster centers and the input sample. Then, the average performance of each classifier on selected clusters is calculated. The best classifier on the nearest cluster and the classifier with the best average performance are picked out. According to the reliability of their outputs which are estimated by confusion matrix, one of them is selected to make the final decision of the system. A number of benchmark data sets from KDD’99, UCI and ELENA database were used to evaluate the proposed method. It can be seen form the experimental results that the proposed method performs well.

Long-term load forecasting is an important issue for a country’s power suppliers to determine the future electric system plan, investment and operation. This paper presents a novel hybrid long-term forecasting method with support vector regression(SVR) and backtracking search algorithm(BSA) optimization algorithm, which is used to obtain the parameters of the SVR. The practical case of China’s annual electricity demand is used to evaluate the effectiveness of the proposed method. According to the results, the performance of the proposed method is better than the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in annual load forecasting.

Integer overflow is one of the most dangerous defects for programs. Many kinds of static analysis techniques and dynamic test methods have been provided to detect it, not only for programs with source code but also with binary code. One of the most important problems which restrict their effectiveness is the test oracle problem. Especially for scientific computing programs and other complex programs integer overflow detection, it is not an easy work because there is often no test oracle to indicate whether it is an integer overflow error, unless the program throws an exception or leads to crash. And more important, in most cases, the program will be an undistinguished performance, when an integer overflow happens. Thus, integer overflows cannot be detected as soon as possible. To help address the problem, this paper proposes a technique in which metamorphic relations are plugged in the program for runtime-testing integer overflows. In order to illustrate the feasibility and evaluate the effectiveness of our method, two case studies are introduced. The results show that, metamorphic relations cannot only alleviate the oracle problem, but can also be used to detect integer overflow effectively, which will prevent the inducing sink accidents. Our method will also be helpful to other kinds of fault detection.

Effectively detecting and preventing Distributed Denial of Service (DDoS) attacks is getting more and more important for internet service quality. Due to computer limitations for counting the number of flows present in network traffic, earlier work on DDoS detection has either focused on offline analysis of log data or ranged in a small number of potential victim destinations. However, those methods are not sufficient for detecting possible DDoS activity in real time over large networks. This paper proposes novel data-streaming algorithms for real-time detection of DDoS activity in large networks. The key idea is a hash-based synopsis data structure for sampling network data streams. This structure can efficiently track, guarantees small space, and offers accurate synopses. It also presents an algorithm for counting the number of potentially malicious (e.g., “half-open”) connections from the network streams. Moreover, the algorithm focuses on counting the distinct destination or source IP by distinguishing difference connection types.

To minimize total construction investment of the gas field gathering and transmission pipeline network, the mathematic model for optimization design of the gathering and transmission pipeline network of the gas field is established, which takes the pipeline network topology layout, pipe diameter, wall thickness and electric tracing power as the decision variants and the pipeline hydraulic equilibrium, thermodynamic equilibrium and actual process requirements as the constraint conditions. Based on the structural features of the model, the mathematic model is decomposed into the pipeline network topology optimization and parameter optimization problem. The hybrid optimization solution strategy is proposed including multiple layers, punishment function and intelligent optimization algorithm. Based on the above work, 7 kernel program classes are developed with the aid of C++ Builder development platform, OLE DB database connection technology and Map Objects component technology. Based on these classes, the gathering and transmission pipeline network layout demonstration, process computing and optimization design is implemented via computers, thus the gathering and transmission pipeline network optimization design platform is developed. This software is used to optimize design of the gathering and transmission pipeline network in a block of Daqing oil field. Compared to the artificial design scheme, the optimization scheme can save 13.9% of the investment cost.

Logistics Web Service optimal composition is the key business of the fourth party logistics service platform and how to construct the optimal logistics Web service composition is a challenge. However, existing logistics Web service composition methods only consider the general quality of service (QoS) and ignore the domain quality attribute of the logistics service, leading to unsatisfactory composite logistics Web services and poor success rate of logistics composition. For solving this problem, a domain quality-driven logistics optimal logistics Web service composition method is proposed. Firstly, domain quality evaluation model of logistics Web service is proposed; secondly, quality evaluation model of logistics Web service composition has been designed in which domain quality is taken as the primary indicator and general QoS attribute is taken as the secondary index; Finally, the improved artificial bee colony algorithm is incorporated within the framework of the cultural algorithm to construct culture artificial bee colony algorithm(C-ABC), and this algorithm is applied to solve the problem of domain quality-driven logistics Web service optimal composition. Experimental results show that the method is effective and feasible.

To solve the problem between large computation and complex process of dynamic modeling on multi-DOF(degree of freedom) robot, the paper introduces Lagrange equation into the problem so that only external force needs to be considered and unknown binding force is no longer to be considered, which is convenient for the design of control system and dynamic simulation. Also, the paper introduces Q matrix into traditional Lagrange dynamic equation of robot and linearizes the dynamic equation under reference frame. Finally, the paper proposes a Lagrange equation simplified calculation method for dynamic modeling on multi-DOF robots and gives the calculation equations. The paper takes five DOF Upper limb rehabilitation robot as example and model the robot using the new method. The simulation result shows that the established mathematical model is accurate and the method is fast, efficient and can be used as a new method for a class of multi-DOF robots modeling.

In urban area, the delay due to the traffic-light should be taken into considerable when searching for the optimal path. So this paper presents an algorithm for find the real-time shortest path in urban area with traffic-light. The core of the algorithm is a translation module which can translate the delay due to the traffic-light(a quantity about time) into length (a quantity about space). In this way to simplify the calculation difficulty and combine the influence due to time delay as well as length simultaneously. Then, this module was added into the heuristic function of A* algorithm to get the improved algorithm. Finally, the simple simulation of the algorithm shown that it can save 5.8% time even when driving more 10% distances. In conclusion, the algorithm is very suitable for the urban traffic and the longer the distance, the more obvious effects it would be.

Ontology, which is regularly used in computer information retrieval and other computer applications, plays an essential role in effectively retrieving the concepts that have highly semantic similarity with the original query concept. Meanwhile, ontology also returns the results to the user. Ontology mapping is used to connect the relationship between different ontologies, and similarity computation is the essence of such applications. In this article, we present a ranking based ontology optimizing algorithm to get the ontology score function which map each ontology vertex to a real number, and the similarity between ontology vertices is determined according to the difference of the scores. The ontology framework is designed relied on the eigenpair computation, and the solution is obtained by means of operator calculation. The result data of simulation experiment implies that our new ontology method has high efficiency and accuracy in ontology similarity measure and ontology mapping in multiple disciplines.

Server cluster has been widely used to improve the performance of various servers. How to optimize the deployment of each server in a cluster so as to minimize cluster’s power consumption while satisfying load capacity requirement is an urgent problem to be resolved. In this paper, we first formulate a typical energy-efficient optimization problem of server cluster as a constrained MIP (Mixed Integer Programming) problem. Then, aimed at the traits of the problem, we propose a hybrid DE (Differential Evolution) algorithm combined with heuristic correction and chaotic search to solve the problem. We introduce a correction operation to traditional DE algorithm, which bases on greedy idea to correct the individuals that do not satisfy constraints. Besides, we generate the initial population based on chaotic sequence so as to enhance population diversity, and implement chaotic search around the best individual of each generation so as to improve solving accuracy and accelerate convergence speed. The algorithm has high solving efficiency and can run online even when applied in large-scale server clusters. Simulation results verify the feasibility and effectiveness of the algorithm.

Maintaining an appropriate environmental condition plays a vital role in preventing food poisoning and food spoilage. Thus, how to allocate a large number of perishable food products with different intrinsic characteristics to a cold store with different cabins is quite important for decision makers. In this paper, we define a mathematical model of the fuzzy cold storage problem (FCSP), which can be regarded as a complex variant of the knapsack problem. Profits of perishable food products are fuzzified and represented by triangular fuzzy numbers (TFNs). To solve the fuzzy system, we use the k-preference integration as the defuzzification method. Given that the FCSP is highly combinatorial, we design a new discrete optimization algorithm which combines the firefly algorithm (FA) and the greedy algorithm to get near-optimal solutions. In this algorithm, we use the Hamming distance to denote the distance between two fireflies and propose a four-phase repair operator to correct and optimize the solutions. Furthermore, processes of initialization and movement of the brightest firefly are also improved to strengthen the algorithm. Finally, computational simulations with randomly generated data are analyzed and reported to evaluate the performance of the proposed algorithm.