• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.141-145
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• 2021

Various types of memory architectures have been developed, and various compiler optimization techniques have been studied to efficiently use them. In particular, since a memory is a major component that determines performance in mobile computing devices, various optimization techniques have been developed to support them. Recently, a lot of research on hybrid type memory architecture is being conducted, so various compiler techniques are being studied to support it. Existing compiler optimization techniques can be used to achieve the required minimum performance and constraint on low power according to market requirements. References for determining the low-power effect and the degree of performance improvement using these optimization techniques are not properly provided yet. This study was conducted to provide the experimental results of the existing compiler technique as a reference for the development of multibank memory architecture.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• ETRI Journal
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• v.30 no.3
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• pp.451-460
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• 2008

Recently, the power consumption of integrated circuits has been attracting increasing attention. Many techniques have been studied to improve the power efficiency of digital signal processing units such as fast Fourier transform (FFT) processors, which are popularly employed in both traditional research fields, such as satellite communications, and thriving consumer electronics, such as wireless communications. This paper presents solutions based on parallel architectures for high throughput and power efficient FFT cores. Different combinations of hybrid low-power techniques are exploited to reduce power consumption, such as multiplierless units which replace the complex multipliers in FFTs, low-power commutators based on an advanced interconnection, and parallel-pipelined architectures. A number of FFT cores are implemented and evaluated for their power/area performance. The results show that up to 38% and 55% power savings can be achieved by the proposed pipelined FFTs and parallel-pipelined FFTs respectively, compared to the conventional pipelined FFT processor architectures.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.111-118
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• 2016

Today, the proliferation of various mobile devices, such as smart phones and tablet PC, brought changes in the existing TV viewing behavior. People use smart devices as secondary device while watching TV. Researches on a wide range of services linked with second-screen devices around the smart TV in the home network have been actively conducted. While there exist several Web-related technologies for connections between devices, specialized techniques for a second screen service are quite insufficient. There are still some problems related to the display of contents from multiple devices and the efficient transfer of these contents. Considering the characteristics of broadcasting systems In this study, we focus on a second screen service that permits a dynamic transfer of contents by connecting a television (TV) with mobile devices. Here, we propose a second screen service model to enable the personalization of TV contents by combining the existing broadcasting and Web-related techniques.

• Membrane and Water Treatment
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• v.5 no.2
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• pp.147-170
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• 2014

This paper focuses on the study of the most recent ultra-filtration techniques, based on porous polymer membranes, used for the treatment of wastewater from oil, mine and hydrometallurgical industries. The performance of porous membranes used in separation and recovery of oil and heavy metals from wastewater, was evaluated by the polymer composition and by the membrane characteristics, as it follows: hydrophobicity or hydrophilicity, porosity, carrier (composition and concentration), selectivity, fouling, durability, separation efficiency and operating conditions. The oil/water efficient separation was observed on ultra-filtration (UF) techniques, with porous membranes, whereas heavy metals recovery from wastewater was observed using porous membranes with carrier. It can be concluded, that in the ultra-filtration wastewater treatments, a hybrid system, with porous polymer membranes with or without carrier, can be used for these two applications: oil/water separation and heavy metals recovery.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.145-152
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• 2008

Nuclear medicine imaging has an unique advantage of absolute quantitation of radioactivity concentration in body. Tracer kinetic analysis has been known as an useful investigation methods in quantitative study of in-vivo physiological function. The use of nuclear medicine imaging and kinetic analysis together can provide more useful and powerful intuition in understanding biochemical and molecular phenomena in body. There have been many development and improvement in kinetic analysis methodologies, but the conventional basic concept of kinetic analysis is still essential and required for further advanced study using new radiopharmaceuticals and hybrid molecular imaging techniques. In this paper, the basic theory of kinetic analysis and imaging techniques for suppressing noise were summarized.

• 전기의세계
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• v.26 no.1
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• pp.87-90
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• 1977

Various simulation techniques were developed in the modeling of biological system during the last decades. Mostly analog and hybrid simulation techniques have been used. The authors chose the Digital Analog Simulation (DAS) technique by using the MIMIC language to simulate the saccadic eye movement system performances on the digital computer. There have been various models presented by many investigators after Young & Stark's sampled-data model. The eye movement model chosen by the authors is Robinson's model III which shows the parallel information processing characteristics clearly to the double-step input stimuli. In the process of simulation, the parameter and constants used were based on the neurophysiological data of the human and animals. The analog model blocks were converted to the corresponding MIMIC block diagrams and programmed into the MIMIC statements. The program was run on the CDC Cyber 72-14 computer. The essential input stimulus was double-step of 5 and 10 degrees, and target durations chosen were 50,100 and 150 msec. The results obtained by the DAS technqiue were in good agreement with analog simulation carried out by other investigators as well as with the experimental human saccadic eye movement responses to double-step target movements.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.73-83
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• 2006

In this paper, a redundancy management system for aircraft is studied, and fault detection and isolation algorithms of inertial sensor system are proposed. Contrary to the conventional aircraft systems, UAV system cannot allow triple or quadruple hardware redundancy due to the limitations on space and weight. In the UAV system with dual sensors, it is very difficult to identify the faulty sensor. Also, conventional fault detection and isolation (FDI) method cannot isolate multiple faults in a triple redundancy system. In this paper, two FDI techniques are proposed. First, hardware based FDI technique is proposed, which combines a parity equation approach with a wavelet based technique. Second, analytic FDI technique based on the Kalman filter is proposed, which is a model-based FDI method utilizing the threshold value and the confirmation time. To provide the reference value for detecting the fault, residuals are calculated using the extended Kalman filter. To verify the effectiveness of the proposed FDI methods, numerical simulations are performed.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.533-539
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• 2012

The performance analysis of wireless geolocation in a non-line-of-sight (NLOS) environment is a very important issue. Since Cramer-Rao lower bound (CRLB) determines the physical impossibility of the variance of an unbiased estimator being less than the bound, many studies presented the performance analysis in terms of CRLB. Several CRLBs for time-of-arrival (TOA), pseudo-range TOA, angle-of-arrival (AOA), and signal strength (SS) based positioning methods have been derived for NLOS environment. However, the performance analysis of time difference of arrival (TDOA) and TDOA/SS based geolocation techniques in a NLOS environment is still an opening issue. This paper derives the CRLBs of TDOA and TDOA/SS based positioning methods for NLOS environment. In addition, theoretical analysis proves that the derived CRLB for TDOA is the same as that of pseudo-range TOA and the TDOA/SS scheme has a lower CRLB than the TDOA (or SS) scheme.