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To solve object detection issues in infrared images, such as a low recognition rate and a high false alarm rate caused by long distances, weak energy, and low resolution, we propose a region-free object detector named YOLO-FIR for infrared (IR) images with YOLOv5 core by compressing channels, optimizing parameters, etc. An improved infrared image object detection network, YOLO-FIRI, is further developed. Specifically, while designing the feature extraction network, the cross-stage-partial-connections (CSP) module in the shallow layer is expanded and iterated to maximize the use of shallow features. In addition, an improved attention module is introduced in residual blocks to focus on objects and suppress background. Moreover, multiscale detection is added to improve small object detection accuracy. Experimental results on the KAIST and FLIR datasets show that YOLO-FIRI demonstrates a qualitative improvement compared with the state-of-the-art detectors. Compared with YOLOv4, the mean average precision (mAP50) of YOLO-FIRI is increased by 21% on the KAIST dataset, the speed is reduced by 62%, the parameters are decreased by 89%, the weight size is reduced by more than 94%, and the computational costs are reduced by 84%. Compared with YOLO-FIR, YOLO-FIRI has an approximately 5% to 20% improvement in AP, AR (average recall), mAP50, F1, and mAP50:75. Furthermore, due to the shortcomings of high noise and weak features, image fusion can be applied to image preprocessing as a data enhancement method by fusing visible and infrared images based on a convolutional neural network.

The increasingly available large-scale bibliographic data that generate a heterogeneous network provide opportunities to detect, track, and predict the evolution of science. Recently, many efforts have been devoted to quantifying the impact of scientific papers within different citation time windows. However, the complex patterns of the citation network make it difficult to predict future citations on the basis of a short time window. Accordingly, we present a data-centric methodology to predict long-term scientific impact by combining numerous bibliographic features and convolutional neural network. More specifically, we first expand the input features from the annual citation records to the features of the whole heterogeneous bibliographic information network that completely represents the topology structure of academic activities. Then, a convolutional neural network model is designed to capture the complex nonlinear relationships between the early network features and the final cumulative citation count. Finally, we conduct an experiment on papers of Markov Chain from 1980 to 1985. The result shows that the prediction performance can be improved by 5% to baseline models under the same problem definition and with the same dataset. Meanwhile, the long-term scientific impacts are strongly correlated with its recognition by authoritative authors or venues in the early stage.

In previous studies of the command and control (C2) modeling and optimization, the C2 process tends to be simplified to a large extent, resulting in a relatively large deviation to actual operation. The paper adopts a simulation method and surrogate technology to transform the black box C2 system into a white box model and try to optimize the C2 seat allocation problem by maximizing the C2 efficiency. Firstly, the dynamic C2 process is modeled based on the simulation tool of ExtendSim, by mining key elements and relations in C2 process. Secondly, with training data generated from the ExtendSim, the artificial neural network (ANN) based surrogate model is adopted to approximate the relation between allocation solution inputs and efficiency outputs. Then, by treating the trained ANN as the objective function, the non-dominated sorting differential evolution (NSDE) algorithm is used to obtain the Pareto set of the problem. Finally, a case is studied to verify the feasibility and effectiveness of the proposed idea and methods.

The weapon system of systems (WSoS) is a complex system composed of independent systems. The capability of WSoS is the ability of the WSoS to accomplish its specific mission. As the scale of the WSoS continues to increase and the types of systems included continue to rise, the capabilities of the WSoS become more and more complex. The measurement of the complexity of the capabilities of the WSoS is conducive to the overall control of the WSoS, which is beneficial to provid a basis for the correct use of the WSoS. In this paper, the capability spacess of the WSoS are described, and their hierarchical structure is analyzed through the capability tree. The capacity decomposition and capacity dependence relationship of the capability spacess are analyzed. Based on the capability decomposition and capacity dependence relationship, the complexity measurement method of the capability spacess of WSoS based on entropy is given. Finally, the feasibility of the method is verified by an example.

With the advent of the era of big data, storing and transferring data is facing tremendous pressure. How to use deep learning to obtain higher compression ratio and higher quality images has become an urgent problem. Recurrent neural network (RNN) can control the bit rate of images with iterative manner to improve compression performance. However, RNN needs to introduce long short term memory (LSTM) to solve the problem of long-term dependence, which leads to the model more complex. In order to speed up training process and reconstruct higher-quality images, firstly, this paper improves the activation function in LSTM to better determine the information to be stored or forgotten, so that the amount of parameters is reduced and the training process is faster. Then, the image recovery block is introduced in the decoder to reconstruct high-resolution images. Finally, instead of L1 loss, we use SmoothL1 loss to accelerate the convergence of loss. Experimental results show that our model can achieve a higher compression ratio, and evaluated by SSIM the value is more nearly to 1.

System of systems (SoS) architecting is a process to bring systems together as a network structure for specific goals with unique capabilities and the cumulative capabilities achieved from their interoperability. How the capability is delivered across the network is crucial for capability-based planning mission. To meet diverse capability needs by architecting different types of systems into a network, we formulate and analyze an SoS architecting problem with network theory as a multi-objective optimization model. More specifically, we construct a three-step capability delivery model from a local and globe perspective to describe the positive and negative consequences caused by system interactions. A three-level response model with a feasibility concept is proposed as the conversion of the capability value to meet different scale combat needs and balance the deployment of new and old weapons. And then, certain intelligent optimization algorithms incorporated with the proposed model is designed to approximate Pareto fronts of three response levels, respectively. Results from a set of numerical studies demonstrate that an efficient structure with fewer nodes performs better than a full set structure, and the deployment of a system should be a comprehensive consideration of its overall structural influence rather than single system performance. In addition, four SoS-level attributes are also defined and calculated as supplements to make the value of solution architecture more comprehensive.

Pricing is important when considering the difficulty of localizing core components. Localization encompasses three essential implicit conditions, under which a two-stage alternative game model reveals the formation of prices of domestic core components: the contraction in price at the beginning of localization is negatively correlated to the cost of domestic core components. Further analysis shows that sellers of domestic core components may use the strategy of second-degree price discrimination to alleviate the contradiction between cost and price reduction. It is also possible to adopt the price skimming method or the experience pricing method based on the order of entering the market.

Focusing on the decision-making confusion of aerospace components manufacturers in market entry order and product development strategy, a market entry game theory model based on the conversion cost and learning curve effects, as well as a product differentiation with downward compatibility game theory model is established. Game analysis shows there is a significant first-mover advantage in the aerospace components market, while adopting a downward compatible strategy may provide a second-mover advantage. The study has two implications. First, companies must strive to be the first mover into the market; second, the incomers should adopt a downward compatible product differentiation strategy.

The military technology planning is a key driving force for improving combat effectiveness. With regard to technological management support, the research on the architecture has been relatively weak. This paper proposed a model based technological management framework for defense acquisition. By analyzing the core decision elements in the technological management process, we define the domain-specific meta-model of the framework to capture the different needs of stakeholders. And then, six products of the technology view (TV) and a specific development process are designed to organize the concepts and relationships into models to support decision-making from different perspectives. The data consistency of models will be ensured by the meta-model. As a consequence, technical analysis activities can be performed based on the data and models in this technology architecture. This model-based approach ensures the availability, integrity, shareability, and compatibility of the architecture. Ultimately, the technology of shipborne unmanned helicopters (SUH) was explored by the ModelLink to demonstrate the applicability and effectiveness of the architecture.

In addition to the preferential attachment mechanism, the evolving citation history of a technical paper is also influenced by time. However, the influence pattern varies a lot in real cases, and cannot be described by a certain distribution like normal distribution. In order to find a universal model to measure the influence of time, this paper proposed a model which combines the normal distribution with Box-Cox transformation. Compared with existing models such as Logistic model, Gompertz model and Wang's model (Science, 2013), our model explain the actual scientific research rules more widely and obtain higher accuracy by conducting experiments in four cases. It can not only describe emerging research topics but also explain the "Sleeping Beauties" phenomenon.

Floating-point arithmetic is an important form in numerical computation, IEEE Standard for Floating-Point Arithmetic — IEEE 754 floats has been applied in overwhelming majority of microprocessors. However, as the increasing of data size and accuracy requirement of applications, bandwidth and precision are highly valued in floating-point computing, optimizing existing floating-point arithmetic is a way to improve current situation. Unum (universal number) arithmetic is a new floating-point arithmetic presented by John L. Gustafson in 2013, being compared with IEEE 754 floats, the outstanding features of unum are clearance of rounding errors, high information-per-bit and variable precision. However, unum was only implemented in software before due to technical complexity, we implement unum arithmetic on FPGA for the first time. We contrast the precision and bit width in computing with IEEE 754 floats, the comparison results show that unum need less bit width than IEEE 754 floats under same precision, it can decrease bandwidth requirement in computing.

The addition of orifice plate near the large container is an effective method to reduce airflow pulsation in the pipeline. It has been widely used in industrial production and has achieved good results. The air flow pulsation takes place regularly in the long-distance gas pipeline, no large container or near large container should not be added the orifice. Therefore, this paper is to study the effect of adding orifice plate on the suppression of airflow pulsation. And it simulates the fluid pressure distribution with CFX, studies the effect of the installation position and the aperture ratio on the airflow pulsation in the pipeline under different working conditions. The results show that, according to the pipeline of the working conditions and the pressure loss, an orifice plate of small aperture ratio should be installed away from the constant pressure position, which has achieved the best suppression effect.

Loop invariants play a major role in software verification. For equality loop invariants with functions, in this paper, a practical generating approach is presented, and a practical verification approach is also presented. The effectiveness of the approach is demonstrated on examples.

In embedded avionic system, time and space partitioning is introduced to achieve stable and secure performance. Tasks must meet the certain time constraints when executed. Proper scheduling scheme is needed to ensure that the hardware is adequately used and all time and function requirements are met. However, existing partition aware scheduling works for no more than 2 partitions only. In this paper, a novel scheduling scheme of task allocation among multiple partitions is proposed. The scheme is based on balancing load of each partition and preferentially execute tasks that can optimize worst respond time. Experiment proved this scheme can reduce the average worst case respond time on the premise of schedulability, making tasks effectively executed.

There are billions of devices in smart grid nowadays. For every data source, it produces thousands of data records every day. Classical relational databases are malfunctioned when dealing with these large-scale data sets. Powerful big data platform is needed to process the information in private clouds of smart grid. HBase is a promising platform to solve these problems. However, finding an effective indexing scheme is still hard because most existing schemes which retrieve data by columns are time-consuming. In this paper, we present a refined secondary index scheme on HBase. It can not only accelerate query process but also save storage space. Experimental results show that when referring to join operation, our proposed indexing scheme provides a minimum 5.584x speedup to a maximum 571.360x speedup compared with a query scheme without any index and it provides a minimum 1.026x speedup to a maximum 4.761x speedup compared with a classical secondary index. Our proposed secondary index scheme is feasible and effective on both query performance and storage efficiency.

Cloud computing is increasingly becoming a significant solution for providing scalable computing resources via Internet [1]. However, as the increasing of data size in cloud computing applications and precision requirement in some special scenarios, bandwidth and precision are highly valued in floating-point associated applications, optimizing existing floating-point arithmetic is a significant way to improve current situation. Unum (universal number) arithmetic is a new floating-point arithmetic presented by John L. Gustafson in 2013, being compared with IEEE 754 floats, the outstanding features of unum are clearance of rounding errors, high information-per-bit and variable precision. However, unum was only implemented in software before due to technical complexity, we implement unum arithmetic on FPGA for the first time. We also implement unum based 16-point FFT on FPGA to demonstrate the advantages of unum arithmetic against IEEE 754 floats. We validate the design and contrast the precision and bit width in computing with IEEE 754 floats, evaluate the power efficiency of the designed module. The comparison results show that unum need less bit width than IEEE 754 floats under same precision, it can decrease bandwidth requirement in computing, and ensure correctness even in some extreme arithmetic cases in which IEEE 754 floats cannot work properly.

Technology evaluation is a research hotspot in technology management. And it is rather difficult to identify and evaluate core technology. This article proposes a framework which combined network topology analysis and text mining to solve it. Firstly, keywords were extracted from technical documents which are related to military requirement, equipment system, technology research and scientific knowledge. Then, superedgeRank algorithm was designed according to node similarity. Finally, a case study about unmanned equipment technology is presented.

Arms race is a typical conflict among rational and non-myopic decision-makers (DMs). Nevertheless, the assumption of basis graph model for conflict resolution (GMCR) is not suitable for this issue that each DM possesses identical priority disregarding the difference of their capabilities. In order to handle with this drawback, this paper proposes a novel methodology, entitled capability-based GMCR. First, performance criteria and capability parameters are investigated through feature analysis for arms race. Then, definitions and algorithms to quantify their impacts are presented for preferences elicitation. Finally, an illustrative example is utilized to demonstrate the proposed approach and analysis results are discussed to summarize related conclusions.

The citation network is considered as a hotpot to investigate the knowledge-transfer evolution tendency. Aiming to research the technology development trace, an advanced analysis method is proposed in this paper. Data of the patents granted between 1976 and 2017 are collected from the website database of United States Patent and Trademark Office (USPTO). In order to determine the knowledge-transfer evolution tendency, destabilization or consolidation, two novel indices, including mD t and mC t , are extracted through network structure analysis and the development trace is discussed with the statistical analysis for assessment results. It is demonstrated that destabilization dominates the citation network in 1980s while the patents granted after 1992 tend to consolidate the development trace. Besides, the results also validate the effectiveness of proposed indexes. Additionally, the contributions of this paper can be utilized and discussed further in detecting technology development opportunities.

A new thresholding algorithm combined double windows with Fisher discrimination is proposed in the paper. For each pixel of image, its two neighbourhoods are chosen to compute local threshold value with two different sizes. The threshold of neighbourhoods is adaptive calculated by using Fisher discrimination. Then, binary each pixel is implemented by compared the grey level with the minimum of two threshold values. The experimental results showed that the proposed method improves thresholding performance efficiently. It has better edge-keeping and noise-tolerant ability.

Cloud computing is a type of Internet-based service computing that provides computing, storage and networking services to multiple users. With the increase of data size, computing capacity runs out quickly in cloud computing services. To fill the shortage of computation capacity, we propose to adopt variable precision by implementing unum (universal number), which is a number format different from IEEE Standard for Floating-Point Arithmetic - IEEE 754 floats. Compared with IEEE 754 floats, the outstanding features of unum are clearance of rounding errors, high information-per-bit and variable precision. As a candidate replacement of IEEE 754 floats, the application of unum can improve the precision in computing, decrease the bit width for high precision numbers. However, unum was only implemented in software model before due to technical complexity, in order to validate the performance on chip, we implement this arithmetic on FPGA for the first time. We also implement an unum based 16-point FFT on FPGA. We validate the design and compare the bit width in computing with IEEE 754 floats, evaluate the power dissipation on FPGA. The experimental results of comparison show that unum arithmetic can ensure correctness even in some extreme arithmetic cases in which IEEE 754 floats cannot work properly, furthermore the bit width of unum is much less than IEEE 754 floats in the same precision.

The SKA (Square Kilometer Array) radio telescope under construction will become the largest telescope in the world by integrating the sampled data from a huge number of small antenna nodes in the array to emulate a giant antenna. Due to the limited storage space, the SKA needs to process massive data in real-time, which makes the SKA scientific data processing become a bottleneck of the computational performance. However, existing off-the-shelf high performance computing solutions cannot meet the computation requirements (5 times more than top 1 supercomputer) as well as low power budget (1/3 of the power of the top 1 supercomputer). In this paper, we explore high efficiency solution design based on FPGA by addressing the most representative key algorithm in SKA data processing, i.e. Gridding, which is the most time and memory consuming. We propose an efficient hardware accelerator design of Gridding algorithm on FPGA, which would the first FPGA based design of Gridding algorithm in this community. In our design, we unfold the third loop in the Gridding algorithm and design corresponding hardware pipeline stages to achieve the high efficiency hardware acceleration. The functionality and performance of our design is verified in both simulation and FPGA prototyping board, whose results show that our proposed hardware implementation achieved great improvement in performance compared with software implementation running on generic CPUs. We believe our design would be a strong candidate design to solve the bottleneck in SKA data processing.

Nowadays, graph theory and matrix theory are developing very rapidly. Solutions to mathematical matrix and graph problems can be applied to solve many realistic problems. Finding submatrix of a specific matrix that satisfies specific constrains is a common and hard problem. Finding all-one submatrix of incidence matrix is a meaningful problem. But for most researches, they focus on finding all-one submatrix that elements of which are adjacent ones. Problem for finding all-one submtrix composed of ones that across rows and columns are not well solved. In this paper, we tried to solve this problem. First, we defined conceptions of Hyper-Submatrix, Maximum Hyper-Submatrix and N order Hyper Submatrix of incidence matrix. Then we come up with two mathematical problems. Problem 1 is how to find Maximum Hyper-Submatrix of an incidence matrix. Problem 2 is how to find N order Hyper-Submatrix of an incidence matrix. For problem 1, we come up with method based on graph theory. An upper bound of size of all-one submatrix of the incidence matrix is obtained with the result of problem 1. For problem 2, we come up with Algorithm 2 to solve it. Time complexity and space complexity are analyzed. Optimized algorithm are proposed and time complexity is optimized to O(m[n(n+1)]/2). Comparison experiments illustrate performance of 2 algorithm we proposed is much better than Apriori algorithm, and a little worse than optimized PrefixSpan algorithm.

In the workflow of SKA-SDP (Square Kilometer Array Radio Telescope-Scientific Data Processing), FFT (Fast Fourier Transform) calculation takes a significant proportion of computation overhead. Moreover, FFT computation has to be done within the tight power budget, which existing generic high performance computing architectures cannot meet. To explore power efficiency of FFT computation, this study is designed with initial evaluation of FFT implementation in variable precision on Xilinx ML605 FPGA (field programmable gate array). The FPGA-based implementation of FFT includes a Xilinx IP Core version 7.1, which supports fixed-point and floating-point computation in single precision. The input data width and phase factor width of fixed-point computation can be varied from 8 bits to 34 bits, allowing that calculation accuracy can be adjusted by setting the width. Since single precision is redundant to accuracy requirement of SKA-SDP, fixed-point calculation is designed to emulate single-precision floating point computation. Calculational power dissipation and throughput with different phase factors on FPGA was measured respectively. The final result demonstrates that the implementation on FPGA ensures sufficient precision at a much less power cost compared with floating-point FFT. In other words, this study indicates variable precision computation would be an efficient way to improve power efficiency.

As a column-oriented, distributed and high fault-tolerant database, HBase is gradually adopted by manufactory industry due to its huge capacity, fast random read and bulk processing performance. Power grid usually generates huge amount of data from multi-sources with hundreds of complicated data types. However, JOIN operation performance is low in HBase since it was not designed to serve relational queries as classic relational database. How to store the data to ensure a sufficient performance of JOIN operation and random read is the critical problem that must to be solved in HBase applications. In this paper, we proposed an event-driven HBase data model to resolve this problem. In our data model, each record of data is defined as a unique event that happened in power grid. Any type of data from any data sources can be distinguished in database. Therefore our data model can store multi-source data generated by power grid devices. In addition, JOIN operation is integrated in our data model, which improves the performance of reading data from multiple data sources. This is realized by designing a specific RowKey in tables. We also proposed a schema including a novel Virtual ColumnFamily, which resolves the compatibility problem of storing data from multi-sources. Virtual ColumnFamily is realized by designing specific qualifiers. To verify the effectiveness of our data model, we conducted empirical experiments on a Hadoop platform to compare our optimized schema and original schema. Experimental results showed that our data model ensures that our optimized schema performs better than the original schema based on original data model.

In order to deal with the subjectivity, epistemic uncertainty and incompleteness of input data, a novel methodology is proposed for interactive decision making between two players based on the graph model for conflict resolution (GMCR) with belief preference. First, a new GMCR model is calibrated, in which belief preferences are generated via evaluating the utilities of feasible states. Second, new definitions of unilateral improvements and four stability concepts are presented for conflict analysis. Finally, an interactive decision case on a weapon system of systems (WSoS) architecting is studied to demonstrate the feasibility of the proposed methodology.

Weapon system planning is of vital importance to a country's national security. Currently, a number of countries are under threat from potential hostile countries, especially those with advanced weapons. In this regard, weapon systems must be developed as the countermeasures that eliminate potential threats. A model of threat-oriented weapon system planning is first proposed, wherein countries must decide how much and when to develop weapons under a number of certain constrains to mitigate or even neutralize threats as much as possible. Then, a novel differential evolution with neighborhood revision algorithm is presented to optimize this problem. Finally, the experimental results illustrate that the proposed differential evolution with neighborhood revision algorithm has a more outstanding performance than two other algorithms.

Despite more than two decades of independent, academic, and industry-related research, software vulnerabilities remain the main reason that undermine the security of our systems. Taint analysis and symbolic execution are among the most promising approaches for vulnerability detection, but either one can't remit the problem separately. In this paper, we try to combine taint analysis and symbolic execution for binary vulnerability mining and proposed a method named directed symbolic execution. Our three-step approach firstly adopts dynamic taint analysis technology to identify the safety-related data, and then uses symbolic execution system to execute the binary software while marks those safety-related data as symbols, and finally discovers vulnerabilities with our check-model. The evaluation shows that our method can be used to detect vulnerabilities in binary software more efficiently.

Despite more than three decades of independent, academic, and industry-related research on symbolic execution, it is still difficult applying symbolic execution to real-world software testing, especially to binary software. The emergence of more and more cloud computing platforms makes it feasible to scale this technique using the concept of distributed computing. In this paper, we proposed a distributed symbolic execution approach, which is a novel schema for scaling symbolic execution to real-world binary software testing. We design our method in peer-to-peer way,that is to say, the work node can do the task alone or together, so it can make full use of the elasticity of cloud computing. Also a prototype system is implemented, which is built based on the S2E platform using its plug-in structure. The evaluation shows that our method can be used to test real-world binary software more efficiently.

In order to solve the problems of traditional SVM classifier for software defect prediction, this paper proposes a novel dynamic SVM method based on improved cost-sensitive SVM (CSSVM) which is optimized by the Genetic Algorithm (GA). Through selecting the geometric classification accuracy as the fitness function, the GA method could improve the performance of CSSVM by enhancing the accuracy of defective modules and reducing the total cost in the whole decision. Experimental results show that the GA-CSSVM method could achieve higher AUC value which denotes better prediction accuracy both for minority and majority samples in the imbalanced software defect data set.

In order to solve the problems of traditional Fuzzing technique for software vulnerability detection, this paper proposes a novel method based on genetic algorithm and dynamic taint analysis. First, static analysis is applied to calculate the critical path information, including danger functions, high cyclomatic number functions and loop structures. Second, dynamic taint analysis is introduced to identify the key bytes to reduce the input space. Third, the genetic algorithm fitness function is constructed based on the critical path information to guide the test case generation and the genetic operators are executed on the reduced input space. Experiments show that the method could obtain higher vulnerability detection accuracy and efficiency.

In order to solve the problems of traditional machine learning methods for automatic classification of vulnerability, this paper presents a novel machine learning method based on LDA model and SVM. Firstly, word location information is introduced into LDA model called WL-LDA (Weighted Location LDA), which could acquire better effect through generating vector space on themes other than on words. Secondly, a multi-class classifier called HT-SVM (Huffman Tree SVM) is constructed, which could make a faster and more stable classification by making good use of the prior knowledge about distribution of the number of vulnerabilities. Experiments show that the method could obtain higher classification accuracy and efficiency.

An approach of the edge feature matching of remote sensing images matching based on parameters decomposition of the affine transformation model is presented. Firstly, the adaptability of the affine transformation model used in the remote sensing images matching is analyzed, and parameters of the affine transformation model are decomposed. Secondly, the sum of squared gradient (SSG) similarity is constructed based on the edge feature in images, and the parameters of the special transformation model between the matching remote sensing images are solved with the Genetic Algorithms (GA) which is used to obtain the global optimum solution of the similarity. Finally, the performance of the approach is analyzed in theory and validated with remote sensing images matching experiments.

Traditional weapon research project pricing method makes the bigger valuation cost, the more profit research department is got. The weapon research department always loses the driver of saving cost and controlling expenditure. This paper identifies the technology units, evaluates technology risk based on technology readiness theory. According to value engineering methods, it provides weapon research project cost evaluation method from the view of technology value, and hopes to supply decision-making references for managers.

In this paper, it is concluded that there is an appropriate scale of public investment with the Barrow model, which is roughly 8% of GDP through empirical study; the scale of public investment in China in recent years has significantly exceeded the appropriate scale and deviated from the optimal efficiency trend, this phenomenon should lead to a high degree of vigilance and be paid much attention by macroeconomic policy-maker.

To study the dynamic development mechanism of used productions recovery logistics network, categories and characteristics of used productions are analyzed aiming at manufacturing companies, recovery network with multi-layer is designed, and a system dynamics model with “social-economic-logistics” triple properties is established based on system dynamics theory. A simulation which takes used mobile phones recovery as a case study is set. The results show that the model established in this paper is valid for simulating the dynamic development processes of used productions recovery logistics and can be served as a computer aided decision tool for managers to control the cooperation organization.

Based on techniques of solving recurrence equations and optimization problems, we present a practical approach for verifying program including loops with assignments only. We implement this approach on the platform of Mathmatica. The experimental results demonstrate the power of our approach.

Constructing program invariants is one of the key problems of program verification. A lot of approaches to invariant generation have been reported, and all of these methods assume that program variables are evaluated over infinite domains such as rational or integer. Actually, during the execution of program, all variables are expressed using bit-vectors with limited width and evaluated over finite domains. Many invariants suit for infinite domains may be invalid in finite domains, and vice versa. This paper proposes an approach to construct invariants for program whose variables are evaluated over finite domains. This method translates a program into constraints that are solved using a QBF (Quantified Boolean Formula) solver to yield desired program invariants. It can be used to discover a rich class of invariants including linear (or polynomial) equality (or inequality) invariants involving operations like addition, multiplication, division, shift, bitwise operation, even quantifiers. And we also show how this technique can be used to prove partial correctness, termination and total correctness.

A structural fairness of electronic commerce protocols is presented. A game-based model of electronic commerce systems is built. And in the model a formalization of fairness is given. They are exactly used to analyze fairness of electronic commerce protocols. Compared with the previous work, the main contributions are the following. Firstly, a strategic game is used to model channels in various qualities and participants' dishonest behavior enables us to analyze cooperative and adversarial behaviors. Secondly, Balance Payoff considers both participants' income and expense but the previous one only considers participants' income. Thirdly, Abortive Fairness is defined because of honest participant's quitting voluntarily or aborting the protocol in response to system failures. These two help us to avoid the confusion that a system satisfies fairness definition but is not fair to some participant factually.

A novel off-line anonymous digital cash protocol utilizing smart card is presented in this paper. It employs smart card as a distributed anonymity and divisibility provider agent based on Schnorr signature scheme. It provides accountable anonymity and divisibility. Its digital cash proves to be unreusable, unexpandable, untraceable, and unforgeable. Compared with Chaum and Wang's scheme, the novel protocol distributes less computation load on customers and cost less communication, thus it is efficient and adapted to mobile payment.