• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.8
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• pp.371-378
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• 2002

This paper presents the rapid and accurate algorithm for fault detection and location estimation in the transmission line. This algorithm uses wavelet transform for fault detection and harmonics elimination and utilizes least square error method for fault impedance estimation. Wavelet transform decomposes fault signals into high frequence component Dl and low frequence component A3. The former is used for fault phase detection and fault types classification and the latter is used for harmonics elimination. After fault detection, an adaptive data window technique using LSE estimates fault impedance. It can find a optimal data window length and estimate fault impedance rapidly, because it changes the length according to the fault disturbance. To prove the performance of the algorithm, the authors test relaying signals obtained from EMTP simulation. Test results show that the proposed algorithm estimates fault location within a half cycle after fault irrelevant to fault types and various fault conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• Physical Therapy Korea
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• v.19 no.3
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• pp.124-131
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• 2012

Most conventional instruments measuring disability rely on total score by simply adding individual item responses, which is dependent on the items chosen to represent the underlying construct (test-dependent) and a test statistic, such as coefficient alpha for the estimate of reliability, varying from sample to sample (sample-dependent). By contrast, item response theory (IRT) method focuses on the psychometric properties of the test items instead of the instrument as a whole. By estimating probability that a respondent will select a particular rating for an item, item difficulty and person ability (or disability) can be placed on same linear continuum. These estimates are invariant regardless of the item used (test-free measurement) and the ability of sample applied (sample-free measurement). These advantages of IRT allow the creation of invariantly calibrated large item banks that precisely discriminate the disability levels of individuals. Computer adaptive testing (CAT) method often requiring a testing algorithm promise a means for administering items in a way that is both efficient and precise. This method permits selectively administering items that are closely matched to the ability level of individuals (measurement precision) and measuring the ability without the loss of precision provided by the full item bank (measurement efficiency). These measurement properties can reasonably be achieved using IRT and CAT method. This article aims to investigate comprehensive overview of the existing disability instrument for back pain and to inform physical therapists of an alternative innovative way overcoming the shortcomings of conventional disability instruments. An understanding of IRT and CAT method will equip physical therapist with skills in interpreting the measurement properties of disability instruments developed using the methods.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.57-69
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• 2022

Recently, as interest in self-driving cars has increased worldwide, research and development on the Advanced Driver Assist System is actively underway. Among them, the purpose of Adaptive Cruise Control (ACC) is to minimize the driver's driving fatigue through the control of the vehicle's longitudinal speed and relative distance. In this study, for the research of the ACC test in the real environment, the real-road test was conducted based on domestic-road test scenario proposed in preceding study, considering ISO 15622 test method. In this case, the distance measurement method using the dual camera was verified by comparing and analyzing the result of using the dual camera and the result of using the measurement equipment. As a result of the comparison, two results could be derived. First, the relative distance after stabilizing the ACC was compared. As a result of the comparison, it was found that the minimum error rate was 0.251% in the first test of scenario 8 and the maximum error rate was 4.202% in the third test of scenario 9. Second, the result of the same time was compared. As a result of the comparison, it was found that the minimum error rate was 0.000% in the second test of scenario 10 and the maximum error rate was 9.945% in the second test of scenario 1. However, the average error rate for all scenarios was within 3%. It was determined that the representative cause of the maximum error occurred in the dual camera installed in the test vehicle. There were problems such as shaking caused by road surface vibration and air resistance during driving, changes in ambient brightness, and the process of focusing the video. Accordingly, it was determined that the result of calculating the distance to the preceding vehicle in the image where the problem occurred was incorrect. In the development stage of ADAS such as ACC, it is judged that only dual cameras can reduce the cost burden according to the above derivation of test results.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.813-820
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• 2018

This paper proposes new frame rate up-conversion algorithms. Adaptive motion estimation based on block complexity information are used to obtain more accurate motion vectors. Because the information on block complexity is extracted from the motion estimation prediction size from the original frame, additional computational complexity is not imparted. In experimental results, the proposed algorithms provide robust frame interpolation performance for whole test sequences. Also, the computational complexity of the proposed algorithm is reduced to a benchmark algorithm.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1992-1995
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• 2003

An adaptive active contour algorithm which shows stable object tracking performance under the moving or deformable environments, is proposed. In order to cope with local deformation of the object, an energy map is generated from the difference of the consecutive images and a new energy function based on the energy map is presented. The algorithm is evaluated on a set of artificial and real images to verify the efficiencies and test results show the stable tracking performance for the moving objects.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.2
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• pp.210-217
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• 1990

For the failure detection of dynamic systems, processing the residuals from the observer of the estimator is the most general method. A failure detection method which use an adaptive predictor to separate the effect of sensor failure from the additive noise in the residuals of a Kalman filter that is employed as an estimator of a dynamic system is addressed here. In the method, the property of the residuals of an optimal Kalman estimator is exploited. The simulation results of this method shows that the proposed method is superior to the sequential probability ratio test for a small failure magnitude.

• The Korean Journal of Food And Nutrition
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• v.1 no.2
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• pp.31-36
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• 1988

An adaptive on-line optimization method has been applied to test the ability to maximize the cellular productivity of a continuous methylotroph culture system which was simulated by a variable yield Monod-type model. Optimum dilution rate and productivity were successively obtained and maintained at all times by the algorithm that utilizes steepest descent technique as optimization method and recursive least-square method with forgetting factor as dynamic model identification.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.188-196
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• 2005

This paper presents a new test data compression and low power scan test method that can reduce test time and power consumption. A proposed method can reduce the scan-in power and test data volume using a modified scan cell reordering algorithm and hybrid adaptive encoding method. Hybrid test data compression method uses adaptively the Golomb codes and run-length codes according to length of runs in test data, which can reduce efficiently the test data volume compare to previous method. We apply a scan cell reordering technique to minimize the column hamming distance in scan vectors, which can reduce the scan-in power consumption and test data. Experimental results for ISCAS 89 benchmark circuits show that reduced test data and low power scan testing can be achieved in all cases. The proposed method showed an about a 17%-26% better compression ratio, 8%-22% better average power consumption and 13%-60% better peak power consumption than that of previous method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4865-4885
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• 2019

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.