• ETRI Journal
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• v.45 no.2
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• pp.267-276
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• 2023

R&D strategies of companies with low and high technological levels are discussed based on the concept of technology convergence and divergence. However, empirically detecting enterprise technology convergence in the distribution of enterprise technology (total productivity increase) over time and identifying key change factors are challenging. This study used a novel statistical indicator that captures the internal technology distribution change with a single number to clearly measure the technology distribution peak as a change in critical bandwidth for enterprise technology convergence and presented it as evidence of each technology convergence or divergence. Furthermore, this study applied the quantitative technology convergence identification method. Technology convergence appeared from the separation of total corporate productivity distribution of 69 IT companies in Korea in 2019-2020 rather than in 2015-2016. Results indicated that when the total technological level was separated from the technology leading and technology catch-up, IT companies were found to be pursuing R&D strategies for technology catch-up.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3985-3995
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• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.

• The KIPS Transactions:PartA
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• v.12A no.4 s.94
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• pp.305-312
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• 2005

In this paper, we present algorithm-level techniques for energy-efficient design at the algorithm level using FPGAs. We then use these techniques to create energy-efficient designs for two signal processing kernel applications: fast Fourier transform(FFT) and matrix multiplication. We evaluate the performance, in terms of both latency and energy efficiency, of FPGAs in performing these tasks. Using a Xilinx Virtex-II as the target FPGA, we compare the performance of our designs to those from the Xilinx library as well as to conventional algorithms run on the PowerPC core embedded in the Virtex-II Pro and the Texas Instruments TMS320C6415. Our evaluations are done both through estimation based on energy and latency equations on high-level and through low-level simulation. For FFT, our designs dissipated an average of $50\%$ less energy than the design from the Xilinx library and $56\%$ less than the DSP. Our designs showed an EAT factor of 10 times improvement over the embedded processor. These results provide a concrete evidence to substantiate the idea that FPGAs can outperform DSPs and embedded processors in signal processing. Further, they show that PFGAs can achieve this performance while still dissipating less energy than the other two types of devices.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.63-72
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• 2005

This paper describes a new approach for the operating system level power management to reduce the energy consumed in the IO devices in a robot platform, which provides various functions such as navigation, multimedia application, and wireless communication. The policy proposed in the paper, which was named the Energy-Aware Job Schedule (EAJS), rearranges the jobs scattered so that the idle periods of the devices are clustered into a time period and the devices are shut down during their idle period. The EAJS selects a schedule that consumes the minimum energyamong the schedules that satisfy the buffer and time constraints. Note that the burst job execution needs a larger memory buffer and causes a longer time delay from generating the job request until to finishing it. A prototype of the EAJS is implemented on the Linux kernel that manages the robot system. The experiment results show that a maximum $44\%$ power saving on a DSP and a wireless LAN card can be obtained with the EAJS.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.135-143
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• 2013

In this study, we suggested effective countermeasure of biodiesel oxidation problems by investigating the oxidation degradation of biodiesels derived from variable resources and the level of oxidation stability of current distributing biodiesel blended fuels (2%) in Korea, and oxidation stability change according to storage time (for 3 month) and biodiesel blending ratio (2, 5, 7, 10%). By the composition analysis results of biodiesel from various resources which are possible to distribute in Korea, the biodiesel from animal fat has poor oxidation stability and cold performance, while the biodiesel from coconut and palm kernel which are considered as future potential raw material showed good oxidation stability and cold performance. The oxidation stability level of current distributing biodiesel blended fuels in Korea was excellent with showing over 30 hours (average 68 hours) stability, but the oxidation stability of the blended fuel with animal fat biodiesel having poor oxidation property (1.22 hours) was rapidly decreased to below 32 hours by mixing only 2%. Therefore, we have to pay attention to quality control of oxidation property, because the oxidation stability problem can be caused by increasing biodiesel blending ratio and diversifying raw materials those have worse property.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.9
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• pp.936-947
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• 2010

In the mobile gaming market, it is desirable for a specific content to be served on various platforms. Recently, it is a very important issue in the market for mobile games. This problem incurs extra cost, because we need to redevelop existing contents to be executed on another platforms. Moreover, the release of a new platform has spent much more effort and cost developing many contents in short period of time. Therefore, we need to research about translation techniques that enable the existing contents to run on a different platform. In this paper, we propose translation techniques for mobile games. The techniques can be applied on converting mobile contents to runnable contents on a new platform. To realize it, we design and implement a source-level contents translator which is targeting Android platform using the suggested techniques. Our source-level contents translator is implemented systematically by applying compiler theory. Also, we solve partially translation problems between different programming languages. The translator has been implemented separately divided into three tasks such as kernel porting, library mapping as well as language translation. We expect these techniques to increase utilization of existing mobile games and contribute to vitalizations of mobile contents industry.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.3
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• pp.324-336
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• 2000

The paper proposes an efficient buffer management scheme to enhance performance of the disk I/O system. Without any user level information, the proposed scheme automatically detects the block reference patterns of applications by associating block attributes with forward distance of a block. Based on the detected patterns, the scheme applies an appropriate replacement policy to each application. We also present a new block allocation scheme to improve the performance of buffer cache when kernel needs to allocate a cache block due to a cache miss. The allocation scheme analyzes the cache hit ratio of each application based on block reference patterns and allocates a cache block to maximize cache hit ratios of system. These all procedures are performed on-line, as well as automatically at system level. We evaluate the scheme by trace-driven simulation. Experimental results show that our scheme leads to significant improvements in hit ratios of cache blocks compared to the traditional schemes and requires low overhead.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1472-1482
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• 2005

In this paper, we propose a socket interface layer for large-scale multimedia servers that adopt TCP/IP Offload Engines (TOE). In order to provide legacy network applications with binary level compatibility, the socket interface layer intercepts all socket-related system calls to forward to either TOE or legacy TCP/IP Protocol stack. The layer is designed and implemented as a kernel module in Linux. The layer is located between BSD socket layer and INET socket layer, and passes the application's socket requests to INET socket layer or TOE. The layer provides multimedia servers and web servers with the following features: (1) All standard socket APIs and file I/O APIs that are supported (2) Support for binary level compatibility of existing socket programs (3) Support for TOE and legacy Ethernet NICs at the same time.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.45-52
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• 2017

This paper presents a high-performance software-hardware dehazing system based on a dedicated hardware accelerator for the haze removal. In the proposed system, the dedicated hardware accelerator performs the dark-channel-prior-based dehazing process, and the software performs the other control processes. For this purpose, the dehazing process is realized as an OpenCL kernel by finding the inherent parallelism in the algorithm and is synthesized into a hardware by employing a high-level-synthesis technique. The proposed system executes the dehazing process much faster than the previous software-only dehazing system: the performance improvement is up to 96.3% in terms of the execution time.