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The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

To address the high complexity, poor real-time performance, and the prevalence of false positives and false negatives in current algorithms for detecting small-target pollutants on UAV-based building facades, this study proposes SDS-YOLOv8. The spatial pyramid pooling structure in the backbone is enhanced to improve feature representation. DySample is incorporated into the neck to adaptively adjust sampling points based on the image feature distribution. Additionally, the SCAM module is introduced to improve the memory of important information, and the loss function is further optimized. Experimental results demonstrate that the accuracy of the proposed algorithm is significantly improved, exhibiting strong generalization capability.

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective.

본 연구에서는 계절적인 요인과 급변하는 상품의 트렌드를 사전예측하기 위해 로지스틱 회귀 알고리즘을 이용 한 상품기획 예측 모형을 제안하고자 수행되었다. 먼저 웹크롤링을 이용하여 포털 사이트 및 온라인 마켓의 소비자의 비정형 데이터를 수집하고 정형 데이터 변환을 위한 전처리 작업을 통해 상품에 대한 의미 있는 정보를 분석하였다. 최종 수집된 11,200개의 데이터셋은 Logistic Regression을 이용하여 상품에 대한 소비자의 만족도, 빈도분석, 상품 에 대한 장점과 단점을 분석할 수 있었다. 분석 결과 소비자의 만족도는 92%이었으며, 빈도분석을 통해 상품에 대한 불량이슈를 확인할 수 있었다. 또한, 개발된 상품 기획 예측 프로그램에 대한 사용 만족도, 시스템 효율성, 시스템 효과 성 항목에 대한 분석결과에서도 만족도가 높게 나타났다. 특히, 불량이슈는 상품에 대한 현 문제를 신속히 인지하고 개선 전략을 수립하는데 필요한 정보를 제공한다는 점에서 매우 의미 있는 자료가 된다.

This study was conducted to propose a product planning prediction model using logistic regression algorithm to predict seasonal factors and rapidly changing product trends. First, we collected unstructured data of consumers in portal sites and online markets using web crawling, and analyzed meaningful information about products through preprocessing for transformation of standardized data. The datasets of 11,200 were analyzed by Logistic Regression to analyze consumer satisfaction, frequency analysis, and advantages and disadvantages of products. The result of analysis showed that the satisfaction of consumers was 92% and the defective issues of products were confirmed through frequency analysis. The results of analysis on the use satisfaction, system efficiency, and system effectiveness items of the developed product planning prediction program showed that the satisfaction was high. Defective issues are very meaningful data in that they provide information necessary for quickly recognizing the current problem of products and establishing improvement strategies.

In this study, AAHT was estimated using machine learning algorithms of ensemble techniques to improve the limitations of existing studies. Among the machine learning algorithms, a random forest algorithm was used, and the traffic volume estimation accuracy was analyzed as MAPE 20.7%. It was analyzed that the higher the traffic volume level, the higher the traffic volume estimation accuracy, and the traffic volume level of 3,000 vehicles/hour or more was analyzed to be 8% or less of MAPE. It was analyzed that the accuracy secured at the current level was very high compared to the existing model.

플라스틱 사출 제품은 다양한 가전제품과 하이테크 제품에 널리 사용되고 있다. 그러나 현재의 치열한 경쟁적 비즈니스 환경에서 플라스틱 사출 제품 제조업자들은 고객을 만족시키면서 경쟁력을 얻기 위하여 다른 경쟁자들보다 먼저 새로운 제품을 시장에 출시하고 신제품의 개발기간을 줄이기 위한 노력을 할 여유가 부족하다. 따라서 무한 경쟁의 시장에서 살아남기 위해서는 제조업자들은 시장 마켓 점유를 빠르게 올리는 것과 동시에 제품의 가격 경쟁력을 가져야 한다. 특징기반 모델의 구조는 현재 연구에서 3D 제작 도구로서 일반적으로 적용되고 있으며 신제품 개발 엔지니어들이 새로운 제품의 개념을 개발하는 데에도 널리 사용되고 있다. 본 연구에서는 특징기반 플라스틱 사출제품을 위한 유전자 알고리즘과 Support Vector Regression (SVR) 기반의 새로운 하이브리드 비용 평가 모델을 제안한다. 제안하는 하이브리드 모델은 기존의 플라스틱 사출제품의 비용평가절차와 계산을 위해 필요로 하는 변수들을 극적으로 간단하게 하고 줄일 수 있다. 사례연구에서는 제안하는 하이브리드 모델과 기존의 multilayer perceptron networks (MLP) 및 pure SVR과의 비교분석을 통하여 제안모델이 플라스틱 사출 제품의 개발단계에서의 비용평가문제를 해결하는데 효율성과 효과성이 있음을 입증한다.

PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.

물 사용량 예측은 최적의 용수 공급 운영 방안을 수립하고 전력 소비량 절감을 위하여 꼭 필요한 과정이라고 할 수 있다. 그러나 수용가 단위의 물 사용량은 용도, 사용자의 패턴, 날씨 등의 다양한 요인으로 인해 변화하는 비선형적 특성을 지니고 있다. 따라서 본 연구에서는 비선형적인 수용가 단위의 물 사용량을 예측하기 위하여 다양한 기법들을 연계한 KWD 프레임워크를 제안하고자 하였다. 즉, 먼저 개별 수용가 마다 용도에 따른 유사한 패턴을 파악하기 위해 K-means (K) 군집분석을 수행하였고, 잡음성분을 제거함으로써 핵심적인 주기패턴을 파악하기 위해 Wavelet (W) 방법을 적용하였다. 또한 비선형적 특성을 학습시키기 위해 Deep learning (D) 알고리즘을 적용하였다. 그리고 기존의 선형 시계열 모형인 ARMA 모형과 비교하여 KWD 프레임워크의 성능을 분석하였다. 그 결과 제안된 모형의 상관성은 92%, ARMA 모형은 약 39%로 KWD 프레임워크가 2배 이상의 성능을 가지는 것으로 분석되었다. 따라서 본 연구에서 제안한 방법을 활용할 경우 정확한 물 사용량 예측이 가능해질 것이며, 상황에 따른 최적의 공급 방안을 수립할 수 있을 것이다.

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

건설공사 프로젝트에서 인력 및 장비 등의 투입자원 증가는 공정간의 작업공간 간섭을 유발할 뿐만 아니라 프로젝트의 생산성과 품질에 영향을 미칠 수 있다. 이를 해결하기 위해 작업공간관련 연구가 수행되고 있으나, 단순히 공정들의 기하학적 위치만을 고려하여 작업공간을 검증하거나, 작업공간의 형상을 세부 작업별 분리된 형상이 아닌 일괄적 하나의 객체 형태로 생성하고 있다. 이러한 방식은 공정의 특성과 자원의 투입 시간에 따라 변화하는 작업공간의 크기를 반영하지 못하므로 합리적인 작업공간 충돌의 분석이 어렵게 된다. 이를 위해 본 연구에서는 공정과 자원의 특성에 적합한 작업공간의 형상과 크기를 생성할 수 있도록 작업공간의 3차원 모델 생성방법과 운용 시스템을 개발한다. 또한 이를 적용한 작업공간 간섭최적화 연계방안과 건설장비의 최적 이동경로 탐색 방법론을 개발하여 실무적 활용성을 검증한다.

The increase of input resources, such as labor and equipment, in a construction project causes workspace interference between activities and it influences on the productivity and quality of construction activities. To solve this problem, many studies related to the workspace interference have been performed, however they verified the workspace concerning with only the geometric location of activities or generated the shape of workspace by a whole object concept not separated units of detailed operations. It is difficult for project manager to reasonably analyze the workspace conflict, because the size of workspace cannot reflect the characteristics of an activity and input time of a resource. This paper presents a methodology that can generate three-dimensional models in order to optimize the workspace shape and size by considering with the characteristics of each activity and input time of each resource. The suggested method can be used for the active BIM system that optimizes the workspace conflict without additional construction duration and for the searching algorithm of optimized moving path for construction equipment.

본 연구는 한우의 도체형질들에 대한 유전적 변이를 분석방법에 따라 어떠한 차이가 있는지를 알아보고자 한우 후대검정자료를 이용하여 분석하였다. 분석에 이용된 도체성적 관련 자료는 가축개량사업소와 한우개량단지에서 1996년부터 2001년까지 태어난 후대검정우 1526두로부터 조사었다. 한우에 있어서 주요 개량형질인 육량과 육질에 영향하는 도체중, 도체율, 배최장근단면적, 등지방두께, 근내지방도를 대상으로 유전력과 유전상관을 추정하였다. 유전모수 추정에 있어서 REML 분석법과 Bayesian추론방법으로써 Gibbs Sampling 분석법을 사용하였는데 각각의 모수들에 대한 Gibbs Sampler는 100,000회 실시하였고 초기 1,000회는 모수의 사후분포에 대한 부정확성으로 간주하여 모수의 사후분포특성 규명에서 제외하였다. 각각의 형질들에 대한 유전변이는 이러한 두 가지 분석방법을 상호 비교 함으로써 최적의 통계분석방법을 모색하였다.도체형질에 대한 유전력 추정은 REML 방법을 통한 추정에서는 근내지방도가 0.51로 고도의 유전력을 보였고, 도체율이 0.25로 중도의 유전력이 추정되었다. Gibbs Sampling 방법을 통한 추정에서도 근내지방도가 0.54로 고도의 유전력을 도체율에서 0.25로 중도의 유전력을 보였다. REML 분석방법과 Gibbs Sampling 분석방법에서의 유전력은 다소 큰 차이는 보이지 않았으나, 대체로 Gibbs Sampling 방법으로 분석한 유전력 추정치가 높게 추정되었다.그리고, 유전상관분석에서는 REML 방법을 통한 분석에서 도체중과 배최장근단면적에서 0.651로 높은 정의 유전상관을 나타내었고, 배최장근단면적과 등지방두께에서 -0.139로 부의 유전상관을 나타내었다. Gibbs Sampling 방법에서는 도체중과 도체율, 배최장근단면적에서 각각 0.814, 0.695으로 높은 정의 상관을 나타내었고, 배최장근단면적과 등지방두께에서 -0.126으로 부의상관을 나타내었다. Gibbs Sampling 방법을 통한 분석에서 특정 형질간 유전상관이 높게 나타났으며, 다소 큰 차이를 보이지 않았다.REML 분석방법과 Bayesian Inference를 바탕으로 한 Gibbs Sampling 분석에서 모수 추정은 큰 차이를 보이지 않았다. 따라서 방대한 현장정보를 활용하여 보다 정확한 모수 추정을 수행하기 위해서는 분석모형에 대한 계수행렬의 역행렬 계산을 통한 REML 방법의 한계성을 극복할 수 있는 방법으로써 Gibbs Sampling 분석 방법이 가능할 것으로 사료된다.

The aims of this study were to estimate genetic parameters for carcass traits on Hanwoo(Korean Native Cattle) and to compare two different statistical algorithms for estimating genetic parameters. Data obtained from 1526 steers at Hanwoo Improvement Center and Hanwoo Improvement Complex Area from 1996 to 2001 were used for the analyses. The carcass traits considered in these studies were carcass weight, dressing percent, eye muscle area, backfat thickness, and marbling score. Estimated genetic parameters using EM-REML algorithm were compared to those by Bayesian inference via Gibbs Sampling to find out statistical properties. The estimated heritabilities of carcass traits by REML method were 0.28, 0.25, 0.35, 0.39 and 0.51, respectively and those by Gibbs Sampling method were 0.29, 0.25, 0.40, 0.42 and 0.54, respectively. This estimates were not significantly different, even though the estimated heritabilities by Gibbs Sampling method were higher than ones by REML method.Since the estimated statistics by REML method and Gibbs Sampling method were not significantly different in this study, it is inferred that both mothods could be efficiently applied for the analysis of carcass traits of cattle. However, further studies are demanded to define an optimal statistical method for handling large scale performance data.

본 연구에서는 코아분석자료와 다상 생산자료를 활용하여 공극률과 투과도 등의 저류층 특성을 규명하기 위해 3차원 저류 전산시뮬레이터와 실수코딩 유전자 알고리즘이 적용된 병렬처리 역산모델을 개발하였다. 개발된 모델을 3개의 층으로 구성된 불균질 저류층에 적용하여 다상 생산자료의 역산을 통해 공극률과 투과도 분포를 산출한 결과, 유전자 알고리즘은 43 세대만에 수렴하였으며 수렴 시 적합도는 0.00107로 양호한 것으로 나타났다. 산출된 최적 투과도 분포로부터 1번 층의 경우 좌측상부에서 우측하부에 걸쳐 저투과도 지역(LPZ)이 존재하고 2번과 3번 층의 경우 좌측과 우측하부에 각각 저투과도 지역이 존재하는 것이 확인되었다. 산출된 최적 공극률과 투과도 분포를 적용하여 저류 전산시뮬레이션을 통해 계산된 압력과 관측 압력을 비교한 결과, 생산정 1에서는 압력 상대오차의 합이 3.3%, 생산정 2는 5.4%로써 매우 양호한 일치를 보였다. 마지막으로, 3개의 노드로 구성된 클러스터에서 병렬처리 연산효율을 평가한 결과, 병렬처리 연산 시 일괄처리 연산에 비해 평균 2.9배 정도 연산시간이 단축됨을 알 수 있었다.

This paper presents the development of a parallelized inverse model equipped with real-coded genetic algorithm, and its application for the characterization of a heterogeneous reservoir with integration of the core analysis data and multi-phase production data. By utilizing the developed model in this study, we performed the inverse calculation with the observed production data obtained from three-layered reservoir system in order to estimate porosity and permeability distributions. As a result, number of generations for the convergence was 43 and the fitness was 0.00107. From the calculated distributions of porosity and permeability, it was confirmed to be low permeability zone located at left-top and right-bottom side in the layer 1. In the case of layer 2 and 3, its zone is located at left and right-bottom side, respectively. According to the pressure matching results, the sum of the relative errors at producer 1 and producer 2 were 3.3% and 5.4%, respectively, that is, it was found that the pressures observed at well almost identical to those calculated by the developed model. Finally, from the estimated calculating efficiency of parallel process with three-nodes cluster, it shows that parallel process was 2.9 times as fast as serial one on an average.

In order to provide a manufacturing system with efficiency and flexibility to cope with the changes in shop floor status, the integration of process planning and operations scheduling is required. In this paper, an integrated system of process planning and operations scheduling based on the concept of process net model and genetic algorithm is suggested. The process net model includes the alternative process plans. The integrated system is applied for prismatic parts.

The operation of agricultural reservoirs during flood seasons requires timely pre-release strategies to comply with restricted water levels and mitigate downstream risks. Effective decision-making depends on accurate inflow, storage, and release data; however, such data are often limited. Reinforcement Learning (RL) offers an advantage in such uncertain environments by learning optimal policies without predefined answers. In this study, we developed a reservoir operation simulation technique using the Proximal Policy Optimization (PPO) algorithm and trained the model. The trained agent demonstrated stable convergence, with reward values concentrated in high-performance ranges and minimal policy fluctuations in later episodes. Simulations applying a maximum allowable release of 2,475 m³/s-based on observed historical operations-resulted in a 1.2% (623 m³) lower total release and a 10% (247.5 m³/s) lower peak release compared to the actual case. Final water levels remained within flood-season restricted water level. In a more conservative scenario reflecting Yedang Reservoir's guideline (1,645 m³/s max release), the RL model achieved a 2.7% (1,439 m³) lower total release and a 33.5% (830 m³/s) reduction in peak release compared to the real operation. Although peak release duration increased from 7 to 15 hours, minimum and maximum water levels matched the observed case, and the final level (21.63 EL.m) was close to the target flood limit (21.5 EL.m). These results suggest that flood response could have been successfully managed even with stricter release constraints.

최근 클라우드 서비스가 주목받으면서 서비스 비용을 줄이기 위한 다양한 연구들이 진행되고 있다. 특히, 이기종 장치를 통해 클라우드 서비스의 상호호환성을 유지할 수 있는 클라우드 관리 방안 기술 연구가 필요하다. 본 논문에서는 클라우드 서비스가 무중단 없이 상호 호환성을 지속할 수 있는 작업 지향 분할 모델을 제안한다. 제안 모델은 클라우드 간 작업 정보 연계가 지속될 수 있도록  개의 작업을 선형 연결하는 것이 목적이다. 제안 모델은 N개의 작업 정보 간 정보 연계성이 높을수록 클라우드 작업 간 관계가 명확하고 작업 정보 간 강한 관계를 유지하기 위해서 작업 정보의 필수 연결을 반복적으로 필터링함으로써 작업의 매개변수 크기를 줄일수 있어서 안 정적인 작업 공유 관계를 작업 전반에 걸쳐 적용할 수 있다. 성능 평가 결과, 상관관계 정보를 이용하여 작업 지향 분할을 반복적으로 필터링함으로써 정확도와 효율성은 각각 평균 4% 향상시켰으며, 클라우드 작업 간 연계 처리의 지연시간은 평균 17% 향상시켰다.

As cloud services have recently attracted attention, various studies are being conducted to reduce service costs. In particular, there is a need for research on technology for cloud management plans that can maintain the interoperability of cloud services through heterogeneous devices. In this paper, we propose a task-oriented segmentation model in which cloud services can continue to be compatible without interruption. The proposed model aims to connect individual tasks linearly so that the linkage of task information between clouds can be continued. The proposed model can apply a stable task sharing relationship throughout the task by repeatedly filtering the essential connection of task information in order to maintain a strong relationship between task information and a clearer relationship between task information. As a result of performance evaluation, by repeatedly filtering the task-oriented segmentation using correlation information, accuracy and efficiency improved by 4% on average, respectively, and the delay time of linkage processing between cloud tasks improved by 17% on average.