Vol. 46, No. 10,
Oct. 2019
Digital Library
An Execution Planning Scheme for Computational Applications using Profiling of a CPU-GPU Container Cluster
Jisun Oh, Sejin Kim, Yoonhee Kim
http://doi.org/10.5626/JOK.2019.46.10.975
As heterogeneous clusters and cloud environments have gained popularity, selection of appropriate resources in an integrated environment is considered essential for enhancement in application performance with reasonable resource utilization. Application characteristics may require specific resource in a certain order during its execution and ask smart job deployment among diverse nodes. Especially, it is necessary to have an execution plan in advance for better performance of CPU and GPU container clusters. In this paper, we propose an execution planning scheme based on runtime profiling history to place jobs on CPU-GPU nodes. Computational applications usually show good performance in GPU. However, the lack of GPU sharing methods leads to failure of co-locating jobs on a GPU node. Based on the profile information, the scheme provides the combination of applications to run at the same time in GPU container clusters and estimate the performance of workload before executing application among CPU-GPU container clusters. We have also demonstrated adjustment of the order of application execution using profile history in order to reduce the execution time of total workload by monitoring GPU memory usage.
An NVM-based Efficient Write-Reduction Scheme for Block Device Driver Performance Improvement
http://doi.org/10.5626/JOK.2019.46.10.981
Recently, non-volatile memory (NVRAM) has attracted substantial attention as a next-generation storage device due to the fact that it shows higher read/write performance than flash-based storage as well as higher cost-effectiveness than DRAM. One way to use NVRAM as a storage device is to modify the existing file system layer or block device layer. Leveraging the NVRAM block device driver is advantageous in terms of overall system compatibility, as it does not require any modification of the existing storage stack. However, when considering the byte-level addressing of the NVRAM device, the block write is not effective in terms of durability or performance. In this paper, we propose a block device driver that attempts to optimize the existing block write operations while considering the existing functionalities of the file system. The proposed block write reduction scheme provides a partial block write by classifying the type of blocks according to the structure of the file system as well as the amount of data modified in the block using XOR operation. Several experiments are performed to validate the performance of the proposed block device driver under various workloads, and the results show that, compared to the conventional block write operations, the amount of writes is reduced by up to 90%.
Partial Movement Authoring for a Reconfigurable Motion Capture
Seonghun Kim, Yangkyu Lim, Youngho Kim, Youngho Chai
http://doi.org/10.5626/JOK.2019.46.10.989
Research on human motion perception has progressed along with the development of technology. The demand for the gesture recognition is increasing daily. In motion capture, data on each part of the body are acquired by using a sensor or camera in order to realize a natural movement as an actual motion. However, it is inefficient to use the sensor every time motion data must be obtained or to make repeat measures because of slight differences in motion. In addition, various problems must be solved in order to transfer the stored data to another person or modify it use for another action.
In this paper, we studied the trends of motion recognition research and analyzed characteristics of the motion reproduction method using keyframe animation and the Labanotation motion recording method. In addition, we proposed an action authoring method which addresses the disadvantages of the existing methods. By visualizing and recording the pattern of partial motions in the units of the images, it is possible to reconstruct other motions, including the meaning of the motion.
Patterns of Detecting Feature Interaction in Autonomous Car
Young Woo Lee, Heung Seok Chae
http://doi.org/10.5626/JOK.2019.46.10.1001
In a system with multiple features working together, unexpected behaviors may arise due to feature interaction. An interaction detection method that only considers the dependencies between a system"s components can cause false positives, because interactions are considered to occur between features that are not actually performed simultaneously. In addition, it does not take into account for the interactions that result from the association, such as speed and direction. This paper proposes a pattern with which to detect interaction based on time-series data of the system. In case studies, I classified interaction types by combining interaction attributes, mapped the patterns to identify each interaction, and then performed interaction detection based on the time series data of an autonomous car. For the ACC, OA, LKA, and EVA features of the autonomous car, interactions between speed and direction variables were detected using a non-continuous partitioning pattern and a repetitive partitioning pattern. The interaction resulting from the association between direction and speed variables was detected using a partition conflict pattern.
Detecting Mode Drop and Collapse in GANs Using Simplified Frèchet Distance
Chung-Il Kim, Seungwon Jung, Jihoon Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2019.46.10.1012
Even though generative adversarial network (GAN) is an excellent model for generating data based on the estimation of real data distribution by of two adversarial learning network, this model often suffers from mode drop that does not learn distribution during learning, or mode collapse that generates only one or very few distribution samples. Most studies to detect these problems have used well-balanced data or additional neural network models. In this paper, we propose a method to detect mode drop and collapse by using a simplified Frèchet distance, which does not require any additional model or well-balanced data. Through various experiments, we showed that our proposed distance metric detected mode drop and collapse more accurately than any other metrics used in GANs.
Sports Broadcasting with Deep Learning
http://doi.org/10.5626/JOK.2019.46.10.1020
Sports broadcasting requires understanding and reasoning of a current situation based on information regarding sports scenes, players, and past knowledge. In this paper, we introduced how scene classifier, player detector, motion recognizer could be used to obtain information on sports images and understand current situations. We created three types of commentaries. One was from web data, another was from 13 scenes with scene classifier, and the other was generated by the position of the players, eight motions, and the ontology. Data from the KBO (Korea Baseball Organization League) games from April 1, 2018, to April 14, 2018, were directly labeled to learn the model.
An Efficient User Interest Region Stitching Method using the RANSAC Algorithm
http://doi.org/10.5626/JOK.2019.46.10.1025
Recently, with the emergence of 5G technology capable of high-capacity wireless data transmission, technologies using 360-degree virtual reality images have attracted attention, and therefore, interest in image stitching is increasing. In this paper, we proposed a user"s interest region matching method using the RANSAC algorithm. The proposed method uses the RANSAC algorithm and assigns a high weight to the region of interest selected by the user to perform image matching. It can be selectively performed in regions requiring natural stitching. The corresponding points included in the region of interest are set to have high weight and are necessarily included in the sample selection of the RANSAC algorithm. Additionally, the degree of matching to a specific region can be adjusted depending on whether several feature points are included. The method of interest region matching consists of setting the region of interest, increasing the weights of the corresponding points in the region of interest, creating the model using the RANSAC algorithm, and setting the inliers and outliers of the corresponding points using the model. The results of this study confirmed that users can get results approximating reality by performing matching based on points corresponding to selected regions of interest.
False-alarm Detection in Model-based API-call Safety Checking of Priority-based Multitasking Programs
http://doi.org/10.5626/JOK.2019.46.10.1035
A multitask program consists of a set of tasks that are executed according to a scheduling policy of the operating system. In order to verify a multitask program, it is necessary to consider the behavior of the operating system. Otherwise, false alarms are reported, e.g., a low-priority task is executed prior to a higher-priority task. Studies introduced a model-based verification method using the formal OS model for the effective verification of multitask programs, but they showed high false-alarm rates due to the abstraction of the application program. This paper proposes an automated false-alarm detection method which identifies false alarms by checking existence of the program path that performs the same sequence of API-calls as the counterexample reported by the model-based verification. The suggested method is applied to two sets of application programs running on an automotive operating system. Results show that 73% of the reported counterexamples were false alarms with an average detection time of 0.199 seconds for the test program and 17.95 seconds for the window control program.
An Autonomous Threat Situation Awareness System for UAV based on Ontology
MyungJoong Jeon, HyunKyu Park, YoungTack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2019.46.10.1044
An autonomous threat situational awareness system is necessary for Unmanned Aerial Vehicles(UAVs) in a variety of fields. Although various of approaches to autonomous threat situational awareness have been proposed, most of them involved reasoning of the semantic information of the object. Therefore, in this paper, based on the existing semantic information of an object, we propose a method to achieve threat situational awareness for a UAV based on reasoning of the relationship between the objects. In this paper, there are three main ways that are used to recognize a threat to a UAV: First, information on the recognized objects is expressed using an LOD(Level of Detail)-based grid map. Second, the concepts of objects around the UAV are defined as ontology while the relationships and situations between objects are defined as SWRL(Semantic Web Rule Language). Third, through the ontology reasoning, the simulator visualizes the recognition of the relationships of objects and threat situations for the UAV.
Prediction of Compound-Protein Interactions Using Deep Learning
http://doi.org/10.5626/JOK.2019.46.10.1054
Characterizing the interactions between compounds and proteins is an important process for drug development and discovery. Structural data of proteins and compounds are used to identify their interactions, but those structural data are not always available, and the speed and accuracy of the predictions made in this way ware limited due to the large number of calculations involved. In this paper, compound-protein interactions were predicted using S2SAE (Sequence-To-Sequence Auto-Encoder), which is composed of a sequence-to-sequence algorithm used in machine translation as well as an auto-encoder for effective compression of the input vector. Compared to the existing method, the method proposed in this paper uses fewer features of protein-compound complex and also show higher predictive accuracy.
A Transport Theoretic Approach for Computational Task Migration in Multi-Access Edge Computing
Sarder Fakhrul Abedin, Md. Shirajum Munir, SeokWon Kang, Choong Seon Hong
http://doi.org/10.5626/JOK.2019.46.10.1061
In the present work, the problem of computational task migration in the Multi-Access Edge Computing (MEC) Network has been addressed and the goal is to minimize the computational cost including the task migration cost of the MEC network. Apparently, at first, we have formulated a Hitchcock-Koopmans transportation problem, which corresponds to the task migration from the over-utilized MEC servers to the under-utilized MEC server. Second, we have solved the transportation problem using the Vogel’s Approximation Algorithm (VAM), where the optimal task migration was achieved. Finally, in the simulation, we have demonstrated that the proposed approach significantly outperforms the baseline approach in terms of the task migration cost, average response time, and average queuing delay in the MEC network.
Multipath-based Bandwidth Estimation Scheme for Improving Network Stability and Responsiveness of HTTP Adaptive Streaming in Wireless Environments
http://doi.org/10.5626/JOK.2019.46.10.1069
As demand for high-quality video streaming services increases, researches on Hyper-Text Transfer Protocol (HTTP) adaptive streaming based on multipath transmission have experienced a continual rise. In the wireless environment, multipath-based streaming cannot be accurately estimated according to transmission scheme and bandwidth estimation scheme. In this paper, we propose a multipath-based HTTP adaptive streaming and available bandwidth estimation scheme to improve stability and responsiveness in wireless environments. Multipath-based HTTP adaptive streaming is a block-based scheduling scheme for improving the performance of HTTP adaptive streaming. The scheme dynamically adjusts the request period based on the buffer state in order to improve the responsiveness to the rapid change in the network. The available bandwidth estimation scheme enhances stability by using the block and segment based available bandwidth measurement to predict frequent and sudden changes of bandwidth in a wireless environment.
Efficient Hybrid Messaging System Architecture and XML element-based Message Filtering Scheme for Tactical Networks
Minho Bae, Kyoungjun Min, Gyudong Park, Hocheol Jeon, Sangyoon Oh
http://doi.org/10.5626/JOK.2019.46.10.1078
To overcome limited network bandwidth conditions which occur frequently in tactical networking environments, an integrated solution that includes architecture, a message format, and a messaging scheme is needed. In this paper, we propose a publish-subscribe based hybrid messaging system architecture and a novel messaging model with an XML element level filtering scheme and data representation to overcome limited networking conditions in tactical environments. Herein, we present the prototype messaging system based on our proposed hybrid architecture. Evaluations of our proposed scheme were conducted to prove its effectiveness. The empirical experimental results showed that our scheme enhanced the message size performance by an average of 34% compared to conventional XML and message encoding performance was also enhanced an average of 7.1 times compared to EXI. The prototype system achieved a 21.7-fold maximum improvement in filtering performance processing time.
Analyzing Semantic Relations of Word Vectors trained by The Word2vec Model
http://doi.org/10.5626/JOK.2019.46.10.1088
As the usage of artificial intelligence (AI) in natural language processing has increased, the importance of word embedding has grown significantly. This paper qualitatively analyzes the representational capability of word2vec models to structure semantic relation in terms of antonymy and hyponymy based on clustering characteristics and t-SNE distribution. To this end, a K-means clustering algorithm was applied to a set of words drawn from 10 categories. Some words in antonymy are found not to be embedded properly. This is attributed to the fact that they typically have many common attributes with a very few opposite ones. It is also observed that words in hyponymy are not properly embedded at all. This can be attributed to the fact that the hyponymic relations of those words are based on the information gathered through a learning process of a knowledge system, as opposed to a natural process of language acquisition. Thus, it appears that word2vec models based on the distributional hypothesis are limited to representing certain antonymic relations and do not properly represent hyponymic relations at all.
Measurement of Pig’s Weight Based on Image Processing of a Normalized Marker
http://doi.org/10.5626/JOK.2019.46.10.1094
In order to reduce the cost of breeding in pig farms, it is necessary to control pig weight through regular growth monitoring and controlling the feed supply. However, it is not easy to measure the weight of pigs due to the difficulty involved with placing a pig on a scale. We propose a weight measurement method based on image processing as part of a smart-farm for the easy management of pigs in rural areas. To this end, we take a picture of a pig with a checker board marker on it. The pig’s weight is then calculated using a regression equation with the measured girth. The weight shows errors within only 3.8%, which is acceptable for the purpose of farmers.
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