A temporal graph is a graph in which vertices communicate with each other at specific time, e.g., A calls B at 11 a.m. and talks for 7 minutes, which is modeled by an edge from A to B with starting time “11 a.m.” an...
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A temporal graph is a graph in which vertices communicate with each other at specific time, e.g., A calls B at 11 a.m. and talks for 7 minutes, which is modeled by an edge from A to B with starting time “11 a.m.” and duration “7 mins”. Temporal graphs can be used to model many networks with time-related activities, but efficient algorithms for analyzing temporal graphs are severely inadequate. We study fundamental problems such as answering reachability and time-based path queries in a temporal graph, and propose an efficient indexing technique specifically designed for processing these queries in a temporal graph. Our results show that our method is efficient and scalable in both index construction and query processing.
This paper addresses the problem of designing efficient logistical arrangements for preparation and delivery of edible food (by a voluntary organization). The short shelf-life of edible, ready-to-eat food items compli...
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ISBN:
(纸本)9781509036660
This paper addresses the problem of designing efficient logistical arrangements for preparation and delivery of edible food (by a voluntary organization). The short shelf-life of edible, ready-to-eat food items complicates the provisioning and distribution networks. The design of the underlying logistical system constitutes an interesting combinatorial optimization problem. Our paper explains the problem background and rigorously defines the underlying mathematical problem. Thereafter, we develop a set of algorithms/techniques (exact and heuristic) to solve the problem faster. We blend the stronger lower bounds (obtained from an alternate MIP formulation) with better upper bounds (obtained using a fast and efficient heuristic approach) to develop a new exact technique. We report the detailed results from computational analysis of our new techniques.
A distributed control strategy for Energy Storage Unit (ESU) in MicroGrid is presented in this paper. In the presence of the stochasticity of renewable generation and load demand, the power balance for MicroGrid shoul...
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A distributed control strategy for Energy Storage Unit (ESU) in MicroGrid is presented in this paper. In the presence of the stochasticity of renewable generation and load demand, the power balance for MicroGrid should be guaranteed by employing real-time control of the charging/discharging power of ESU. A distributed gradient algorithm is firstly employed to construct the distributed power balance control, which is used to balance the active power in MicroGrid. The objective of the proposed control is minimizing the total active power loss during the charging/discharging process, with the linear relationship among charging/discharging efficiency, charging/discharging rate and State of Charge (SOC) considered. The simulation results demonstrate the effectiveness of the proposed method.
Machine learning (ML) based applications that require data stream processing have become quite common over the past few years. To deal with continuous and massive streams of data, low computational and memory costs ar...
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Machine learning (ML) based applications that require data stream processing have become quite common over the past few years. To deal with continuous and massive streams of data, low computational and memory costs are required from the ML techniques employed; these requirements can be partially fulfilled by using constructive neural networks (CoNN) algorithms. The automatic definition of the Neural Network (NN) architecture, as well as its fast training, promote the high adaptability of such NNs, which allows to skip the conventional model selection phase during the learning process. However, such advantages usually come with the drawback of promoting lower accuracy rates, when compared to other learning approaches. The work described in this paper proposes an ensemble of CoNNs combined with functional expansion techniques to cope with data stream classification. The experiment results, followed by a comparative analysis, showed that CoNN accuracy performance along streaming data does improve with the use of functional expansion. Considering that to functionally expand the input data has a low computational cost, the obtained results turn CoNN algorithms an interesting approach to be considered in data stream classification.
In recent years, with the extensive application of the path planning technology, the development prospect of this technology is more and more broad, and the scientific research value of it is also more and more import...
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ISBN:
(纸本)9781509035595
In recent years, with the extensive application of the path planning technology, the development prospect of this technology is more and more broad, and the scientific research value of it is also more and more important. It is very necessary to apply it to the underground coal mine with complex terrain, and the algorithm is the core content of the research on path planning. We add simulated annealing algorithm into the process of ant colony algorithm formed simulated annealing ant colony algorithm in this paper, and apply it to the path planning, solve the local optimum problem caused by precocity in ant colony algorithm. For further ensure to find the optimal path, entropy increase strategy was used, that means using multiple update path to avoid algorithm premature, it's easier to find global optimal solution. Simulation result shows that the method presented in this paper obtains the optimization path and proves that the algorithm is efficient and reliable.
For mobile robots, being able to find a suitable route through an environment filled with varied obstacles, and to ensure that they can successfully reach their target point from the starting point in the most efficie...
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For mobile robots, being able to find a suitable route through an environment filled with varied obstacles, and to ensure that they can successfully reach their target point from the starting point in the most efficient manner is very important, and a necessary research topic. This article proposes a combination of Artificial Bee Colony algorithm (ABC) and Rapidly-Exploring Random Tree (RRT) to produce a novel algorithm to meet these navigation requirements. This algorithm is then compared with the traditional ABC for path planning. Unlike previous algorithms, this study uses the RRT algorithm to find several extend points, choose the best extend point to move the bees. Because the Artificial Bee Colony algorithm is simply structured, easy to operate and quickly converges, it is able to address the problems of slow convergence and easy entrapment in local optimal solutions encountered in previous path planning algorithms. Although RRT has excellent characteristics in terms of the search area which is unknown, it is unstable for each planning. Thus, this thesis combines the characteristics of the artificial bee colony algorithm with the RRT algorithms, and considers the problem of robot path simulation with obstacles.
Data mining is the process in that analyzing of data from different perspective and summarizes that data into some useful information which can be used to enhance the revenue generation, cost cutting etc. In data mini...
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ISBN:
(纸本)9781509020850
Data mining is the process in that analyzing of data from different perspective and summarizes that data into some useful information which can be used to enhance the revenue generation, cost cutting etc. In data mining, cluster formation plays a vital role which is data can be divided into different groups. Clustering is the technique in which grouping is based on similar type of data relevant to different attributes. WEKA is the most important tool of data mining which is used to allocate and clustering of data with use of various machine learning algorithms. The purpose of this paper is to compare different algorithms of machine learning on the subject of types of data set, their size, number of clusters and cyber privacy platform. We also discuss different types of cyber threats in computing world.
High-level synthesis for FPGA designs (FPGA-HLS) is recently required in various applications. Since wire delays are becoming a design bottleneck in FPGA, we need to handle interconnection delays and clock skews in FP...
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ISBN:
(纸本)9781467394994
High-level synthesis for FPGA designs (FPGA-HLS) is recently required in various applications. Since wire delays are becoming a design bottleneck in FPGA, we need to handle interconnection delays and clock skews in FPGA-HLS flow. In this paper, we propose an FPGA-HLS algorithm optimizing critical path with interconnection-delay and clock-skew consideration. By utilizing HDR architecture, we floorplan circuit modules in HLS flow and, based on the result, estimate interconnection delays and clock skews. To reduce the critical-path delay(s) of a circuit, we propose two novel methods for FPGA-HLS. Experimental results demonstrate that our algorithm can improve circuit performance by up to 24% compared with conventional approaches.
RFID applications are used in the Internet of Things (IoTs) which uses multiple readers to read the IDs of multiple tags and form the RFID network. In such a network, unguarded reader deployment may generate over-crow...
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ISBN:
(纸本)9781467398732
RFID applications are used in the Internet of Things (IoTs) which uses multiple readers to read the IDs of multiple tags and form the RFID network. In such a network, unguarded reader deployment may generate over-crowded readers, cause interferences. The deployment is crucial for RFID system performance Radio Frequency Identification (or RFID) tags have seen its wide range of applications in the recent years and has a very promising future. In the present scenario, Ubiquitous computing plays an important role in our daily lives and has a vast range of applications in many fields. In this paper we have proposed a system which uses RFID tags for guidance of tourists. The system uses RFID tags to guide and inform the tourists about a given location and surrounding. We have proposed an algorithm which describes the system's implementation even for densely populated RFID Reader based geographical area. The system requires multiple readers and it collaborates to read the RFID tag data.
In this paper, a new approach based on Binary Black Hole algorithm (BBHA) and Adaptive Boosting version Ml (AdaboostM1) is proposed for finding genes that can classify the group of cancers correctly. In this approach,...
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In this paper, a new approach based on Binary Black Hole algorithm (BBHA) and Adaptive Boosting version Ml (AdaboostM1) is proposed for finding genes that can classify the group of cancers correctly. In this approach, BBHA is used to perform gene selection and AdaboostM1 with 10-fold cross validation is adopted as the classifier. Also, to find the relation between the biomarkers for biological point of view, decision tree algorithm (C4.5) is utilized. The proposed approach is tested on three benchmark microarrays. The experimental results show that our proposed method can select the most informative gene subsets by reducing the dimension of the data set and improve classification accuracy as compared to several recent studies.
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