• 한국지능시스템학회논문지
• /
• 제22권4호
• /
• pp.414-424
• /
• 2012

문제 인스턴스 탐색 혹은 자동 생성은 알고리즘 분석 및 테스트에 적용될 수 있으며, 하드웨어, 소프트웨어 프로그램, 계산 이론 등 다양한 수준에서 연구되어온 주제이다. 본 연구에서는 해(解) 공간에 사용된 목적값-거리 상관관계 분석을 문제 인스턴스 공간에 적용하였다. 문제 인스턴스의 목적값은 문제에 따라 알고리즘의 수행 시간과 최적해를 잘 구하는 정도로 정의하였다. 이러한 정의는 문제 인스턴스의 난이도로 해석할 수 있다. 상관관계는 3가지 측면에서 분석하였다: 첫째, 알고리즘과 거리 함수에 따른 상관관계 차이, 둘째, 알고리즘의 개선 전/후의 상관관계 변화, 셋째, 문제 인스턴스 공간과 해당 문제의 해 공간 사이의 연관성. 본 논문은 문제 인스턴스 공간에 상관계수 분석이 어떻게 적용될 수 있는지 보여주며, 문제 인스턴스 공간 분석을 본격적으로 다루는 첫번째 시도이다.

• 대한수학회보
• /
• 제46권2호
• /
• pp.245-253
• /
• 2009

A new hard problem called the vector decomposition problem (VDP) was recently proposed by Yoshida et al., and it was asserted that the VDP is at least as hard as the computational Diffie-Hellman problem (CDHP) under certain conditions. Kwon and Lee showed that the VDP can be solved in polynomial time in the length of the input for a certain basis even if it satisfies Yoshida's conditions. Extending our previous result, we provide the general condition of the weak instance for the VDP in this paper. However, when the VDP is practically used in cryptographic protocols, a basis of the vector space ${\nu}$ is randomly chosen and publicly known assuming that the VDP with respect to the given basis is hard for a random vector. Thus we suggest the type of strong bases on which the VDP can serve as an intractable problem in cryptographic protocols, and prove that the VDP with respect to such bases is difficult for any random vector in ${\nu}$.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제15권4호
• /
• pp.217-231
• /
• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• Management Science and Financial Engineering
• /
• 제2권1호
• /
• pp.147-176
• /
• 1996

The main objective of this study is to uncover the differences in the programming behavior between methodology creators and methodology users. We conducted an experiment with methodology creators who have invented one of the major object-oriented methodologies and with professional programmers who have used the same methodology for their software-development projects. In order to explain the difference between the two groups, we propose a theoretical framework that views programming as search in four problem spaces: representation, rule, instance and paradigm spaces. The main problem spaces in programming are the representation and rule spaces, while the paradigm and instance spaces are the supporting spaces. The results of the experiment showed that the methodology creators mostly adopted the paradigm space as their supporting space, while the methodology users chose the instance space as their supporting space. This difference in terms of the supporting space leads to different search behaviors in the main problem spaces, which in turn resulted in different final programs and performance.

• Journal of Information Processing Systems
• /
• 제13권5호
• /
• pp.1277-1298
• /
• 2017

Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon single-instance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user's score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

• 한국컴퓨터정보학회논문지
• /
• 제24권9호
• /
• pp.9-20
• /
• 2019

In this paper, we study the university timetabling problem, which consists of two subproblems, the university course timetabling problem and the examination timetabling problem. Given a set of classrooms, students, teachers, and lectures, the problem is to assign a number of courses (and examinations) to suitable timeslots and classrooms while satisfying the given set of constraints. We discuss the modeling and solution approaches to construct course and examination timetables for one of the largest Korean university. By using binary integer programming formulations, we describe these two complex real-world problems. Then, we propose a solution method, called NOGOOD, to solve the examination timetabling model. The computation results show that NOGOOD finds the optimal examination schedule for the given instance. Although we consider a specific instance of the university timetabling problem, the methods we use can be applicable to modeling and solving other timetabling problems.

• 대한산업공학회지
• /
• 제41권6호
• /
• pp.540-550
• /
• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• Management Science and Financial Engineering
• /
• 제20권1호
• /
• pp.17-19
• /
• 2014

In this paper, we consider an interesting variant of the inverse minimum traveling salesman problem. Given an instance (G, w) of the minimum traveling salesman problem defined on a metric space, we fix a specified Hamiltonian cycle $HC_0$. The task is then to adjust the edge cost vector w to w' so that the new cost vector w' satisfies the triangle inequality condition and $HC_0$ can be returned by the minimum spanning tree algorithm in the TSP-instance defined with w'. The objective is to minimize the total deviation between the original and the new cost vectors with respect to the $L_1$-norm. We call this problem the inverse metric traveling salesman problem against the minimum spanning tree algorithm and show that it is closely related to the inverse metric spanning tree problem.

• 한국지능시스템학회:학술대회논문집
• /
• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
• /
• pp.283-286
• /
• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• 스마트미디어저널
• /
• 제8권4호
• /
• pp.64-71
• /
• 2019

The performance of an OMR (Optical Music Recognition) system is usually determined by the characterizing features of the input music score images. Low resolution is one of the main factors leading to degraded image quality. In this paper, we handle the low-resolution problem using the super-resolution technique. We propose the use of a deep neural network with instance normalization to improve the quality of music score images. We apply instance normalization which has proven to be beneficial in single image enhancement. It works better than batch normalization, which shows the effectiveness of shifting the mean and variance of deep features at the instance level. The proposed method provides an end-to-end mapping technique between the high and low-resolution images respectively. New images are then created, in which the resolution is four times higher than the resolution of the original images. Our model has been evaluated with the dataset "DeepScores" and shows that it outperforms other existing methods.