• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.53-62
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• 2022

With increasing size of transformer-based neural networks, a light-weight algorithm and efficient AI accelerator has been developed to train these huge networks in practical design time. In this article, we present a survey of state-of-the-art research on the low-precision computational algorithms especially for floating-point formats and their hardware accelerator. We describe the trends by focusing on the work of two leading research groups-IBM and Seoul National University-which have deep knowledge in both AI algorithm and hardware architecture. For the low-precision algorithm, we summarize two efficient floating-point formats (hybrid FP8 and radix-4 FP4) with accuracy-preserving algorithms for training on the main research stream. Moreover, we describe the AI processor architecture supporting the low-bit mixed precision computing unit including the integer engine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.693-709
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• 2018

In this paper, we propose a compact Convolutional Neural Network (CNN)-based tracker in conjunction with a particle filter architecture, in which the CNN model operates as an accurate candidates estimator, while the particle filter predicts the target motion dynamics, lowering the overall number of calculations and refines the resulting target bounding box. Experiments were conducted on the Online Object Tracking Benchmark (OTB) [34] dataset and comparison analysis in respect to other state-of-art has been performed based on accuracy and precision, indicating that the proposed algorithm outperforms all state-of-the-art trackers included in the OTB dataset, specifically, TLD [16], MIL [1], SCM [36] and ASLA [15]. Also, a comprehensive speed performance analysis showed average frames per second (FPS) among the top-10 trackers from the OTB dataset [34].

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.173-178
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• 2019

Mongolian historical and cultural monuments on settlement areas of stone inscriptions, stone images, rock-drawings, remains of cities, architecture are still telling us their stories. These monuments depict the understanding of the word, philosophical and artistic outlook, beliefs, religion, national art, language, culture and traditions of Mongols [1]. Nowadays computer science, especially computer vision is applying in the other science fields. The main problem is how to apply and which algorithm can detect and classify the objects correctly. In this paper, we propose a method to detect object from Mongolian nomadic environment images. This work proposes a method for object detection that is the combination of the binary operations in the edge detection results. We found out the best method and parameters of state-of-the-art machine learning algorithms. In experimental result, we evaluate our results with 10-fold cross validation and split 66% strategies.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.127.1-127
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• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.41-47
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• 2016

This paper considers simple polygon search problem. How many searchers find a mobile intruder that is arbitrarily faster than the searcher within polygon art gallery? This paper uses the visibility graph that is connected with edges for mutually visible vertices. Given visibility graph, we select vertex u that is conjunction ${\Delta}(G)$ in $N_G(v)$ for $d_G(v){\leq}4$. We decide 1-searchable if $1{\leq}{\mid}u{\mid}{\leq}2$ and 2-searchable if ${\mid}u{\mid}{\geq}3$. We also present searcher's shortest path. This algorithm is verified by varies 1 or 2-searchable polygons.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.443-450
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• 2003

In this paper, we proposed the novel hierarchical algorithm for the recognition of English calling cards that processes multiresolution images of calling cards hierarchically to extract individual characters and recognizes the extracted characters by using the enhanced neural network method. The hierarchical recognition algorithm generates multiresolution images of calling cards, and each processing step in the algorithm selects and processes the image with suitable resolution for lower processing overhead and improved output. That is, first, the image of 1/3 times resolution, to which the horizontal smearing method is applied, is used to extract the areas including only characters from the calling card image, and next, by applying the vertical smearing and the contour tracking masking, the image of a half time resolution is used to extract individual characters from the character string areas. Lastly, the original image is used in the recognition step, because the image includes the morphological information of characters accurately. And for the recognition of characters with diverse font types and various sizes, the enhanced RBF network that improves the middle layer based on the ART1 was proposed and applied. The results of experiments on a large number of calling card images showed that the proposed algorithm is greatly improved in the performance of character extraction and recognition compared with the traditional recognition algorithms.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.89-102
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• 2013

The paper presents a fuzzy based impulse noise filter for both gray scale and color images. The proposed approach is based on the technique of boundary discriminative noise detection. The algorithm is a multi-step process comprising detection, filtering and color correction stages. The detection procedure classifies the pixels as corrupted and uncorrupted by computing decision boundaries, which are fuzzified to improve the outputs obtained. In the case of color images, a correction term is added by examining the interactions between the color components for further improvement. Quantitative and qualitative analysis, performed on standard gray scale and color image, shows improved performance of the proposed technique over existing state-of-the-art algorithms in terms of Peak Signal to Noise Ratio (PSNR) and color difference metrics. The analysis proves the applicability of the proposed algorithm to random valued impulse noise.

• Journal of the Korean Graphic Arts Communication Society
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• v.30 no.3
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• pp.1-11
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• 2012

According as the latest printing technology is converted from analogue to digital, life cycle of a printing technology is shortened and the existent printing companies were faced always in a new technology. Specially, way of foreign countries export opened because globalization of printing market is accelerated. But, printing buyers of advanced nation require standard printing process control. fect at product process step. Emphasized in IPA technical conference for past several years tendency about graphic art color proofing and technical analysis and comparison going through Color Proofing RoundUP. These researchers have developed a color management technology. A specially developed printing technology and reference characterization data brought certain high quality elevation in a graphic art proofing technology. When excessive GCR method application supervise printing, width of color conversion necks by requiring a lot of color conversions than proofing. But, these point is lacking relatively than a lot of effects that GCR gives. Therefore, correct interests of GCR algorithm and verification step to forecast beforehand result about actuality application are positively necessary. Therefore, this research forced into input file which is applied with different levels from input to print for printing optimization that consider standard printing with eco-friendly by method to solve these problem. And experimented using manuscript who GCR level is applied as is different in each field, and analyzed the result. Also, it is verification method by step to last printing from input file that solve been the various quality who generate in actuality field through these analysis result. ICC color management confirmed printing optimization process applying GCR algorithm improved to base.

• IE interfaces
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• v.10 no.2
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• pp.51-56
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• 1997

This paper concerns with the problem of mixed model assembly sequencing using neural net. In recent years, because of two characteristics of it, massive parallelism and learning capability, neural nets have emerged to solve the problems for which more conventional computational approaches have proven ineffective. This paper proposes a method using neural net that can consider line balancing and grouping problems simultaneously. In order to solve the mixed model assembly sequencing of the motor industry, this paper uses the modified ART1 algorithm.