• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.361-378
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• 2012

Most of the current frequency hopping (FH) based dynamic spectrum access (DSA) methods concern a reactive channel access scheme with synchronous inter-channel spectrum sensing, i.e., FH is reactively triggered by the primary user (PU)'s return reported by spectrum sensing, and the PU channel to be switched to is assumed precisely just sensed or ready to be sensed, as if the inter-channel spectrum sensing moments are synchronous. However, the inter-channel spectrum sensing moments are more likely to be asynchronous, which risks PU suffering more interference. Moreover, the spectrum sensing is usually erroneous, which renders the problem more complex. To address this problem, we propose a proactive FH based DSA method against both erroneous spectrum sensing and asynchronous inter-channel spectrum sensing (moments). We term it as proactive DSA. The optimal FH sequence is obtained by dynamic programming. The complexity is also analyzed. Finally, the simulation results confirm the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2212-2230
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• 2014

Cognitive radio (CR) users, known as secondary users (SUs), should avoid interference with primary users (PUs) who own the licensed band, while trying to access it; when the licensed band is unused by the PUs. To detect PUs, spectrum sensing should be performed over in-band channels that are currently in use by SUs. If PUs return to access the band, SUs need to vacate it, disrupting the SUs' communication unless a non-utilized band is discovered. Obtaining a non-utilized band in a short period facilitate seamless communication for SUs and avoid interference on PUs by vacating from the channel immediately. Searching for a non-utilized band can be done through proactive out-of-band (OB) sensing. In this paper, we suggest a proactive OB sensing scheme that minimizes the time required to discover a non-utilized spectrum in order to continue communication. Although, the duration spent on OB sensing reduces the throughput of the CR networks that can be achieved on band being utilized, the lost throughput can be compensated in the new discovered band. We demonstrate that, the effect of our proposed scheme on the throughput owing to OB sensing is insignificant, while exhibiting a very short channel discovery time.

• 산업경영시스템학회지
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• 제22권52호
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• pp.171-180
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• 1999

Industrial equipment reliability improvement and maintenance is gaining attention as the next great opportunity for manufacturing productivity improvement. Reactive maintenance is expensive because of extensive unplanned downtime and damage to machinery. To avoid such an unplanned machine downtime, it is needed to use proactive maintenance approach by either using historical maintenance data or by sensing machine conditions. This paper discusses failure diagonosis and prediction based on the condition-based maintenance and reliability technique. Thus, by enabling such a framework, it can bring us more efficient planning and execution of maintenance to reduce costs and/or increase profits.

• 대한안전경영과학회지
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• 제9권5호
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• pp.79-85
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• 2007

Intelligent machines respond to external environments on the basis of decisions that are made by sensing the changes in the environment and analyzing the obtained information. This study focuses on the construction of a knowledge base which enables decision making with that information. Approximately 70% of all errors that occur in machine tools are caused by thermal error. In order to proactive deal with these errors, a system which measures the temperature of each part and predicts and compensates the displacement of each axis has been developed. The system was built in an open type controller to enable machine tools to measure temperature changes and compensate the displacement. The construction of a machining knowledge base is important for the implementation of intelligent machine tools, and is expected to be applicable to the network based intelligent machine tools which look set to appear sooner or later.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• 한국구조물진단유지관리공학회 논문집
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• 제16권1호
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• pp.120-130
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• 2012

최근 장대 교량 및 복잡한 교량의 형상이 자주 건설됨에 따라, 교량의 안전도 및 건전성 평가에 많은 관심이 집중되고 있다. 장대교량의 경우 다양한 종류의 계측기 들이 설치되어, 측정된 센싱(Sensing)자료를 신호처리를 통해 케이블을 이용하여 장거리 전송하거나, Smart Health 모니터링 개념으로 교량 현장에서 게이트웨이(Gateway)를 통해 외부 무선통신망에 연결하여 정보를 전송하는 최신 무선통신 기술을 적용하고 있다. 하지만, 전 세계적으로 발생한 교량 관련 안전사고의 경우 위험 또는 사고인지에 따른 실시간 예방적, 지능적 조치가 미흡하여 대형사로를 유발한 것으로 보고되고 있다. 이런 문제점을 해결하고자 본 논문에서는 첨단 무선통신인 RFID(Radio Frequency Identification)/USN (Ubiquitous Sensor Network)기술의 기본 개념인 "Communication Among things" 사물 간 통신 개념을 교량 계측모니터링에 적용하여, 교량에 탑재된 다양한 계측 센서 노드로부터 내구성/안전성에 관련된 위험신호를 추출하여 긴박한 안전사고 등이 인지된 경우 사고예방개념에서 사물 간 통신개념으로, 교량의 센서노드가 바로 교량 인근의 교통신호등에 RF 무선 전파를 송신하여 교량의 교통을 차단하며, 대형 사고를 예방할 수 있는 USN기반의 지능형 교량 시스템을 구축을 위한 센서노드모듈을 설계 하였으며, TinyOS 기반 미들웨어 설계와 센서 자유공간 송수신거리 테스트를 실시하여 센서의 성능을 검증 하였다.

• 한국통신학회논문지
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• 제31권2B호
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• pp.98-106
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• 2006

본 논문에서는 무선LAN에서 실시간 트래픽의 효율적인 전송을 위해 새로운 재밍방식을 사용한 충돌 방지 방안을 제안하고 기존 MAC과의 성능을 비교 분석하였다. 기존의 무선LAN에서는 단말들간 프레임 전송 충돌을 방지하기 위하여 이진 지수 분포(BinaryExponential) 백오프 알고리즘을 사용하기 때문에, 망의 부하가 크거나, 망에 존재하는 무선 단말의 수가 많을수록 프레임 충돌이 발생할 확률이 증가하므로 실시간 트래픽 전송에 불리하다. 이 점에 착안하여 본 논문에서 제안한 재밍 기반의 재전송 방식은 실시간 트래픽의 전송 중 충돌이 발생하면, 충돌에 개입한 각 무선 단말이 동시에 충돌을 감지하고 자신의 채널 사용 횟수를 기록한 데이터 베이스를 참조하여, 서로 상이한 재밍 윈도우 기간 동안 재밍 신호를 송신함으로써, 다른 단말의 접근을 일단 차단시킨다. 이후, 자신의 재밍 윈도우 기간 만기시 채널이 비어있는 경우에만 자신의 프레임을 재전송하도록 함으로써 해당 프레임의 재충돌을 방지한다. 이 과정에서 송신단말이 동시에 충돌을 감지할 수 있도록 모든 연결의 실시간 프레임들은 고정된 길이를 가지도록 하여 전송한 프레임에 대한응답 프레임을 수신하기까지 걸리는 시간은 동일한 것으로 가정하였다. 제안된 방식과 기존 MAC의 성능을 모의 실험으로 비교 분석한 결과, 제안 방식의 경우 프레임의 평균 충돌횟수, 평균 백오프 시간 그리고 프레임의 평균 전송대기 시간이 기존 방식보다 우수하였다. 제안된 방식을 실시간 트래픽 양이 많은 무선랜에서 활용한다면, 실시간 트래픽의 전송 지연 시간을 단축시킴으로써, 무선LAN의 실시간 응용에 적절하게 적용될 수 있을 것이다.