• Title/Summary/Keyword: multi-core systems

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Implementation of the AMBA AXI4 Bus interface for effective data transaction and optimized hardware design (효율적인 데이터 전송과 하드웨어 최적화를 위한 AMBA AXI4 BUS Interface 구현)

  • Kim, Hyeon-Wook;Kim, Geun-Jun;Jo, Gi-Ppeum;Kang, Bong-Soon
    • Journal of the Institute of Convergence Signal Processing
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    • v.15 no.2
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    • pp.70-75
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    • 2014
  • Recently, the demand for high-integrated, low-powered, and high-powered SoC design has been increasing due to the multi-functionality and the miniaturization of digital devices and the high capacity of service informations. With the rapid evolution of the system, the required hardware performances have become diversified, the FPGA system has been increasingly adopted for the rapid verification, and SoC system using the FPGA and the ARM core for control has been growingly chosen. While the AXI bus is used in these kinds of systems in various ways, it is traditionally designed with AXI slave structure. In slave structure, there are problems with the CPU resources because CPU is continually involved in the data transfer and can't be used in other jobs, and with the decreased transmission efficiency because the time not used of AXI bus beomes longer. In this paper, an efficient AXI master interface is proposed to solve this problem. The simulation results show that the proposed system achieves reductions in the consumption clock by an average of 51.99% and in the slice by 31% and that the maximum operating frequency is increased to 107.84MHz by about 140%.

Metamorphosis Hierarchical Motion Vector Estimation Algorithm for Multidimensional Image System (다차원 영상 시스템을 위한 변형계층 모션벡터 추정알고리즘)

  • Kim Jeong-Woong;Yang Hae-Sool
    • The KIPS Transactions:PartB
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    • v.13B no.2 s.105
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    • pp.105-114
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    • 2006
  • In ubiquitous environment where various kinds of computers are embedded in persons, objects and environment and they are interconnected and can be used in my place as necessary, different types of data need to be exchanged between heterogeneous machines through home network. In the environment, the efficient processing, transmission and monitoring of image data are essential technologies. We need to make research not only on traditional image processing such as spatial and visual resolution, color expression and methods of measuring image quality but also on transmission rate on home network that has a limited bandwidth. The present study proposes a new motion vector estimation algorithm for transmitting, processing and controlling image data, which is the core part of contents in home network situation and, using algorithm, implements a real time monitoring system of multi dimensional images transmitted from multiple cameras. Image data of stereo cameras to be transmitted in different environment in angle, distance, etc. are preprocessed through reduction, magnification, shift or correction, and compressed and sent using the proposed metamorphosis hierarchical motion vector estimation algorithm for the correction of motion. The proposed algorithm adopts advantages and complements disadvantages of existing motion vector estimation algorithms such as whole range search, three stage search and hierarchical search, and estimates efficiently the motion of images with high variation of brightness using an atypical small size macro block. The proposed metamorphosis hierarchical motion vector estimation algorithm and implemented image systems can be utilized in various ways in ubiquitous environment.

Immersive Smart Balance Board with Multiple Feedback (다중 피드백을 지원하는 몰입형 스마트 밸런스 보드)

  • Seung-Yong Lee;Seonho Lee;Junesung Park;Min-Chul Shin;Seung-Hyun Yoon
    • Journal of the Korea Computer Graphics Society
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    • v.30 no.3
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    • pp.171-178
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    • 2024
  • Exercises using a Balance Board (BB) are effective in developing balance, strengthening core muscles, and improving physical fitness and concentration. In particular, the Smart Balance Board (SBB), which integrates with various digital content, provides appropriate feedback compared to traditional balance boards, maximizing the effectiveness of the exercise. However, most systems only offer visual and auditory feedback, failing to evaluate the impact on user engagement, interest, and the accuracy of exercise postures. This study proposes an Immersive Smart Balance Board (I-SBB) that utilizes multiple sensors to enable training with various feedback mechanisms and precise postures. The proposed system, based on Arduino, consists of a gyro sensor for measuring the board's posture, a communication module for wired/wireless communication, an infrared sensor to guide the user's foot placement, and a vibration motor for tactile feedback. The board's posture measurements are smoothly corrected using a Kalman Filter, and the multi-sensor data is processed in real-time using FreeRTOS. The proposed I-SBB is shown to be effective in enhancing user concentration and engagement, as well as generating interest, by integrating with diverse content.

Application Plan of Goods Information in the Public Procurement Service for Enhancing U-City Plans (U-City계획 고도화를 위한 조달청 물품정보 활용 방안 : CCTV 사례를 중심으로)

  • PARK, Jun-Ho;PARK, Jeong-Woo;NAM, Kwang-Woo
    • Journal of the Korean Association of Geographic Information Studies
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    • v.18 no.3
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    • pp.21-34
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    • 2015
  • In this study, a reference model is constructed that provides architects or designers with sufficient information on the intelligent service facility that is essential for U-City space configuration, and for the support of enhanced design, as well as for planning activities. At the core of the reference model is comprehensive information about the intelligent service facility that plans the content of services, and the latest related information that is regularly updated. A plan is presented to take advantage of the database of list information systems in the Public Procurement Service that handles intelligent service facilities. We suggest a number of improvements by analyzing the current status of, and issues with, the goods information in the Public Procurement Service, and by conducting a simulation for the proper placement of CCTV. As the design of U-City plan has evolved from IT technology-based to smart space-based, reviews of limitations such as the lack of standards, information about the installation, and the placement of the intelligent service facility that provides U-service have been carried out. Due to the absence of relevant legislation and guidelines, however, planning activities, such as the appropriate placement of the intelligent service facility are difficult when considering efficient service provision. In addition, with the lack of information about IT technology and intelligent service facilities that can be provided to U-City planners and designers, there are a number of difficulties when establishing an optimal plan with respect to service level and budget. To solve these problems, this study presents a plan in conjunction with the goods information from the Public Procurement Service. The Public Procurement Service has already built an industry-related database of around 260,000 cases, which has been continually updated. It can be a very useful source of information about the intelligent service facility, the ever-changing U-City industry's core, and the relevant technologies. However, since providing this information is insufficient in the application process and, due to the constraints in the information disclosure process, there have been some issues in its application. Therefore, this study, by presenting an improvement plan for the linkage and application of the goods information in the Public Procurement Service, has significance for the provision of the basic framework for future U-City enhancement plans, and multi-departments' common utilization of the goods information in the Public Procurement Service.

Feasibility of Deep Learning Algorithms for Binary Classification Problems (이진 분류문제에서의 딥러닝 알고리즘의 활용 가능성 평가)

  • Kim, Kitae;Lee, Bomi;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.23 no.1
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    • pp.95-108
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    • 2017
  • Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

Recent Progress in Air-Conditioning and Refrigeration Research : A Review of Papers Published in the Korean Journal of Air-Conditioning and Refrigeration Engineering in 2016 (설비공학 분야의 최근 연구 동향 : 2016년 학회지 논문에 대한 종합적 고찰)

  • Lee, Dae-Young;Kim, Sa Ryang;Kim, Hyun-Jung;Kim, Dong-Seon;Park, Jun-Seok;Ihm, Pyeong Chan
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.29 no.6
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    • pp.327-340
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    • 2017
  • This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.

A Study on the Records and Archives Management System in Japan : Focusing on the Electronic Public Documents Management (일본의 기록관리 제도 연구 법령과 전자공문서 관리를 중심으로)

  • Yi, Kyoung Yong
    • The Korean Journal of Archival Studies
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    • no.45
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    • pp.219-253
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    • 2015
  • The Records Management System in Japan has been developed to a comprehensive and unitary records management system based on the records life-cycle principle from the enactment of 'the Public Records and Archives Management Act' in 2009 and its implementation in April, 2011. The scope of objects has also been extended to documents of independent administrative institutions and specific confidential documents on diplomacy and defense. In addition, a series of Electronic Documents Management Systems have been built for the transfer of electronic records to the National Archives of Japan, which is called the Electronic Records Archives of Japan, in connection with the records and archives management systems covering creation, management, transfer, preservation, and use of electronic records. This paper deals with the core contents and characteristics of the records management system of Japan, focusing on the operational structure of the records and archives management law and electronic documents management. Firstly, The Cabinet Office and professional groups in records and archives management started to work on reformation of the records management system from 2003 and resulted in enactment of the Public Records and Archives Management Act in 2009. In that sense, the Public Records and Archives Management Act can be evaluated as a result of constant activities of the records management community in Japan for realization of accountabilities of government agencies to the general public. Secondly, the Public Records Management Act of Japan has a coherent multi-layer structure from the law, enforcement ordinances, guidelines, and to institutional documents management regulations in the operational system. This is a systematic structure for providing practical business units of each administrative agency with detailed standards on the basis of guidelines and making them to prepare their own specific application standards related to their unique businesses. Unlike the past, the National Archives of Japan became to be able to identify specific historial documents which should be transferred to the archives by selecting important historical records as early as possible after creating and receiving them in each institution through the retention schedule. Thirdly, Japan started to operate a system in regard to electronic records transfer and preservation in 2011. In order to prepare for it, each administrative agency has used EDMS in creation and management of electronic records. A Guideline for the Standard Format and Media released by the Cabinet Office in 2010 is also for the transfer of electronic records to the Electronic Records Archives of Japan. In future, it is necessary to conduct further studies on activities of the records and archives management community in Japan, relating to long-term preservation and use of electronic records.

Analysis of the 'Problem Solving and Invention' Units of Technology and Home Economics 1 Textbook (기술.가정 1 교과서 '문제해결과 발명' 단원 분석)

  • Jung, Jin Woo
    • 대한공업교육학회지
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    • v.38 no.1
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    • pp.49-67
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    • 2013
  • The purpose of this study is to analyze the external systems and the units 'problem solving and invention' of the middle school technology and home economics 1 textbooks of the revised 2011 national curriculum in an effort to provide some information on the content system of invention education in technology class, as invention education was provided as part of a regular subject for the first time. The findings of the study were as follows: First, 'Technology and Inventions' chapter of Technology and Home Economics 1 Textbooks occupied 10-18% share, with the subchapter of 'Problem Solving and Invention' unit taking up 6.7-29% of the textbooks. Second, for most textbooks, 'Technological Problem Solving', 'Idea Generation' 'Multi-dimensional Projection Method', 'Expansive Thought-Processing Methodology', 'Converging Thought Methodology' and 'Invention in Everyday Lives' were included as main contents based on the accomplishment criteria presented in education process interpretation documents. Third, the detailed structures were generally made up as follows: Introduction (Broad Chapter Title, Subchapter Table of Contents, Introduction, Subchapter Title, Study Objectives, Open Thinking); Development (Unit Title, Thinking Ahead, Core Terms, Main Text, Study Helper, Activities, Research Exercises, Supplemental Readings, In-depth Study Topics, Technology in Everyday Lives, Reading Topics, Discussion Topics, and Career Helpers); and Summary (Subchapter Summary, Study Summary, Terms Summary, Writing Follow-up, Self Review, Broad Chapter Evaluation). Fourth, based on the analysis of figures included, photographs had the largest share, followed by figures, tables, and graphs. The photos were used to illustrate various inventions, invention methodologies, and exercise activities, while figures were included to depict the contents included in the main text, and the tables to assist to preparation of process diagrams or materials lists. Fifth, based on the analysis of content weights, greater weights were placed on 'Inventions and Thoughts', and 'Invention Experiment Activities,' while 'Understanding Inventions' and 'Invention and Patents' chapters did not have a lot of texts involved. Sixth, based on the analysis of content presentation methods, most textbooks combined figures, tables, illustrations and texts to discuss the topics. Based on the above study results, we suggest the following: First, a consistent education curriculum should be developed over the topic of invention; and second, more precise and systematic analysis of textbooks would need to be performed.

Business Application of Convolutional Neural Networks for Apparel Classification Using Runway Image (합성곱 신경망의 비지니스 응용: 런웨이 이미지를 사용한 의류 분류를 중심으로)

  • Seo, Yian;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.1-19
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    • 2018
  • Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.