Earticle

본 연구는 중국의 섬유산업에 대한 상황분석을 통하여 비용, 환경오염, 외부 경쟁, 불안정한 외부환경에 대한 문 제를 확인하려는 목적을 지닌다. 특히 본 연구는 양쯔강 지역의 생산 현황, 수출입 상황, 고용현황, 의복 생산을 SWOT분석을 통하여 살펴보는데 초점을 둔다. 지속가능한 발전을 위한 능력을 형성하는 것은 5가지의 영역으로 나 뉘는데, 정부, 산업 협회, 연구기관들, 회사, 개인이다. 본 연구는 특히 산업, 정부, 대학 간의 협력 및 조합의 중요 성을 제기한다.

Chinese textile industry is competitive and high dependence on international trade，also the comparative advantages of the traditional pillars of enterprise. China is textile power but is not textile great-power. Especially in the context of economic globalization, Chinese textile industry is facing many problems to be solved, at the same time, development is also facing a bottleneck. Let the textile industry continues to contribute to the national economy, it’s necessary to ensure sustainable development, avoid become a burden textile industry from the pillar industries. This paper through situation analysis of China’s textile industry, identify problems about cost, pollution, external competition and unstable external environment. Analyze Production status, Import and Export situation, Employment status and clothing production about Yangtze River Delta through SWOT analysis. Building capacity for sustainable development will be divided into five parts, incloud Government, Industry associations, Research Institutions, Company and Personal, propose the Combination of Industry, Official and University. Government should intensify investment in research funding, to improve research and development capabilities, actively cooperate with universities and train more professionals, provide advanced technology and more personnel. Industry associations should supervise company, at the same time, provide more policy guidance for company, change the company’s greener and more efficient production methods.

In earlier days, most of the data carried on communication networks was textual data requiring limited bandwidth. With the rise of multimedia and network technologies, the bandwidth requirements of data have increased considerably. If a network link at any time is not able to meet the minimum bandwidth requirement of data, data transmission at that path becomes difficult, which leads to network congestion. This causes delay in data transmission and might also lead to packet drops in the network. The retransmission of these lost packets would aggravate the situation and jam the network. In this paper, we aim at providing a solution to the problem of network congestion in mobile ad hoc networks [1, 2] by designing a protocol that performs routing intelligently and minimizes the delay in data transmission. Our Objective is to move the traffic away from the shortest path obtained by a suitable shortest path calculation algorithm to a less congested path so as to minimize the number of packet drops during data transmission and to avoid unnecessary delay. For this we have proposed a protocol named as Congestion Aware Selection Of Path With Efficient Routing (CASPER). Here, a router runs the shortest path algorithm after pruning those links that violate a given set of constraints. The proposed protocol has been compared with two link state protocols namely, OSPF [3, 4] and OLSR [5, 6, 7, 8].The results achieved show that our protocol performs better in terms of network throughput and transmission delay in case of bulky data transmission.

The performance of traditional routing protocols for mesh networks can decrease significantly in disaster recovery situations due to dynamic changes to the network environment. A number of multi-path routing protocols have been proposed to address such issues. MMQR is a multi-path routing protocol for OFDM?TDMA mesh networks which use multiple paths that can be replaced immediately by one another in case of link failures. In this paper we extend the MMQR such that the alternative paths are reordered adaptively as the communication environment changes based on the heuristic model of attractor selection mechanism by which biological entities are known to adapt to dynamically changing environment. Simulation results show that the multi-path routing protocol with attractor selection mechanism outperforms existing multi-path routing protocols in terms of packet delivery ratio, throughput and QoS.

주택채무변제계약과 관련한 분쟁은 그 해결에 있어 복잡하다는 특성이 있다. 현행법률과 사법해석으로 인해 이에 대한 주택채무변제계약의 성격과 효력을 회피하고, 계약의 자유라는 원칙을 제대로 적용되지 않는다. 채권자가 우선변제 권을 가지고 있는지 등의 문제가 있다. 이에 따라 주택채무변제계약과 같은 분쟁의 성격과 효력에 대한 대물청산와 담 보 등은 채권의 채권의 변제와 다르게 사법의 획일적 적용을 제약하고, 사법의 본 위를 약화시킨다. 따라서 주택담보부채권이 낙성계약에 속하는 것이 명확하고, 이 를 실제로 이행함에 있어 주택담보부채권의 구성요건을 무효화는 경우, <해석> 제 24조의 세분화와 <해석> 제24조의 예외적용 등에서 사법적 보완을 가하여야 한다. 또한 인공지능과 사법재판이 점점 더 결합되고 있는 가운데, 인공지능 빅데 이터 처리, 정보분류, 문자생성 등의 기능을 활용하여 구체적인 합의에 따른 법 적 관계, 내용의 복잡정도, 관련 법조문의 수, 내용 추출 및 분류를 할 필요가 있다. 주택채무변제계약 분쟁 사례에 대한 데이터베이스의 보완과 주택담보부 분쟁 알고리즘의 해석성 강화 및 주택담보부 분쟁의 지능화된 연구개발 역량을 강화하여 재판의 결과를 예측할 필요가 있을 것이다. 이는 현대사회의 현실에 부합하고, 계약자유의 원칙과 당사자 법익에 부합하는 지능화된 재판을 통해 인공지능과 사법재판의 융합을 실현할 수 있을 것이다.

While frequent housing-for-debts-repayment disputes have complex and blended characteristics, current law and its judicial interpretation do not demonstrate the contractual freedom, leaving such matters as how to judge the nature and effect of relevant agreements and whether the creditor has a priority of compensation unresolved. As a result, such judges as datio in solutum, transfer guarantee, post-transfer guarantee, flow escort item or renewal of debts were made in judicial practice when dealing with the housing-for-debts-repayment agreement, which has inhibited the unified application of law and impaired judicial authority. Therefore, it is essential to make clear that the housing-for-debts-repayment agreement is a promissory contract requiring actual performance. Meanwhile, in order to improve the judging rules, it’s of necessity to clear and definite elements of constitution, ineffective circumstances, further illustration of Article 24 of the Provisions on the Application of Law in the Trial of Private Lending Cases and exceptions to the application of Article 24. In the context of the increasing combination of artificial intelligence and judicial trials, utilizing the big data processing, information classification, text generation and other functions, AI can complete text extraction and classification based on legal relationship contained under specific agreements, the complexity of the content and the number of relevant articles. At the same time, by means of improving the case database of housing-for-debts-repayment disputes, enhancing the interpretability of housing-for-debts-repayment algorithm and strengthening the research of intellectualization in such disputes, intellectualized trial can be used to predict the judgment and the same judgment in similar cases can be used to improve the litigation efficiency. Thus, an intellectualized trial path will be established which not only is in line with the social reality in China, but also conforms to the principle of freedom of the contract law and the legal interests of the parties, realizing the effective integration of artificial intelligence and jurisdiction.

以房抵债民事纷争频发, 且具有复杂性、混合性特质。由于现行法律及其司法 解释对此规定, 回避了以房抵债协议性质和效力、未彰显契约自由精神, 债权人 是否具有优先受偿权等问题, 导致实践中出现了对该类纠纷的性质与效力作出 代物清偿、让与担保、后让与担保、流押条款或债的更新等不同界定, 制约司法 的统一适用, 减损了司法权威。因而, 需要明确以房抵债属于诺成契约且现实履 行, 从以房抵债的构成要件、无效情形、≪解释≫第24条的细化和 ≪解释≫第 24条的例外适用等方面完善裁判规则。且在人工智能与司法审判日趋结合的当 下, 利用AI大数据処理、信息分类、文字生成等功能, 根据具体协议包含的法律 关系、内容的复杂程度、关联法条的数量, 进行内容提取与分类, 从完善以房抵 债纠纷的案例数据库、増强以房抵债纷争算法的可解释性和强化以房抵债纷争 智能化的研发力度着手, 发挥智能化审判对以房抵债裁判结果的预测、类案同判 实现和诉讼效益提升的显性功效, 以达至一条既契合中国社会现实, 也符合债法 自由原则和当事人法益的智能化裁判路径, 真正实现人工智能与司法裁判有效 融合。

강화학습의 기법 중 Q-Learning은 주어진 상태에서 행동을 수행하면서 미래의 효율적인 기댓값을 예측하는 Q 함수를 학습하면서 최적의 정책을 학습하는 것이다. Q-Learning은 강화학습의 기본적인 알고리즘으로 많이 활용하고 있다. 본 논문에서는 Q-Learning을 바탕으로 정책과 보상을 설계하여 효율적인 경로를 선택하고 학습하는 효용성에 대하여 연구하였다. 또한 Frozen Lake 게임의 8x8 그리드 환경에 동일한 학습 횟수를 적용하여 기존 알고리즘 및 처벌 보상 정책과 제시한 처벌강화 정책의 결과를 비교하였다. 해당 비교를 통해 본 논문에서 제시한 Q-Learning의 처벌강화 정책이 통상적인 알고리즘의 적용보다 학습 속도를 상당히 높일 수 있는 것으로 분석되었다.

Among the techniques of reinforcement learning, Q-Learning means learning optimal policies by learning Q functions that perform actionsin a given state and predict future efficient expectations. Q-Learning is widely used as a basic algorithm for reinforcement learning. In this paper, we studied the effectiveness of selecting and learning efficient paths by designing policies and rewards based on Q-Learning. In addition, the results of the existing algorithm and punishment compensation policy and the proposed punishment reinforcement policy were compared by applying the same number of times of learning to the 8x8 grid environment of the Frozen Lake game. Through this comparison, it was analyzed that the Q-Learning punishment reinforcement policy proposed in this paper can significantly increase the learning speed compared to the application of conventional algorithms.

An efficient algorithm is proposed to select the proper tools and generate their paths for NC rough cutting of dies and molds with sculptured surfaces. Even though a milling process consists of roughing, semi-finishing, and finishing, most material is removed by a rough cutting process. Therfore it can be said that the rough cutting process occupy an important portion of the NC milling process, and accordingly, an efficient rough cutting method contributes to an efficient milling process. In order work, the following basic assumption is accepted for the efficient machining. That is, to machine a region bounded by a profile, larger tools should be used in the far inside and the region adjacent to relatively simple portion of the boundary while smaller tools are used in the regions adjacent to the relatively complex protion. Thus the tools are selected based on the complexity of the boundary profile adjacent to the region to be machined. An index called cutting path ratio is proposed in this work as a measure of the relative complexity of the profile with respect to a tool diameter. Once the tools are selected, their tool paths are calculated starting from the largest to the smallest tool.

본 논문에서는 다중 홉 시스템에서 하나의 홉에 대한 채널 정보만을 이용하는 효율적인 부분 중계 노드 선택(efficient partial relay selection, EPRS) 기법에 대한 아웃티지 확률을 레일레이 페이딩 채널에서 분석한다. 특히, 디코딩 후 전달 중계 기반의 EPRS 기법에 대한 정확한 아웃티지 확률의 식을 유도한다. 또한, 다중 홉 시스템에서 EPRS 기법의 실효성을 확인하기 위하여, 임의의 중계 경로에서 종단간 신호 대 잡음비와 각 홉의 신호 대 잡음비의 상관관계를 분석한다. 그리고, 수치적 결과를 통해 EPRS 기법과 모든 채널 정보를 이용하는 중계 노드 선택 기법의 아웃티지 성능을 비교한다.

In this paper, the outage probability of efficient partial relay selection(EPRS) using only one-hop channel information in multi-hop systems is analyzed in Rayleigh fading channels. In particular, we derive an exact and closed-form expression for the outage probability of decode-and-forward relaying based EPRS. In order to prove the usefulness of EPRS in multi-hop systems, we also analyze the correlation between the end-to-end signal-to-noise ratio(SNR) and the SNR for each hop at an arbitrary relaying path. Furthermore, through numerical investigation, we compare the outage performances for EPRS and the best relay selection using all channel information.

최근 스마트 폰과 같은 다양한 단말의 보급이 확대됨에 따라 지속적인 트래픽 증가로 인해 이를 처리하기 위한 전달 망 및 통신망 제어기술 또한 급속히 발전하고 있다. 이러한 추세를 반영하여 차세대 네트워크는 다양한 스위칭 계층으 로 구성되고 이를 Generalized Multi-Protocol Label Switching(GMPLS) 프로토콜로 제어하는 형태로 구성될 것이다. 그러나 다계층 네트워크에서의 장애는 보호 복구 기능들 간의 충돌을 유발하여, 복구 시간의 지연, 망 자원의 비효율적 사용 등을 초래할 수 있다. 따라서 계층별 보호 복구 기능 간의 신속한 복구 경로설정이 다계층 네트워크에서 가장 중 요한 과제가 되었다. 본 논문에서는 PCE(Path Computation Element)를 기반으로 다계층, 다중 도메인 네트워크에서 최 적의 트래픽 엔지니어링을 지원하기 위한 기법을 제안하였다.

The explosive growth of Internet traffic cause by smart equipment such as smart phone has led to a dramatic increase in demand for data transmission capacity and network control architecture, which requires high transmission rates beyond the conventional transmission capability. Next generation networks are expected to be controlled by Generalized Multi-Protocol Label Switching(GMPLS) protocol suite and operating at multiple switching layers. In order to ensure the most efficient utilization of multilayer network resources, effective global provisioning that providing the network with the possibility of reacting in advance to traffic changes should be provided. In this paper, we proposes a new path selection scheme in multilayer optical networks based on the vertical PCE architecture and a different approach to efficiently exploit multiple PCE cooperation.

릴레이 시스템은 커버리지 확장과 셀 경계(Cell-Edge)의 시스템 처리량 향상을 목적으로 4세대 이동통신 시스템에 적용되어 왔다. 릴레이 시스템은 커버리지 확장과 시스템 처리량 증대에 효과적이지만 기존 단일 홉 시스템과 달리 추가 자원을 사용하기 때문에 릴레이 시스템에 특화된 경로선택 및 무선자원 할당 알고리즘의 적용을 요구한다. 본 논문에서는 협력 통신을 이용하는 LTE-Advanced 릴레이 시스템을 위한 통합 경로선택 및 자원할당 기법을 제안한다. 제안하는 기법은 라그랑지 승수 기반의 휴리스틱 알고리즘으로, 다중차원 다중선택 배낭 문제(Multi -dimensional Multi-choice Knapsack Problem)의 형태로 정의된 협력 통신 기반의 LTE-Advanced 릴레이 시스템 하향링크 처리율 최대화 문제의 근사 해를 구한다. 제안된 기법에 의해 도출된 근사 해의 성능이 최적 해의 성능에 충분히 근접할 수 있음을 시뮬레이션을 통해 보인다.

Mobile relay systems have been adopted by 4th generation mobile systems as an alternative method to extend cell coverage as well as to enhance the system throughput at cell-edges. In order to achieve such performance gains, the mobile relay systems require path selection and resource allocation schemes that are specifically designed for these systems which make use of additional radio resources not needed in single-hop systems. This paper proposes an integrated path selection and resource allocation scheme for LTE-Advanced relay systems using collaborative communication. We first define the problem of maximizing the downlink throughput of LTE-Advanced relay systems using collaborative communication and transform it into a multi-dimensional multi-choice backpacking problem. The proposed Lagrange multiplier-based heuristic algorithm is then applied to derive the approximate solution to the maximization problem. It is shown through simulations that the approximate solution obtained by the proposed scheme can achieve a near-optimal performance.

Aluminum alloys have low density, relatively high strength and yield strength, good plasticity, good machinability, and high corrosion and acid resistance. Therefore, they are suitable for large containers for the food, chemical and other industries. Large containers are often bodies of revolution consisting of shell courses, stiffening rings, heads and other elements joined by annular welds. Larger containers have longer welds and require greater leak-tightness and higher weld mechanical properties. The LNG tank consists of aluminum plates with various sizes, so its construction should by divided by several sections. Moreover, each section has its own sub-section consisted of several aluminum plates. To guarantee the quality of huge LNG tank, therefore, the precise control of plate dimension should by urgently needed in conjunction with the appropriate selection of process parameters such as cutting speed, depth of cut, rotational speed and so on. In this paper, a manufacturing system was developed to implement automatic circular tracking in height direction and automatic circular interpolation in depth of cut direction. Also, the neural network based on the backpropagation algorithm was used to predict the cutting quality and motor load related with the life time of the developed system. It was revealed that the manufacturing system and the neural network could be effectively applied to the bevelling process and to predict the quality of machined area and the motor load.

This research is to select a path planning algorithm to maximize survivability for Unmanned Aerial Vehicle(UAV). An UAV is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). In this research, a mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and verified by using ILOG CPLEX. A path planning algorithm for UAV is selected by comparing of SPP(Shortest Path Problem) algorithms which transfer MRPP into SPP.

One of the main design issues of wireless sensor network (WSN) is to prolong the network lifetime and prevent connectivity degradation by employing energy management techniques. But a single route does not make any solution of in terms of energy because it rapidly drains energy of relay nodes. As a result these nodes die quickly than others. In this paper, we present a novel level based path selection and data forwarding techniques for large WSN for hierarchical architecture. In this structure cluster head (CH) is responsible for data fusion and data forwarding. So CHs expense more energy than other nodes. In this work base station constructs multiple routes among the CHs based on the path cost. In the data forwarding phase a CH selects a path from its route cache by considering both path cost and residual energy. Simulation result shows that our proposed technique achieves much higher performance than the classical routing protocols such as LEACH, flooding.

Nowadays, many network devices have more than one network interface. Meanwhile, many protocols have been proposed to utilize multiple paths simultaneously. MPTCP (Multipath TCP) is a transport layer protocol that transfers data by multiple paths simultaneously, which is designed to improve the throughput and increase robustness of end to end transmission. However, when we use MPTCP, we find that MPTCP can not improve the throughput but even reduce the throughput in some cases. In this paper, we study this issue. Firstly, we study the effects of path characteristics for throughput of MPTCP, and find that path characteristics especially those bad paths have a great impact on the performance of MPTCP. Secondly, we analyze the reasons why the bad path characteristics have a serious impact on throughput of MPTCP, and propose a path selection method based on throughput prediction model and available bandwidth to improve the throughput. Finally, we validate our path selection method by simulations, and the results show that our method can improve the throughput of MPTCP.

The selection of a path in the exponentially growing internet is a difficult task. The upcoming high-speed networks are expected to support a wide variety of communication-intensive real-time multimedia applications. There are many significant factors which affect the performance of a computer network such as choice of routing algorithm, heavy traffic, etc. There must be a mechanism to effectively use the network. Using shortest paths conserves network resources, but it causes some resources of the network to be over utilized while other resources remain under utilized. As the path selection directly affects the throughput, an efficient path selection algorithm is needed which periodically finds the optimum path. In this paper, we would like propose an algorithm that can be used in both datagram and virtual circuit environment. This is a small step in building throughput-aware and traffic-sensitive path selection. Ultimately, our idea contributes to find a route in an integrated network environment.

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

Evacuation to disaster shelters can be optimized by giving instructions to evacuees to help them find the most suitable shelters and evacuation paths from their location. To generate such information in real-time, construction of spatial databases and complex spatial analysis models through Web Map Services (WMS) and Web Feature Services (WFS) based on Open Geospatial Consortium Web Services (OWS) is required. Focusing on the implementation of distributed OWS resources to provide more immediate information in the case of an emergency, we propose a Web-based system that identifies suitable shelters and provides the shortest evacuation path to each shelter for civilians who are within the danger zone. Specifically, (1) a search framework based on the OWS architecture is proposed and utilized to develop both Web-based and smartphone applications connected with WMS types of spatial data on the Web; (2) the spatial analysis models proposed include three sub-models, namely, data construction, shelter selection using scores by natural breaks, and an evacuation path model using network analysis. We identify candidate shelters by considering proximity to health care facilities and to major road, and coverage of each shelter in danger zone.

본 논문은 실시간 GPS 항법시스템에서 최단경로 탐색에 일반적으로 적용되고 있는 Dijkstra 알고리즘의 수행 복잡도 을 선형인 으로 단축시킬 수 있는 알고리즘을 제안하였다. Dijkstra 알고리즘은 출발 노드부터 시작하여 모든 노드를 방문하여 최소 경로 길이를 계산한다. 따라서 “노드 수 -1”회를 수행하야 하기 때문에 복잡한 도로로 구성된 도시에서 실시간으로 최단경로 정보를 제공할 수 없는 경우도 발생한다. 제안된 알고리즘은 먼저, 그래프를 트리로, 출발 노드를 근 노드로 치환하여 트리의 각 레벨에 해당하는 외부근방 (Out-Neighbourhood) 노드 집합을 구성하고, 외부근방간과 외부근방 내부의 최소 경로 길이를 계산하는 방법을 적용하였다. 제안된 알고리즘을 양방향과 일방통행로로 구성된 복잡한 2개 그래프에 대해 알고리즘을 적용한 결과 Dijkstra 알고리즘과 동일하게 모든 노드의 최소 경로 길이를 얻는데 성공하였다. 또한, 알고리즘 수행속도를 “노드 수 -1”회에서 “레벨 수 -1”회로 약 4배 정도 단축시키는 효과를 얻었다. 제안된 알고리즘을 GPS 실시간 시스템에 적용하여 러시아워나 차량 사고로 인한 병목현상이 발생하였을 때, 최단 경로 우회 도로 정보를 실시간으로 제공할 수 있다면 운전자의 만족도를 크기 향상시킬 수 있을 것이다.

This paper suggests an algorithm that can shorten the complexity O(n²) of Dijkstra algorithm that is applied to the shortest path searching in real-time GPS Navigation System into an up-to-date O(n). Dijkstra algorithm manipulates the distance of the minimum length path by visiting all the nodes from the starting node. Hence, it has one disadvantage of not being able to provide the information on the shortest path every second, in a city that consists of sophisticated roads, since it has to execute number of node minus 1. The suggested algorithm, firstly, runs by means of organizing the set of out-neighbourhood nodes at each level of the tree, and root node for departure node. It also uses a method of manipulating the distance of the minimum path of all out-neighborhoods and interior of the out-neighborhoods. On applying the suggested algorithm to two sophisticated graphs consisted of bi-direction and uni-direction, we have succeeded to obtain the distance of the minimum length path, just as same as Dijkstra algorithm. In addition, it has an effect of shortening the time taken 4 times from number of node minus1 to number of level minus 1. The satisfaction of the drivers can be increased by providing the information on shortest path of detour, every second, when occurs any rush hour or any traffic congestion due to car accident, by applying this suggested algorithm to the real-time GPS system.