• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.483-503
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• 2015

This paper analyzes the optimal user density and power allocation for Device-to-Device (D2D) communication underlaying cellular networks on multiple bands with the target of maximizing the D2D transmission capacity. The entire network is modeled by Poisson point process (PPP) which based on stochastic geometry. Then in order to ensure the outage probabilities of both cellular and D2D communication, a sum capacity optimization problem for D2D system on multiple bands is proposed. Using convex optimization, the optimal D2D density is obtained in closed-form when the D2D transmission power is determined. Next the optimal D2D transmission power is obtained in closed-form when the D2D density is fixed. Based on the former two conclusions, an iterative algorithm for the optimal D2D density and power allocation on multiple bands is proposed. Finally, the simulation results not only demonstrate the D2D performance, density and power on each band are constrained by cellular communication as well as the interference of the entire system, but also verifies the superiority of the proposed algorithm over sorting-based and removal algorithms.

• 한국멀티미디어학회논문지
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• 제3권6호
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• pp.674-684
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• 2000

병렬처리 과정 중에서 고려되어야할 가장 중요한 점은 프로세서사이에 통신을 어떻게 효율적으로 처리하는가 하는 것이다. 그 중 하나의 접근방법이 익스팬드 그래프를 이용하여 최적의 지연시간을 달성하는 방법이다. 익스팬드 그래프를 기초로 하여 효율적인 네트워크 구성과 수행시간이 빠른 병렬 알고리즘을 개발하기 위한 시도가 이루어져왔다. 병렬알고리즘 수행에서의 중요한 결과인 O(logN)시간의 AKS정렬 알고리즘은 익스팬드를 기초로 한다. 익스팬드 그래프는 다시 집중기(concentrator)와 초집중기(superconcentrator)에 적용될 수 있으며 Margulis가 선형 익스팬드 그래프의 구성하는 방법을 구체적으로 제시한 후 몇 개의 익스팬드가 제시되었다. 그러나 익스팬드 그래프를 이용한 구체적인 구조는 제시하지 않았다. 본 논문에서 hypercube 구조에서의 익스팬드 네트워크 구조를 조사하고 그리고 각 단에서의 확장성을 분석하고 다단으로 확장한다. 본 논문은 hypercube에서의 익스팬드 네크워크의 이론적 분석을 제시한다.

• 한국지능시스템학회논문지
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• 제21권1호
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• pp.49-54
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• 2011

본 논문에서는 실내 환경에서 발생 할 수 있는 각종 냄새를 구분하고 농도를 검출하여 냄새 발생지로의 탐색과 이에 관련된 정보를 전송하는 지능 로봇을 구현 하였다. 온도변화에 따라 구동 되는 반도체식 가스센서를 이용하여 에탄올, 암모니아 및 이들의 혼합가스를 신경망 알고리즘을 통해 구분하고, 퍼지 추론방식으로 농도 측정이 가능한 후각 기능을 구현 하여 탑재함과 동시에 비전 시스템을 이용하여 장애물 회피 이동이 가능하게 하였다. 또한 로봇은 센서 네트워크 내에서 동작하며 유해 가스 및 냄새 관련 정보와 이에 따른 경고 메시지를 멀티홉 방식으로 네트워크 내 다른 노드로 전송한다. 비전 시스템과 후각 센서를 이용한 알고리즘으로 냄새의 종류 구분 및 농도 측정, 발생지 탐색이 가능 하였고 센서 네트워크 내 동작으로 이러한 정보전송이 가능한 로봇을 제시 하였다. 실제 실험을 통하여 냄새 인식 및 농도검출, 그리고 냄새 발생지로의 탐색 알고리즘 및 정보전송 성능의 효용성을 입증 하였다.

• Smart Structures and Systems
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• 제26권5호
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• pp.559-573
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• 2020

One of the powerful data management tools is Building Information Modeling (BIM) which operates through obtaining, recalling, sharing, sorting and sorting data and supplying a digital environment of them. Employing SHM, a BIM in monitoring systems, would be an efficient method to address their data management problems and consequently optimize the economic aspects of buildings. The recording of SHM data is an effective way for engineers, facility managers and owners which make the BIM dynamic through the provision of updated information regarding the occurring state and health of different sections of the building. On the other hand, digital transformation is a continuous challenge in construction. In a cloud-based BIM platform, environmental and localization data are integrated which shape the Internet-of-Things (IoT) method. In order to improve work productivity, living comfort, and entertainment, the IoT has been growingly utilized in several products (such as wearables, smart homes). However, investigations confronting the integration of these two technologies (BIM and IoT) remain inadequate and solely focus upon the automatic transmission of sensor information to BIM models. Therefore, in this composition, the use of BIM based on SHM and IOT is reviewed and the economic application is considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4214-4230
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• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.374-384
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• 2016

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2240-2254
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• 2021

We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 1998년도 춘계학술발표논문집
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• pp.366-370
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• 1998

One major limitation of the efficiency of parallel computer designs has been the prohibitively high cost of parallel communication between processors and memories. Linear order concentrators can be used to build theoretically optimal interconnection schemes. Current designs call for building superconcentrators from concentrators, then using these to recursively partition the connection streams O(log2N) times to achieve point-to-point routing. Since the superconcentrators each have O(N) hardware complexity but O(log2N) depth, the resulting networks are optimal in hardware, but they are of O(log2N) depth. This pepth is not better than the O(log2N) depth Bitonic sorting networks, which can be implemented on the O(N) shuffle-exchange network with message passing. This paper introduces a new method of constructing networks using linear order concentrators and expanders, which can be used to build interconnection networks with O(log2N) depth as well as O(Nlog2N) hardware cost. (All logarithms are in base 2 throughout paper)

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.271-274
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• 1997

This paper presents an adaptive quantization of image sequences using the Radial Basis Function Network(RBFN) which classifies interframe image blocks. The clssification algorithm consists of two steps. Blocks are classified into NA(No Activity), SA(Small Activity), VA(Verical Activity), and HA(Horizontal Activity) classes according to edges, image activity and AC anergy distribution. RBFN is trained using the classification results of the above algorithm, which are nonlinear classification features are acquired from the complexity and variability of difference blocks. Simulation result shows that the the adaptive quantization using the RBFN method produced better results better results than that of the sorting and MLP methods.