• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.574-576
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• 2020

최근에는 딥러닝 기술의 발달로 물체 인식 및 검출에 관한 기술들 또한 발탄하고 있다. 검출에 관한 여러 기법(Faster R-CNN, R-CNN, YOLO, SSD 등) 중 SSD는 다른 기법들과는 다르게 높은 정확도와 빠른 속도가 특징이다. 동시에 여러 detection network들도 쉽게 이용이 가능하다. 본 논문에서는 detection netowork중 Mobilenet V2 network를 이용하여 SSD와 결합해 모델을 훈련하고, TensorRT engine을 이용하여 더 빠른 속도로 검출할 수 있는 방법에 대해 논의한다. 이 방법을 통해 face detector를 만들어 여러 상황에서 쓰일 수 있도록 한다.

• Journal of Information Technology Applications and Management
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• v.10 no.2
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• pp.33-41
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• 2003

It is not easy to find desired information in the world wide web. In this research, we introduce a notion of concept network that is useful in finding information if it is used in search engines that are specialized in domains such as medicine, law or engineering. The concept network that we propose is a network in which nodes represent significant concepts in the domain, and links represent relationships between the concepts. We may use the concept network constructor as a preprocessor to speci-alized search engines. When user enters a target word to find information, our system generates and displays a concept network in which nodes are con-cepts that are closely related with the target word. By reviewing the network, user may confirm that the target word is properly selected for his intention, otherwise he may replace the target word with better ones discovered in the network. In this research, we propose a detailed method to construct concept net-work, implemented a prototypical system that constructs concept networks, and illustrate its usefulness by demonstrating a practical case.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.243-246
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• 2005

Recent ubiquitous computing environments increasingly impact on our lives using the current technologies of sensor network and ubiquitous services. In this paper, we propose ubiquitous architectural framework for ubiquitous sleep aid service(UbiSAS) in the subset of ubiquitous computing for refreshing of human's sleep. And we examine technical feasibility. Human can recover his health through refreshing sleep from fatigue. Ubiquitous architectural framework for UbiSAS in digital home offers agreeable sleeping environment and improves recovery from fatigue. So we present new concept of ubiquitous architectural framework dissolving stress. Specially, we apply context to context-aware framework module. This context is transferred to context adaptive inference engine which has service invocation function in intelligent agent module. Ubiquitous architectural framework for UbiSAS using context adaptive rule inference engine without user intervention is technical issue. That is to say, we should take sleep comfortably during our sleeping. And sensed information during sleeping is changed to context-aware information. This presents significant information in context adaptive rule inference engine for UbiSAS. This information includes all sleeping state during sleeping in context-aware computing technique. So we propose more effective and most suitable ubiquitous architectural framework using context adaptive rule inference engine for refreshing sleep in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.26-31
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• 2011

In this study, it made to run conventional single direct injection(DI) diesel engine, which adapted bulk combustion system not following spark ignition system without any ignition apparatus. It was heated and controlled inlet-air into conventional single DI diesel engine. The maximum value of brake thermal efficiency was at 35 region of air-fuel ratio. On the contrary, when the region of air-fuel ratio leaner than 35, brake thermal efficiency was decreased suddenly. And brake thermal efficiency was increased as much as inlet-air heating temperature increased. So, when air-fuel ratio was decreased and inlet-air heating temperature was higher, the engine was in optimal operation condition.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2006.04a
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• pp.56-60
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• 2006

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.99-99
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• 2004

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.804-810
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• 2004

There are two important things for the general purpose neural network processor. The first is a capability to build various structures of neural network, and the second is to be able to support suitable learning method for that neural network. Some way to process various learning algorithms is required for on-chip learning, because the more neural network types are to be handled, the more learning methods need to be built into. In this paper, an improved hardware structure is proposed to compute various kinds of learning algorithms flexibly. The hardware structure is based on the existing modular neural network structure. It doesn't need to add a new circuit or a new program for the learning process. It is shown that rearrangements of the existing processing elements can produce several neural network learning modules. The performance and utilization of this module are analyzed by comparing with other neural network chips.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.571-577
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• 2021

This study is a study on System On Chip (SOC) that implements virtual engine sound in electric vehicles without engines, and realizes vivid engine sound by combining Adaptive Difference PCM (ADPCM) method and frequency modulation method for satisfaction of driver's needs and safety of pedestrians. In addition, by proposing an electronic sound synthesis algorithm applying Musical Instrument Didital Interface (MIDI), an engine sound synthesis method and a constitutive model of an engine sound generation system are presented. In order to satisfy both drivers and pedestrians, this study uses Controller Area Network (CAN) communication to receive information such as Revolution Per Minute (RPM), vehicle speed, accelerator pedal depressed amount, torque, etc., transmitted according to the driver's driving habits, and then modulates the frequency according to the appropriate preset parameters We implemented an interaction algorithm that accurately reflects the intention of the system and driver by using interpolation for the system, ADPCM algorithm for reducing the amount of information, and MIDI format information for making engine sound easier.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.617-628
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• 2023

This study aimed to evaluate the engine load factor (LF) of a 90 kW agricultural combine harvester. The combine harvester used in this study is equipped with an electronic engine, and real-time engine data (torque and speed) was collected through a controller area network. The speed of the combine harvester during harvesting operation was divided into three levels (4, 5, and 6 km/h) for the representative operation speed range of 4 to 6 km/h. The LF was calculated using the engine load data measured in real time during harvesting. A weight was applied to the LF for each condition based on a survey of the usage. Results of the field test showed that the LF was 0.53, 0.64, and 0.87 at working speeds of 4, 5, and 6 km/h, respectively. The highest engine load factor was recorded at 6 km/h. Finally, based on the weight for the usage applied, the integrated engine LF was analyzed to be 0.69, which is approximately 144% higher than the currently applied LF of 0.48. A study on LF analysis for the entire work cycle, including idling and driving of the combine harvester, will be addressed in a future study.