• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.12
• /
• pp.2313-2321
• /
• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.3
• /
• pp.28-34
• /
• 1999

In this paper. we propose the method of Scene Change Detection for video sequence using the DC components and the moving vectors in the Macro Blocks in the DCT blocks. The proposed method detects the Scene Change which would not be related with the specific sequences in the compressed MPEG domain. To do this. we define new metrics for Scene Change Detection using the features of picture component and detect the exact Scene Change point of B-pictures using the characteristics of B-picture's sharp response for the moving vectors. In brief, we will detect the cut point using I-picture and the gradual scene changes such as dissolve, fade, wipe, etc. As a results, our proposed method shows good test results for the various MPEG sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2976-2990
• /
• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

• Journal of the Korean Society of Visualization
• /
• v.21 no.1
• /
• pp.18-25
• /
• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.46 no.9
• /
• pp.14-24
• /
• 2009

Memory cell array and peripheral circuits are designed for system on panel style frame buffer. Moreover, a parallel test methodology to test multiple blocks of memory cells is proposed to overcome low yield of system on panel processing technologies. It is capable of faster fault detection compared to conventional memory tests and also applicable to the tests of various embedded memories and conventional SRAMs. The various patterns of conventional test vectors can be used to enhance fault coverage. The proposed testing method is also applicable to hierarchical bit line and divided word line, one of design trends of recent memory architectures.

• Structural Engineering and Mechanics
• /
• v.21 no.5
• /
• pp.553-570
• /
• 2005

Dynamic and static analyses of existing structures are very important to obtain reliable information relating to actual structural properties. For this purpose a series of material test, dynamic test and static collapse test of the existing two brick chimneys, in Tokoname, are carried out. From the material tests, Young's modulus and compressive strength of the brick used for these chimneys are estimated to be 3200 MPa and 7.5 MPa, respectively. The results of static collapse test of the existing two brick chimneys are discussed in this paper and composed with the results from FEA (Finite Element analysis). From the results of dynamic tests, the fundamental frequencies of Howa and Iwata brick chimneys are estimated to be about 2.69 Hz and 2.93 Hz, respectively. Their natural modes are identified by ARMAV (Autoregressive Moving Average Vectors) model. On the basis of the static and dynamic experimental tests, a numerical model has been prepared. According to the European code (Eurocode n. 8: "Design of structures for earthquake resistance") non-linear static (Pushover) analysis of the two chimneys is carried out and they seem to be vulnerable to earthquakes with 0.25 to 0.35 g.

• Journal of Broadcast Engineering
• /
• v.23 no.3
• /
• pp.409-420
• /
• 2018

In this paper, we propose a Motion Vector-based Particle Filter(MVPF) for object tracking on bitstreams and a object tracking system using the MVPF. The MVPF uses motion vectors to both the transition and the observation models of a general particle filter to improve the accuracy while maintaining the number of particles. In the proposed object tracking system, the state of the target object can be predicted using the histogram of motion vectors extracted from the bitstream. In terms of precision, F-measure and IOU(Intersection Of Union), the proposed method is about 30%, 17%, and 17% better on average, respectively, in MPEG test sequences and VOT2013 sequences. Furthermore, When the tracking results are displayed in box form for subjective performance evaluation, the proposed method can track moving objects more robust than the conventional methods in all test sequences.

• International Journal of Aeronautical and Space Sciences
• /
• v.16 no.3
• /
• pp.437-444
• /
• 2015

Satellite selection and exclusion techniques have been applied to the global navigation satellite system (GNSS) with the aim of achieving a balance between navigational performance and computational efficiency. Conventional approaches to satellite selection based on the best dilution of precision (DOP) are excessively computational and complicated. This paper proposes a new method that applies a geometric sensitivity index of individual GNSS satellites. The sensitivity index is derived using the inner product of the line of sight (LOS) vector of each satellite. First, the LOS vector is computed, which accounts for the geometry between the satellite and user positions. Second, the inner product of each pair of LOS vectors is calculated, which indicates the proximities of the satellites to one another. The proximity can be determined according to the sensitivity of each satellite. A post-processing test was conducted to verify the reliability of the proposed method. The proposed index and the results of a conventional approach that measures the dilution of precision (DOP) were compared. The test results demonstrate that the proposed index produces results that are within 96% of those of the conventional approach and reduces the computational burden. This index can be utilized to estimate the sensitivity of individual satellites, obtaining a navigation solution. Therefore, the proposed index applies to satellite selection and exclusion as well as to the sensitivity analyses of multiple GNSS applications.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.3 no.1
• /
• pp.235-244
• /
• 1999

This Paper proposes a new compaction algorithm for IDDQ testing in CMOS Circuits. A primary test pattern is generated by the primitive fault pattern which is able to detect GOS(gate-oxide short) and the bridging faults in an internal primitive gate. The new algorithm can reduce the number of the test vectors by decreasing the don't care(X) in the primary test pattern. The controllability of random number is used on processing of the backtrace together four ones of heuristics. The simulation results for the ISCAS-85 benchmark circuits show that the test vector reduction is more than 45% for the large circuits on the average compared to static compaction algorithms.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.2 s.302
• /
• pp.11-18
• /
• 2005

To detect vehicles in image, first the image is transformed with the steerable pyramid which has independent directions and levels. Feature vectors are the collection of filter responses at different scales of a steerable image pyramid. For the detection of vehicles in image, feature vectors in feature points of the vehicle image is used. First the feature points are selected with the grid points in vehicle image that are evenly spaced, and second, the feature points are comer points which m selected by human, and last the feature points are corner Points which are selected in grid points. Next the feature vectors of the model vehicle image we compared the patch of the test images, and if the distance of the model and the patch of the test images is lower than the predefined threshold, the input patch is decided to a vehicle. In experiment, the total 11,191 vehicle images are captured at day(10,576) and night(624) in the two local roads. And the $92.0\%$ at day and $87.3\%$ at night detection rate is achieved.