Web Intelligence

The core problems of Web Service Composition (WSC) are to satisfy user preferences while at the same time facilitate reasonable construction for WSC. This poses two problems. First, the user real preferences has not been fully expressed when qualitative preference is used to measure composite service. Second, when considering whether the WSC is reliable, some studies use the trust value as the reference attribute of the composite service. However, this is not sufficient as an evaluation index. To solve these problems, we first use the neural network to adjust initial weights in qualitative preference, making qualitative preference accurately measure user preferences when it changes. Second, we redefine the service invocation structures based on the travel plan. Third, we propose a new indicator: availability. Based on the service invocation structures, global availability of WSC is obtained from availability of a single service. Finally, WSC in three aspects: qualitative, quantitative and availability are used to get the final optimal composite service by multi-objective optimization algorithm. Results show that our method is reasonable and efficient compared with other counterparts.

In cloud computing environment, a larger number of tasks are executed simultaneously, and therefore task scheduling strategy is a key factor to determine the performance of the system. For the problem of static scheduling regarding related tasks in cloud environment, this paper minimizes the scheduling length and keeps load balance as the main goal. By combining the list scheduling and task duplication algorithms, a new task scheduling algorithm is proposed. This algorithm is composed of three steps of operations. Firstly, a task scheduling queue is constructed by computing the priority value for each task; secondly, in order to reduce communication latency between tasks, the parent tasks of the current task are duplicated selectively by taking advantage of the timeslots of the current virtual machine rationally; lastly, each task is assigned to the virtual machine that made the task have the earliest execution time and keep load balanced of system. The experimental results display the influence of the number of tasks on the performance of the algorithm when CCR is different and the new task scheduling algorithm balances the loads among virtual machines in the cloud computing system, and improves the resource utilization effectively.

We aimed to control complex wind power generation parameters’ contributions to achieve healthful and energy-saving operating conditions for wind turbines and improve the stability of wind power generation and processing capacity of wind grid output data. Hence, a design method of complex wind power generation parameter control system based on embedded control combined with Internet of Things (IoT) is proposed. In this method, in the IoT environment, the overall model of the system is determined, and embedded control for big data of the complex wind power generation parameters is performed to achieve adaptive fusion of control instruction information, and optimize the big data transmission and scheduling of control instruction sets. Test results show that the complex wind power generation parameter control system designed through this method has relatively strong parameter data control processing capability, good stability of control instruction transmission and high parallel processing capability of control instruction sets, and this method improves the control quality of the overall system.

In order to improve the intelligence of higher mathematics teaching resource scheduling, a higher mathematics teaching resource scheduling system based on cloud computing is designed. A balanced modulation control algorithm for higher mathematics teaching resources is proposed on the overall design of this system and an association rule constraint model for higher mathematics teaching resource allocation is constructed based on the design results. The adaptive weighted control method is adopted for distributed control of higher mathematics teaching resources and a priority attribute list of higher teaching resource scheduling is established. The higher mathematics teaching resource allocation and adaptive information fusion are realized according to the priority list. On this basis, the higher mathematics teaching resource scheduling system based on cloud computing is designed optimally through the embedded control technology. The simulation results show that, in the allocation of teaching resources, the balance coefficient of higher mathematics teaching resource scheduling system based on cloud computing has increased by 12.54%, and the accuracy of sending and receiving teaching data provided by this system has improved by 13.6%, which indicates that this system has relatively good intelligence and strong human-computer interaction ability.

In this paper, a hybrid model composed of the correlation ratio analysis and the Support Vector Machine (SVM) is proposed for trip mode recognition. The correlation ratio analysis algorithm is applied to determine the optimal time window of the input attributes so as to optimize the input parameters. The SVM is applied to carry out the trip mode recognition for the whole trip. On this basis, the influence of data sampling frequency on trip mode recognition is further evaluated using large-scale field test data. The results show that: (1) the correlation ratio analysis and the SVM hybrid model attains the best performance for trip mode recognition, the average mode recognition precision reaches 89.8% at the sampling frequency of 1 s; (2) the data sampling frequency significantly affects the trip mode recognition effect; when the sampling frequency is high (less than 5 s), the mode recognition precision is above 70%, however, when the sampling frequency is relatively low (more than 30 s), the bus and car mode recognition precision falls rapidly below 37%. These results provide a reference for using the smartphone sensor datasets to supplement or even replace household travel surveys in transportation planning in the future.

A crowd sensing network takes the use of the dense distribution, frequent encountering, and social property of mobile terminals in real society. In a crowd sensing network, the datasensing transmission is based on the collaborative opportunistic transmission, the final message data will be forwarded to the user who is willing to report data (e.g. high battery capacity and with low-cost Internet link equipment), and then report the perceptual data to the backdrop data center. In the mobile crowdsensing network, there is a big difference in the social ability of each node carrier, so that the data forwarding capability of each node is also different. In this paper, we first analyze the shortcomings of node based on node quantization. On these grounds, this paper proposes a data forwarding mechanism based on node function and node centricity, and gets its performance improved effectively, and reduces the energy consumption of nodes.

In order to solve the frequent communication obstacle problem in multiple-input and multiple-output (MIMO) system communication caused by routing conflicts, it is necessary to quickly locate the optimal nodes for communication, so a fast optimal node locating algorithm for MIMO system communication based on link balance control is proposed in this paper. In this algorithm, a MIMO system communication channel model is constructed through the link balance control method, and interference features of signals are separated by analyzing the spectrum of communication signals based on the model. Simulation results show that this algorithm can provide 100% accuracy in optimal node locating under signal-to-noise ratio of 80 dB, an average improvement of 13.8% over that in traditional algorithms. The results show that our algorithm can provide high accuracy in locating the optimal nodes for communication, and can ensure high locating efficiency, which effectively improves the communication transmission quality of MIMO system.