• Title/Summary/Keyword: Optimum Algorithm

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Robust Object Tracking based on Weight Control in Particle Swarm Optimization (파티클 스웜 최적화에서의 가중치 조절에 기반한 강인한 객체 추적 알고리즘)

  • Kang, Kyuchang;Bae, Changseok;Chung, Yuk Ying
    • The Journal of Korean Institute of Next Generation Computing
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    • v.14 no.6
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    • pp.15-29
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    • 2018
  • This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.

A Study on Suction Pump Impeller Form Optimization for Ballast Water Treatment System (선박평형수 처리용 흡입 펌프 임펠러 형상 최적화 연구)

  • Lee, Sang-Beom
    • Journal of the Korean Society of Industry Convergence
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    • v.25 no.1
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    • pp.121-129
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    • 2022
  • With the recent increase in international trade volume the trade volume through ships is also continuously increasing. The treatment of ballast water goes through the following five steps, samples are taken and analyzed at each step, and samples are obtained using a suction pump. These suction pumps have low efficiency and thus need to be improved. In this study, it is to optimize the form of the impeller which affects directly improvements of performance to determine the capacity of suction pump and to fulfill the purpose of this research. To do it, we have carried out parametric design as an input variable, geometric form for the impeller. By conducting the flow analysis for the optimum form, it has confirmed the value of improved results and achieved the purpose to study in this paper. It has selected the necessary parameter for optimizing the form of the pump impeller and analyzed the property using experiment design. And it can reduce the factor of parameter for local optimization from findings to analyze the property of form parameter. To perform MOGA(Multi-Objective Genetic Algorithm) it has generated response surface using parameters for local optimization and conducts the optimization using multi-objective genetic algorithm. with created experiment cases, it has performed the computational fluid dynamics with model applying the optimized impeller form and checked that the capacity of the pump was improved. It could verify the validity concerning the improvement of pump efficiency, via optimization of pump impeller form which is suggested in this study.

Reinforced concrete structures with damped seismic buckling-restrained bracing optimization using multi-objective evolutionary niching ChOA

  • Shouhua Liu;Jianfeng Li;Hamidreza Aghajanirefah;Mohammad Khishe;Abbas Khishe;Arsalan Mahmoodzadeh;Banar Fareed Ibrahim
    • Steel and Composite Structures
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    • v.47 no.2
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    • pp.147-165
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    • 2023
  • The paper contrasts conventional seismic design with a design that incorporates buckling-restrained bracing in three-dimensional reinforced concrete buildings (BRBs). The suboptimal structures may be found using the multi-objective chimp optimization algorithm (MEN-ChOA). Given the constraints and dimensions, ChOA suffers from a slow convergence rate and tends to become stuck in local minima. Therefore, the ChOA is improved by niching and evolutionary operators to overcome the aforementioned problems. In addition, a new technique is presented to compute seismic and dead loads that include all of a structure's parts in an algorithm for three-dimensional frame design rather than only using structural elements. The performance of the constructed multi-objective model is evaluated using 12 standard multi-objective benchmarks proposed in IEEE congress on evolutionary computation. Second, MEN-ChOA is employed in constructing several reinforced concrete structures by the Mexico City building code. The variety of Pareto optimum fronts of these criteria enables a thorough performance examination of the MEN-ChOA. The results also reveal that BRB frames with comparable structural performance to conventional moment-resistant reinforced concrete framed buildings are more cost-effective when reinforced concrete building height rises. Structural performance and building cost may improve by using a nature-inspired strategy based on MEN-ChOA in structural design work.

Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles

  • Mahzad Esmaeili-Falak;Reza Sarkhani Benemaran
    • Geomechanics and Engineering
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    • v.32 no.6
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    • pp.583-600
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    • 2023
  • The resilient modulus (MR) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The MR determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the MR of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviatoric stress (σd). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ3, and σd is recognized as the most suitable model, with R2 and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in MR prediction procedure.

Design of Low-Complexity FSM based on Viterbi for Optimum Bluetooth GFSK Signal Receiver (최적의 Bluetooth GFSK 신호 수신을 위한 Viterbi 기반 저복잡도 FSM 설계)

  • Kwon, Taek-Won;Lee, Kyu-Man
    • Journal of Digital Convergence
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    • v.20 no.1
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    • pp.185-190
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    • 2022
  • Bluetooth is a common wireless technology that is widely used as a connection medium between various consumer electronic devices. The Bluetooth receiver usually adopts a Viterbi algorithm to improve signal-to-noise ratio performance, but requires complex hardware and calculations for continuous search and estimation for the irrational modulation indexes at the transmission. This paper proposes a non-coherent maximum estimation based 8-State Viterbi FSM to solve these complexity problems. The proposed optimal Viterbi FSM can detect Gaussian frequency-shfit keying symbol without any prior information and estimation for the modulation indexes. The HV1/HV2 packets are used for the estimation of the proposed algorithm and the simulation results have shown performance improvements with about 2dB for 10-3 BER compared to other ideal approaches such as decision direct method.

Predicting concrete's compressive strength through three hybrid swarm intelligent methods

  • Zhang Chengquan;Hamidreza Aghajanirefah;Kseniya I. Zykova;Hossein Moayedi;Binh Nguyen Le
    • Computers and Concrete
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    • v.32 no.2
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    • pp.149-163
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    • 2023
  • One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R2). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R2=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

Development of Travel Time Estimation Algorithm for National Highway by using Self-Organizing Neural Networks (자기조직형 신경망 이론을 이용한 국도 통행시간 추정 알고리즘)

  • Do, Myungsik;Bae, Hyunesook
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.28 no.3D
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    • pp.307-315
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    • 2008
  • The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.

An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand

  • Danial Jahed Armaghani;Haleh Rasekh;Panagiotis G. Asteris
    • Computers and Concrete
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    • v.33 no.1
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    • pp.77-90
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    • 2024
  • Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

A Study on the Effect of On-Dock System in Container Terminals - Focusing on GwangYang Port - (컨테이너터미널에서 On-Dock 시스템 효과분석에 관한 연구 - 광양항을 중심으로 -)

  • Cha, Sang-Hyun;Noh, Chang-Kyun
    • Journal of Navigation and Port Research
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    • v.39 no.1
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    • pp.45-53
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    • 2015
  • These days Container Terminals are focusing on increasing the quantity of containers and shipping lines choose Terminals by referring to the key elements of a terminal to perform the overall operation the fastest such as the location of the terminal, discharging ability, keeping environment, and other elements related to shipping in general. Container terminal is able to offer On-Dock service has become an important factor for shipping lines to choose that terminal. In this paper, we propose an algorithm for On-Dock system work algorithm, the algorithm Empty container exports, Full Container algorithm and The aim of our study focus on both container's gate out time and search for the effective terminal operation which is using the general On-Dock system through several algorithm like container batch priority, gate in and out job priority and empty container yard equipment allocation rule based on the automatic allocation method and manual allocation scheme for container. Gathering these information, it gives the priority and yard location of gate-out containers to control. That is, by selecting an optimum algorithm container, container terminals Empty reduces the container taken out time, it is possible to minimize unnecessary re-handling of the yard container can be enhanced with respect to the efficiency of the equipment. Operations and operating results of the Non On-Dock and On-Dock system is operated by the out work operations (scenarios) forms that are operating in the real Gwangyang Container Terminal derived results. Gwangyang Container terminal and apply the On-Dock system, Non On-Dock can be taken out this time, about 5 minutes more quickly when applying the system. when managing export orders for berths where On-Dock service is needed, ball containers are allocated and for import cargoes, D/O is managed and after carryout, return management, container damage, cleaning, fixing and controlling services are supported hence the berth service can be strengthened and container terminal business can grow.

A Study on Optimum Control of Marine Traffic -In the Domain of Control Sector- (해상 교통량의 효율적 관리 방안에 관하여 -(1) 교통 관제 해역의 경우-)

  • 윤명오;이철영
    • Journal of the Korean Institute of Navigation
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    • v.15 no.2
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    • pp.39-47
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    • 1991
  • As per the rapid development of world economics the marine traffic volume was increased accordingly and caused frequent disasters in human lives and natural environment in the consequence of accidents. As the result of the above they started to establish Vessel Traffic System(VTS) and separation scheme in waterway from 1960' to prevent the marin traffic accident but the problem of safety at sea appears now as neither fully defined nor sufficiently analysed. At the present, the dominant factor in establishing the strategy of marine traffic has been safety of navigation concerning only with the ship, but the risk of society derives almost wholly from the nature of cargo. To measure the degree of danger for each ship there is suggested concept of safety factor numbers denoting the level of latent danger in connection with ship and her cargo. In this paper, where the strategy of VTS is put on controlling density of safety factor for control area. it suggested algorithms how to assign the vessels and also to get optimal sequence of vessels located to a sector in the sense of minimizing the passage delay. For the formulation of problem, min max and 0-1 programming methods are applied and developed heuristic algorithm is presented with numerical example to improve the efficiency of calculation.

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