Depth-first Research Articles

Sistem Informasi Pelayanan dan Pengelolaan Rukun Tetangga (SIPP RT) Menggunakan Metode Depth First Search Berbasis Web

The government system in Indonesia is divided into several levels, starting from the central government, regional government, to the smallest at the RT (neighborhood) level. At the RT level, administrative services and management, such as citizen data collection, financial reporting, providing information, and so on, are still mostly done manually so that their service practices are not optimal. Therefore, we need a system that can improve services at the RT level. With the design of a service information system and neighborhood management using the web-based DFS (depth first search) method, it is hoped that it can improve the services of RT management and make it easier for residents to search for information at the RT level. This system is made using the PHP programming language and MySQL as the database. The method used is DFS (depth first search) because it can optimize the web in searching for information based on the keywords entered so as to shorten the time in getting the information needed in RT services and management

Efficient semi-external depth-first search

As graphs grow in size, many real-world graphs are difficult to load into the primary memory of a computer. Thus, computing depth-first search (DFS) results (i.e., depth-first order or DFS-Tree) on the semi-external memory model is important to investigate. Semi-external algorithms assume that the primary memory can at least hold a spanning tree T of a graph G and gradually restructure T into a DFS-Tree, which is nontrivial. In this paper, we present a comprehensive study for the semi-external DFS problem. Based on a theoretical analysis of this problem, we introduce a new semi-external DFS algorithm called EP-DFS with a lightweight index N+-index. Unlike traditional algorithms, we focus on addressing such a complex problem efficiently with fewer I/Os, simpler CPU calculations (implementation-friendly), and less random I/O accesses (key-to-efficiency). Extensive experimental evaluations are performed on both synthetic and real graphs, and experimental results confirm that the proposed EP-DFS algorithm markedly outperforms existing algorithms.

Autocomplete Indonesian Dictionary with Trie and Depth-First Search Algorithm


 In the current era of technology, the internet provides many conveniences for our daily lives. There are no limitations in accessing the internet, we can even search for information that we do not know through the internet. One of them is by using an electronic dictionary. By using the electronic dictionary the user only needs to enter a keyword and the machine will search for the appropriate data. But when the user finishes entering the word in the search engine, the word is not necessarily found, this makes the electronic dictionary less than optimal in terms of time usage. Therefore, we use the autocomplete feature with the trie data structure and the dfs algorithm in order to shorten the time the user is typing a word, where this feature will display a list of words that the user might mean without having to type the word in full.
 

Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation

Operation optimization for large-scale offshore wind farms can cause the fatigue loads of single wind turbines to exceed their limits. This study aims to improve the economic profit of offshore wind farms by conducting multi-objective optimization via decoupled group operations of turbines. To do this, a large-scale wind farm is firstly divided into several decoupled subsets through the parallel depth-first search (PDFS) and hyperlink-induced topic search (HITS) algorithms based on the wake-based direction graph. Next, three optimization objectives are considered, including total output power, total fatigue load, and fatigue load dispatch on a single wind turbine (WT) in a wind farm. And then, the combined Monte Carlo and beetle swarm optimization (CMC-BSO) algorithms are applied to solve the multi-objective non-convex optimization problem based on the decentralized communication network topology. Finally, the simulation results demonstrate that the proposed method balances the total power output, fatigue load, and single fatigue loads with fast convergence.

A new approach for generator startup sequence online decision making with a heuristic search algorithm and graph theory

Most system restoration strategies following blackouts are obtained by offline methods, which are efficient in theory. But this may not be the case. A new method is proposed to decide the generator startup sequence during the system restoration. The Monte Carlo tree search algorithm is used to search for the generator to be recovered according to the real-time situation of the power system. All paths from the restored system to the chosen generator are obtained by Depth-First Search. And the K-shortest paths are obtained by Yen’s Algorithm to decide the best restoration path. Then, key constraints are verified. Especially, the constraint on the hot start of generators is considered. Finally, the feasible scheme with the maximum power generation is selected for implementation. In addition, the cranking process of G33 to G32 in the IEEE39-bus test system is taken as an example to illustrate the efficiency of this method. Depth-First Search detects 42 restoration paths, and Yen’s Algorithm selects three most reasonable paths from them, which greatly improves the efficiency of online decision making.

Implementation Of Backtracking Algorithm In Elena Puzzle Game Setup

Elena Puzzle is a puzzle game that appears on an anime called Phi Brain in episode 10, Elena puzzle game is a puzzle game where players have to put the 18 pieces of pawns on the game board size 6x6. Pawns are placed consists of the three different colors red, blue and yellow. Pawns put on in the game area must be in accordance with the instructions on the edge of the arena. The instructions are on the sidelines of arena explain that the first color that was encountered in the edge of the arena is a color that corresponds to these instructions. In each row and column in the game of arena can not have two same colors and in each row and column must have three different colors. The purpose of this research is to build an artificial intelligence agent that can solve the puzzle. The methodology used is the backtrack algorithm, that is an algorithm based on Depth-First Search, so the search for solutions is done by tracing the rooted tree-shaped structure in preorder. This process is characterized by the expansion of the deepest node until no longer found the successor of a node. The results will be obtained from existing applications is backtracking algorithm implementation in building artificial intelligence agencies in order to complete the puzzle in accordance with the instructions provided.

Modular configuration design of a special machine tool for variable hyperbolic circular-arc-tooth-trace cylindrical gears

Abstract. In this paper, the machining principle of variable hyperbolic circular-arc-tooth-trace (VH-CATT) cylindrical gears is analysed, and a modular configuration design of this special machine tool is carried out. Based on the importance of parameter and knowledge dependence of rough set theory, the degree of influence of the index hidden in the data on the decision-making results is mined, and an algorithm for calculating the objective weight of the index is given. The comprehensive weight of the index is then obtained, combining the analytic hierarchy process. After obtaining the results of the index's comprehensive weight and module attribute classification, a module retrieval strategy is formulated using the fuzzy similarity priority ratio algorithm. Based on the modular division of this special gear-making machine tool, the modular configuration sequence of the machine tool using the depth-first search algorithm is determined. According to the module retrieval strategy and configuration sequence, a traversal of module instances is carried out, and a variety of modular configuration schemes of the machine tool is obtained, which provides a practical and efficient method for the rapid design of a special machine tool for VH-CATT cylindrical gears.

Backpropagation Neural Tree

We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological properties, BNeuralT is a single neuron neural tree model with its internal sub-trees resembling dendritic nonlinearities. BNeuralT algorithm produces an ad hoc neural tree which is trained using a stochastic gradient descent optimizer like gradient descent (GD), momentum GD, Nesterov accelerated GD, Adagrad, RMSprop, or Adam. BNeuralT training has two phases, each computed in a depth-first search manner: the forward pass computes neural tree’s output in a post-order traversal, while the error backpropagation during the backward pass is performed recursively in a pre-order traversal. A BNeuralT model can be considered a minimal subset of a neural network (NN), meaning it is a “thinned” NN whose complexity is lower than an ordinary NN. Our algorithm produces high-performing and parsimonious models balancing the complexity with descriptive ability on a wide variety of machine learning problems: classification, regression, and pattern recognition.

Fast rule-based graph programs

• Rooted rules are essential for fast rule-based graph algorithms. • Cycle graphs, trees, binary DAGs can be recognised by graph reduction in linear time. • Topological sorting has a linear-time implementation in the rule-based language GP 2. • Depth-first search and 2-colouring have linear-time implementations in GP 2. Implementing graph algorithms efficiently in a rule-based language is challenging because graph pattern matching is expensive. In this paper, we present a number of linear-time implementations of graph algorithms in GP 2, an experimental programming language based on graph transformation rules which aims to facilitate program analysis and verification. We focus on two classes of rule-based graph programs: graph reduction programs which check some graph property, and programs using a depth-first search to test some property or perform an operation such as producing a 2-colouring or a topological sorting. Programs of the first type run in linear time without any constraints on input graphs while programs of the second type require input graphs of bounded degree to run in linear time. Essential for achieving the linear time complexity are so-called rooted rules in GP 2, which, in many situations, can be matched in constant time. For each of our programs, we prove both correctness and complexity, and also give empirical evidence for their runtime.

Graph label prediction based on local structure characteristics representation

A recent study has shown that the real-time anti-noise challenges faced by molecular activity prediction algorithms can be solved by using the part structure features of the molecular graph. However, the sub-structures selected by this method are distributed in a scattered manner such that although they include as many block features as possible, they do not fully consider the connections between these blocks. Therefore, this study was conducted to fully consider the physical interpretation of the betweenness centrality node in the graph, and a sub-structure was obtained by depth-first search (DFS) from this node. This sub-structure not only contains the characteristics of each region but also retains the connections between each region. Then, a cascading multi-layer perception (MLP) model was designed to learn the characteristic representation of the graph from its local structure features. Experiments demonstrated that the performance of our algorithm is superior to that of other algorithms when evaluated on different datasets.

METHODOLOGY OF USING DEPTH-FIRST SEARCH (DFS) FOR SOLVING PROGRAMMING COMPETITION PROBLEMS

Depth First Search (DFS) is a well-known method for traversing a graph. In various situations, particularly in competition problems, the need for an effective application of DFS is masked by the conditions of the problem, or the data structures used. In these cases, the problem is either to identify the need to use DFS in the solution or to adapt the DFS to the particular conditions of the problem. This article is a study of representative competition problems, with the identification of DFS solutions, accompanied by IT solutions. The results will be useful to all those who are passionate about competitive programming, but also to those who study graph algorithms.

Automatic Preidentification of Fault Structural Traps From Graph View

Fault Structural Traps (FSTs) are one of the main gathering places for oil and gas, so their identification on the structure map is of great importance and thus a fundamental work in exploration. In the society of exploration geophysics, such a work is accomplished by experienced experts manually, which motivates us to propose a more effective method to identify the traps automatically. From the graph view, we propose a workflow to realize the automatic pre-identification of FSTs preliminarily according to the subsurface faults distribution. The automatic pre-identification strategy includes three main components: fault information acquisition, traps coarse identification, and rock body correction. First, the fault information acquisition is a fundamental work to obtain the connectivities between faults. Second, in the traps coarse identification, an undirected graph (undigraph) model is established to digitally describe the relationship between faults. Meanwhile, the Alpha Shape (AS) algorithm and Depth First Search (DFS) algorithm are used to add auxiliary lines for connected subgraphs and find basic loops of the undigraph respectively. Finally, a correction algorithm is designed to find the traps formed by blocky rock bodies and their connected faults. The effectiveness of the FSTs pre-identification (i.e., annotating all potential FSTs on the structure map) method is verified by illustrative examples in the Shengli Oilfield, which are almost consistent with the expertise.

Near-Optimal Dispersion on Arbitrary Anonymous Graphs

Given an undirected, anonymous, port-labeled graph of $n$ memory-less nodes, $m$ edges, and degree $\Delta$, we consider the problem of dispersing $k\leq n$ robots (or tokens) positioned initially arbitrarily on one or more nodes of the graph to exactly $k$ different nodes of the graph, one on each node. The objective is to simultaneously minimize time to achieve dispersion and memory requirement at each robot. If all $k$ robots are positioned initially on a single node, depth first search (DFS) traversal solves this problem in $O(\min\{m,k\Delta\})$ time with $\Theta(\log(k+\Delta))$ bits at each robot. However, if robots are positioned initially on multiple nodes, the best previously known algorithm solves this problem in $O(\min\{m,k\Delta\}\cdot \log \ell)$ time storing $\Theta(\log(k+\Delta))$ bits at each robot, where $\ell\leq k/2$ is the number of multiplicity nodes in the initial configuration. In this paper, we present a novel multi-source DFS traversal algorithm solving this problem in $O(\min\{m,k\Delta\})$ time with $\Theta(\log(k+\Delta))$ bits at each robot, improving the time bound of the best previously known algorithm by $O(\log \ell)$ and matching asymptotically the single-source DFS traversal bounds. This is the first algorithm for dispersion that is optimal in both time and memory in arbitrary anonymous graphs of constant degree, $\Delta=O(1)$. Furthermore, the result holds in both synchronous and asynchronous settings.

Critical Pattern Selection Method for Full-Chip Source and Mask Optimization Based on Depth-First Search

Critical Pattern Selection Method for Full-Chip Source and Mask Optimization Based on Depth-First Search

Depth-first Search Algorithm with the Method of Successive Averages for Solving the Logit-based Stochastic User Equilibrium Problem

Depth-first Search Algorithm with the Method of Successive Averages for Solving the Logit-based Stochastic User Equilibrium Problem

Population Synthesis by Disaggregating OD Matrices with Time-Progressive Graphs for Agent-based Simulations

In the last decades, a shift from traditional travel demand modeling approaches to more fine-granular agent-based simulations occurred. This transition was supported by technological advances in computation capabilities and research regarding the capture of individual travel behaviors. While agent-based simulations have been well established, Origin-Destination (OD) matrices are still widely adopted, especially since much historical data is available in these formats. Furthermore, defining models with OD matrices requires less input than detailed microsimulation models. OD matrix-based modeling frameworks may be adequate for mostly car-and public transit-oriented traffic models; however, OD matrices fail to capture the interaction between different trips and do not regard the traveler as a central entity for travel decisions. These limits become especially apparent when regarding intermodal trips. Hence, we want to bridge the gap between traditional OD matrix-based travel demand analysis and modern agent-based simulations. We propose to compute realistic activity schedules from OD matrices that we refine with external statistics. The algorithm converts OD matrices into a graph, where each path represents a valid activity schedule. With a modified depth-first search, we extract activity schedules from the graph and assign them to agents. The practicability of the algorithm is demonstrated by a runtime analysis and a case study. The results show that the algorithm computes activity schedules while keeping the original flow from the OD matrices.

Effective method for detecting error causes from incoherent biological ontologies.

Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies based on four patterns. Each pattern is represented as a directed graph and the depth-first search strategy is used to find the axiom paths relevant to the MinAs of the unsatisfiable class. However, DOBP is inefficient when a debugging large incoherent ontology with a lot of unsatisfiable classes. To solve the problem, we first extract a module responsible for the erroneous classes and then compute the MinAs based on the extracted module. The basic idea of module extraction is that rather than computing MinAs based on the original ontology O, they are computed based on a module M extracted from O. M provides a smaller search space than O because M is considerably smaller than O. The experimental results on biological ontologies show that the module extracted using the Module-DOBP method is smaller than the original ontology. Lastly, our proposed approach optimized with the module extraction algorithm is more efficient than the DOBP method both for large-scale ontologies and numerous unsatisfiable classes.

Pemanfaatan Algoritma Generate and Test Dalam Kasus Travelling Salesman Problem Pencarian Jalur Terpendek

The Generate and Tesr algorithm is a very simple method in a heuristic search. The completion process of this algorithm is a combination of depth-first search with backtracking because the solution must be generated completely before testing. systematically and following procedures will find a solution. The problem in this study is that if the problem discussed in the Generate test algorithm is very broad, it takes a long time to solve it. The main purpose of this study is to find the shortest alternative route to find a solution in order to obtain the smallest route so as to save transportation costs. The final result of this study obtained 8 paths to the final destination, 2 paths that have the shortest route, namely the A-C-F-I-T and A-C-E-G-T routes. So that the tests carried out can pass through 2 paths to be able to visit the city.

Optimized Graph Search Algorithms for Exploration with Mobile Robot

Graph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS) and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known.

NWP-Miner: Nonoverlapping weak-gap sequential pattern mining

Nonoverlapping sequential pattern mining (SPM) is a type of SPM with gap constraints that can mine valuable information in sequences. One of the disadvantages of nonoverlapping SPM is that any characters can match with gap constraints. Hence, there can be a significant difference between the trend of a pattern and those of its occurrences. To tackle this issue, we propose nonoverlapping weak-gap sequential pattern (NWP) mining, where characters are divided into two types: weak and strong. This allows discovering frequent patterns more accurately by limiting the gap constraints to match only weak characters. To discover NWPs, we propose NMP-Miner which involves two key steps: support calculation and candidate pattern generation. To efficiently calculate the support of candidate patterns, depth-first search and backtracking strategies based on a simplified Nettree structure are adopted, which effectively reduce the time and space complexities of the algorithm. Moreover, a pattern join approach is applied to effectively reduce the number of candidate patterns. The experimental results show that NWP-Miner is more efficient than other competitive algorithms. More importantly, the case study of time series shows that NWP-Miner can effectively filter out noise patterns and discover more meaningful patterns. Algorithms and datasets can be downloaded fromhttps://github.com/wuc567/Pattern-Mining/tree/master/NWP-Miner.