• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.238-242
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• 2005

This paper considers a routing problem on a RPR(Resilient Packet Ring). The RPR is one of the ring-type data telecommunication network. Our major problem is to find an optimal routing algorithm for a given data traffic on the network under no splitting the traffic service, where the maximum load of a link is minimized. This paper characterizes the Minmax problem and develops two heuristic algorithms. By using the numerical comparison, we show that our heuristic algorithm is valuable for efficient routing the data traffic on a RPR.

• Journal of Applied and Pure Mathematics
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• v.4 no.5_6
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• pp.269-286
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• 2022

In this article we exhibit univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on ℝ^N , N ∈ ℕ. When they are also uniformly continuous we have pointwise and uniform convergences. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.127-135
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• 1995

The diffusion neural network forms a Gaussian distribution by transferring an excitation to the surround. A DOG(difference of two Gaussians) is obtained by the diffusion neural network. This type of the DOG, which can detect the intensity changes of an image, has the same shape as a LOG(Laplacian of a Gaussian:${\Delta}^2$G) and narrow band pass characteristics. In this paper we show that another type of the DOG which has a very narrow Gaussian for the excitatory and a very wide Gaussian for the inhibitory, can be formed by the diffusion process of this network, This type of the DOG has a wide band width in spatial frequency domain and can be used efficiently in detecting special type of edges.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.536-544
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• 2011

In this paper, a controller for two wheeled inverted pendulum type robot is designed to have more stable balancing capability than conventional controllers. Traditional PID control structure is chosen for the two wheeled inverted pendulum type robot, and proper gains for the controller are obtained for specified user's weights using trial-and-error methods. Next a neural network is employed to generate PID controller gains for more stable control performance when the user's weight is arbitrarily selected. Through simulation studies we find that the designed controller using the neural network is superior to the conventional PID controller.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.29-36
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• 2016

A wireless passive sensor network is a network which, by letting separate RF sources supply energy to sensor nodes, is able to live an eternal life without batteries. Against expectations about an eternal life, however, a wireless passive sensor network still has many problems; scarcity of energy, non-simultaneity of energy reception and data transmission and inefficiency in resource allocation. In this paper, we focus on a wireless passive sensor network providing a packet service which is tolerable to packet losses but requires timely delivery of packets. Perceiving the practical constraints, we then consider a contending-type MAC scheme, rooted in framed and slotted ALOHA, for supporting many sensor nodes to deliver packets to a sink node. Next, we investigate the network-wide throughput achieved by the MAC scheme when the packets transmitted by geographically scattered sensor nodes experience path losses hence capture phenomena. Especially, we derive an exact formula of network-wide throughput in a closed form when 2 sensor nodes reside in the network. By controlling design parameters, we finally optimize the contending-type MAC scheme as to attain the maximum network-wide throughput.

• Genomics & Informatics
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• v.13 no.3
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• pp.76-80
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• 2015

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

• KSCI Review
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• v.1 no.2
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• pp.73-94
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• 1995

In this paper, design of efficient NDB(Network Data Base) for the activation plan of chain information network. The DB structure build up, logical structure, store structure, physical structure, the data express for one's record, and the express using linked in the releation of data. Also express as hierarchical model on the DSD(Data Structure Diagram) from the database with logical structure. Each node has express on record type, the linked in course of connective this type, the infuence have efficent of access or search of data, in the design for connection mutually a device of physical, design for database, and construction a form of store for logical. Also activation of chain information network of efficent, using POS(Point Of Sale) system in OSI(Open Systems Interconnection) environment for network standardization, and build up network a design for system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.570-573
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• 1996

The evaluation test for the operation of rotary type surface mounters which consist of the reel axis, the index table and the X-Y table, has been performed by comparing the new method with the old one in only fields. Because the problem seeking for the optimal operation of rotary type surface mounters, is NP complete, it is almost impossible to get the optimal solutions of large problems. This paper deals with a dynamic network modeling, which can reduce the effort, the cost, and the time used for the performance test of rotary type surface mounters.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.