Earticle

이 논문에서는 초등수학에서 평행의 정의에 등장하는 ‘한 직선에 수직인 두 직 선’이라는 문구에 주목하고 그것이 어떤 쓰임이 있는지 알아보았다. 이를 위해 우 리나라 중학교 교과서와 초등학교 교과서를 대비하여 초등의 특징을 찾고, 일본과 싱가포르의 초등학교 수학 교과서에서는 그 특징이 나타나는 양상을 살펴보았다. 이어 제1차부터 현재까지 초등수학 교과서에서 평행과 평행선이 어떻게 정의되어 왔는지를 살펴보고 각 교과서에서 ‘한 직선에 수직인 두 직선’이 어떤 쓰임을 가졌는지를 조사하였다. ‘한 직선에 수직인 두 직선’은, 평행선 여부 판정하기, 평행선 긋기, 평행선 사이의 거리 구하기, 사각형에서 평행 여부 판단하기, 평행사 변형 그리기 등 모두 다섯 가지 쓰임이 있다. 5차와 6차에는 이 다섯 가지 쓰임이 모두 교과서에 드러나 있는 반면에 2015 개정 교육과정에서는 상당히 축소된 것으 로 나타났다. 교사나 교과서 저자들이‘한 직선에 수직인 두 직선’이라는 아이디 어가 평행 지도에서 어떤 의미가 있는지를 인식하고 더 나아가 교과용 도서에 이 를 어떻게 반영할 것인지 판단할 때 이 논문이 기초 자료가 될 수 있을 것이다.

In this paper, we wanted to find out why the idea of 'two straight lines perpendicular to one line' is contained in the parallel definition of elementary school mathematics. To this end, we examined how parallel and parallel lines have been defined in the primary math textbooks presented from the first to the present, and what use 'two straight lines perpendicular to one line' in each textbook was. The "two straight lines perpendicular to one straight line" had five uses. While all five were shown in textbooks in the fifth and sixth rounds, the 2015 revised curriculum showed a sharp reduction. In elementary mathematics, the mathematics education community needs to recognize what the idea of "two straight lines perpendicular to one line" means in parallel lines and reflect them in textbooks.

It is widely said that shi conjunctive postposition has two functions. One is to connect more than two phrases or clauses in a parallel relation. Another function is to present one or more causes or reasons for a result or an affirmation stated in the main clause. However, if we closely examine the function and the meaning of shi, not only in a sentence but in context, we can observe when shi connects phrases or clauses, they always act as causes or reasons for a result or an affirmation. From these observations we can conclude that shi conjunctive postposition connects two or more phrases or clauses in a parallel relation and make them act as causes or reasons in wide context.

Scrypt is a password-based key derivation function proposed by Colin Percival in 2009 that has a memory-hard structure. Scrypt has been intentionally designed with a memory-intensive structure to make password cracking using ASICs, GPUs, and similar hardware more difficult. However, in this study, we thoroughly analyzed the operation of Scrypt and proposed strategies to maximize computational parallelism in GPU environments. Through these optimizations, we achieved an outstanding performance improvement of 8284.4% compared with traditional CPU-based Scrypt computations. Moreover, the GPU-optimized implementation presented in this paper outperforms the simple GPU-based Scrypt processing by a significant margin, providing a performance improvement of 204.84% in the RTX3090. These results demonstrate the effectiveness of our proposed approach in harnessing the computational power of GPUs and achieving remarkable performance gains in Scrypt calculations. Our proposed implementation is the first GPU implementation of Scrypt, demonstrating the ability to efficiently crack Scrypt.

In this paper, we present an approach to transmit data from the source to the destination through a minimal path (least-cost path) in a computer network of n nodes. The motivation behind our approach is to address the problem of finding a minimal path between the source and destination. From the work we have studied, we found that a Steiner tree with bounded Steiner vertices offers a good solution. A novel algorithm to construct a Steiner tree with vertices and bounded Steiner vertices is proposed in this paper. The algorithm finds a path from each source to each destination at a minimum cost and minimum number of Steiner vertices. We propose both the sequential and parallel versions. We also conducted a comparative study of sequential and parallel versions based on time complexity, which proved that parallel implementation is more efficient than sequential.

In this paper, we propose efficient algorithms for parallel prefix computation and sorting on a recursive dual-net. The recursive dual-net $RDN^k$(B) for k > 0 has $(2n_o)^{2K}/2$ nodes and $d_0$ + k links per node, where $n_0$ and $d_0$ are the number of nod es and the node-degree of the base-network B, respectively. Assume that each node holds one data item, the communication and computation time complexities of the algorithm for parallel prefix computation on $RDN^k$(B), k > 0, are $2^{k+1}-2+2^kT_{comm}(0)$ and $2^{k+1}-2+2^kT_{comp}(0)$, respectively, where $T_{comm}(0)$ and $T_{comp}(0)$ are the communication and computation time complexities of the algorithm for parallel prefix computation on the base-network B, respectively. The algorithm for parallel sorting on $RDN^k$(B) is restricted on B = $Q_m$ where $Q_m$ is an m-cube. Assume that each node holds a single data item, the sorting algorithm runs in $O((m2^k)^2)$ computation steps and $O((km2^k)^2)$ communication steps, respectively.

Many studies have been done on the integral imaging pickup whose objective is to get efficiently elemental images from a lens array with respect to three-dimensional (3D) objects. In the integral imaging pickup process, it is necessary to render an elemental image from each elemental lens in a lens array for 3D objects, and then to combine them into one total image. The multiple viewpoint rendering (MVR) is one of various methods for integral imaging pickup. This method, however, has the computing and rendering time problem for obtaining element images from a lot of elemental lens. In order to solve the problems, in this paper, we propose a parallel MVR (PMVR) method to generate elemental images in a parallel through distribution of elemental lenses into multiple threads simultaneously. As a result, the computation time of integral imaging using PMVR is reduced significantly rather than a sequential approach and then we showed that the PMVR is very useful.

A large-scale analysis to evaluate complex material and structural behaviors is one of interesting topic in diverse engineering and scientific fields. Also, the utilization of massively parallel processors has been a recent trend of high performance computing. The objective of this paper is to introduce a parallel process system which consists of general purpose finite element analysis solver as well as parallelized PC cluster. The later was constructed using eight processing elements and the former was developed adopting both hierarchical domain decomposition method and balancing domain decomposition method. Then, to verify the efficiency of the established system, it was applied for structural analysis of steam generator in nuclear power plant. Since the prototypal evaluation results agreed well to the corresponding reference solutions it is believed that, after reinforcement of PC cluster by increasing number of processing elements, the promising parallel process system can be utilized as a useful tool for advanced structural integrity evaluation.

This paper presents the first fully parallelized optimization of GCM in a GPU environment. As the era of IoT emerges, a large number of clients communicate with servers, necessitating encrypted communications for security. GCM is a type of AEAD and is currently used in various security protocols, including TLS 1.3 and IPsec. Due to the burden of performing encrypted communication with numerous clients, there has been significant research on utilizing GPUs for high-speed parallel processing in encryption. However, to date, there has been no fully parallelized implementation of GCM on GPUs. This paper proposes a method for parallelizing the challenging GHASH computation in GCM mode, leading to a high-speed parallel implementation of AES-GCM that can exceed 400Gb/s, meeting the requirements of next-generation communication systems. The proposed approach is algorithm-independent and can be applied to any block ciphers. Our implementation on an RTX 4090 demonstrates a performance improvement of *15.38 compared to the maximum processing throughput of a multi-threaded Intel(R) Core(TM) i7-13700K. It also achieves a *17.87 improvement compared to a hybrid CPU?GPU system. Compared to the most researched FPGA implementation for GCM, specifically Xilinx Ultrascale FPGA, our implementation achieves *1.11 better performance. For not only throughput but also power efficiency also better than other implementation, it achieves *3.33 compared to CPU implementation on Intel Xeon E3-1220, also it achieves *21.09 compared to FPGA implementation for AES on Xilinx Virtex 7 series, which is not including full GCM.

In the autonomous driving environment, each vehicle performs numerous signing and verification while sending and receiving BSMs(Basic Security Messages) in real-time. We present an optimized CRYSTALS-Dilithium software which can quickly process each signingand verification in parallel using embedded Graphic Processing Unit. For efficiency, we propose several optimization techniques such asdummy operation-based warp divergence reducing technique, parallel implementation NTT (Number Theoretic Transform)-based polynomialmultiplication, optimization of rejection sampling process using rejection sequence table, and so on. The proposed CRYSTALS-Dilithiumsoftware provides a performance improvement of up to 19.41 times compared to the Dilithium Software on CPUs on NVIDIA Jetson AGXXavier which is an off-the-shelf autonomous vehicle OBU (On-Board Unit).

The scale invariant feature transform (SIFT) is an effective algorithm used in object recognition, panorama stitching, and image matching. However, due to its complexity, real-time processing is difficult to achieve with current software approaches. The increasing availability of parallel computers makes parallelizing these tasks an attractive approach. This paper proposes a novel parallel approach for SIFT algorithm implementation using a block filtering technique in a Gaussian convolution process on the SIMD Pixel Processor. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and input/output capabilities of the processor, which results in a system that can perform real-time image and video compression. We apply this implementation to images and measure the effectiveness of such an approach. Experimental simulation results indicate that the proposed method is capable of real-time applications, and the result of our parallel approach is outstanding in terms of the processing performance.

To address the requirement for high-speed encryption of large amounts of data, this study improves the widely adopted cipher block chaining (CBC) mode and proposes a controllable parallel cipher block chaining (CPCBC) block cipher mode of operation. The mode consists of two phases: extension and parallel encryption. In the extension phase, the degree of parallelism n is determined as needed. In the parallel encryption phase, n cipher blocks generated in the expansion phase are used as the initialization vectors to open n parallel encryption chains for parallel encryption. The security analysis demonstrates that CPCBC mode can enhance the resistance to byte-flipping attacks and padding oracle attacks if parallelism n is kept secret. Security has been improved when compared to the traditional CBC mode. Performance analysis reveals that this scheme has an almost linear acceleration ratio in the case of encrypting a large amount of data. Compared with the conventional CBC mode, the encryption speed is significantly faster.

기업의 네트워크가 대규모화되고 보안시스템 수가 많아짐에 따라 엄청난 양의 보안시스템 이벤트로부터 이상 트래픽을 신속하게 탐지하기란 쉽지 않다. 본 논문에서는 방화벽 등 정보보호 시스템의 보안이벤트를 실시간 탐지하고 분석할 수 있는 트래픽 시각화 분석시스템(FDANT-PCSV)를 제안한다. FDANT-PCSV는 보안이벤트 중 5가지 인자(출발지 IP, 목적지 IP, 목적지 포트, 패킷 길이, 처리상태)를 이용한 Parallel Coordinates 시각화와 4가지 인자(출발지 IP, 목적지 IP, 이벤트 수, 데이터 크기)를 이용한 Sankey 시각화로 구성된다. 또한, 빅데이터 기반 SIEM을 이용하기 때문에 인터넷 및 인트라넷에서 발생하는 네트워크 공격과 네트워크 장애 트래픽을 실시간 탐지할 수 있다. FDANT-PCSV는 사이버 보안 관제요원과 네트워크 관리자가 네트워크 이상 트래픽을 빠르고 쉽게 탐지하여 네트워크 위협에 신속히 대응할 수 있도록 해준다.

As a company's network structure is getting bigger and the number of security system is increasing, it is not easy to quickly detect abnormal traffic from huge amounts of security system events. In this paper, We propose traffic visualization analysis system(FDANT-PCSV) that can detect and analyze security events of information security systems such as firewalls in real time. FDANT-PCSV consists of Parallel Coordinates visualization using five factors(source IP, destination IP, destination port, packet length, processing status) and Sankey visualization using four factors(source IP, destination IP, number of events, data size) among security events. In addition, the use of big data-based SIEM enables real-time detection of network attacks and network failure traffic from the internet and intranet. FDANT-PCSV enables cyber security officers and network administrators to quickly and easily detect network abnormal traffic and respond quickly to network threats.

An edge-located electromagnetic bandgap (EL-EBG) structure using a defected ground structure (DGS) is proposed to suppress resonant modes induced by edge excitation in a two-dimensional planar parallel plate waveguide (PPW). The proposed EL-DGS-EBG PPW significantly mitigates multiple transverse-magnetic (TM) modes in a wideband frequency range corresponding to an EBG stopband. To verify the wideband suppression, test vehicles of a conventional PPW, a PPW with a mushroom-type EBG structure, and an EL-DGS-EBG PPW are fabricated using a commercial process involving printed circuit boards (PCBs). Measurements of the input impedances show that multiple resonant modes of the previous PPWs are significantly excited through an input port located at a PPW edge. In contrast, resonant modes in the EL-DGS-EBG PPW are substantially suppressed over the frequency range of 0.5 GHz to 2 GHz. In addition, we have experimentally demonstrated that the EL-DGS-EBG PPW reduces the radiated emission from -24 dB to -44 dB as compared to the conventional PPW.

The unlimited possibilities of machine learning have been shown in several successful reports and applications. However, how to make sure that the searched results of a machine learning system are not tampered by anyone and how to prevent the other users in the same network environment from easily getting our private data are two critical research issues when we immerse into powerful machine learning-based systems or applications. This situation is just like other modern information systems that confront security and privacy issues. The development of blockchain provides us an alternative way to address these two issues. That is why some recent studies have attempted to develop machine learning systems with blockchain technologies or to apply machine learning methods to blockchain systems. To show what the combination of blockchain and machine learning is capable of doing, in this paper, we proposed a parallel framework to find out suitable hyperparameters of deep learning in a blockchain environment by using a metaheuristic algorithm. The proposed framework also takes into account the issue of communication cost, by limiting the number of information exchanges between miners and blockchain.

South Korea has allowed parallel importation since 1995. Parallel importation causes competition among importers in the logistics process allowing, consumers to purchase foreign brand products at low prices. Most parallel importers base product pricing on subjective judgements. Fashion products in particular, have different sales rates depending on trends and seasons, so sales performance varies greatly depending on selling price timing and policy. The merchandiser (MD) set the price on parallel importation products by aggregating information on imported products and pricing goods. However, this customized process is very time consuming for the MD. This is because the logistics process of parallel importation's customs clearance procedures and repair works is complicated and takes a significant amount of time. In this paper, we propose an improved parallel importation logistics process based on big data, which automatically sets the price of parallel importation products.

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

Opportunistic routing was originally introduced in various multihop network environments to reduce the number of hops in such a way that, among the relays that decode the transmitted packet for the current hop, the one that is closest to the destination becomes the transmitter for the next hop. Unlike the conventional opportunistic routing case where there is a single active S-D pair, for an ad hoc network in the presence of fading, we investigate the performance of parallel opportunistic multihop routing that is simultaneously performed by many source-destination (S-D) pairs to maximize the opportunistic gain, thereby enabling us to obtain a logarithmic gain. We first analyze a cut-set upper bound on the throughput scaling law of the network. Second, computer simulations are performed to verify the performance of the existing opportunistic routing for finite network conditions and to show trends consistent with the analytical predictions in the scaling law. More specifically, we evaluate both power and delay with respect to the number of active S-D pairs and then, numerically show a net improvement in terms of the power-delay trade-off over the conventional multihop routing that does not consider the randomness of fading.

With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.

Although conventional contour parallel tool paths obtained from geometric information have successfully been used to produce desired shapes, they seldom consider physical process concerns such as cutting forces and chatter. In this paper, we introduce an optimized contour parallel path that maintains a constant material removal rate at all times. The optimized tool path is based on a conventional contour parallel tool path. Additional tool path segments are appended to the basic path to achieve constant cutting forces and to avoid chatter vibrations over the entire machining area. The algorithm was implemented for two-dimensional contiguous end milling operations with flat end mills, and cutting tests were conducted to verify the performance of the proposed method.

Diabetic Foot Ulcer (DFU) is a complication of diabetes that causes lower limb amputation. In this work, a unique stacked parallel convolution layers-based network (DFU_SPNet) is proposed to perform DFU vs. normal skin classification. The main objective of this work is to design an effective CNN-based classification model, along with proper fine-tuning of optimizer settings. DFU_SPNet consists of 3 blocks of parallel convolution layers with multiple kernel sizes, for local and global feature abstractions. The proposed DFU_SPNet, trained using SGD (with momentum) optimizer with learning rate on the DFUNet dataset, outperformed the current state-of-the-art results with an AUC of 0.974.