Earticle

도시부 도로에서 버스정류장의 위치는 이용자가 이용하기 편리하고 버스의 정차가 기존 교 통류에 주는 영향이 최소화되는 지점에 설치하여야 한다. 하지만 교차로로부터 버스정류장까 지의 적정 이격거리에 대한 연구가 미흡하여 여유 공간 확보 등 현장여건에 따라 버스정류장 의 위치가 결정되고 있다. 본 연구에서는 버스정류장 부근의 교통 및 기하구조 변수를 활용하 여 교통사고 예측모형을 개발하였고, 최적화 기법을 통해 교통사고를 최소화시킬 수 있는 교 차로로부터 버스정류장까지의 적정 이격거리를 산정하였다. 연구 결과, 교통량이 1,000대/시에 서 3,000대/시 수준에서 주도로 차로수가 2~4차로인 도로구간에서는 버스정류장을 교차로에서 약 87~166m 정도 떨어진 미드-블록(mid-block) 형태로 설치하는 것이 적정하고, 주도로 차로수 가 5~6차로인 구간에서는 교차로에서 약 42~97m 정도로 근접하게 설치하는 것이 바람직한 것 으로 나타났다.

The location of the bus stop on urban roads should be installed at a point where it is convenient for users and the impact of bus stops on the traffic flow is minimized. However, the location of the bus stops is determined indiscriminately due to the lack of related research. Therefore, this study developed a traffic accident prediction model and calculated the proper separation distance for the bus stops through an optimization technique. The result of the study indicates that the bus stop can be installed in the form of a mid-block approximately 87 to 166 m away from the intersection in the road section. This result is valid if the number of main road lanes in the road section is 2 to 4 with a level of traffic from 1,000 to 3,000 v/h. In the section with 5 to 6 lanes, it is desirable to install a bus stop close to the intersection by about 42 to 97 m.

The research work was undertaken to determine an effective fertilization medium, sperm separation method and sperm capacitating agent for optimum in vitro fertilization (IVF) rates of indigenous zebu cow oocytes. In experiment 1, tissue culture medium (TCM 199), Tyrode’s albumin lactate pyruvate (TALP) and Brackett and Oliphant (BO) medium were used as basic medium for IVF of oocytes of indigenous zebu cows. In experiment 2, three sperm separation methods namely centrifugation, swim up and percoll gradient methods were used for separation of motile and viable spermatozoa for IVF. In experiment 3, for capacitation of spermatozoa, IVF medium supplemented with the heparin, mixture of penicillamine, hypotaurine and epinephrine (PHE) or the combination of heparin with PHE were used for fertilization. In vitro culture (IVC) of presumptive zygotes was done in modified synthetic oviduct fluid (mSOF) medium using standard procedure 24 h after sperm-oocytes co-culture. The cleavage rate was determined to evaluate the efficacy of fertilization medium, sperm separation method and sperm capacitating agent 24 h after IVC. The cleavage rate was higher in oocytes fertilized in TALP (63.3%) than in TCM 199 (47.5%) (p < 0.05). The cleavage rate was higher in oocytes fertilized by spermatozoa separated by percoll gradient method (62.3%) than by centrifugation (51.6%) (p < 0.05). The cleavage rate of oocytes was higher when insemination was done with spermatozoa capacitated in TALP supplemented with heparin and PHE (61.3%) compared to control (40.9%) (p < 0.05). In conclusions, TALP based medium and percoll gradient sperm separation followed by capacitation with combination of heparin and PHE are suitable for IVF of indigenous zebu cow oocytes in Bangladesh.

시스템 최적화는 소스코드의 변경 없이 중복된 모듈을 추출하고, 추출된 모듈의 재사용을 위하여 프로그램 의 구조를 변경하는 기법이다. 구조지향 개발과 객체지향 개발은 크로스커팅 영역의 모듈화에는 효율적이나 크로스 커팅 개념을 모듈화 할 수 없다. 기존 시스템에서 크로스커팅 개념을 적용하기 위해, 각 시스템 내에 분산되어 있는 시스템 최적화 대상 모듈을 크로스커팅 영역으로 추출하는 기술이 필요하다. 본 논문에서는 개발이 완료된 시스템에 서 중복 모듈을 추출하기 위한 방법을 제안한다. 제안하는 방법은 소스코드 분석을 통해 데이터 의존관계와 제어 의 존관계를 분석하여 중복되는 요소를 추출한다. 추출된 중복된 요소는 시스템 최적화를 위하여 프로그램 의존 관계 분석에 사용될 수 있다. 중복된 의존관계 분석 결과는 제어 흐름 그래프로 변환되며, 이를 활용하여 최소 크로스커 팅 모듈을 생성할 수 있다. 의존 관계 분석을 통해 추출된 요소는 크로스커팅 영역 모듈로 설정함으로써 시스템 내 중복된 코드를 최소화 할 수 있는 시스템 최적화 방법을 제시한다.

The system optimization is a technique that changes the structure of the program in order to extract the duplicated modules without changing the source code, reuse of the extracted module. Structure-oriented development and object-oriented development are efficient at crosscutting concern modular, however can’t be modular of crosscutting concept. To apply the crosscutting concept in an existing system, there is a need to a extracting technique for distributed system optimization module within the system. This paper proposes a method for extracting the redundant modules in the completed system. The proposed method extracts elements that overlap over a source code analysis to analyze the data dependency and control dependency. The extracted redundant element is used to program dependency analysis for the system optimization. Duplicated dependency analysis result is converted into a control flow graph, it is possible to produce a minimum crosscutting module. The element extracted by dependency analysis proposes a system optimization method which minimizes the duplicated code within system by setting the crosscutting concern module.

본 연구에서는 형상 최적설계 기법으로 주목 받고 있는 Level set method의 기본 개념 및 이론을 설명 하였다. 연속체 민감도해석을 이용하여 맥스웰방정식으로 대표되는 전자장시스템을 결합된 Level set method 기 반 최적화방법을 기술한 후 Level set method의 응용사례로서 전장 내에서 유전체전극에서 최적설계문제, 자장 내에서 동기릴럭턴스 전동기의 회전자 형상최적설계 모델링 및 해석문제, 자기장 내에서 자성입자의 거동 및 체인 현상과 약물 표적화 시스템에 활용에 대하여 수치해석 결과를 보였다. Level set method는 형상뿐만 아니라 위상 최적화에도 적용이 가능하며 유한요소법을 이용할 경우 요소재분할 과정 없이 형상의 변화를 해석할 수 있는 유 용한 설계기법이다.

In this study, the basic concept and theory of the level set method(LSM), which has recently attracted attention as an innovative optimal design technique, were explained. Using the continuum sensitivity analysis, an optimization method based on the level set method combined with the electromagnetic field system represented by the Maxwell's equation was described. As an application example of the level set method, the problem of optimal design in the dielectric electrode within the electric field, the modeling and analysis of the optimal design of the rotor shape of the synchronous reluctance motor within the magnetic field, the behavior of magnetic particles in the magnetic field, chain phenomenon, and drug targeting systems. Numerical analysis results were shown for the application. Level set method can be applied not only to shape but also to topology optimization, and when finite element analysis is used, it is a useful design technique that can analyze shape change without element remesh process.

본 논문에서는 타겟의 RCS Calibration의 정확도를 향상시키기 위해 Time-Gating 조건에 대한 최적화 기법을 제안하였다. Matlab 기반의 코드를 이용하여 RCS Calibration의 최적화 코드를 개발하였으며, 그 조건을 찾기 위해 S-Parameter 신호 처리 알고리즘을 제시하였다. 안테나와 Far-field 조건을 만족한 거리에 있는 타겟 을 두고 측정실험을 진행하였다. 제안하는 신호 최적화 방법의 핵심은 실험에서 측정된 시간 도메인 S-Parameter 값을 확인하여 폭과 레퍼런스 포인트를 조절하는 것이다. 그 방법의 적용 과정을 본 논문에서 서술하였으며, 시간 축에서의 S21의 신호를 확인하여 타겟의 위치를 기준으로 한 Time-Gating 최적화를 진행하였다. 결과적으로 게 이팅 최적화 코드를 적용하였을 때 최적화가 진행되지 않았을 경우와 비교하여 약 12배만큼 정의된 에러 값이 감 소함을 확인할 수 있었다.

In this paper, an optimization technique for time-gating conditions is proposed to improve the accuracy of target RCS calibration. An RCS calibration optimization code was developed using Matlab-based code, and an S-Parameter signal processing algorithm was presented to find the condition. The measurement experiment was conducted with the antenna and the target at a distance that satisfies the far-field condition. The signal optimization method is to check time domain S-parameter value measured in the experiment and adjust the width and reference point. The process of applying the method was described in this paper, and time-gating optimization was performed based on the position of the target by checking the signal of S21 on the time axis. As a result, when the gating optimization code was applied, it was confirmed that the defined error value was reduced by about 12 times compared to the case where optimization was not performed.

본 연구에서는 강우관측소의 고도별 공간분포의 적정성을 평가하기 위한 방안으로 고도별 강우관측소의 최근린지수를 산정하고, 현재 강우관측소 공간분포의 적정성을 평가하였다. 등면적비를 이용하여 고도를 구분하고, 고도마다 다른 지형적인 조건을 고려하기 위하여 주어진 지형조건내에서 가능한 최대 NNI을 최적화 기법의 하나인 화음탐색법을 이용하여 산정하였다. 이와 같이 고도별로 현재 상태 및 최대 NNI를 산정한 후 이 두 값의 차이를 바탕으로 고도별로 강우관측소 분포를 평가하였다. 그 결과 고도가 높아질수록 공간분포가 상대적으로 취약함을 확인하였다. 추후 강우관측망을 신설할 경우 고도별 특성을 반영한 다면 보다 효율적인 강우관측망의 구축이 가능할 것으로 판단된다.

In this study, we estimate the NNI(Nearest Neighbor Index) which is considered altitude of rain gauge network as a method for evaluating appropriateness of spatial distribution and the current rain gauge network is evaluated. The altitude is divided by equal-area-ratio and optimal NNI within given basin condition is estimated using harmony search method for considering geographical conditions that vary from altitude to altitude. After calculating current state and optimal NNI for each altitude, the distribution of the rain gauge network is evaluated based on the difference between the two NNIs. As a result, it founds that the density of rain gauge networks is relatively thin as the altitude increases. Furthermore, it will be possible to construct an efficient rain gauge network if the characteristics of different altitudes are considered when a new rain gauge network is newly constructed.

본 논문에서는 커널 Extreme Learning Machine을 기반으로 하여 최적화 기법들 중의 하나인 입자 군집 최적화 기법을 이용한 설계 기법을 제시한다. 제안된 Kernel Extreme Learning Machine은 기존에 사용되어지는 뉴럴 네트워크의 단점을 개선한 네트워크이다. 다시 말하면, 뉴럴 네트워크의 단점인 느린 학습속도를 개선한 네 트워크이다. 일반적으로 뉴럴 네트워크의 히든 노드들은 랜덤 초기화 후 오류 역전파 알고리즘을 이용하여 학습 한다. 이와 같은 오류 역전파 알고리즘은 매우 느린 학습속도를 보인다. 이와 같은 단점을 해결하기 위하여, Kernel Extreme Learning Machine의 히든 노드들은 랜덤 초기화 되고 학습되지 않고 출력층의 연결 하중만 학습 되어진다. 이와 같은 장점을 가진 Kernel Extreme Learning Machine의 구조를 최적화하기 위하여 입자 군집 최적 화 기법을 사용한다. 제안된 설계 방법을 적용하여 설계된 모델의 일반화 성능의 우수성을 보이기 위하여, 다수 의 머신러닝 데이터들을 이용하여 실험하고 실험을 통해 얻은 결과를 비교 평가하였다.

In this paper, we proposed the design method of Extreme Learning Machine which is optimized by using Particle Swarm Optimization Technique. Extreme Learning Machine is the improved version of the conventional neural networks which have a very slow learning speed based on the back-propagation algorithm. In the conventional neural networks, the connection weights between the input layer and the hidden layers are initialized randomly and then optimized by using the gradient decent method. The speed of the learning method for the conventional neural networks is slow. In order to overcome the drawback of the conventional neural networks, the connection weights of the hidden nodes are just initialized randomly and will not be optimized, and the only connection weights of the output nodes are learned by using least square estimation not the iterative learning method. In addition, we use Particle Swarm Optimization to optimize the proposed Extreme Learning Machine. Several machine learning bench-mark data sets are used to show the generalization performance of the proposed design method and to compare their performance with the other already studied models.

In the automobile manufacturing industry, lightweight design is one of the essential challenges to be solved fundamentally. The vehicle wheels are classified as safety related components as the main substructure of the vehicle. In this study, we illustrate a technique for selecting the appropriate number of spokes. Based on the basic model of the selected number of spokes, we propose a method to maintain stiffness and design lightweight using topology optimization software. Based on the basic model of the selected number of spokes, it was redesigned to be lightweight while maintaining stiffness by utilizing topology optimization software. By comparing and reviewing the structural analysis results of the basic model and the redesigned model, a design technique that can maintain structural safety and reduce wheel mass was proposed.

소형발전용 연료전지 시스템에 있어 개질장치는 탄화수소계의 연료를 수소가 풍부한 가스로 개질하여 주는 장치이다. 개질장치는 시스템 전체의 안정성과 성능의 관점에서 중요한 핵심 지표를 가지게 되는데 개질기의 핵심평가지표 중 가장 중요한 것은 배출가스 중의 CO농도이다. 시스템의 효율, 성능 및 안정성을 위하여 CO 농도를 5ppm 이하로 관리되어야 한다. 본 연구에서는 개질기의 배출가스 내의 CO농도에 영향을 미치는 핵심인자를 도출한다. 개질기의 운전 및 설계에 있어 six-sigma 기법 중의 실험계획법을 도입하여 CO 농도에 영향을 미치는 핵심인자들을 도출해내고 도출된 인자들의 개선을 통하여 최적화된 운전조건을 제시하였다. 연료전지용 개질기에 있어서 가장 중요한 CO의 농도를 제어하기 위하여 도출된 인자들은 MTS, LTS, Prox와 같은 각 개질기내의 온도제어 및 그에 관한 결과로서의 CO 농도에 대한 최적 운전조건을 도출하였다.

A reformer for a small fuel cell system is an apparatus which converts hydrocarbon fuel into hydrogen-rich gas. Among many indices of a reformer, the most crucial index of a reformer is CO concentration in the off-gas out of reformer which must be controled under 5ppm for the efficiency and performance of a system. This paper suggests the criteria of a reformer operation for the stability of a reformer in a fuel cell system by deducing crucial indices and improving processes. The six-sigma technique was applied to verify the optimum control and operation of a reformer of a fuel cell combined heat and power system. The result of temperature control of each parts of a reformer system is the concentration of CO which is the most important factor for the operation of a fuel cell system. The temperature of the parts of a reformer, MTS, LTS and Prox, were controled so that the concentration of CO.

Spatial Partitioning Fragmentation (SPF) is a popular method to partition data in Distributed Spatial Databases (DSDBs). The issue of cross-border queries is an inherent problem however with distributed spatial data queries based on partitioning fragmentation given a continuity and strong correlation of geospatial data. In the case of partitioning fragmentation, a global spatial join can be translated into multiple sub-joins, and then divided into 2 groups: Cross-Border Joins (CBJs) and Non-Cross-Border Joins (NCBJs). The CBJ approach is essential for process efficiency in a distributed spatial query. A compound join based on a topological relationship inquiry and a buffering analysis is a crucial class of spatial queries. This article studies compound join optimization for spatial queries in a DSDB, and proposes a set of theorems and rules for the optimization of CBJs, contributing a removal rule and a filtering rule. This article supplies a Partition Fragmentation Join Strategy (PFJS) to resolve the compound join problem based on these rules. Experimental results show that the PFJS can improve the efficiency of CBJs, when compared with the Naive Join Strategy (NJS) or the Spatial Semi-Join Strategy (SSJS). The PFJS contributes to the optimization of spatial compound joins.

In this paper, a new approach based on the Honey bee mating optimization (HBMO) technique is proposed to tune the parameters of the multi-machine power system stabilizers (PSSs). The honey- bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. The PSSs parameters tuning problem is converted to an optimization problem with time domain- based objective function which is solved by a HBMO algorithm. To ensure the robustness of the proposed stabilizers, the design process takes a wide range of operating conditions into account. The performance of the newly designed PSSs is evaluated in a three -machine power system subjected to the different types of operating conditions in comparison with the genetic algorithm based PSSs. The effectiveness of the proposed technique is demonstrated through nonlinear time-domain simulation studies over a wide range of loading condition.

Most RFID applications in the Internet of Things (IoTs) use multiple readers to read the IDs of multiple tags and form the RFID network. In such a network, unguarded reader deployment may generate over-crowded readers, cause interferences and, as a result, increases the deployment cost while degrading tag detection. Seeing that desirable reader deployment is crucial for RFID system performance, this paper introduces an optimization-based IGAA approach which outperforms existing RFID topology designs by turning up more favorable reader deployment and system performance. The new approach employs an advanced multi-objective fitness function and improved genetic annealing algorithms (GAA) to pursue a better RFID topology design. By involving an improved gene-stirring operation to help preserve good genes and locate optimal solutions for reader deployment, it is simple in operation but effective in practice. Experimental evaluation shows that when compared with related approaches, IGAA can yield better solution quality with less search time.

As one of the driving forces changing the way research and industry uses virtualization, distributed computing, internet, software and web services today, cloud computing stands tall. A cloud is an ecosystem of data centers, distributed servers, and clients all interconnected to each other. The unique selling point of a cloud is its reduced cost of ownership in comparison to traditional models. This primary advantage is complemented by fault tolerance, high availability opportunity, scalable and flexible structure, reduced infrastructure overheads for users, and services that can be accessed as and when required. One of the challenges that face cloud computing is load balancing. Load balancing assures optimum use of available resources, thereby, enabling consistency and performance of the overall system. An imbalance of load causes a single node or nodes to operate beyond its optimum levels. As a result, there could be either a gradual or a rapid loss in overall efficiency of the system leading to increase in emission rates and inefficient use of energy. In this paper, we have focused on resourceful load balancing coupled with a technique which reduces flooding. We have discussed how a combination of these is able to ensure efficient routing with reduced carbon emission.

The aim of this work is to design and simulate an armature and field control systems using state feedback controller based on bacterial foraging optimization (BFO) technique for controlling the speed of separately excited dc motor (SEDM). The controller's performances have been optimized based on social foraging behavior of Escherichia (E. Coli) bacteria. The state feedback controller's parameters (controller's gains K1 & K2) are tuned using foraging strategy. The SEDM is loading for different loads ranging from no-load to full-load to test the controller behavior and robustness for wide range of loadings variations. First the SEDM is simulated feeding back the armature current and angular speed (armature control method), second the SEDM is simulated with feeding back the field current and angular speed (field control method). For both controlling methods the controller's gains are tuned using BFO.

Wireless Sensor Network (WSN) is a collection of tiny sensor nodes capable of sensing and processing the data. These sensors are used to collect the information from the environment and pass it on to the base station. A WSN is more vulnerable to various attacks. Among the different types of attacks, sinkhole attack is more vulnerable because it leads to a variety of attacks further in the network. Intrusion detection techniques are applied to handle sinkhole attacks. One of effective approach of intrusion detection mechanism is using Swarm Intelligence techniques (SI). Particle Swarm Optimization is one of the important swarm intelligence techniques. This research work enhances the existing Particle Swarm Optimization technique and the proposed technique is tested in a simulated environment for performance. It is observed that the proposed Enhanced Particle Swarm Optimization (EPSO) technique performs better in terms of Detection rate, False Alarm rate, Packet delivery ration, Message drop and Average delay when compared to the existing swarm intelligence techniques namely, Ant Colony Optimization and Particle Swarm Optimization.

Cloud Computing is a distributing computing technology which is used as pay per use basis. Now a day’s Cloud Computing is the most reputed topic due to its ability to offer guaranteed quality of service atmosphere, dynamic IT infrastructures, and conFigureurable software services. But many users could not satisfy on cloud services completely due to their uncovering security purpose for handling large numbers of data. Even the network becomes uncontrollable, when large numbers of user’s request to the server create network congestion and data losses vigorously. Content Delivery Network OR CDN is an eminent solution of this problem. Our objective is to create local and global server and connect the global server to system manager, which is worked over Content Delivery Network to deliver and direct the user request to the nearest global edge server except local server and establish the connection between them and transfer the respective content. For optimization of edge selection process and reduce the load over content delivery network we approach local server concept in this paper.

The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy to use, and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes. In this research, we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction. Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.