• 한국멀티미디어학회논문지
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• 제11권4호
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• pp.454-461
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• 2008

본 논문에서는 이동변위를 기반으로 하는 휴대기기의 새로운 입력 방법을 제안한다. 이를 위하여 휴대기기에 장착되어 있는 카메라를 이용하여 영상을 연속적으로 획득하고, 획득된 영상간의 변위를 실시간으로 계산함으로써 휴대기기의 이동 변위를 추정하였다. 제안하는 알고리즘은 획득된 영상간의 변위를 실시간으로 계산하기 위하여 계산량이 적은 SUSAN 코너 검출기를 사용하여 두 영상에서 특징점 들을 추출하였다. 다음으로 추출된 특징점 사이의 매칭작업을 수행하기 위하여 투 패스 알고리즘을 적용한 보로노이 평면을 생성하고, 두 영상의 거리 값인 SAD (Sum of absolute difference)를 계산함으로써 두 영상간의 변위를 계산하였다. 실험결과에서는 총 1500장의 영상을 이용하여 변위 추정알고리즘의 성능을 평가하였다. 그 결과 최대 90% 이상 매칭 성공률을 보였으며, 연산 속도는 5 ms 이내였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.666-670
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• 2004

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using sequential Monte Carlo Simulation (MCS) techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential MCS to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. And also the proposed approach has been considered multi-state as well as two-state of the generating units. In addition, we have applied the variance reduction technique to enhance the efficiency of the simulation. To show the efficiency and effectiveness of the suggested method the numerical studies have been performed for the modified IEEE reliability test system.

• 산업경영시스템학회지
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• 제42권1호
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• pp.8-20
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• 2019

The vehicle routing problem is one of the vibrant research problems for half a century. Many studies have extensively studied the vehicle routing problem in order to deal with practical decision-making issues in logistics. However, developments of new logistics strategies have inevitably required investigations on solution methods for solving the problem because of computational complexity and inherent constraints in the problem. For this reason, this paper suggests a simulated annealing (SA) algorithm for a variant of vehicle routing problem introduced by a previous study. The vehicle routing problem is a multi-depot and multi-trip vehicle routing problem with multiple heterogeneous vehicles restricted by the maximum permitted weight and the number of compartments. The SA algorithm generates an initial solution through a greedy-type algorithm and improves it using an enhanced SA procedure with three local search methods. A series of computational experiments are performed to evaluate the performance of the heuristic and several managerial findings are further discussed through scenario analyses. Experiment results show that the proposed SA algorithm can obtain good solutions within a reasonable computation time and scenario analyses show that a transportation system visiting non-dedicated factories shows better performance in truck management in terms of the numbers of vehicles used and trips for serving customer orders than another system visiting only dedicated factories.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3733-3755
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• 2019

E-cash has its merits comparing with other payment modes. However, there are two problems, which are how to achieve practical/complete tracing and how to achieve it in compact E-cash. First, the bank and the TTP (i.e., trusted third party) have different duties and powers in the reality. Therefore, double-spending tracing is bank's task, while unconditional tracing is TTP's task. In addition, it is desirable to provide lost-coin tracing before they are spent by anyone else. Second, compact E-cash is an efficient scheme, but tracing the coins from double-spender without TTP results in poor efficiency. To solve the problems, we present a compact E-cash scheme. For this purpose, we design an embedded structure of knowledge proof based on a new pseudorandom function and improve the computation complexity from O(k) to O(1). Double-spending tracing needs leaking dishonest users' secret knowledge, but preserving the anonymity of honest users needs zero-knowledge property, and our special knowledge proof achieves it with complete proofs. Moreover, the design is also useful for other applications, where both keeping zero-knowledge and leaking information are necessary.

• 한국전자통신학회논문지
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• 제14권1호
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• pp.147-154
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• 2019

이 연구에서는 분자들이 노즐과 터빈내부에서 운동하는 거동을 모사하기 위해 분자동력학 시뮬레이션 시스템의 모델을 설계하고 개발하였다. Lennard-Jones Potential 모델을 이용하여 분자들간에 상호 작용을 계산하고, Verlet 알고리듬을 뉴턴의 운동 방정식을 적산하기 위한 수치해석 방법으로 사용하였다. Lennard-Jones Potential 함수를 계산하기 위해, 분자 개수 N에 대해 $O(N^2)$ 계산량을 cutoff $r_c$를 이용하여 O(N)으로 줄여서 계산하여 CPU 시간을 절약할 수 있도록 구현하였다.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.256-266
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• 2021

The Convolutional Neural Network (CNN) has recently made potential improvements in face verification applications. In fact, different models based on the CNN have attained commendable progress in the classification rate using a massive amount of data in an uncontrolled environment. However, the enormous computation costs and the considerable use of storage causes a noticeable problem during training. To address these challenges, we focus on relevant data trained within the CNN model by integrating a lifting method for a better tradeoff between the data size and the computational efficiency. Our approach is characterized by the advantage that it does not need any additional space to store the features. Indeed, it makes the model much faster during the training and classification steps. The experimental results on Labeled Faces in the Wild and YouTube Faces datasets confirm that the proposed CNN framework improves performance in terms of precision. Obviously, our model deliberately designs to achieve significant speedup and reduce computational complexity in deep CNNs without any accuracy loss. Compared to the existing architectures, the proposed model achieves competitive results in face recognition tasks

• Journal of Information Processing Systems
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• 제17권1호
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• pp.191-202
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• 2021

In an Internet of Things (IoT)-configured system, each device executes on-chip software. Recent IoT devices require fast execution time of complex services, such as analyzing a large amount of data, while maintaining low-power computation. As service complexity increases, the service requires high-performance computing and more space for embedded space. However, the low performance of IoT edge devices and their small memory size can hinder the complex and diverse operations of IoT services. In this paper, we propose a remote on-demand software code execution unit using the cloudification of on-chip code memory to accelerate the program execution of an IoT edge device with a low-performance processor. We propose a simulation approach to distribute remote code executed on the server side and on the edge side according to the program's computational and communicational needs. Our on-demand remote code execution unit simulation platform, which includes an instruction set simulator based on 16-bit ARM Thumb instruction set architecture, successfully emulates the architectural behavior of on-chip flash memory, enabling embedded devices to accelerate and execute software using remote execution code in the IoT environment.

• 로봇학회논문지
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• 제16권2호
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• pp.112-121
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• 2021

Unlike a typical small-sized robot navigating in a free space, an autonomous vehicle has to travel in a designated road which has lanes to follow and traffic rules to obey. High-Definition (HD) maps, which include road markings, traffic signs, and traffic lights with high location accuracy, can help an autonomous vehicle avoid the need to detect such challenging road surroundings. With space constraints and a pre-built HD map, a new type of path planning algorithm can be conceived as a substitute for conventional grid-based path planning algorithms, which require substantial planning time to cover large-scale free space. In this paper, we propose an obstacle-avoiding, cost-based planning algorithm in a continuous space that aims to pursue a globally-planned path with the help of HD map information. Experimentally, the proposed algorithm is shown to outperform other state-of-the-art path planning algorithms in terms of computation complexity in a typical urban road setting, thereby achieving real-time performance and safe avoidance of obstacles.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1552-1559
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• 2021

Convolution neural networks (CNNs) show notable performance in image processing and are used as representative core models. CNNs extract and learn features from large amounts of train dataset. In general, it has a structure in which a convolution layer and a fully connected layer are stacked. The core of CNN is the convolution layer. The size of the kernel used for feature extraction and the number that affect the depth of the feature map determine the amount of weight parameters of the CNN that can be learned. These parameters are the main causes of increasing the computational complexity and memory usage of the entire neural network. The most computationally expensive components in CNNs are fully connected and spatial convolution computations. In this paper, we propose a Fourier Convolution Neural Network that performs the operation of the convolution layer in the Fourier domain. We work on modifying and improving the amount of computation by applying the fast fourier transform method. Using the MNIST dataset, the performance was similar to that of the general CNN in terms of accuracy. In terms of operation speed, 7.2% faster operation speed was achieved. An average of 19% faster speed was achieved in experiments using 1024x1024 images and various sizes of kernels.

• 자동차안전학회지
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• 제14권2호
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.