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이 연구는 사회정책의 ‘필요’(needs) 개념에 입각하여 기본소득에 대해 검토하였다. 인간의 필요(need) 는 사회정책의 심장이며, 복지국가의 중추인 소득보장제도는 필요충족을 위한 사회권을 통해 제도화되었기 때문이다. 이 연구는 이점에 근거하여 소득보장제도와 비교를 통해 접근하였으며, 기본소득이 사회정책의 ‘필요’ 개념에 부합하지 않는다는 점을 지적하였다. 이와 같은 비판은 기본소득이 필요에 따른 재분배체계가 아니며, 반(反)집합적이며 개인주의적 정책에 가깝다는 점에 기초했다. 기본소득은 필요충족의원칙에 위배된다. 전통적 의미에서 사회정책과 무관하며, 조세정책이나 부의 재분배 정책에 가까운 것이다. 또한 필요충족을 위한 사회권과 무관한 기본소득은 복지국가의 재상품화 경향과 조응할 수 있다. 소득보장제도의 탈상품화 기능을 철폐함으로써 한계노동인구의 노동공급을 촉진하여 노동시장의 분절구조를더욱 공고히 할 공산이 크다. 기본소득은 근로유인에 친화적이며 임금보조정책의 성격을 가졌기 때문이다. 또한 개인화된 접근의 특성상 기본소득은 표준적인 필요에 기초하여 집합적으로 조직된 소득보장 시스템을 해체하고 사회서비스 시장화에 조응할 가능성이 충분하다. 기본소득이 복지국가를 보완하거나, 대안이 되기엔 위험한 도구인 것이다. 따라서 이 연구는 기본소득이 아닌 소득보장제도의 확충에 대한 논의에 집중해야 한다고 주장했다. 불안정한 노동시장을 주어진 숙명으로 받아드리면서 복지국가가 이에 적응하는 데 초점을 맞추는 접근에서 벗어나야할 필요성도 언급했다. 그리고 인간의 필요와 사회권의 관점에서 노동시장 개혁과 소득보장제도의 확충을 함께 모색해야 한다고 제언했다.

This article reviewed Unconditional Basic Income(UBI) based on the concept of ‘needs ‘ in social policy, because needs is at the heart of social policy and Income Maintenance Institutions became institutionalized in order to ensure social right to satisfaction needs. Therefore, this article examined UBI by comparing it with income maintenance institutions, and pointed out that it does not support the concept of ‘needs’ in social policy. The criticism was based on the grounds that (i) UBI is not redistribution according to needs, and (ii) it is closer to individualised measure than to collectivist one. UBI violates the principle of needs satisfaction. It is irrelevant to social policy in the traditional sense, and, rather, similar to tax policy or redistribution one of wealth in its nature. Also, UBI, which is irrelevant to social right to need satisfaction, may correspond to the re-commodification tendency in welfare states. It is likely to reinforce the labour market segmentation by eliminate de-commodification enabled by income maintenance institutions and, thereby, accelerating the supply of labour powers. UBI supports work incentives and supplements wage. Also, considering its individualized approach, UBI entails considerable probability to dismantle income maintenance systems and support marketisation of social services. It is because UBI is a risk instrument for supplementing or providing an alternative to welfare state. Therefore, this article suggested the relevant discussions focus on expanding income maintenance institutions rather than adoption UBI, and discussed the need to accept instability of the labor market as well as to change the current approach to and focus on adaptation to it. Furthermore, the article proposed examination of the labour market reform and expansion of income maintenance institutions from the viewpoints of human need and social right.

A general procedure for the numerical solution of coupled, nonlinear, differential two-point boundary-value problems, solutions of which are crucial to the controller design, has been developed and demonstrated. A fixed-end-points, free-terminal-time, optimal-control problem, which is derived from Pontryagin's Maximum Principle, is solved by an extension of Davidenko's method, a differential form of Newton's method, for algebraic root finding. By a discretization process like finite differences, the differential equations are converted to a nonlinear algebraic system. Davidenko's method reconverts this into a pseudo-time-dependent set of implicitly coupled ODEs suitable for solution by modern, high-performance solvers. Another important advantage of Davidenko's method related to the time-optimal problem is that the terminal time can be computed by treating this unkown as an additional variable and sup- plying the Hamiltonian at the terminal time as an additional equation. Davidenko's method uas used to produce optimal trajectories of a single-degree-of-freedom problem. This numerical method provides switching times for open-loop control, minimized terminal time and optimal input torque sequences. This numerical technique could easily be adapted to the multi-point boundary-value problems.

개미군락시스템(Ant Colony System, ACS)은 조합 최적화 문제를 해결하기 위한 기법으로 생물학적 기반의 메타휴리스틱 접근법이다. 지나간 경로에 대하여 페로몬을 분비하고 통신 매개물로 사용하는 실제 개미들의 추적 행 위를 기반으로 한다. 최적 경로를 찾기 위해서는 보다 다양한 에지들에 대한 탐색이 필요하다. 기존 개미군락시스템 의 지역 갱신 규칙에서는 지나간 에지에 대하여 고정된 페로몬 갱신 값을 부여하고 있다. 그러나 본 논문에서는 현 재 선택한 노드에 대한 이전 iteration 에서 방문한 총 빈도수를 고려한 페로몬 부여 방법을 지역갱신규칙에 사용하고 자 한다. 탐색을 위해서는 부경로를 이용한 ACS알고리즘을 사용하였다. 보다 많은 정보를 탐색에 활용함으로써 기 존의 방법에 비해 지역 최적화에 빠지지 않고 더 나은 해를 찾을 수 있다.

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

The analytic hierarchy process is known as a useful tool for the group decision making methods. This tool has been area such as investment, R&D management, manufacturing, production and marketing. Typically, transportation problems have addressed by mathematical programming. In this paper, an optimal solution of transportation problem was determined by the analytic hierarchy process.

This study shows a variance regularization method in order to obtain the stable optimal solutions, when a numerical method accelerating design variables is used for material topology optimization algorithm. Since a moved and regularized Heaviside function used in the accelerated method is composed of nonlinear concave and convex functions in a given design domain between 0 and 1, design variables below 0.5 can move fast toward the value of 0 and those over 0.5 are rapidly located to the value of 1. However optimal solutions may be not stable due to singularity of element stiffness, while the accelerated design variables are too closed to value 0. In particular this instability may occur to the accelerated method-based material topology optimization algorithms much repeating the moved and regularized Heaviside function. In order to resolve the problem, in this study, a variance regularization is formulated within a linear governing equation for structural analyses of optimization procedures. Numerical examples for topologically optimally modeling a linear elastostatic MBB-beam verify that the accelerated method of design variables take numerical stability of topological optimal solutions by being associated with the variance regularization method.

Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Particle swarm optimization is an optimization method. It is an optimization algorithm, which is based on swarm intelligence. Optimization problems are widely used in different fields of science and technology. Sometimes such problems can be complex due to its practical nature. Particle swarm optimization (PSO) is a stochastic algorithm used for optimization. It is a very good technique for the optimization problems. But still there is a drawback that it gets stuck in local minima. To improve the performance of PSO, the researchers have proposed some variants of PSO. Some researchers try to improve it by improving the initialization of swarm. Some of them introduced new parameters like constriction coefficient and inertia weight. Some define different methods of the inertia weight to improve performance of PSO and some of them work on the global and local best. This paper transplants some of the parameters used to enhance the performance of Particle Swarm Optimization technique.

This paper aims to present a comprehensive study for various Asset Tracking technologies. Our study scales down from introduction of chief underlying tracking and localization principles to technologies and systems at higher level. At lower tier, we introduce schemes like triangulation, Time difference of Arrival (TDOA or trilateration), multilateration, Angle of Arrival (AOA), Doppler, Signal Strength (RSSI), Beam forming etc. At surfacetier, our primary focus has been laid on RFID and Bluetooth based technologies while an overview of other technologies like satellites (GPS), cellular (GSM) or data connectivity (WIFI) etc. An insight to associated schemes like Active and Passive, Indoor and Outdoor, and Behavioral Sensing augment this. Each technology is further analyzed with benefits and drawbacks for a tracking solution. After technical insight, we will focus our study on Personal Asset tracking and highlight the pros and cons of previously mentioned tracking technologies in line with five aspects; Accuracy, Budget, Energy, Host and Platform-independence. Within these five regimes, we tabulate the requirements of an affordable, efficient and practicable scheme and illustrate with current examples. We keep our focus on a common user who has a cell phone in-hand and resolve an optimal tracking solution within the resources available to him. As a consequence, we conclude Bluetooth Low Energy based tracking schemes to be optimal candidate. We foresee our endeavor to serve as a compendium for the readers who wish to get an overview of such technologies without going into discrete technical details, and propose the Bluetooth based tacking scheme as a viable and affordable solution for a common user.

본 논문은 지금까지 해결하지 못한 난제 중 하나인 외판원 문제의 최적 해를 구하는 발견적 알고리즘을 제안 한다. 제안된 알고리즘은 초기 경로를 결정하기 위해 기존의 DNN을 변형한 SW-DNN, DW-DNN과 DC-DNN을 제 안하였다. 초기 해는 DNN, SW-DNN, DW-DNN과 DC-DNN을 적용하여 최소 경로 길이를 가진 방법을 선택한다. 초기 해에 대해 최적 해를 구하기 위해 먼저 삭제 대상 간선을 선택하는 방법을 결정하였으며, 이들 간선들에 대해 지 역 탐색 방법인 k-opt 중에서 2, 2.5, 3-opt를 먼저 적용하고, 삭제 대상 간선들 중 삭제되지 않은 간선들에 대해 4-opt를 적용하였다. 제안된 알고리즘을 대규모의 TSP인 26개의 유럽 도시들을 방문하는 TSP-1과 49개의 미국 도시 들을 방문하는 TSP-2에 적용한 결과 모두 최적 해를 구하는데 성공하였다. 제안된 알고리즘은 지금까지 발견적 방법 으로는 TSP의 최적 해를 구하지 못한다는 미신을 타파하였고, TSP의 알고리즘으로 적용할 수 있을 것이다.

This paper introduces a heuristic algorithm to NP-hard travelling salesman problem. The proposed algorithm, in its bid to determine initial path, applies SW-DNN, DW-DNN, and DC-DNN, which are modified forms of the prevalent Double-sided Nearest Neighbor Search and searches the minimum value. As a part of its optimization process on the initial solution, it employs 2, 2.5, 3-opt of a local search k-opt on candidate delete edges and 4-opt on undeleted ones among them. When tested on TSP-1 of 26 European cities and TSP-2 of 49 U.S. cities, the proposed algorithm has successfully obtained optimal results in both, disproving the prevalent disbelief in the attainability of the optimal solution and making itself available as a general algorithm for the travelling salesman problem.

본 논문은 수송 문제의 최적 해를 찾는 방법을 제안하였다. 수송 문제는 공급량과 요구량이 동일한 균형 수 송과 공급량과 요구량이 다른 불균형 문제로 구분된다. 수송문제의 최적 해를 얻는 대표적인 TSM은 먼저, 불균형 수 송 문제인 경우 가상의 행이나 열을 추가하여 균형 수송 문제로 변환시킨다. 다음으로 NCM, LCM, VAM 등 다양한 방법을 적용하여 초기 해를 구한다. 마지막으로 초기 해가 최적 해인지 검증하는 MODI를 적용한다. 따라서 최적 해 를 구하는 과정이 복잡하다. 제안된 방법은 불균형을 균형 수송 문제로 변환하는 과정을 거치지 않고 직접 적용한다. 또한, 초기 해가 최적해인지 검증하는 과정도 수행하지 않는다. 제안된 방법은 첫 번째로, 행에 대해 공급량을 비용 오름차순으로 요구량을 만족하도록 배정한다. 두 번째로, 각 열에 대해 배정된 량이 요구량을 초과하는 순으로 배정 량을 조정한다. 배정량 조정 방법은 다음 수행 순위 열의 비용과의 차이인 손실비용이 가장 큰 셀에 우선 배정하고 나머지 셀에 대해서는 배정량을 조정한다. 조정된 배정량은 요구량을 만족하지 못하는 수행 순위 오름차순 셀들에 추 가된다. 모든 열에 대해 배정량이 조정되면 마지막으로 행의 최소 비용에 미 배정되었거나 열의 최대 비용에 배정된 경우 배정량을 상호 교환하는 방법으로 추가 조정한다. 불균형 배송 2개와 균형 배송 13개 데이터에 제안된 방법을 적용한 결과 모두 최적 해를 구하는데 성공하였다. 또한, 기존의 방법들이 최적해를 구하지 못한 4개 데이터에 대해 서 추가로 최적 해를 구하였다. 따라서 제안된 방법은 수송 문제에 대해 일반화된 단일 방법으로 적용할 수 있을 것 이다.

This paper proposes an algorithm designed to obtain the optimal solution for transportation problem. The transportation problem could be classified into balanced transportation where supply meets demand, and unbalanced transportation where supply and demand do not converge. The archetypal TSM (Transportation Simplex Method) for this optimal solution firstly converts the unbalanced problem into the balanced problem by adding dummy columns or rows. Then it obtains an initial solution through employment of various methods, including NCM, LCM, VAM, etc. Lastly, it verifies whether or not the initial solution is optimal by employing MODI. The abovementioned algorithm therefore carries out a handful of complicated steps to acquire the optimal solution. The proposed algorithm, on the other hand, skips the conversion stage for unbalanced transportation problem. It does not verify initial solution, either. The suggested algorithm firstly allocates resources so that supply meets demand, in the descending order of its loss cost. Secondly, it optimizes any surplus quantity (the amount by which the initially allocated quantity exceeds demand) in such a way that the loss cost could be minimized Once the above reallocation is terminated, an additional arrangement is carried out by transferring the allocated quantity in columns with the maximum cost to the rows with the minimum transportation cost. Upon application to 2 unbalanced transportation data and 13 balanced transportation data, the proposed algorithm has successfully obtained the optimal solution. Additionally, it generated the optimal solution for 4 data, whose solution the existing methods have failed to obtain. Consequently, the suggested algorithm could be universally applied to the transportation problem.

일반적으로 최적화문제는 간단하게 해결하기 어렵다. 그 이유는 주어진 문제가 단순하면 바로 해결되지만, 복잡 할수록 그 경우의 수는 방대하기 때문이다. 본 연구는 인공신경망 최적화에 대한 연구이다. 여기에서 우리가 다루고 있는 것은, 인공신경망을 구축하기 위한 완화법으로써, 최적화하는 방법이다. 주요 논제로는, 신경망 네트워크 전체의 안정성 과 불안정성, 경비 절감, 에너지 절감과 같은 비결정적인 문제를 다루고 있다. 이를 위하여, 우리는 연상기억 모델 즉, 국소적 최소인 기억정보가 가짜인 정보를 선택하지 않는 방법을 제시한다. 그리고, 시물레이티드 어닐링법으로써, 이것 은 가급적 낮은값을 가지고 있는 그 방향을 예측하고 그 이전의 낮은값과 결합해 나가서 더 낮은값으로 반복 수정해 나가는 방법이다. 그리고, 비선형 계획문제는, 방대한 조합상태의 수를 목적함수 합의 최소화를 위하여 적절한 최소하강 법을 적용하여 입출력을 확인하여 수정해 나가는 방법이다. 결국 본 연구는 최적화문제를 해결하기 위한 이론적인 접근 방법으로써 완화법으로서의 접근가능한 유용한 방법을 제시하였다. 따라서, 본연구는 새롭게 인공신경망을 구축할 때, 효율적으로 적용 할 수 있는 좋은 제안이 될 것으로 생각한다.

In general, optimization problems are difficult to solve simply. The reason is that the given problem is solved as soon as it is simple, but the more complex it is, the very large number of cases. This study is about the optimization of AI neural network. What we are dealing with here is the relief method for constructing AI network. The main topics deal with non-deterministic issues such as the stability and unstability of the overall network state, cost down and energy down. For this one, we discuss associative memory models, that is, a method in which local minimum memory information does not select fake information. The simulated annealing, this is a method of estimating the direction with the lowest possible value and combining it with the previous one to modify it to a lower value. And nonlinear planning problems, it is a method of checking and correcting the input / output by applying the appropriate gradient descent method to minimize the very large number of objective functions. This research suggests a useful approach to relief method as a theoretical approach to solving optimization problems. Therefore, this research will be a good proposal to apply efficiently when constructing a new AI neural network.

그래프에서의 배달문제는 m개의 정점으로 구성된 그래프에서 n개의 서로 다른 속도를 갖는 로봇 에이전트들을 이용하여 배달물을 그래프의 한 노드에서 다른 노드로 배달하는 최소 배달순서를 구하는 문제이다. 본 논문에서는 그래프에서의 배달문제에 대하여 최적해를 계산하는 O(㎥n)과 O(㎥)시간복잡도를 갖는 두 개의 알고리즘을 제안한다. 알고리즘은 그래프의 모든 쌍에 대한 최단경로를 구하는 전처리를 한 후, 최소배달시간이 작은 정점의 순으로 최단배달경로를 구하는 방법으로 개발하였다. 이 문제에서 그래프가 문제를 해결하고자 하는 지형을 반영하고 있다고 하면, 다양한 로봇 에이전트의 배치에 대하여 전처리를 1회만 실행되면 되므로 O(㎥) 알고리즘은 실제로 O(㎡n)의 시간복잡도를 갖는다고 할 수 있다.

The delivery problem on a graph is that of minimizing the object delivery time from one vertex to another vertex on a graph with m vertices using n various speed robot agents. In this paper, we propose two optimal solution algorithms for the delivery problem on a graph with time complexity of O(㎥n) and O(㎥). After preprocessing to obtain the shortest path for all pairs of the graph, our algorithm processed by obtaining the shortest delivery path in the order of the vertices with the least delivery time. Assuming that the graph reflects the terrain on which to solve the problem, our O(㎥) algorithm actually has a time complexity of O(㎡n) as only one preprocessing is required for the various deployment of n robot agents.

This paper suggests heuristic algorithm to obtain optimal solution of minimum number of delay aircraft in airport arrivals/departures problem. This problem can be solved only mathematical optimization method. The proposed algorithm selects the minimum delays capacity in various airport capacities for number of arrivals/departures aircraft in $i^{th}$ time interval (15 minutes). In details, we apply median selection method and left-right selection method. This algorithm can be get the optimal solution of minimum number of delay aircraft for sixes actual experimental data.

Finding the right balance between the customer service and personnel expenditure has been an important task both in practice and academia and in academia this problem was usually approached using a total cost problem. However, it is difficult to directly apply this formulation in practice and hence recent works in queueing systems have investigated this problem with staffing minimization with constraints on quality-of-service constraints. Our work contributes on this stream of works by studying TSF measure, which shows the percentage of customers who waited beyond the prespecified threshold. TSF measures the proportion of customers whose waiting time exceed a certain threshold. It is one the most prevalent kind of quality of service measure, along with ASA, which measures the average of waiting time. TSF measure is intuitive to understand and hence widely been used but has not received sufficient attention in academia. Part of the reason comes from the unnatural property of TSF measure. Once a customer waits more than the threshold, the incentive to serve that customer starkly diminishes since decreasing the eventual waiting time of him does not enhance the performance of the system. Hence a policy that is far from FIFO turns out to be more efficient. Especially we are interested in context where service level differentiation among various classes of customers. Hence, we work on V-model where multiple classes of customer and homogeneous pool of servers exist. Our task is to come up with the minimum possible number of servers while maintaining TSF measure on the pre-defined level. The decision variables are two-kind: number of servers and prioritization policy. The prioritization policy defines which class of customer to server first when a server becomes available. The nature of TSF measure, which tends to give lower priority to the customers who had already waited beyond the threshold, makes the problem more difficult to solve and hence it was usually treated in literature more added conditions or constraints added. We devise an optimal solution of TSF constrained problem without adding more structures. Since the problem is difficult to solve in exact analysis, we apply the heavy traffic technique to solve the problem. First, we show that our suggested staffing level is the lower bound for all the feasible solutions. Second, it is proved that the proposed prioritization policy combined with our staffing level actually is feasible and hence asymptotically optimal.

Parameter identification problem of a three species (predator, mutualist-prey, and mutualist) ecological system with reaction-diffusion phenomenon is investigated in this paper. The mathematical model of the parameter identification problem is constructed and continuous dependence of the solution for the direct problem on the parameters identified is obtained. Finally, the existence of optimal solution and an optimality necessary condition for the parameter identification problem are given.

In this paper we study the problem of three-dimensional layout optimization on the simplified rotating vessel of satellite. The layout optimization model with behavioral constraints is established and some effective and convenient conditions of performance optimization are presented. Moreover, we prove that the performance objective function is locally Lipschitz continuous and the results on the relations between the local optimal solution and the global optimal solution are derived.

We consider discrete-time factorial Markov Decision Processes (MDPs) in multiple decision-makers environment for infinite horizon average reward criterion with a general joint reward structure but a factorial joint state transition structure. We introduce the "localization" concept that a global MDP is localized for each agent such that each agent needs to consider a local MDP defined only with its own state and action spaces. Based on that, we present a gradient-ascent like iterative distributed algorithm that converges to a local optimal solution of the global MDP. The solution is an autonomous joint policy in that each agent's decision is based on only its local state.cal state.

When a fault occurs in a power system, the existing analytic models for the power system fault diagnosis could generate multiple solutions under the condition of one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or an alarm or alarms of these PRs and/or CBs failing. Therefore, this paper presents an improved analytic model addressing the above problem. It takes into account the interaction between the uncertainty involved with PR operation and CB tripping and the uncertainty of the alarm reception, which makes the analytic model more reasonable. In addition, the existing analytic models apply the penalty function method to deal with constraints, which is influenced by the artificial setting of the penalty factor. In order to avoid the penalty factor's effects, this paper transforms constraints into an objective function, and then puts forward an improved immune clonal multi-objective optimization algorithm to solve the optimal solution. Finally, the cases of the power system fault diagnosis are served for demonstrating the feasibility and efficiency of the proposed model and method.

Experiments were carried out to develop an optimal nutrient solution for the single-stemmed rose (Rosa hybrida L.) 'Red velvet' in a closed aeroponic system. Plants were grown in 1/3, 1/2, 1, or 3/2 strength of the nutrient solution of National Horticultural Research Station in Japan (NHRS). Significantly less changes of pH and EC ($dS{\cdot}m^{-1}$) in the drainage were observed in 1/2 strength treatment as compared to other treatments. The $NO_3-N$, K, Ca, and Mg concentrations in the drainage solution of 1/2 strength treatment were maintained at optimal levels. These results indicated that the rose uptakes of both nutrients and water was more stable than those in other concentration. The concentration of macronutrients in nutrient solution were adjusted based on the ratio of nutrient:water (n/w) taken up by plants grown in the 1/2 strength solution. The composition of the new solution (classified the University of Seoul (UOS) solution) was as follow; $NO_3-N$ 8.8, $NH_4-N$ 0.67, P 2.0, K 4.8, Ca 4.0, Mg 2.0 $me{\cdot}L^{-1}$. To further evaluate new solution on crop growth, the rose 'Red Velvet' was grown again in l/2, 1, and 2 strength UOS solution to compare with 1.0 strength PBG (proefstion voor bloemisterij en glasgroenpe) solution. Overall the plant growth, including the stem length and number of five-leaflet leaves was higher in 1.0 strength of UOS solution than other treatments. Results presented in this study indicate that the nutrients in the UOS solution are well balanced for the single-stemmed rose in the closed aeroponic system.