Depth-first Research Articles

Adaptive tiling for parallel N-body simulations on many core

The N-body simulations consist of computing mutual gravitational forces exerted on each body in O ( N ) . The Barnes–Hut approximation allows processing a group of bodies in O ( 1 ) if they are far enough from a given body, which drops the complexity of the whole simulation to O ( N L o g N ) . The octree is used to ease the pruning process but at the cost of some irregularity in the access pattern. In a parallel N-body implementation the bodies are partitioned among threads that are executed on multiple cores. The depth-first traversal of the octree is used for processing each body, which causes repeated cache misses during traversal. This paper proposes different types of tiling methods to improve the performance of N-body simulations. It presents an experimental analysis of octree traversal by using these tiling methods to identify the potential of cache data reuse. It then evaluates these tiling methods for varying tile sizes with different galaxy sizes and a varying number of threads on several machine architectures. The efficiency of tiling approaches depends on the chosen tile size. It is shown that a speedup of 8 times can be achieved by choosing the appropriate tile size on a 60-core Intel accelerator. In order to determine appropriate tile size, the paper proposes an adaptive tiling approach to implicitly adapt the tile size to the distribution of threads, the cache capacity, cache latency, problem size and dynamic changes in the access pattern over the iterations. The proposed adaptive tiling approach can be used as an optimization option in parallel compilers. • Proposes tiling methods to improve cache data reuse for Barnes-Hut simulation. • Provides experimental analysis of octree traversal with proposed tiling approaches. • Evaluates different galaxy sizes and number of threads on several architectures. • A hybrid tiling approach with a suitable tile size provides better performance. • Proposes an adaptive tiling approach to identify an optimal tile size.

R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

Efficient algorithms for deriving complete frequent itemsets from frequent closed itemsets

When mining frequent itemsets (abbr. FIs) from dense datasets, it usually produces too many itemsets and results in the mining task to suffer from a very long execution time and high memory consumption. Frequent closed itemset (abbr. FCI) is a compact and lossless representation of FI. Mining FCIs can not only reduce the execution time and memory usage, but also reserve the complete information of FIs derived from FCIs. Although many studies have been proposed with various efficient methods for mining FCIs, few of them have developed algorithms for efficiently deriving FIs from FCIs. In this work, we propose two efficient algorithms named DFI-List and DFI-Growth for efficiently deriving FIs from FCIs. The both algorithms adopt depth-first search and divide-and-conquer methodology to derive all the FIs. DFI-List efficiently derives all the FIs with a vertical index structure called Cid List. DFI-Growth compresses the information of FCIs into tree structures and applies pattern-growth strategy to derive FIs from the trees. Empirical experiments show that DFI-List is the most efficient and scalable algorithm on the dense datasets. For example, when the minimum support threshold is set to 50% on the Chess dataset, DFI-List runs faster than LevelWise (Pasquier et al. Inf Syst 24(1): 25-46, 1999b) over 100 times. As for DFI-Growth, it is the most stable and memory efficient algorithm on the sparse datasets. Both DFI-Growth and DFI-List are superior to the state-of-the-art algorithm (Pasquier et al. Inf Syst 24(1): 25-46, 199b) in terms of execution time.

Virtual Restoration of Mural Color Based on Artificial Intelligence

In recent years, the development of artificial intelligence can be described as rapid change, which provides a safer method for the virtual restoration of mural colors. This article mainly studies the virtual restoration of mural color based on artificial intelligence. This paper constructs the minimum spanning tree of the segmentation line and transforms it into a graph, finds out the closed sub-ring as the segmentation line from the graph, and uses the depth-first search algorithm to extract the surface in the segmentation line area. This paper performs threshold segmentation on the mural image to obtain the drop-off area. The threshold T is selected based on experience; the pixel points of the same attribute are merged by 8 adjacent methods, that is, the pixel value is 1, and then the closed operation is used to achieve the purpose of continuous drop-off information. In order to control the tendency of global optimization and neighborhood correlation, a proportional parameter needs to be added, and the parameters are adjusted to obtain a balance between neighborhood correlation and global optimization, and the best matching point can be obtained. The data shows that the average objective evaluation score of Criminisi algorithm is 0.794603, and the average objective evaluation score of the improved algorithm in this paper is 0.848665. The results show that artificial intelligence technology plays an extremely important role in the virtual restoration of mural colors.

A Functionality Testing Method Based on Exploratory Testing for Android Mobile Apps

The research is based on the characteristics of mobile Applications (Apps), taking an Android mobile App of a small examination system as an application case, this study is devoted to exploring a feasible functionality testing method based on exploratory testing for Android mobile Apps, which includes fundamental functionality logic verification, Graphical User Interface (GUI) automated testing, and special testing. Furthermore, further work is put forward to apply this functionality testing method to IOS mobile Apps, adding new test items for mobile Apps and improving the method of depth-first search for control traversal in GUI automated testing.

Comprehensive study of schedulability tests and optimal design for rate-monotonic scheduling

Schedulability analysis of the rate-monotonic (RM) scheduling algorithm is an important research field in real-time systems. New dimensions in RM have been explored, ranging from faster schedulability tests to the optimal design of real-time systems. Recently, researchers have extended the schedulability problem to a more generalized case: the rate-monotonic optimal-design problem (RM-ODP), wherein the execution time is limited to an interval rather than a sole point. The RM-ODP processes task-execution-time adjustment such that (i) the system is RM schedulable and (ii) certain system performances (e.g., processor utilization) are optimized. In this paper, we review RM scheduling and summarize three aspects of the related research: (1) evaluate existing relevant feasibility tests by categorizing them based on inexact or exact conditions; (2) comprehensively review RM-ODP, summarize associated advantages and disadvantages, and provide some recommendations on schedulability tests to achieve the optimal system design under RM-scheduling policies; and (3) propose a hybrid search method based on linear programming with respect to both depth-first search and breadth-first search using the dynamic pruning strategy.

Some new perspectives for solving 0–1 integer programming problems using Balas method

Egon Balas’s additive algorithm, also known as implicit enumeration, is a technique that uses a branch-and-bound (B&B) approach to finding optimal solutions to 0–1 integer programming problems. Three common search strategies in B&B are depth-first search, breadth-first search and best-first search. The B&B approach generates a list of pending nodes to be evaluated and storage of these nodes becomes a memory issue for larger problems. In this paper, we propose a simple bookkeeping method that tracks the state of the problem using a single array when performing a depth-first search, dramatically reducing memory requirements. The method also provides the ability to calculate, at any point of the search, the theoretical maximum number of remaining nodes to be evaluated. We note in this paper that when using the best-first search strategy, the first candidate solution found is the optimal solution.

Encoding and decoding algorithms for unlabeled trees

Trees considered in this article, in which at least two receivers emanate from each internal vertex, are found in the works by E. Schroder, R. Stanley, O.V. Kuzmin, etc. Encoding and decoding algorithms for unlabeled planar rooted trees with a given number of end vertices, root receivers, and a sequence of degrees of internal tree vertices during depth-first search are built. Encoding is done in non-decreasing tuples, by assigning labels to the internal vertices of the tree. The proposed algorithms made it possible to prove the existence of a one-to-one correspondence between the studied set of trees and the set of non-decreasing tuples. To find the cardinality of a set of tuples, we used an approach based on the generalized Pascal pyramid.

Distribution Network Topology Identification Based on IEC 61850 Logical Nodes

Distributed control has good real-time performance and can better meet the control requirements of active distribution networks with a large number of distributed generations. Some distributed applications require real-time feeder topology to achieve control. In this paper, the demand for distributed control applications for feeder real-time topology is analyzed. Based on IEC 61850 modeling method, a new cell topology logic node and a new topology slice node are built to express feeder topology. Using the topology information of smart terminal unit (STU) configuration and the current status information of switchgear, based on the depth-first search, the feeder real-time topology identification can be realized, which meets the application requirements of distributed control. The study case verified the effectiveness of the method.

FHUQI-Miner: Fast high utility quantitative itemset mining

High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task is useful to understand customer behavior, an important limitation is that high utility itemsets do not provide information about the purchase quantities of items. Recently, some algorithms were designed to address this issue by finding quantitative high utility itemsets but they can have very long execution times due to the larger search space. This paper addresses this issue by proposing a novel efficient algorithm for high utility quantitative itemset mining, called FHUQI-Miner (Fast High Utility Quantitative Itemset Miner). It performs a depth-first search and adopts two novel search space reduction strategies, named Exact Q-items Co-occurrence Pruning Strategy (EQCPS) and Range Q-items Co-occurrence Pruning Strategy (RQCPS). Experimental results show that the proposed algorithm is much faster than the state-of-art HUQI-Miner algorithm on sparse datasets.

Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networks

With the power of social media being harnessed to coordinate events and revolutions across the globe, it is important to identify the key sets of individuals that have the power to mobilize crowds. These key sets have higher resources at their disposal and can regulate the flow of information in social networks. They can maximize information spread and influence/manipulate crowds when they are coordinating. But due to the inherent drawbacks in node-based and network-based community detection algorithms, neither of these types of algorithms can be used to detect/identify these key sets. In this study, we present a bi-level max-max optimization approach to identify these key sets, where the degree centrality is used to identify individuals’ influence at the commenter-level, while the network-level is designed to evaluate the spectral modularity values. We also present a set of evaluation metrics that can be used to rank these key sets for an in-depth investigation. We demonstrated the efficacy of the proposed model by identifying key sets hidden in a YouTube network spreading fake news about the conflict in South China Sea. The network consisted of 47,265 comments, 8477 commenters, and 5095 videos. A co-commenter network was constructed, where two commenters were linked together if they comment on same video. The proposed model efficiently identified key sets of commenters spread information to the whole network to manipulate YouTube’s recommendation and search algorithm to increase the information dissemination. Moreover, the projected approach could identify sets of commenters that were key connectors to multiple groups, high influence across the network, higher interactions, and reachability than other regular communities. Besides, the Girvan–Newman modularity method, the depth-first search method, and text analysis was applied to validate the outcomes, categorize the identified key sets, and monitor the commenters’ behaviors and information spread strategies in the network. In addition, the model considered a multi-criteria problem to rank these key sets of commenters based on the small real-world networks’ features.

A Hybrid Teaching-Learning-Based Optimization Algorithm for the Travel Route Optimization Problem alongside the Urban Railway Line

Accurate travel route optimization is essential to promote and grow tourism in modern society. This paper investigates a travel route optimization problem alongside the urban railway line and proposes a hybrid teaching–learning-based optimization (HTLBO) algorithm. First, a mathematical programming model is established to minimize the total traveling time, in which the routes between and in different cities have to be appropriately determined. Then, a hybrid metaheuristic named HTLBO is proposed for solution generation. In HTLBO, depth first search (DFS) is utilized to obtain the optimal routes of any two stations in railway network, and a three-level coding method is designed to accommodate the problem characteristic. Besides, opposition-based learning (OBL) is embedded into teaching-learning-based optimization (TLBO) for enhancing HTLBO’s exploration ability, while variable neighborhood descent (VND) is used to enhance the algorithm’s exploitation ability. Finally, a case study is presented and simulation results verify HTLBO’s feasibility and effectiveness.

Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage

Purpose Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage. Design/methodology/approach Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution. Findings Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time. Originality/value Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

High-Utilization, High-Flexibility Depth-First CNN Coprocessor for Image Pixel Processing on FPGA

Recently, CNNs are increasingly exploited for pixel processing tasks, such as denoising, which opens up new challenges due to the increased activation and operation count. This article presents a CNN coprocessor architecture to solve these challenges on field-programmable gate array (FPGA) through four main contributions. First, the I/O communication between the host processor and the FPGA is reduced to a minimum using a depth-first (DF) principle. Three new DF approaches are presented. Second, to ensure high throughput, the increased parallelization opportunities of the proposed line-based DF operation are analyzed. Third, introducing programmability to the compute array is introduced to enable a broad deployment while maintaining high utilization of the available multipliers digital signal processings (DSPs), independently of the kernel dimensions and without control of the host processor. This is in contrast with many state-of-the-art FPGA implementations, focusing on only one algorithm and/or one kernel topology. Fourth, a model is built to investigate the influence of architecture parameters and show the benefits of DF. The scalable design can be deployed on a wide range of FPGAs, maintaining 78%-93% DSP utilization across all algorithms (denoising, optical flow, depth estimation, segmentation, and super-resolution) and FPGA platforms. Up to 695 GOPS is achieved on a Zynq XCZU9EG board, matching state-of-the-art performance with a more flexible design. The throughput is compared with other pixel processing architectures on FPGA.

Adaptive UAV-Assisted Geographic Routing With Q-Learning in VANET

The Q-learning based geographic routing approaches suffer from problems of low converging speed and inefficient resources utilization in VANET due to the dynamic scale of Q-value table. In addition, the next hop selection based on local information can not always be conducive to the global message forwarding. In this letter, we propose an adaptive unmanned aerial vehicle (UAV) assisted geographic routing with Q-Learning. The routing scheme is divided into two components. In the aerial component, the global routing path is calculated by the fuzzy-logic and depth-first-search (DFS) algorithm using the UAV-collected information like the global road traffic, which is then forwarded to the ground requesting vehicle. In the ground component, the vehicle maintains a fix-sized Q-table converged with a well-designed reward function and forwards the routing request to the optimal node by looking up the Q-table filtered according to the global routing path. The simulation results show the proposed approach performs remarkably well in packet delivering and end-to-end delay.

SARVE-2: Exploiting Social Venue Recommendation in the Context of Smart Conferences

Globally, the superfluity of scholarly research conferences in varying disciplines has introduced the issue of scholarly big data and information overload related to both research papers and conference proceedings/sessions. This evident scholarly expansion in different disciplines has increased the collaborative importance of conferences. Consequently, the problem regarding attendees selecting the right conference session(s) to attend in academic conferences requires further and urgent attention. Using a smart conference scenario, this paper aims to address the problem above by proposing an improved venue recommender algorithm called Socially-Aware Recommendation of Venues and Environments-2 (SARVE-2). Using a closeness centrality approach, SARVE-2 initially employs Breadth First Search (BFS) and Depth First Search (DFS) strategies to search for relevant presenters for a target attendee. Then, the tie strength of the (searched) presenter and target attendee is computed to generate reliable social (conference session) recommendations for the target attendee. Through the utilization of a relevant (real-world) dataset, our benchmark experiments reveal that, in comparison with other contemporary methods, SARVE-2 exhibits better performance in terms of effective social recommendation search, as well as social recommendation quality, coverage and accuracy.

Fast single-phase fault location method based on community graph depth-first traversal for distribution network

With the increasing complexity of distribution network structure originated from high penetration of renewable energy and responsive loads, fast and accurate fault location technology of distribution network is a prerequisite for rapid isolation of faults and restoration of power supply. In this paper, a fault location method based on community graph depth-first traversal is proposed for fast location of single-phase ground faults in distribution network. First, this paper defines the fault graph weight of the vertices in the distribution network graph model, which are used to reflect the topology of the vertices and fault points as well as the fluctuation of the vertices' currents. Then, the vertices on the graph model are clustered by using an improved parallel louvain method (IPLM). Finally, the community formed by IPLM is used as the smallest unit for depth-first traversal to achieve fast and accurate location of the fault section. The paper builds the distribution network graph model of IEEE 33-bus system on the graph database for testing. And three other methods were selected for comparison with IPLMDF. The test results show that IPLMDF can achieve fast and accurate fault location when half of the nodes in the distribution network are equipped with D-PMUs. When some of the D-PMUs lose time synchronization, it is still possible to locate the fault section, and at the same time, the locating results can be avoided to fall into local optimal solutions.

Effect of Various Incremental Conductance MPPT Methods on the Charging of Battery Load Feed by Solar Panel

The presented work in this paper deals with various step sizes used in incremental conductance (INC) related to the maximum power point tracking (MPPT) technique. In the solar photovoltaic system, the variable step size selection method for INC is proposed and compared. The MATLAB/Simulink and hardware setup are used for assessing and analyzing step size methods. The variable step size (DVS), fixed step size (DFS) are comprehensively studied and compared. This DVS method is having a lower ON delay time (Td <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ON</sub> ) as 148 msec as regard to 164 msec in the DFS method. On the other hand, the lowest peak-peak oscillations in load current as 0.04 amp for DVS as compared to 0.5A for the DFS method, lower peak current as 1.96A for DVS as compare to 2.37A for the DFS method. In this way, the performance of the DVS method is found superior as it is analyzed and compared with the DFS algorithm.

Algorithms Effectiveness comparison in solving Nonogram boards

Solving logic puzzles using selected algorithms is an interesting problem in which the efficiency of the methods is essential. One such task is solving nonograms, where board fields should be filled to meet the conditions for rows and columns, respectively. The number of methods possible to use opens the possibility to compare their efficiency and quality of operation.In this paper, we decided to analyze two algorithms belonging to different groups of methods to determine their effectiveness. A modified Depth-First Search (DFS) method and a soft computing method based on permutations generation were chosen and used to solve selected nonograms. The research was conducted on four board sizes, and the results showed that the effectiveness of the used methods mainly depends on the level of nonogram complexity. The algorithm results with permutations were stable, while in contrast to the DFS method, it did not always guarantee a complete solution.

An Energy‐Efficient One‐Shot Scheduling Algorithm for Wireless Sensor Networks

In low‐load wireless sensor networks, the power consumption of the node consists mainly of two parts: data transmission and node state switching. The lower node workload causes low energy consumption on data transmission, and the state switching energy of the node cannot be ignored. This paper proposes a one‐shot time division multiple access (TMDA) scheduling with unlimited channels (SUC) on the assumption that the number of available channels is unlimited. SUC combines the receiver‐based consecutive slot allocation with channel allocation, which minimises the number of node state switching and optimizes energy efficiency. Theoretical analysis demonstrates that the number of channels required by SUC does not exceed , where N indicates the number of nodes. Seeing that the number of available wireless channels is limited in practice, the paper proposes the scheduling with limited channels (SLC) and uses a Lookahead Search mechanism to solve slot conflict. For the scalability of the algorithm, a distributed implementation based on the token change is proposed. The algorithm uses the depth‐first‐search (DFS) to pass the token to all nodes and terminates slot and channel assignment. The simulation results show our algorithm can reduce the energy consumption by minimizing the number of state switching and shorten the data aggregation time by reusing slots among nodes.