• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.224-231
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• 2019

The current paper was conducted to unravel the influence of affect induction as a background emotion in the process of cognitive task to judge the degree of sequence in groups with or without anxiety symptoms. Four types of affect induction and two sequential task types were used as within-subject variables, and two types of college students groups classified under the Beck Anxiety Inventory (BAI) as a between-subject variable were selected to determine reaction times involving sequential judgment among the lexical relevance information. DmDx5 was used to present a series of stimuli and elicit a response from subjects. Repeated measured ANOVA analyses revealed that reaction times and error rates were significantly larger with anxiety participants compared to the normal group regardless of affect and task types. Within-subject variable effects found that specific affect type (sorrow condition) and number-related task type showed a more rapid response compared to other affect types and magnitude-related task type, respectively. In sum, these findings confirmed the difference in tendency with reaction time and error rates that varied as a function of accompanying affect types as well as anxiety level and task types suggesting the that underlying background affect plays a major role in processing affect-cognitive association tasks.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.807-816
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• 2005

Sequential pattern mining finds all of the frequent sequences satisfying a minimum support threshold in a large database. However, when mining long frequent sequences, or when using very low support thresholds, the performance of currently reported algorithms often degrades dramatically. In this paper, we propose a novel sequential pattern algorithm using only closed frequent sequences which are small subset of very large frequent sequences. Our algorithm generates the candidate sequences by depth-first search strategy in order to effectively prune. using bitmap representation of underlying databases, we can effectively calculate supports in terms of bit operations and prune sequences in much less time. Performance study shows that our algorithm outperforms the previous algorithms.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.6
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• pp.896-902
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• 2008

The objective of this research was to create a new probiotic candy with good flavor and healthy benefits by using the response surface method and a sequential quadratic programming technique. The endpoint was to increase the varieties of dairy products and enhance their market values. In this study, milk was mixed with yogurt cultures (Lactobacillus bulgaricus, Streptococcus thermophilus) and probiotics (L. paracasei, Bifidobacterium longum) and incubated at $37^{\circ}C$ for 20 h. The samples were blended with lyoprotectants (galactose, skim milk powder and sucrose), freeze dried and then mixed with sweeteners (lactose and xylitol) to improve the texture for forming tablets. The processing conditions were optimized in two steps: the first step constructed a surface model using response surface methodology; the second step optimized the model with a sequential quadratic programming procedure. Results indicated that skim milk inoculated with L. delbrueckii subsp. Bulgaricus, S. thermophilus, L. paracasei subsp. paracasei and B. longum and blended with 6.9% of galactose, 7.0% of sucrose and 8.0% of skim milk powder would produce a new probiotic candy with the highest viability of probiotics and good flavor. A relatively higher survival of probiotics can be achieved by placing the probiotic candy product in a glass bottle with deoxidant and desiccant at $4^{\circ}C$. These probiotic counts remained at 106-108 CFU/g after being stored for two months.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.287-293
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• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.5
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• pp.65-73
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• 2002

In this paper, we propose and verify an implementation method for a single-chip 2048 complex point FFT/IFFT in terms of sequential data processing. For the sequential processing of 2048 complex data, buffers to store the input data are necessary. Therefore, DRAM-like pipelined commutator architecture is used as a buffer. The proposed structure brings about the 60% chip size reduction compared with conventional approach by using this design method. The 16-point FFT is a basic building block of the entire FFT chip, and the 2048-point FFT consists of the cascaded blocks with five stages of radix-4 and one stage of radix-2. Since each stage requires rounding of the resulting bits while maintaining the proper S/N ratio, the convergent block floating point (CBFP) algorithm is used for the effective internal bit rounding and their method contributed to a single chip design of digital audio broadcasting system.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.1997-2012
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• 1998

It is necessary to find out the parallelism in tlle sequential Java program to execute it on the parallel machine. The loop is a fundamental source to exploit parallelism as it process a large portion of total execution time in sequential Java program on the parallel machine. However, a complete parallel execution can hardly be achieved due to data dependence. This paper proposes the method of exploiting the implicit parallelism by structuring a dependence graph through the analysis of data dependence in the existing Java programming language having a nested loop structure. The parallel code generation method through the restructuring compiler and also the translation method of Java source program into multithread statement. which is supported by the Java programming language itself, are proposed here. The perforance evaluatlun of the program translaed into the thread statement is conducted using the trip cunt of loop and the trip Count of luop and the thread count as parameters The resttucturing compiler provides efficient way of exploiting parallelism by reducing manual overhead conveliing sequential Java program into parallel code. The execution time for the Java program as a result can be reduced un the parallel machine.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.183-195
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• 2013

Real-time high-precision image georeferencing is important for the realization of image based precise navigation or sophisticated augmented reality. In general, high-precision image georeferencing can be achieved using the conventional simultaneous bundle adjustment algorithm, which can be performed only as post-processing due to its processing time. The recently proposed sequential bundle adjustment algorithm can rapidly produce the results of the similar accuracy and thus opens a possibility of real-time processing. However, since the processing time still increases linearly according to the number of images, if the number of images are too large, its real-time processing is not guaranteed. Based on this algorithm, we propose a modified fast algorithm, the processing time of which is maintained within a limit regardless of the number of images. Since the proposed algorithm considers only the existing images of high correlation with the newly acquired image, it can not only maintain the processing time but also produce accurate results. We applied the proposed algorithm to the images acquired with 1Hz. It is found that the processing time is about 0.02 seconds at the acquisition time of each image in average and the accuracy is about ${\pm}5$ cm on the ground point coordinates in comparison with the results of the conventional simultaneous bundle adjustment algorithm. If this algorithm is converged with a fast image matching algorithm of high reliability, it enables high precision real-time georeferencing of the moving images acquired from a smartphone or UAV by complementing the performance of position and attitude sensors mounted together.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1130-1137
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• 1996

Every neural network has some kind of feedback. For the sake of analyzing fundamental aspects of information processing in neural nets, a net without feedbacks is an important theoretical model. But here we focus on a recurrent neural net which delay synapses as a realistic dynamical model of nervous systems. Synaptic connections are determined by a version of the Hebb rule (correlation type rule). We use a statistical neurodynamic method to explain the retrieval dynamics of the network. The result of the analysis for the sequential associativ e memory with delay synapses is compared with computer simulation. We have succeeded in explaining the dynamics of this network by theoretical analyses.

• Spatial Information Research
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• v.18 no.3
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• pp.73-83
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• 2010

This study presents a method to segment an image, employing the statistics observed at each pixel location across sequential frame images. In the acquisition and analysis of spatial information, utilization of digital image processing technique has very important implications. Various image segmentation techniques have been presented to distinguish the area of digital images. In this study, based on the analysis of the spectroscopic characteristics of sequential frame images that had been previously researched, an image segmentation method was proposed by using the randomness occurring among a sequence of frame images for a same scene. First of all, we computed the mean and standard deviation values at each pixel and found reliable pixels to determine seed points using their standard deviation value. For segmenting an image into individual regions, we conducted region growing based on a T-test between reference and candidate sample sets. A comparative analysis was conducted to assure the performance of the proposed method with reference to a previous method. From a set of experimental results, it is confirmed that the proposed method using a sequence of frame images segments a scene better than a method using a single frame image.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.353-360
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• 2004

The study on scene change detection in the compressed MPEG videos has been done by various approaches. However, most of these approacher accomplished scene change detection by carrying out decoding processes and then by comparing pixels with pixels. This approach it not suitable for real time applications owing to much computing time of decoding processes. Recently, the study on scene change detection algorithms using only information of compressed domain is becoming Increasingly important. In this paper, we propose a sequential access method as an efficient scene change detection algorithm in the compressed domain. According to the type of pictures in the compressed MPEG video streams (divided in I-blocks and each I-block into P-blocks), the proposed algorithm provides effective scene change detection by applying sequential access and block by block mechanism. The proposed sequential access method provides fast and accurate detection operation by reducing checking procedures of unnecessary pictures due to molt of operations in compressed domain and checking by block units. Also, this approach uses optimal algorithm to provide fast and accurate detection operation.