• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.539-546
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• 2006

The Air Quality Sensor(AQS), located near the fresh air inlet, serves to reduce the amount of pollution entering the vehicle cabin through the HVAC(heating, ventilating, and air conditioning) system by sending a signal to close the fresh air inlet door/ventilation flap when the vehicle enters a high pollution area. The sensor module which includes two independent sensing elements for responding to diesel and gasoline exhaust gases, and temperature sensor and humidity sensor was designed for intelligent AQS in automobile. With this sensor module, AVR microcontroller was designed with back propagation neural network to a powerful gas/vapor pattern recognition when the motor vehicles pass a pollution area. Momentum back propagation algorithm was used in this study instead of normal backpropagation to reduce the teaming time of neural network. The signal from neural network was modified to control the inlet of automobile and display the result or alarm the situation in this study. One chip microcontroller, ATmega 128L(ATmega Ltd., USA) was used for the control and display. And our developed system can intelligently reduce the malfunction of AQS from the dampness of air or dense fog with the backpropagation neural network and the input sensor module with four sensing elements such as reducing gas sensing element, oxidizing gas sensing element, temperature sensing element and humidity sensing element.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1090-1095
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• 1992

This paper describes the systematic control method of process information transfer and machine cell control in FMS implementation. We have constructed an experimental FMS computer network and control system. The system hardware consists of host computer to manage process data and information transfer of machine cells, cell control computers to control machine cells(NC lathe, machining center). On the other hand, software is made up of oredr management module, NC program searching and generation module, NC part program error check module and cell control module. In this study, we could arrive at conclusion as following : The first, each task could be accomplihed by the efficient information transfer in hierachical computer network. The second, data base system of part programs and process control data is needed for the efficint information transfer and production management. Lastly, expansion of FMS control system could be achieved by the hierachical and decentralized computer control system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.04c
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• pp.54-57
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• 2008

A new modular Lateral Information Propagation Networks can be implemented in a IC chip with the circuit VLSI technology for detection of bad materials. The proposed modular architecture is propagated the neural network through inter module connections. For such inter module connections, the host(computer or logic) mediates the exchange of information among modules. Also border nodes in each module have capacitors for temporarily retaining the information from outer modules. For detecting of Faulty Insulator, $4\;{\times}\;4$ neural network modules has been designed and simulation of interpolation with the designed networks has been done.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.718-723
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• 1993

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classification accuracy but also for being able to classify objects whose attribute values do not have clear boundaries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. The transformed input is processed in the postprocessing module. The experimental results indicate the superiority of the backpropagation network with fuzzy preprocessor in comparison to the conventional backpropagation network.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1654-1660
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• 2004

In this paper, we describes the design and implementation of LonWorks communication module for distributed intelligent control using LonWorks technology of Echelon. LonWorks communication module can be divided hardware and firmware. First, hardwares is divided into microcontroller attaching sensors and LonWorks components for working together control network and data network. Hardwares are consisted of neuron chip, microcontroller, transceiver, LONCard. Second, operating firmware is realized with neuron C using NodeBulider 3.0 development tool. Produced and implemented LonWorks communication module is pretested using LTM-10A, Gizmo 4 I/O board, parallel I/O Interface. For field test, microcontroller module part is tested by HyperTerminal, communication procedure in data network is certified by transmitting and receiving short message using LonMaker for Windows tool. Herewith, LON technology is based on network communication technique using LonWorks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.795-798
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• 2005

In this paper, we analyses of additive crypto-module architecture for a sensor network. Recent research in sensor networks has raised security issues for small embedded devices. Security concerns are motivated by the development of a large number of sensor devices in the field. Limitations in processing power, battery life, communication bandwidth and memoryconstrain devices. A mismatch between wide arithmetic for security and embedded data buscombined with lack of certain operations. Then, we compared the architecture of crypto-module in this paper.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.53-58
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• 2002

the popularity of uses for internet has been needed to information security. thereforce, intrusion, information leakage and modification, change or intentional efflux to computer system aspects of information security have been resulted in requirement of intrusion detection from outer at user authentication. this problem Presents design of PUM(Processing Unit Module) which analyze both the host log generated by sever host systems that various case for intellectualized intrusion method and network_packet on networks in large scale network.