• Geomechanics and Engineering
• /
• v.34 no.3
• /
• pp.329-339
• /
• 2023

The objective of this study is to develop a Stability Evaluation System for retaining walls to assess their safety in real-time during excavation. A ground investigation is typically conducted before construction to gather information about the soil properties and predict wall stability. However, these properties may not accurately reflect the actual ground being excavated. To address this issue, the study employed a differential evolution algorithm to estimate the soil parameters of the actual ground. The estimated results were then used as input for an artificial neural network to evaluate the stability of the retaining walls. The study achieved an average accuracy of over 90% in predicting differential settlement, wall displacement, anchor force, and structural stability of the retaining walls. If implemented at actual excavation sites, this approach would enable real-time prediction of wall stability and facilitate effective safety management. Overall, the developed Stability Evaluation System offers a promising solution for ensuring the stability of retaining walls during construction. By incorporating real-time soil parameter analysis, it enhances the accuracy of stability predictions and contributes to proactive safety management in excavation projects.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.4
• /
• pp.15-20
• /
• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• Journal of Drive and Control
• /
• v.20 no.4
• /
• pp.17-26
• /
• 2023

A forklift is an industrial vehicle that lifts or transports heavy objects using a hydraulically operated fork, and is equipped with an automatic transmission for the convenience of repetitive transportation, loading, and unloading work. The Transmission Control Unit (TCU) is a key component in charge of the shift control function of an automatic transmission. It consists of an electric circuit with an input/output signal interface function and firmware running on a microcontroller. To develop TCU firmware, the development process of shifting algorithm design, firmware programming, verification test, and performance improvement must be repeated. A simulator is a device that simulates a mechanical system having dynamic characteristics in real time and simulates various sensor signals installed in the system. The embedded transmission simulator is a simulator that is embedded in the TCU firmware. information related to the mechanical system that is necessary for TCU normal operation. In this study, an embedded transmission simulator applied to the originally developed forklift TCU firmware was designed and used to verify various forklift shift control algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.2
• /
• pp.199-207
• /
• 2001

In this paper, we suggest an edge-based surface segmentation algorithm of 3D image using curvature. For the first, in this proposed method, we approximate 3D depth data to second order curves by each scan line and decide splitting points of 3D edges by curvature of the approximated curves. And finally make a group as 3D surface with the region of input image by the 3D edges. In the conventional algorithms, there are some difficulties in detecting 3D edge with the separated processes for the jump edge and the crease edge and especially, in deciding the ambiguous discontinuity of surface directions about the crease edge. The proposed algorithm decides curvature discontinuity using curvature which is simply calculated by a geometrical approximation. Furthermore, the algorithm has a cooperated process to calculate the jump and crease edges. The results of computer simulations with several 3D images show that the proposed method yields better performance as comparing with the conventional methods.

• Journal of KIISE:Computer Systems and Theory
• /
• v.30 no.3_4
• /
• pp.168-185
• /
• 2003

This paper presents a progressive algorithm that not only can narrow down the search domain in the course of face identification but also can fast reconstruct various 3D objects from a sketch drawing. The sketch drawing, edge-vertex graph without hidden line removal, which serves as input for reconstruction process, is obtained from an inaccurate freehand sketch of a 3D wireframe object. The algorithm is executed in two stages. In the face identification stage, we generate and classify potential faces into implausible, basis, and minimal faces by using geometrical and topological constraints to reduce search space. The proposed algorithm searches the space of minimal faces only to identify actual faces of an object fast. In the object reconstruction stage, we progressively calculate a 3D structure by optimizing the coordinates of vertices of an object according to the sketch order of faces. The progressive method reconstructs the most plausible 3D object quickly by applying 3D constraints that are derived from the relationship between the object and the sketch drawing in the optimization process. Furthermore, it allows the designer to change viewpoint during sketching. The progressive reconstruction algorithm is discussed, and examples from a working implementation are given.

• Journal of KIISE:Computer Systems and Theory
• /
• v.27 no.3
• /
• pp.255-263
• /
• 2000

A multistage interconnection network is a suitable class of interconnection architecture for constructing large-scale multicomputers. Broadcast and multicast communication are fundamental in supporting collective communication operations such as reduction and barrier synchronization. In this paper, we propose a new multicast technique in wormhole-switched bidirectional multistage banyan networks for constructing large-scale multicomputers. To efficiently support broadcast and multicast with simple additional hardware without deadlock, we propose a two-phase multicast algorithm which takes only two transmissions to perform a broadcast and a multicast to an arbitrary number of desired destinations. We encode a header as a cube and adopt the most upper input link first scheme with periodic priority rotation as arbitration mechanism on contented output links. We coalesce the desired destination addresses into multiple number of cubes. And then, we evaluate the performance of the proposed algorithm by simulation. The proposed two-phase multicast algorithm makes a significant improvement in terms of latency. It is noticeable that the two-phase algorithm keeps broadcast latency as efficient as the multicast latency of fanout 2^m where m is the minimum integer satisfying $2^m{\geq} {\sqrt{N}}$ ( N is a network size).

• Journal of KIISE
• /
• v.43 no.2
• /
• pp.229-236
• /
• 2016

Theta join is one of the essential and important types of queries in database systems. As the amount of data needs to be processed increases, processing theta joins with a single machine becomes impractical. Therefore, theta join algorithms using distributed computing frameworks have been studied widely. Although one of the state-of-the-art theta-join algorithms uses M-Bucket-I heuristic, it is hard to use since running time of M-Bucket-I heuristic, which computes a mapping from a record to a reducer (i.e., reducer mapping), is O(n) where n is the size of input data. In this paper, we propose MBI-I algorithm which reduces the running time of M-Bucket-I heuristic to $O(r_{max}log\;n)$ and gives the same result as M-Bucket-I heuristic does. We also conducted several experiments to show algorithm and confirmed that our algorithm can improve the performance of a theta join by 10%.

• Smart Structures and Systems
• /
• v.22 no.4
• /
• pp.413-424
• /
• 2018

The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.37 no.5
• /
• pp.19-28
• /
• 2000

It is not so easy to control the wheeled mobile robot because of some causes like non-holonomic constraints. To overcome these problems, a controller that PD system is combined with fuzzy process is composed of several steps that have each separate algorithm and niche search algorithm and immune algorithm is applied partly. Output term set is changed by search that is performed to get optimal elements and then the rule base is also reformed. The fitness for the altered system is estimated and the surplus elements are removed. After the adjustment of output term set and rule base is finished, input and output membership functions is tuned.

• Journal of KIISE:Computer Systems and Theory
• /
• v.29 no.10
• /
• pp.550-560
• /
• 2002

External sort on cluster computers requires not only fast internal sorting computation but also careful scheduling of disk input and output and interprocessor communication through networks. This is because the overall time for the execution is determined by reflecting the times for all the jobs involved, and the portion for interprocessor communication and disk I/O operations is significant. In this paper, we improve the sorting performance (sorting throughput) on a cluster of PCs with a low-speed network by developing a new algorithm that enables even distribution of load among processors, and optimizes the disk read and write operations with other computation/communication activities during the sort. Experimental results support the effectiveness of the algorithm. We observe the algorithm reduces the sort time by 45% compared to the previous NOW-sort[1], and provides more scalability in the expansion of the computing nodes of the cluster as well.