Earticle

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

본 연구의 목적은 한국의료패널 2012년 자료를 이 용하여 고지혈증 유병에 영향을 미치는 변수를 확인 하고 이를 예측하는 분류모형을 개발하는데 있다. 분류모형에 투입되는 변수 선정을 위해 로지스틱 회 귀분석, 의사결정트리 c4.5, 유전자 알고리즘을 각 각 적용하여 선정하였다. 고지혈증 유병을 예측하기 위해 SVM과 meta learning 알고리즘을 이용하였다. 먼저 SVM의 경우 변수를 6개만 투입하였을 때 정확도가 가장 높았으 며, meta learning의 경우 메타분류기를 SVM으로 하 여 변수 6개를 투입한 경우가 가장 높았다. 본 연구는 기존 연구에서 많이 다루지 않은 고지 혈증을 예측하는 모형을 개발했다는 점과 여러 변수 기법을 적용하여 모델 정확도를 기여하였다. 그러나 메타러닝 성과가 크게 향상되지 않은 점은 본 연구 의 한계이자 추후 관련 연구에서 보완되어야 할 부분이다.

NIST PQC 표준화 Round 3에 제시된 PKE/KEM 알고리즘인 SABER 알고리즘은 격자 기반 문제 중 Module-LWR 문제를 기반으로 하는 알고리즘으로 Meta-PKE 구조로 되어 있다. 이때, 암호화 과정에서 사용되는 비밀 정보를 임시 키라고 부를 것이며 본 논문에서는 Meta-PKE 구조를 활용한 임시 키 재사용 공격에 대해 설명한다. NIST에서 요구하는 보안 강도 5, 3, 1을 만족하는 각각의 파라미터에 대해 4, 6, 6번의 쿼리를 사용하여 공격한 선행 연구에 대해 자세한 분석과 함께 이를 향상하여 3, 4, 4번의 쿼리만 사용하는 방법을 제시한다. 그리고 추가로 한 번의 쿼리를 통해 임시 키를 복원하는 계산 복잡도를 n차 격자 위에서 각각의 파라미터에 대해 전수조사 복잡도인 2^7.91×n, 2^10.51×n, 2^12.22×n에서 2^4.91×n, 2^6.5×n, 2^6.22×n으로 감소시키는 방법을 소개하며 그에 대한 결과 및 한계점을 제시한다.

The SABER algorithm, a PKE/KEM algorithm presented in NIST PQC Standardization Round 3, is an algorithm based on the Module-LWR problem among lattice-based problems and has a Meta-PKE structure. At this time, the secret information used in the encryption process is called a ephemeral key, and in this paper, the ephemeral key reuse attack using the Meta-PKE structure is described. For each parameter satisfying the security strengths required by NIST, we present a detailed analysis of the previous studies attacked using 4, 6, and 6 queries, and improve them, using only 3, 4, and 4 queries. In addition, we introduce how to reduce the computational complexity of recovering ephemeral keys with a single query from the brute-force complexity on the n-dimension lattice, 2^7.91×n, 2^10.51×n, 2^12.22×n to 2^4.91×n, 2^6.5×n, 2^6.22×n, for each parameter, and present the results and limitations.

An Intrusion Detection System is an application which observes movements or action happen on the network and determine it for any kind of harmful activity that can disturb computer security policy. With progress of increase the usage rate of the internet, there is a widely increase in the number of internet attacks as well, so contests arise towards the network security due to the arrival of new approaches of attacks. To classify these attacks, a new hybrid method with the help of data mining based on decision tree C4.5 and Meta algorithm is planned. This method gives a classifier which expands the whole accuracy of detection. Many data mining techniques have been settled for detecting intrusion. For recognition of anomalies a hybrid technique based on decision tree C4.5 with Meta algorithm is offered that provides better accuracy and reduces the problem of high false alarm ratio. The assessment of the given approach is made with other data mining techniques. With this given approach detection rate is improved significantly. KDD Cup 1999 dataset use for experimental work.

Multiple Unmanned Underwater Vehicles Is a typical combinatorial optimization problem, to achieve multiple AUV, coordinated, collaborative tasks to complete complex jobs subsea. Through analyzing the ant colony optimization algorithm, the paper proposed An Meta-heuristic ant colony optimization algorithm the Implementation to solve the multi AUVs to achieve the task allocation problem, and had simulation test based on the consolidated analyze the advantages of multiple unmanned underwater vehicle .results show that the ant colony optimization algorithms in solving multi-task allocation problem of multiple unmanned underwater vehicle showed a good performance.

Although meta-heuristic optimization algorithms have been used to solve many optimization problems, they still suffer from two main difficulties: What are the best parameters for a particular problem? How do we escape from the local optima? In this paper, a new, efficient meta-heuristic optimization algorithm inspired by wild dog packs is proposed. The main idea involves using three self-competitive parameters that are similar to the smell strength. The parameters are used to control the movement of the alpha dogs and, consequently, the movement of the whole pack. The rest of the pack is used to explore the neighboring area of the alpha dog, while the hoo procedure is used to escape from the local optima. The suggested method is applied to several unimodal and multimodal benchmark problems and is compared to five modern meta-heuristic algorithms. The experimental results show that the new algorithm outperforms other peer algorithms.

A clustering algorithm is proposed in this paper, which is based on discussion of multi-agent meta-heuristic architecture of the ant colony optimization algorithm. The multi-agent architecture of ant colony optimization meta-heuristic includes three levels. Level-0 agents build solutions, level-l agents improve solutions and level-2 agents update pheromone matrix. The updated pheromone then provides feedback information for the next iteration of solution construction. Mutation probability p and pheromone resistance ρ are the adaptive parameters, which can be adjusted automatically during the evolution progress. With the adaptive variable, the algorithm can solve the contradiction between convergence speed and precocity and stagnation. The algorithm has been tested and compared with the clustering algorithm based on Genetic and Simulate annealing. Experimental results show that the proposed algorithm is more effective, and the clustering quality and efficiency are promising.

본 연구는 만성질환 중의 하나인 고지혈증 유병을 예측하는 분류모형을 개발하고자 한다. 이를 위해 SVM과 meta-learning 알고리즘을 이용하여 성과를 비교하였다. 또한 각 알고리즘에서 성과를 향상시키기 위해 변수선정 방법을 통해 유의한 변수만을 선정하여 투입하여 분석하였고 이 결과 역시 각각 성과를 비교하였다. 본 연구목적을 달성하기 위해 한국의료패널 2012년 자료를 이용하였고, 변수 선정을 위해 세 가지 방법을 사용하였다. 먼저 단계적 회귀분석(stepwise regression)을 실시하였다. 둘째, 의사결정나무(decision tree) 알고리즘을 사용하였다. 마지막으로 유전자 알고리즘을 사용하여 변수를 선정하였다. 한편, 이렇게 선정된 변수를 기준으로 SVM, meta-learning 알고리즘 등을 이용하여 고지혈증 환자분류 예측모형을 비교하였고, TP rate, precision 등을 사용하여 분류 성과를 비교분석하였다. 이에 대한 분석결과는 다음과 같다. 첫째, 모든 변수를 투입하여 분류한 결과 SVM의 정확도는 88.4%, 인공신경망의 정확도는 86.7%로 SVM의 정확도가 좀 더 높았다. 둘째, stepwise를 통해 선정된 변수만을 투입하여 분류한 결과 전체 변수를 투입하였을 때보다 각각 정확도가 약간 높았다. 셋째, 의사결정나무에 의해 선정된 변수 3개만을 투입하였을 때 인공신경망의 정확도가 SVM보다 높았다. 유전자 알고리즘을 통해 선정된 변수를 투입하여 분류한 결과 SVM은 88.5%, 인공신경망은 87.9%의 분류 정확도를 보여 주었다. 마지막으로, 본 연구에서 제안하는 meta-learning 알고리즘인 스태킹(stacking)을 적용한 결과로서, SVM과 MLP의 예측결과를 메타 분류기인 SVM의 입력변수로 사용하여 예측한 결과, 고지혈증 분류 정확도가 meta-learning 알고리즘 중에서는 가장 높은 것으로 나타났다.

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner wa

The core power control is an important issue for the study of dynamic characteristics in China initiative accelerator driven subcritical system (CiADS), which has direct impact on the control strategy and safety analysis process. The CiADS is an experimental facility that is only controlled by the proton beam intensity without considering the control rods in the current engineering design stage. In order to get the optimized operation scheme with the stable and reliable features, the variation of beam intensity using the continuous and periodic control approaches has been adopted, and the change of collimator and the adjusting of duty ratio have been proposed in the power control process. Considering the neutronics and the thermal-hydraulics characteristics in CiADS, the physical model for the core power control has been established by means of the point reactor kinetics method and the lumped parameter method. Moreover, the multi-inputs single-output (MISO) logical structure for the power control process has been constructed using proportional integral derivative (PID) controller, and the meta-heuristic algorithm has been employed to obtain the global optimized parameters for the stable running mode without producing large perturbations. Finally, the verification and validation of the control method have been tested based on the reference scenarios in considering the disturbances of spallation neutron source and inlet temperature respectively, where all the numerical results reveal that the optimization method has satisfactory performance in the CiADS core power control scenarios.

This paper presents a study on optimizing the size and geometry of 3D frames using an improved meta-heuristic algorithm. 3D frames are integral parts of various engineering designs and require efficient optimization techniques to improve their performance and minimize material consumption. The proposed meta-heuristic algorithm builds on existing methods and incorporates novel improvements to increase search efficiency and solution quality. Through rigorous testing on benchmark problems, the algorithm demonstrates superior performance in achieving optimal design solutions that ensure structural integrity while reducing overall weight and cost. The results exhibit the potential of the improved algorithm to advance the field of structural optimization.

In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.

The novel permanent magnetic actuator (PMA) and its optimal design method were proposed in this paper. The proposed PMA is referred to as the separated permanent magnetic actuator (SPMA) and significantly superior in terms of its cost and performance level over a conventional PMA. The proposed optimal design method uses the evolutionary strategy algorithm (ESA), the kriging meta-model (KMM), and the multi-step optimization. The KMM can compensate the slow convergence of the ESA. The proposed multi-step optimization process, which separates the independent variables, can decrease time and increase the reliability for the optimal design result. Briefly, the optimization time and the poor reliability of the optimum are mitigated by the proposed optimization method.

Usability evaluation(UE) experiments are conducted to provide UE practitioners with guidelines for better outcomes. In UE research, significant quantities of empirical results have been accumulated in the past decades. While those results have been anticipated to integrate for producing generalized guidelines, traditional meta-analysis has limitations to combine UE empirical results that often show considerable heterogeneity. In this study, a new data mining method called weighted bi-clustering(WBC) was proposed to partition heterogeneous studies into homogeneous subsets. We applied the WBC to UE empirical results and identified two homogeneous subsets, each of which can be meta-analyzed. In addition, interactions between experimental conditions and UE methods were hypothesized based on the resulting partition and some interactions were confirmed via statistical tests.

Due to their natural and social revelation, also their ease and flexibility, human collective behavior and teamwork sports are inspired to introduce optimization algorithms to solve various engineering and scientific problems. Nowadays, meta-heuristic algorithms are becoming some striking methods for solving complex real-world problems. In that respect in the present study, the authors propose a novel meta-innovative algorithm based on soccer teamwork sport, suitable for optimization problems. The method may be referred to as the Soccer League Optimization-based Championship Algorithm, inspired by the Soccer league. This method consists of two main steps, including: 1. Qualifying competitions and 2. Main competitions. To evaluate the robustness of the proposed method, six different benchmark mathematical functions, and two engineering design problem was performed for optimization to assess its efficiency in achieving optimal solutions to various problems. The results show that the proposed algorithm may well explore better performance than some well-known algorithms in various aspects such as consistency through runs and a fast and steep convergence in all problems towards the global optimal fitness value.

다양한 교육 분야에 기억법을 도입함에 있어서 모바일 환경에서 동작하는 프로그램은 접근성을 높이고 교육의 효과를 높이는 목적으로 활용할 수 있다. 의미가 부여된 단어를 기억하는 것은 연도와 같은 숫자 정보를 기억하는 일에 비하면 훨씬 쉽다. 교육적 효과를 높이고자 하는 입장에서 어플리케이션의 도움을 받아 보완되어야 할 부분이라 생각되는 부분은 수치 정보라 할 수 있다. 기존의 수치 기억법과 연관된 대부분의 연구는 숫자를 이미지화해 기억에 도움을 주는 형태에 초점이 맞춰져 있다. 모바일 환경에서 기억법 기반 메타레코드 알고리즘 논문에서는 이전 연구에서 개발한 어플리케이션은 입력한 수치 정보에 대해 사용자가 실수할 수 있는 부분을 발견하고 단순 수정하는 방법에 그쳐 이를 보완하고자 한다. 본 연구에서는 개인화된 로그 정보를 기반으로 메타데이터를 구성하여 실수를 수정함으로써 기억률을 높이고자 한다. 이를 위해 모바일 환경에 적합한 어플리케이션을 개발하고 메타레코드 데이터의 구조를 제안하고 메타레코드 적용 알고리즘을 구현하고 평가한다.

In introducing memory methods in various educational fields, programs in a mobile environment can be used for the purpose of increasing accessibility and enhancing the effectiveness of education. It is much easier to remember words with meaning than to remember numerical information such as years. From the standpoint of increasing the educational effect, the part that needs to be supplemented with the help of the application can be said to be numerical information. Most studies related to conventional numerical memory have focused on the form that helps memory by imaging numbers. In the paper on memory-based meta-record algorithms in the mobile environment, the application developed in the previous study attempts to supplement this by discovering and simply modifying the user's mistakes in the entered numerical information. In this study, we aim to increase the memory rate by constructing metadata based on personalized log information and correcting mistakes. To do this, applications suitable for the mobile environment are developed, a structure of meta-record data is proposed, and meta-record application algorithms are implemented and evaluated.

Droplet impingement on solid surfaces is pivotal for a range of spray and heat transfer processes. This study aims to optimize the cooling performance of single droplet impingement on heated textured surfaces. We focused on maximizing the cooling effectiveness or the total contact area at the droplet maximum spread. For efficient estimation of the optimal values of the unknown variables, we introduced an enhanced Genetic Algorithm (GA) and Particle swarm optimization algorithm (PSO). These novel algorithms incorporate its developed theoretical backgrounds to compare proper optimized results. The comparison, considering the peak values of objective functions, computation durations, and the count of penalty particles, confirmed that PSO method offers swifter and more efficient searches, compared to GA algorithm, contributing finding the effective way for the spray and droplet impingement process.

소프트웨어 테스트는 시스템의 신뢰도를 판단하는 중요한 작업이지만, 많은 노력과 비용이 요구된다. 모델 기반 테스트는 시스템 요구사항을 정형적으로 표현한 모델로부터 테스트 설계를 자동화함으로써 이러한 비용을 줄이기 위한 방안으로 제안되었다. 모델의 각 경로마다 입력값을 생성하여 테스트를 수행하는데, 이 때, 적절한 입력 값을 찾기 위해 메타-휴리스틱 기법을 사용한다. 본 논문은 슬라이싱 기법과 우선순위 정책을 적용한 테스트 데이터 자동 생성 기법을 제안하며, 목적 경로와 관련이 없는 변수를 제외하여 불필요한 계산을 억제한다. 실험을 통해 기존의 기법보다 효과적으로 테스트 데이터를 생성함을 보인다.

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.

컨테이너 터미널에서 효율적인 적하작업 계획을 자동으로 생성하는 알고리즘을 연구하였다. 실제 터미널에서 계획자들이 적하작업 계획시에 고려하는 제약소건 및 효율적인 계획을 위한 고려사항을 조사하였다. 이를 바탕으로 1단계에서는 개미시스템(ant system)이라는 인공지능기법을 적용하여 제약조건을 만족시키면서 원활한 적하작업이 진행될 수 있도록 컨테이너 크레인과 트랜스퍼 크레인의 이동순서와 위치를 결정하고, 2단계에서는 1단계에서의 결과를 바탕으로 빔탐색법(beam search)을 사용하여 컨테이너 개개의 작업순서를 결정하는 알고리즘을 개발하였다. 또한 개발된 시스템의 성능을 검증하기 위하여 최근의 대형선반에 대한 실제 현장자료를 바탕으로 실험을 수행하였다.

본 논문에서는 퍼지 제어기의 설계를 위한 다중 돌연변이 연산자를 갖는 Niche Meta 유전 알고리즘을 제안한다. 제안된 알고리즘에서 유전자는 유전 알고리즘에 사용되는 교배율이나 돌연변이율과 같은 구조 매개변수와 퍼지 제어기의 입, 출력 소속함수를 나타내는 매개변수로 구성된다. 제안된 알고리즘은 부개체군들에 대해 퍼지 제어기의 소속함수의 매개변수를 최적화시키는 지역적 탐색을 수행하면서 전체 개체군에 대해서 최적의 구조 매개변수에 대한 전역적인 탐색을 수행한다. 다중 돌연변이 연산자는 지역적 진화의 결과에 따라 진화에 가장 적합한 돌연변이 방법으로 선택된다. 제안된 알고리즘의 효율성을 입증하기 위해 2 자유도를 구륜이동 로봇에 대한 모의 실험을 수행한다.

본 연구에서 개발된 Self-Adaptive Vision Correction Algorithm (SAVCA)은 광학적 특성을 모방하여 개발된 Vision Correction Algorithm (VCA)의 총 6개의 매개변수 중 자가 적응형태로 구축된 Division Rate 1 (DR1) 및 Division Rate 2 (DR2)를 제외한 Modulation Transfer Function Rate (MR), Astigmatic Rate (AR), Astigmatic Factor (AF) 및 Compression Factor (CF) 등 4개의 매개변수를 변경하여 사용성을 증대시키기 위해 제시되었다. 개발된 SAVCA의 검증을 위해 기존 VCA를 적용하였던 2개 변수를 갖는 수학 문제 (Six hump camel back 및 Easton and fenton) 및 30개 변수를 갖는 수학 문제 (Schwefel 및 Hyper sphere)에 적용한 결과 SAVCA는 비교한 다른 알고리즘 (Harmony Search, Water Cycle Algorithm, VCA, Genetic Algorithms with Floating-point representation, Shuffled Complex Evolution algorithm 및 Modified Shuffled Complex Evolution)에 비해 우수한 성능을 보여주었다. 마지막으로 공학 문제인 Speed reducer design에서도 SAVCA는 가장 좋은 결과를 보여주었다. 복잡한 매개변수 조절과정을 거치지 않은 SAVCA는 여러 분야에서 적용이 가능할 것이다.

The Self-Adaptive Vision Correction Algorithm (SAVCA) developed in this study was suggested for improving usability by modifying four parameters (Modulation Transfer Function Rate, Astigmatic Rate, Astigmatic Factor and Compression Factor) except for Division Rate 1 and Division Rate 2 among six parameters in Vision Correction Algorithm (VCA). For verification, SAVCA was applied to two-dimensional mathematical benchmark functions (Six hump camel back / Easton and fenton) and 30-dimensional mathematical benchmark functions (Schwefel / Hyper sphere). It showed superior performance to other algorithms (Harmony Search, Water Cycle Algorithm, VCA, Genetic Algorithms with Floating-point representation, Shuffled Complex Evolution algorithm and Modified Shuffled Complex Evolution). Finally, SAVCA showed the best results in the engineering problem (speed reducer design). SAVCA, which has not been subjected to complicated parameter adjustment procedures, will be applicable in various fields.