• 사회경제평론
• /
• v.29 no.1
• /
• pp.99-117
• /
• 2016

This paper aims to explore the institutional traits of the federal employees pension system in the United States and the direction of its reform. Currently the United States has two systems of the federal employees pension. One is CSRS, the other is FERS. The former was firstly introduced in 1920 as a generous DB pension well before the establishment of the Social Security System(OASDI). What led to the latter, FERS was the Social Security Amendment Act of 1983 and Federal Employee Retirement System Act of 1986. The crucial difference between the CSRS and the FERS is the contrasting characteristic of their relationships with OASDI. The CSRS has just one source of retirement benefit(DB pension) without OASDI benefit, whereas the FERS has three sources(OASDI benefit, basic annuity(DB), DC typed TSP benefit). When it comes to FERS, what matters most is TSP(Thrift Savings Plan).

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.45
• /
• pp.113-120
• /
• 1998

본 논문에서는 에너지 함수의 직접적인 평가에 기초해 홉필드 신경망을 진화시킴에 따라 적응적으로 에너지 계수를 결정하는 기법을 제시하고자 한다. 이 기법에 근거하여 구해지는 계수들의 효과를 검증하기 위해 응용 모델인 TSP(Traveling Salesman Problem)에 적용하여, 실험을 통한 계수 값의 변화 추이를 분석하고 그 결과를 기존의 기법들과 비교한다. 또한 제안된 방법에 필수적인 각 단계에서의 에너지 값의 평가를 위한 부가적인 연산을 줄이기 위해 단계적으로 증감분만을 계산하는 효율적인 연산법을 제시한다.

• The Journal of the Korea Contents Association
• /
• v.5 no.3
• /
• pp.193-199
• /
• 2005

One of the important fields for heuristics algorithm is how to balance between Intensificationand Diversification. Ant Colony System(ACS) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem(TSP). In this paper, we propose Multi Colony Interaction Ant Model that achieves positive negative interaction through elite strategy divided by intensification strategy and diversification strategy to improve the performance of original ACS. And, we apply multi colony interaction ant model by this proposed elite strategy to TSP and compares with original ACS method for the performance.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.237-242
• /
• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2004.05a
• /
• pp.495-498
• /
• 2004

In the sound insulation measurements, the influence of background (extraneous) noise is often serious problem and how to reduce its effect and to improve the signal-to-noise(S/N) ratio is an important theme. As the background noise, such extraneous noises as road traffic noise and machine noise often disturb the measurement. In laboratory measurements on specimens with high sound insulation performances, even the internal noise of the measurement system can become a problem. To improve the signal-to-noise ratio and to improve the measurement accuracy, various kinds of digital signal processing techniques can be applied. In this paper, four kinds of digital signal processing techniques are applied and their effectiveness is examined by a simple sound insulation measurement.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2004.11a
• /
• pp.441-444
• /
• 2004

In the sound insulation measurements, the influence of background (extraneous) noise is often serious problem and how to reduce its effect and to improve the signal-to-noise(S/N) ratio is an important theme. As the background noise, such extraneous noises as road traffic noise and machine noise often disturb the measurement. In laboratory measurements on specimens with high sound insulation performances, even the internal noise of the measurement system can become a problem. To improve the signal-to-noise ratio and to improve the measurement accuracy, various kinds of digital signal processing techniques can be applied. In this paper, four kinds of digital signal processing techniques are applied and their effectiveness is examined through field measurements.

• The KIPS Transactions:PartB
• /
• v.11B no.1
• /
• pp.77-82
• /
• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• Analytical Science and Technology
• /
• v.23 no.4
• /
• pp.371-382
• /
• 2010

TSP and PM2.5 atmospheric aerosols have been collected at Gosan site of Jeju Island, and their compositions were analyzed to understand the pollution characteristics. The composition ratios of nss (non-sea salt)-$SO_4^{2-}$ and $NH_4^+$ were higher in Gosan site than those in other Korean background and urban sites. However the composition ratio of $NO_3^-$ was conversely lower in Gosan site. From the study of aerosol components according to particle sizes, the anthropogenic nss-$SO_4^{2-}$, $NO_3^-$ and $NH_4^+$ components were mostly existed in the fine particles. But the nss-$Ca^{2+}$, $Na^+$, $Cl^-$ and $Mg^{2+}$ originated from soil and marine sources were distributed relatively in the coarse particles. In the seasonal comparison, the concentrations of nss-$Ca^{2+}$, Al, Fe, Ca and $NO_3^-$ increased in spring season, and nss-$SO_4^{2-}$ showed higher concentration in summer and spring seasons. Based on the factor analysis, the atmospheric aerosols in Gosan site have been found to be influenced largely by anthropogenic sources, and next by marine and soil sources. The backward trajectory analyses showed that the concentrations of nss-$SO_4^{2-}$, $NO_3^-$, Pb and nss-$Ca^{2+}$ increased when the air mass moved from Chinese continent to Jeju area. On the other hand, their concentrations decreased when the air mass moved in from the North Pacific Ocean.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.33 no.2
• /
• pp.17-26
• /
• 2008

The traveling salesman problem is to find tours through all cities at minimum cost ; simply visiting the cities only once that a salesman wants to visit. As such, the traveling salesman problem is a NP-complete problem ; an heuristic algorithm is preferred to an exact algorithm. In this paper, we suggest an effective cost relaxation using a candidate arc set which is obtained from a regression function for the traveling salesman problem. The proposed method sufficiently consider the characteristics of cost of arcs compared to existing methods that randomly choose the arcs for relaxation. For test beds, we used 31 instances over 100 cities existing from TSPLIB and randomly generated 100 instances from well-known instance generators. For almost every instances, the proposed method has found efficiently better solutions than the existing method.

• Journal of Environmental Science International
• /
• v.5 no.2
• /
• pp.229-241
• /
• 1996

This study was performed to find the most desirable emission reduction for each mobile source pollutant and the optimal control strategy at a given level of expenditures in Pusan City in 2000 by using the interactive s-constraint method developed by Chang-Hyo Lee and Hyung-Wook Kim, which isone of the mathematical programming models. The most desirable emission reduction is 7093 ton/year for particulate (TSP), 4871 ton/year for NOx, 5148 ton/year for HC and 36779 ton/year for CO. The optimal control strategy is as follows; 1. As to passenger car and taxi, limiting VKT (vehicle kilometers travelled) in congested areas will be necessary. In addition to this, improving vehicie inspection Program should be enforced. 2. As to small-gasoline bus, traffic adaptive control system will be necessary. 3. As to small-diesel bus, non-adjustable engine parameters will have to be applied. .4. As to heal bus and heart truck, catalytic trap oxidizer and limiting VKT in congested areas will do necessary. 5. As to motorcycle, 2-cycle motorcycles should be converted to 4-cycle motorcycles.