• 국제학술발표논문집
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• The 10th International Conference on Construction Engineering and Project Management
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• pp.1308-1308
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• 2024

Computer vision techniques have been widely employed in automated construction management to enhance safety and prevent accidents at construction sites. However, previous research in the field of vision-based approaches has often overlooked small-sized construction tools. These tools present unique challenges in data collection due to their diverse shapes and sizes, as well as in improving model performance to accurately detect and classify them. To address these challenges, this study aimed to enhance the performance of vision-based classifiers for small-sized construction tools, including bucket, cord reel, hammer, and tacker, by leveraging synthetic images generated from a 3D virtual environment. Three classifiers were developed using the YOLOv8 algorithm, each differing in the composition of the training dataset: (i) 'Real-4000', trained on 4,000 authentic images collected through web crawling methods (1,000 images per object); (ii) 'Hybrid-4000', consisting of 2,000 authentic images and 2,000 synthetic images; and (iii) 'Hybrid-8000', incorporating 4,000 authentic images and 4,000 synthetic images. To validate the performance of the classifiers, 144 directly-captured images for each object were collected from real construction sites as the test dataset. The mean Average Precision at an IoU threshold of 0.5 (mAP_0.5) for the classifiers was 79.6%, 90.8%, and 94.8%, respectively, with the 'Hybrid-8000' model demonstrating the highest performance. Notably, for objects with significant shape variations, the use of synthetic images led to the enhanced performance of the vision-based classifiers. Moreover, the practical applicability of the proposed classifiers was validated through confidence scores, particularly between the 'Hybrid-4000' and 'Hybrid-8000' models. Statistical analysis using t-tests indicated that the performance of the 'Hybrid-4000' model would either matched or exceeded that of the 'Hybrid-8000'model based on confidence scores. Thus, employing the 'Hybrid-4000' model may be preferable in terms of data collection efficiency and processing time, contributing to enhanced safety and real-time automation and robotics in construction practices.

• 대한원격탐사학회지
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• 제25권6호
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Geomechanics and Engineering
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• 제28권6호
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1659-1663
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• 1991

A new control design methodology is presented here which is based on a nonlinear time-series reference model. It is indicated by highly nonlinear simulations that such designs successfully stabilize troublesome aircraft maneuvers undergoing large changes in angle of attack as well as large electric power transients due to line faults. In both applications, the nonlinear controller was significantly better than the corresponding linear adaptive controller. For the electric power network, a flexible a.c. transmission system (FACTS) with series capacitor power feedback control is studied. A bilinear auto-regressive moving average (BARMA) reference model is identified from system data and the feedback control manipulated according to a desired reference state. The control is optimized according to a predictive one-step quadratic performance index (J). A similar algorithm is derived for control of rapid changes in aircraft angle of attack over a normally unstable flight regime. In the latter case, however, a generalization of a bilinear time-series model reference includes quadratic and cubic terms in angle of attack. These applications are typical of the numerous plants for which nonlinear adaptive control has the potential to provide significant performance improvements. For aircraft control, significant maneuverability gains can provide safer transportation under large windshear disturbances as well as tactical advantages. For FACTS, there is the potential for significant increase in admissible electric power transmission over available transmission lines along with energy conservation. Electric power systems are inherently nonlinear for significant transient variations from synchronism such as may result for large fault disturbances. In such cases, traditional linear controllers may not stabilize the swing (in rotor angle) without inefficient energy wasting strategies to shed loads, etc. Fortunately, the advent of power electronics (e.g., high-speed thyristors) admits the possibility of adaptive control by means of FACTS. Line admittance manipulation seems to be an effective means to achieve stabilization and high efficiency for such FACTS. This results in parametric (or multiplicative) control of a highly nonlinear plant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3166-3187
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• 2017

For "generic" multivariate public key cryptography (MPKC) systems, experts believe that the Unbalanced Oil-Vinegar (UOV) scheme is a feasible signature scheme with good efficiency and acceptable security. In this paper, we address two problems that are to find inversion solution of quadratic multivariate equations and find another structure with some random Oil-Oil terms for UOV, then propose a novel signature scheme based on hyper-sphere (HS-Sign for short) which directly answers these two problems. HS-Sign is characterized by its adding Oil-Oil terms and more advantages compared to UOV. On the one side, HS-Sign is based on a new inversion algorithm from hyper-sphere over finite field, and is shown to be a more secure UOV-like scheme. More precisely, according to the security analysis, HS-Sign achieves higher security level, so that it has larger security parameters choice ranges. On the other side, HS-Sign is beneficial from both the key side and computing complexity under the same security level compared to many baseline schemes. To further support our view, we have implemented 5 different attack experiments for the security analysis and we make comparison of our new scheme and the baseline schemes with simulation programs so as to show the efficiencies. The results show that HS-Sign has exponential attack complexity and HS-Sign is competitive with other signature schemes in terms of the length of the message, length of the signature, size of the public key, size of the secret key, signing time and verification time.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 추계학술대회 논문집(제1권)
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• pp.399-404
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• 2006

컨테이너 크레인은 항만전체 하역 효율에 큰 영향을 미치는 하역 장비로서 그동안 컨테이너 크레인의 작업효율을 높이는 연구가 진행되어 왔다. 특히 화물이 목표치에 도달했을 때 흔들림을 단시간에 제어하는 데 초점이 맞추어져 왔다. 일반적으로 컨테이너 크레인을 제어하기 위해서 PID제어나 LQ제어가 주로 사용되었는데, 이는 제어기 설계가 용이하고, 주어진 제어 환경 하에서 우수한 제어성능을 발휘하기 때문이다. 본 연구에서는 LQ 제어의 관점에서 실수코딩 유전알고리즘을 이용한 상태 피드백 제어기의 설계 방법을 제안한다. 즉, 실수코딩 유전알고리즘을 이용하여 상태 피드백 이득을 탐색하는 방법이다. 실수코딩 유전알고리즘은 주어진 목적함수를 최소가 되도록 상태 피드백 이득을 최적으로 탐색한다. 컴퓨터 시뮬레이션은 이렇게 탐색한 상태 피드백 이득을 컨테이너 크레인의 선형 및 비선형 모델에 적용하여 그 유효성을 확인한다.

• 한국전자통신학회논문지
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• 제5권1호
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• pp.58-65
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• 2010

본 연구는 선불 또는 후불 교통카드 사용에 있어서 사용불가 카드 또는 사용 가능 카드 정보만을 구성하여 시스템 운영의 응답 시간을 단축하여 선별하는 방법과 그 시스템에 관한 것으로 기존의 카드 방식에서 제안하고 있는 방법의 차이점을 개선하므로 정보를 구성하고 있는 용량을 개선하여 처리 용량 대비 업데이트 속도를 개선하여 시스템의 효율성을 향상 시키고자 하였다. 이에 본 연구에서는 각각의 파일은 다수의 섹션으로 구성하고 또한 각 섹션은 다수개의 블록으로 구성하고 각 블록은 다수개의 셀 단위의 크기로 분할하여 구성한 인덱스 부와 사용 불가 또는 사용 가능 카드 정보 중에 더 낮은 비율을 차지하는 정보로 구성하는 데이터 부의 영역으로 구성하여 시스템의 성능을 개선하였다.

• 수산해양기술연구
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• 제26권1호
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• pp.88-100
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• 1990

이상과 같이 엑튜에이터의 동특성 해석을 행하고 PID 제어기 및 최적 제어기를 설계하여 응답시뮬레이션을 한 결과 다음과 같은 결론을 얻었다. 1) 엑튜에이터 부의 시정수는 엔진 부의 시정수에 비해 아주 작아 생략하여 제어계를 구성할 수 있다. 2) 한계 감도법에 의해 PID 제어기를 시뮬레이션 한 결과 PID 제어 가버너는 전반적으로 오버슈트가 크고 중속 및 고속 상태에서는 정정시간이 비교적 짧지만 저속에서는 정상 상태에 도달하는데 상당한 시간이 걸린다. 3) 중량 matrix를 적당히 선택하여 최적 피이드 백 게인을 구한 후 마이크로 프로세서에 저장하여 제어기를 구성하면 PID 제어기 보다 양호한 응답 특성을 갖는 제어기를 설계 할 수 있다.

• 한국컴퓨터정보학회논문지
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• 제26권4호
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• pp.63-74
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• 2021

위치 기반 서비스(LBS)는 가장 바쁜 시간에 동시에 도착하는 최단 경로 및 k-최근접 이웃 질의를 포함한 다양한 공간 질의를 효과적으로 처리한다. 동시에 도착하는 공간 질의를 빠르게 처리하기 위한 간단한 해결 방법은 LBS 서버를 추가하는 것이다. 이 방법은 서비스 운영 비용을 많이 증가시킨다. 최근에는 공유 가능한 계산을 사용하여 일련의 질의를 한꺼번에 모아서 처리하는 일괄 처리 방법이 제안되었다. 본 연구에서는 교통 상황에 따라 각 도로 구간의 이동 시간이 빈번하게 변하는 동적 공간 네트워크에서 움직이는 k-최근접 이웃 질의를 한꺼번에 처리하는 방법을 연구한다. 순차적 질의 처리를 기반으로 하는 LBS 서버는 중복 계산으로 인해 한꺼번에 요청이 들어오는 움직이는 k-최근접 이웃 질의를 효과적으로 처리하지 못한다. 본 연구의 목표는 움직이는 k-최근접 이웃 질의를 한꺼번에 처리하고 공유 가능한 계산을 재사용하여 알고리즘을 효율성을 개선한다. 실제 지도 데이터를 사용한 실험 평가는 최신 방법보다 제안된 방법이 우수하다는 것을 보여준다.