• The KIPS Transactions:PartB
• /
• v.16B no.6
• /
• pp.463-470
• /
• 2009

In this paper, we propose the advanced fire-flame detection algorithm using camera image for better performance than previous sensors-based systems which is limited on small area. Also, previous works using camera image were depend on a lot of heuristic thresholds or required an additional computation time. To solve these problems, we use statistical values and divide image into blocks to reduce the processing time. First, from the captured image, candidate flame regions are detected by a background model and fire colored models of the fire-flame. After the probability models are formed using the change of luminance, wavelet transform and the change of motion on time axis, they are used for membership function of fuzzy logic. Finally, the result function is made by the defuzzification, and the probability value of fire-flame is estimated. The proposed system has shown better performance when it compared to Toreyin's method which perform well among existing algorithms.

• Journal of Korean Society of Steel Construction
• /
• v.18 no.2
• /
• pp.213-223
• /
• 2006

Conventional truss optimization approaches, while often sophisticated and computationally intensive, have been applied to simple, minimum weight-cost models. These approaches do not perform well when applied to real-world trusses, which have costmodels that are complex and which often involve multiple objectives. Thus, this paper describes the optimization strategies that a clustering technique, which identifies members that are likely to have the same product type, uses for the optimal design of truss structures with real- world cost functions that consider the costs on the weight of the truss, the number of products in the design, the number of joints in the structures, and the costs required in the site.At first, the clustering technique is applied to identify the members and to generate a proper initial solution. A simple taboo search technique is then used, which attempts to generate the optimal solution by starting with the solution from the previous technique. For example, the proposed approach is a plied to a typical problem and to a problem similar to relative performances. The results show that this algorithm generates not only better-quality solutions but also more efficient ones

• Journal of Intelligence and Information Systems
• /
• v.14 no.1
• /
• pp.117-129
• /
• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.

• Journal of the Economic Geographical Society of Korea
• /
• v.15 no.3
• /
• pp.343-357
• /
• 2012

The Seoul metropolitan subway system can be represented by a graph which consists of nodes and edges. In this paper, we study classification of subway stations and trip behaviour of subway passengers through partitioning the graph of the subway system into roughly equal groups. A weight of each edge of the graph is set to the number of passengers who pass the edge, where the number of passengers is extracted from the transportation card transaction database. Since the graph partitioning problem is NP-complete, we propose a heuristic algorithm to partition the subway graph. The heuristic algorithm uses one of two alternative objective functions, one of which is to minimize the sum of weights of edges connecting nodes in different groups and the other is to maximize the ratio of passengers who get on the subway train at one subway station and get off at another subway station in the same group to the total subway passengers. In the experimental results, we illustrate the subway stations and edges in each group by color on a map and analyze the trip behaviour of subway passengers by the group origin-destination matrix.

• Journal of KIISE:Software and Applications
• /
• v.31 no.6
• /
• pp.829-839
• /
• 2004

Methods based on integer programming have been shown to be very effective in solving various crew pairing optimization problems. However, their applicability is limited to problems with linear constraints and objective functions. Also, those methods often require an unacceptable amount of time and/or memory resources given problems of larger scale. Heuristic methods such as neighborhood search, on the other hand, can handle large-scaled problems without too much difficulty and can be applied to problems having any form of objective functions and constraints. However, neighborhood search often gets stuck at local optima when faced with complex search spaces. This paper presents ,i hybrid algorithm of neighborhood search and integer programming, which nicely combines the advantages of both methods. The hybrid algorithm has been successfully tested on a large-scaled crew pairing optimization problem for a real subway line.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.11
• /
• pp.189-196
• /
• 2012

This paper proposes Swap algorithm for solving Dynamic Economic Dispatch (DED) problem. The proposed algorithm initially balances total load demand $P_d$ with total generation ${\Sigma}P_i$ by deactivating a generator with the highest unit generation cost $C_i^{max}/P_i^{max}$. It then swaps generation level $P_i=P_i{\pm}{\Delta}$, (${\Delta}$=1.0, 0.1, 0.01, 0.001) for $P_i=P_i-{\Delta}$, $P_j=P_j+{\Delta}$ provided that $_{max}[F(P_i)-F(P_i-{\Delta})]$ > $_{min}[F(P_j+{\Delta})-F(P_j)]$, $i{\neq}j$. This new algorithm is applied and tested to the experimental data of Dynamic Economic Dispatch problem, demonstrating a considerable reduction in the prevalent heuristic algorithm's optimal generation cost and in the maximization of economic profit.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.5
• /
• pp.155-162
• /
• 2015

This paper proposes a deterministic optimization algorithm to solve economic load dispatch problem with quadratic convex fuel cost function. The proposed algorithm primarily partitions a generator with prohibited zones into multiple generators so as to place them afield the prohibited zone. It then sets initial values to $P_i{\leftarrow}P_i^{max}$ and reduces power generation costs of those incurring the maximum unit power cost. It finally employs a swap optimization process of $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$ where $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_j+{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}=1.0,0.1,0.01,0.001$. When applied to 3 different 15-generator cases, the proposed algorithm has consistently yielded optimized results compared to those of heuristic algorithms.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.5
• /
• pp.1151-1161
• /
• 1998

In this paper, we are considering a heterogeneous processor system in which each processor may have different performance and reliability characteristics. In other to fully utilize this diversity of processing power it is advantageous to assign the program modules of a distributed program to the processors in such a way that the execution time of the entire program is minimized. This assignment of tasks to processors to maximize performance is commonly called load balancing, since the overloaded processors can perform their own processing with the performance degradation. For the task assignment problem, we propose a new objective function which formulates this imbalancing cost. Thus the task assignment problem is to be carried out so that each module is assigned to a processor whose capabilities are most appropriate for the module, and the total cost is minimized that sum of inter-processor communication cost and execution cost and imbalance cost of the assignment. To find optimal assignment is known to be NP-hard, and thus we proposed an efficient heuristic algorithm with time complexity $O(n^2m)$ in case of m task modules and n processors.

• Journal of Intelligence and Information Systems
• /
• v.13 no.4
• /
• pp.121-135
• /
• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.2
• /
• pp.253-266
• /
• 2023

The Null Pointer Dereference vulnerability is a significant vulnerability that can cause severe attacks such as denial-of-service. Previous research has proposed methods for detecting vulnerabilities, but large and complex programs pose a challenge to their efficiency. In this paper, we present a lightweight tool for detecting specific functions in large binaryprograms through symbolizing variables and emulating program execution. The tool detects vulnerabilities through data dependency analysis and heuristics in each execution path. While our tool had an 8% higher false positive rate than the bap_toolkit, it detected all existing vulnerabilities in our dataset.