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Innovative learning ability is the core competitiveness of university students, the essential ability of university students to solve problems and a fundamental quality that can be universally applied by university students in both theoretical and practical spheres. This paper analyses the necessity of innovative learning ability cultivation for engineering students in applied universities, and introduces constructivist learning theory into curriculum teaching. The logistic function is improved as 1/(1 + e −αk i ), and the value α varies with the number of iterations. Data are obtained through questionnaires; data analysis techniques are used to analyse the factors affecting the cultivation of creative ability in terms of the elaborate teaching processes, creative training courses, emphasis on social practice innovation, promotion of knowledge construction, creation of academic atmosphere and encouragement of questioning authority; and positive coping strategies are given.

Recently, fresh agricultural cold-chain logistics have been greatly developed with the increasing needs of people’s life. Reducing costs of cold-chain distribution has become the main object of loss control in logistics enterprises. The objective of this research is to find a set of optimal routes that minimize the total loss, including fuel cost, refrigeration cost, soft time window penalty cost, and cargo damage cost over transit time. In this paper, the definition and model construction of vehicle routing problem (VRP) with multiobjective minimum lost are introduced first. Then, an ant colony optimization (ACO) algorithm combined with Pareto local search (PLS) is put forward to solve the minimum loss model. In order to avoid the influence of complex road conditions during distribution, the distance matrix and the transit time matrix are both derived from the recommended navigation road based on E-map API. At last, a compare experiment between the traditional method and our proposed method is performed. The results indicate that our method has strong applicability and potential advantages in cold-chain logistic and has important practical significance and application value.

Learning ability is the core competitiveness of college students, the most essential problem-solving ability of college students, and the fundamental quality of college students in both theoretical study and practical scope that can be generally applicable. This paper analyzes the necessity of learning ability cultivation for engineering students, introduces constructivist learning theory and cognitive psychology information processing theory into curriculum teaching and student innovation and learning ability cultivation, explores the strategy of constructing learning ability cultivation for college students, and explores changing the teaching mode and guiding students to construct cognitive structure on their own initiative, in order to cultivate students to use scientific learning methods to obtain information, process and use information, from learning knowledge to learning, in order to improve the learning ability of self-improvement of professional knowledge, follow up professional development, and improve the overall learning quality of students.

Hyperspectral remote sensing sensors can provide plenty of valuable information. Fusion of spectral and spatial information plays a key role in the field of HyperSpectral Image (HSI) classification. In this paper, a novel two stages spectral-spatial HSI classification method based on Mathematical Morphology (MM) post-processing is proposed. In first stage, Support Vector Machine (SVM) is adopted to obtain the initial classification results. In second stage, in order to remove salt and pepper noise, MM is used to refine the obtained results of above stage. Experiments are conducted on the Indian Pines dataset. The evaluation results show that the proposed approach achieves better accuracy than several recently proposed post-processing HSI classification methods.

Hyperspectral remote sensing sensors can provide plenty of valuable information with hundreds of spectral bands at each pixel. Feature selection and spectral-spatial information play an important role in the field of hyperspectral image (HSI) classification. In this paper, a novel two-stage spectral-spatial HSI classification method is proposed. In first stage, the standard particle swarm optimization (PSO) is adopted to optimize the parameters, and a novel binary PSO with mutation mechanism is used for feature selection simultaneously. Then, the support vector machine classifier is performed. In second stage, in order to reduce salt and pepper phenomenon, mathematical morphology post-processing is used to further refine the obtained results of the above stage. Experiments are conducted on two real hyperspectral data sets. The evaluation results show that the proposed approach achieves better accuracy than several state-of-the-art methods.

Based on the shortest scheduling time of emergency cold chain logistics resources, the mathematical model of emergency cold chain logistics scheduling, including the loss of the vehicle, refrigeration consumption and damage of goods over time, is constructed. Considering the traffic situation of the actual distribution route, based on the network optimization idea and Baidu map API, the heuristic algorithm is used to solve the emergency cold chain logistics scheduling model. The proposed optimization model adjusts the pheromone updating strategy adaptively which can balance the convergence rate and diversification of solutions. Combined with Pareto Local Search (PLS) is to avoid local-best solutions and accelerate the search so as to obtain the uniform Pareto optimal frontier and keep the diversity of Pareto solution set. The experiments show that our method has strong applicability and potential advantages in the rescue of sudden crisis events, and has important practical significance and application value.

In recent years, cold chain logistics, especially for agricultural products has been greatly developed. It aims to keep agricultural product fresh before arrive to the designated locations. In this paper, the definition and current situation of cold chain logistics are introduced first. Then a novel approach for path distribution of cold chain is proposed according to time-consumption. After that, we introduce the Ant Colony Optimization (ACO) method and the way to get realistic deliver time. At last, we put forward a method for distribution path optimization based on delivery time and ACO. Experiments show that the proposed method can achieve the optimal solution effectively.

Recently, cold chain which aims to keep agricultural product fresh before arrive to the designated customers has been greatly developed. The distribution routing optimization problem of cold chain has been an attractive research hotspot. In this paper, we introduce the basic PSO method and it’s advantaged about good robustness, global convergence, and intelligent search. Then, we put forward a method for distribution path optimization based on PSO. Taking an example, the evaluation results show that our proposed method can achieve the optimal solution effectively.

In recent years, people placed great emphasis on food quality and safety. About half million people die of food hygiene problems every year in the world. How to effectively trace the source of various kinds of food with various origins has become the core issue of food safety research. Based on EPC, RFID and Baidu Map API, this paper designs a tracking and traceability platform and establishes a food life cycle model to ensure food safety and improve service level. The main research contents include: developing EPC coding system of the research object; solving the life cycle of the research object.

With the rapid growing of universities, the campus infrastructure continues to expand. Visitors are not clear the position of their destination. Traditional online mapping service updating not timely can only supports customers to browse and query incomplete description of the destination. In this paper, a spatial information inquiry system of campus is designed based on Baidu map API with JavaEE technologies. The system can provide the basic map function for almost all important things on campus. Based on this method, Beijing Union University campus E-map has been developed successfully, which demonstrates the feasibility of this solution.

International audience; Hyperspectral remote sensing sensors can capture hundreds of contiguous spectral images and provide plenty of valuable information. Feature selection and classification play a key role in the field of HyperSpectral Image (HSI) analysis. This paper addresses the problem of HSI classification from the following three aspects. First, we present a novel criterion by standard deviation, Kullback-Leibler distance, and correlation coefficient for feature selection. Second, we optimize the SVM classifier design by searching for the most appropriate value of the parameters using particle swarm optimization (PSO) with mutation mechanism. Finally, we propose an ensemble learning framework, which applies the boosting technique to learn multiple kernel classifiers for classification problems. Experiments are conducted on benchmark HSI classification data sets. The evaluation results show that the proposed approach can achieve better accuracy and efficiency than state-of-the-art methods.

Feature selection approaches aim to maximize relevance and minimize redundancy to the target by selecting a small subset of features in classification. This paper proposes a feature selection method based on mutual information (MI). We select a feature subset with minimal redundancy maximal relevance criteria. Multiple kernel learning (MKL) and ensemble learning (EL) have been applied in hyperspectral image classification. Our method applies Adaptive Boosting (AdaBoost) approach to learning multiple kernel-based classifier for multi-class classification problem. Classification experiments with a challenging Hyperspectral imaging (HSI) task demonstrate that our approach outperforms current state-of-the-art HSI classification methods.

In this paper, firstly, application areas of Operations Research are introduced. Some computer tool-kits used to solve OR problems are outlined, together with some programming languages, which can be used to model and solve the optimization problems. Secondly, an optimization programming language-IBM ILOG OPL is presented. Background knowledge about a high level programming language, data structure in OOP and relational database will facilitate the process of learning due to its highly abstract syntax and semantics. The connection between background knowledge and OPL is illustrated in detail. Lastly, the typical problems that IBM ILOG OPL can solve are introduced. This paper highlights the language features of OPL, focuses on its powerful data processing capability and

In this paper, Optimization problem is introduced. Some computer tool-kits used to solve optimization problems are outlined with their respective strength and weakness. A state of the art optimization programming language-IBM ILOG OPL is presented. This high level OPL facilitates modeling and solving optimal problem in all application fields significantly. An IBM ILOG OPL model of the shortest-path problem is implemented. Then, with IBM ILOG OPL model of Shortest-path problem as building block, an algorithm of center of graph is illustrated through calling this building block but with different data in consecutive way.This algorithm can be implemented with IBM ILOG Script language.This approach enable engineers in all kinds of field to solve optimal problems since no underlying mechanism of optimization need to be grasped.

Collaborative learning is a new teaching mode that overcomes important non-functional requirements arisen in distributed environments. It is important to improve student's cooperative spirit, creative ability and improvement of utilization of the learning resource. In this paper, we present a generic platform, called Collaborative learning system, which is based on flexible Web-services for the systematical construction of collaborative learning applications. In the last place, we demonstrated the detailed analysis of implementation of each function module.

In this paper we propose a service-oriented architecture for data exchanging between applications using heterogonous databases. Heterogeneous data makes it difficult to be shared across organizations. In these organizations, data is often produced with diverse applications relying on a mix of software and information systems. Each application uses its individual system to increase efficiency, but sharing data across the enterprise is a near impossibility. We discuss a service-oriented architecture of using web services which has been adopted for integrating diverse repositories of data. Based on our practical tests, the proposed framework proved to be an efficient solution for sharing and accessing to Heterogeneous data.

In order to solve costly procedure of search and premature convergence for continuous function optimization problem, an improved particle swarm optimization algorithm combined with Iterated local search (ILS) method is proposed. During the global search process, our algorithm can enhance the local search ability of particle swarm optimization thought adding random perturbation to local search. Some optimization tests of the standard benchmark function confirm that our method has a stronger ability of global optimization and a faster convergence.

A (t,n) threshold proxy signature scheme enables a proxy signer to sign messages on behalf of the original signer. Hwang et al. recently proposed an practical (t, n) threshold proxy signature scheme. In this paper we propose an efficient threshold proxy signature scheme based on RSA signature scheme, which improves security of their scheme. Our scheme divides the document M into m blocks and add some redundant bits to each message block. It is embedded in the proxy signature issuing phase to avoid the conspiracy attack. The proposed signature scheme satisfies all the secure requirements and is efficient.

Information is an irreversible general trend of social and world economy’s development. E-government means to make full use of information technology to enhance the effective management of the government, in order to promote transformation of government functions and improve management level. However, E-government faces with some problems, such as security issues. First, this paper analyzes the characteristics of E-government system and points out its security threats. Then this paper gives outs an frame of E-government security system based on the user role certificates. In the end, the implementation model based on web service is described in detail.