Earticle

How to generate the task-oriented optimal agent coalition is a key issue of multi-agent system, which is a typical optimization problem. In this paper, an improved particle swarm optimization (IPSO) is proposed to solve this problem. In order to overcome the premature and local optimization problem in traditional particle swarm optimization (PSO), we proposed a variation of inertia weight PSO algorithm by analyzing the feasibility of particle optimization process in PSO. Compared with several well-known algorithms such as PSO, ACO, experimental results show that the global search capability of IPSO has been significantly improved and IPSO can effectively avoid premature convergence problem. Also it can solve the multi-agent coalition formation problem effectively and efficiently.

As one of the driving forces changing the way research and industry uses virtualization, distributed computing, internet, software and web services today, cloud computing stands tall. A cloud is an ecosystem of data centers, distributed servers, and clients all interconnected to each other. The unique selling point of a cloud is its reduced cost of ownership in comparison to traditional models. This primary advantage is complemented by fault tolerance, high availability opportunity, scalable and flexible structure, reduced infrastructure overheads for users, and services that can be accessed as and when required. One of the challenges that face cloud computing is load balancing. Load balancing assures optimum use of available resources, thereby, enabling consistency and performance of the overall system. An imbalance of load causes a single node or nodes to operate beyond its optimum levels. As a result, there could be either a gradual or a rapid loss in overall efficiency of the system leading to increase in emission rates and inefficient use of energy. In this paper, we have focused on resourceful load balancing coupled with a technique which reduces flooding. We have discussed how a combination of these is able to ensure efficient routing with reduced carbon emission.

Nowadays, traditional model construction techniques are often used in the self-adaptive software development environment. However, due to its adaptation problems, such as low reusability and high complexity, it is difficult to meet the incremental requirements of users. In order to achieve the combination of models and the results verified, this paper regards improving the reusability of adaptation models as a starting point, introducing the formal method of partial behaviour model to the description of adaptation behaviour. With the help of three-valued logic model description language KMTS, we research the consistent judgment algorithm of related models, and propose a fusion method of consistent models for supporting online fusion of adaptation models. Finally we use a model instance and its application to analyze and verify the correctness and effectiveness of the fusion result.

This paper gives a solution of the synchronic distance between any two transitions in a fair Petri net. In order to describe the synchronic distance between two transitions more accurately, the concept of weighted synchronic distance is adopted. The solution method is firstly to add a weighted observe-place between two transitions, and a net system with a weighted observe-place is constructed by assigning a suitable weight function for an arc between a transition and an observe-place. Then the initial tokens of a weighted observe-place are obtained by constructing an augumented coverability tree of a net system with a weighted observe-place. The synchronic distance between two transitions is finally yielded by constructing a coverability tree of a net system with a weighted observe-place, and the corresponding solution algorithms are also given.

A tremor is an involuntary, somewhat rhythmic, muscle contraction and relaxation involving to and from movements (oscillations or switching) of one or more body parts. It is the most common of all involuntary movements and can affect the hands, arms, eyes, face, head, vocal folds, trunk, and legs. Most tremors occur in the hands. In some people, tremor is a symptom of another neurological disorder. This research focuses on the design and analysis of a model reference-based controller for highly nonlinear dynamic multi degrees of freedom, in presence of hand tremors. In order to provide a high performance nonlinear methodology, the inverse dynamical controller is selected. The inverse dynamic controller is the type of model reference controller. It is nonlinear and stable controller. To reduce the challenges of this controller hybrid fuzzy controller is used. However, this method has many advantages, but it has challenged in presence of uncertainties. To solve this challenge model reference-based direct adaptive methodology is used and applied to hybrid inverse dynamic fuzzy controller.

This paper takes the automatic classification of the large-scale Uyghur text collected from the network as research background, designed the functional block structure of the Uyghur text classification system, and chose the KNN algorithm as the classification engine, and programmed the classification system using C sharp. In the preprocessing part, combining with the Uyghur language’s lexical characteristics, we introduced the stem extraction method into the procedure, and then have greatly reduced the whole feature dimensions. the classification experimental results on the basis of large-scale text corpus includes more than 3000 documents which are belongs to different 10 categories are given, and the results of the classification experiments for the different number of features selected by using x2 statistical method are also given. The results show that only 3% to 5% of the whole high dimensional features are crucial to higher classification accuracy, so it is possible how to determine what those best features are or further reducing the feature space dimensions which are the interesting issues to be further continued.

In this paper, a new version of non-ideal model 08-bit current mode pipeline ADC has been developed. It is based on Matlab and Simulink environment with special focus given to the analog building blocks. Those blocks are current Sample-and-Hold(S/H), current Sub-ADC and current Sub-DAC. In this model, the sub-ADC is implemented by two current comparators and encoder logic circuit. For the current S/H and sub-DAC, they are implemented by using a switch current and current source. The main advantage of current mode approach is its low power dissipation, low cost and high speed. However, there are some technical limitations; using the model and running the simulation with the introduction of the main non idealities components such as a current offset, clock feed through, charge injection, clock jitter, switching noise, mismatch errors and non idealities in current amplifier, demonstrated clear degradation of the performance of the ADC.

Today‘s marketing climate raised a significant challenge for the marketers- finding a marketing solution that’s both economical and efficient when it comes to responding to the ever-changing customer environment. It is important to address the main factors affecting the demand of customers in order to generate the best response. This paper is an attempt to investigate the main factors affecting the demand of the clothing customization products for the online shoppers in retail market in China. GRA (Grey Relational Analysis) and Fisher’s exact test is applied to identify the main affecting factors of online performance of the selected e-commerce clothing customization companies. The results could be the guideline for the online clothing customization companies in terms of improving their online popularity and making further progress in the clothing customization industry.

Traditional Chinese Medicine (TCM) plays an important role in South-East Asia, and increasingly in the West. However, learning and mastering TCM knowledge are still an obscure problem for researchers all over the world, especially for beginners. With the development of computer science, serious game rising in a wide range of fields like education, training and simulation, etc. In this paper, we design and implementation a knowledge-intensive serious game for education of TCM. When the users play this game, they will finding learning is an interesting thing. We chose the Unity 3D as our game engine, 3ds Max 2013 as model design tools. When the users log in the game, it will presents a series of scenes include some tasks and questions about the TCM knowledge. After a series tasks and test, the reslut shows that serious game for TCM education proved effective to help the user remember and comprehend the concept of TCM.

In 1984, Shamir proposed the concept of the ID-based cryptosystem (IBC). Instead of generating and publishing a public key for each user, the ID-based scheme permits each user to choose his name or network address as his public key. This is advantageous to public-key cryptosystems because the public-key verification is so easy and direct. In such a way, a large public key file is not required. Since new cryptographic schemes always face security challenges and many integer factorization and discrete logarithm based cryptographic systems have been deployed, therefore, the purpose of this paper is to design a transformation process that can transfer the entire integer factorization problem and discrete logarithm problem based cryptosystems into the ID-based systems rather than re-invent a new system. We consider the security against a conspiracy of some entities in the proposed system and show the possibility of establishing a more secure system.

Collaborative Filtering is of particular interest because its recommendations are based on the preferences of similar users. This allows us to overcome several key limitations. This paper explains the need for collaborative filtering, its benefits and related challenges. We have investigated several variations and their performance under a variety of circumstances. We also explored the implications of these results when weighing K Nearest Neighbor algorithm for implementation. Based on the relationship of individuals, putting forward a new incremental learning collaborative filtering recommendation system, discovery it is a better way to acquire optimum results.

As the international video coding standard, MPEG-2 has been widely used in today’s digital video applications. However, its quantization operation is complex. To simplify this operation, we propose a new algorithm for the intra frame transform-coding in this paper, instead of the conventional algorithm that using discrete cosine transform (DCT). This new algorithm is based on the all phase biorthogonal transform (APBT) theory, which has three kinds of forms in accordance with different transform matrices, referred to as the all phase Walsh biorthogonal transform (APWBT), the all phase discrete cosine biorthogonal transform (APDCBT), and the all phase inverse discrete cosine biorthogonal transform (APIDCBT). Compared with the conventional DCT, APBT reduces the inter-pixel redundancy and the computational complexity using the uniform quantization for the intra frames transform-coding. Experimental results show that the peak signal to noise ratio (PSNR) of the proposed algorithm performs close to the DCT for the tested frames, and there is no difference in visual quality.

Data stream is a continuous, time varying, massive and infinitely ordered sequence of data elements. The streaming data are fast changing with time, it is impossible to acquire all the elements in a data stream. Therefore, each data element should be examined at most once in data streams. Memory usage for mining data stream should be limited due to the new data elements are continuously generated from the streams. It is essential to ensure that newly arrived stream should be immediately available whenever it is requested made this task much challenging and necessary for fraud detection in stream, taking out knowledge, for business improvement and other applications where data arrived in stream. This paper tries to highlight important issues and research challenges of data stream by means of a comprehensive review.

Designated verifier signature (DVS) allows the signer to convince a verifier the validity of a statement but prevent the verifier from transferring the conviction. Strong designated verifier signature (SDVS) is a variant of DVS, which could make it possible for a signer to convince only the designated verifier that the signature is made by the signer. Recently, many strong designated verifier signature schemes have been presented in identity based setting. Unfortunately, most of them can not satisfy the strongness and non-delegatability properties, and cannot be rigorously proved secure. Moreover, for some special applications, these schemes suffer from larger data size of communication. In this paper, we present an efficient identity-based strong designated verifier signature scheme with message recovery, and then rigorously analyze its security properties in the random oracle model. As far as we know, it is the first provably secure identity-based strong designated verifier signature scheme with message recovery, which can be used to sign an arbitrary long message, and can satisfy all the security properties including the existential unforgeability, strongness property, non-transferability and non-delegatability, etc.

Trust management is fundamental to identify malicious, selfish and compromised nodes which have been authenticated. It has been widely studied in many network environments such as peer-to-peer networks, grid and pervasive computing and so on. In the paper, we propose a trust model (TM) based on fuzzy logic inferences, which can better handle uncertainty, fuzziness, and incomplete information in cloud trust reports. The experiments are performed in order to test the accuracy of the TM as compared to a data storage system where no trust model is implemented.

The main idea behind any hash function is to find a one to one correspondence between a key value and an index in the hash table where the key value can be placed. In closed hashing, it is very difficult to handle the situation of table overflow in a satisfactory manner. Key values are haphazardly placed and generally majority of the keys are placed far away from their hash location. Thus, the number of probes is greatly increased which degrades the overall performance. To resolve this problem another hashing method known as open hashing (separate chaining) is employed. But this type of hashing is still not efficient in case of searching because here all the elements that have the same hash function are inserted in a sequential order. Due to this, traversal of all the previously inserted elements is required when we are searching for the last element. So the overall complexity will be O (n). In this paper, we propose a hybrid chaining model which is a combination of Binary Search Tree and AVL Tree to achieve a complexity of O (log n).

With the development of the cloud computing, increasing application and service based on cloud can be used by end-users. However, for these applications, the end-users can only use them passively. Without the programming experience, the end-users hardly involve their knowledge and awareness into the logic of applications. Cloud computing can provide large storage and computing resources for end-users on demand, and the Web of Things and sensor network can provide various kinds of information which is beyond the users’ own sense. In our opinion, the end-users in cloud computing environment have the ability to develop and design the function of applications by themselves through their knowledge and awareness. Therefore, we proposed the Cloud-Brain, a knowledge-based developing approach for end-user in cloud computing. In our approach, the presentation of fact and knowledge in cloud is considered. The rule engine and service-oriented architecture are involved in processing the knowledge in the cloud. End-users can upload their knowledge and awareness in the form of rule into cloud. At the same time, the system collects facts and data from Web of Things or sensor network. The actions in these rules, which have matching facts, call the web service or other function for users. A prototype was implemented to verify the feasibility of this method. Here, we also present a case study to demonstrate the functionality, performance, and potential of the approach.

In Ant Colony System, the agents or the ants travel in search of foods by following one another that is they show the swarming behavior along the searching path of the food source and return path towards the hive. In the artificial ant system or the robot system, the same operation of the robot can be observed by the suitable design of the travelling path. The path design and the travelling technique are already proposed in my two previous works. Those designs include different types of algorithms for different opeartions. Kruskel’s algorithm is applied to find the shortest path in between any two nodes, one is the source and another is the destination node. But the disadvantage of this design is the longer simulation time as three algorithms is required to travel from one node to another node and naturally several numbers of the databases are required to hold all the information about the entire robot colony. In this paper, a single database is proposed that can hold all the connectivity and which can be driven with a single algorithm. The proposed method is compared with the other previous techniques of finding the shortest path and the result set represented here showing the efficiency of the proposed method.

With the single chip microcomputer as the core, a kind of multi-function detection system of vehicle fuel oil is designed by using sensor and signal processing circuit. Signal can be converted into electrical signal and connected to A/D converter through the oil signal acquisition channel, instantaneous oil signal acquisition channel, respectively. In the system, output data is displayed through the software programming and the corresponding circuits. The tests show that the system can automatically and accurately monitor all cases of the fuel oil and it is very important for the drivers to know the conditions of the vehicle fuel oil.

By analyzing a large number of web pages, we proposed a page segment algorithm which is based on DOM tree structural features and visual features. The algorithm segments the pages to the small particles. It produces basic processing units for recognition algorithms. After segmenting the pages, we extracted the structural and visual features of the pages, and proposed a method to identify the body of the web article. The method uses clustering algorithm and heuristic rules to produce an automatic wrapper. A testing experiment demonstrated the efficacy of the algorithm.

With the tremendous amount of research publications online, finding relevant ones for a particular research topic can be an overwhelming task. As a solution, papers recommender systems have been proposed to help researchers find their interested papers or related papers to their fields. Most of existing papers recommendation approaches are based on paper collections, citations and user profile which is not always available (not all users are registered with their profiles). The existing approaches assume that users have already published papers and registered in their systems. Consequently, this neglects new researcher without published papers or profiles. In this paper, we propose an academic researcher papers recommendation approach that is based on the paper’s topics and paper’s main ideas. The approach requires as input only a single research paper and extracts its topics as short queries and main ideas’ sentences as long queries which are then submitted to existing online repositories that contains research papers to retrieve similar papers for recommendation. Four query extraction and one paper recommendation methods are proposed. Conducted experiments show that the proposed method presents good improvement.

Compared with traditional PWM converters, the matrix converter with high development potential has many advantages, such as simpler structure, better controllable performance, higher power factor, less harmonic pollution to the grid and no large-capacity DC capacitor. Currently, among a variety of modulation algorithms for the matrix converter, the double SVPWM control algorithm is the most mature. This paper analyzes the double SVPWM control strategy and the realization of the variable frequency and variable voltage. Aiming at the pollution problem from the input side current harmonics to grid, a damping LC filter is designed in the input side. However, the input filter will cause reactive power increase and result in power factor decrease of the grid side. To ensure the unit power factor of the grid side, this paper deduces the relationship between the actual power factor angle of the grid side and damping filter. By adjusting the setting value of the input side power factor, the unit power factor of the grid side can be obtained. Simulation results verify the validity of the theoretical analysis.

Soft set theory, proposed by Molodtsov, has been regarded as a generic mathematical tool for dealing with uncertainty. Recently, researches of decision making based on soft sets have got some progress, but few people consider both incomplete information and group decision making. This paper introduces the concept of Dempster-Shafer fuzzy soft sets combined Dempster-Shafer theory and fuzzy soft sets. We study the FUSE operation on both Dempster-Shafer fuzzy soft sets, and the relationship between incomplete fuzzy soft sets and D–S fuzzy soft sets. At last, we present a new method of evaluation based on Dempster-Shafer fuzzy soft sets and apply it into the information systems quality evaluating to illuminate the practicability and validity.

As a result of stochasticity of the spreading process and topological complexity of the cloud computing, it is an arduous task to characterize the nodes which are far more influential than others. The entire objective of this paper is to examine complex networking based cloud computing. Complex networks are those networks whose structure is irregular, complex and evolving dynamically. Its main focus may be the analysis of small networks to that particular of system with thousands or millions of node. It has been found from the review that none of the technique is way better for cloud computing. Therefore, this paper ends with the future scope to solve these issues.

Mixing matrix estimation (MME) algorithm was proposed for the mixing matrix estimation problem of underdetermined blind source separation. The algorithm is based on a combination of processing of isolated time–frequency points from local directional density detection and Hough transform (HT). Firstly, signal sparsity was strengthened through the processing of single-source time–frequency points in the transform domain. Next, HT was applied to the directional straight lines in the scatter plot and realized the spatial transformation. The number of source signals and mixing matrix were estimated by determining the local maxima of cumulative array. To deal with the peak values clustering issue that commonly arises with HT, the local directional density detection method was used to identify and eliminate isolated time–frequency points. HT was then used to improve the accuracy of MME. The experimental results indicate that the proposed method is able to achieve MME under the condition when the number of source signals is unknown. Further, the accuracy of estimation is better than other commonly-used methods such as K-means.

In recent trends, the rapidly increasing power of personal mobile devices (smartphones, tablets, etc.) is providing much richer contents and social interactions to users on the move. This trend however is throttled by the limited battery lifetime of mobile devices and unstable wireless connectivity, making the highest possible quality of service experienced by mobile users not feasible. The recent cloud computing technology, with its rich resources to compensate for the limitations of mobile devices and connections, can potentially provide an ideal platform to support the desired mobile services. In this paper, the design of a Cloud-based novel Mobile Social TV system (CloudMoV) is proposed. The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users who can interact socially while sharing the video. Given the battery life as a key performance bottleneck, the use of burst transmission from the surrogates to the mobile users, and carefully decide the burst size which can lead to high energy efficiency and streaming quality is advocated. Social interactions among the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient designs of data storage with Big Table and dynamic handling of large volumes of concurrent messages in a typical PaaS cloud.

In view of the problems of easily relapsing into local extremum and low convergence accuracy of fruit fly optimization algorithm (FOA), this paper proposes a adaptive fruit fly optimization algorithm based on velocity variable (VFOA). The idea of this algorithm is based on the flight characteristics of fruit fly, using particle swarm optimization (PSO) concept of particle velocity, based on fruit fly optimization algorithm, improved the convergence speed of fruit fly optimization algorithm by adding the particle velocity variable parameter. Finally, simulation comparison experiment tests are conducted on 13 benchmark functions, test results show that adaptive fruit fly optimization algorithm based on velocity variable VFOA compared to swarm intelligence algorithms of FOA, PSO, CS, and so on, the convergence speed and accuracy are improved obviously.

This paper proposes an algorithm to assess the spatial distribution uniformity in 2-dimensional discrete space. Spatial distribution uniformity refers to how uniform some objects distribute in a space. The algorithm takes a matrix of logical values that represents the distribution pattern as the input and outputs a number that represents the degree of the uniformity. A pseudo energy attenuation model and the uniformity coefficients in the irrigation field are leveraged in this algorithm. Experiment results show that this algorithm is effective in assessing spatial distribution uniformity in 2-dimensional discrete space.