• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Journal of Environmental Health Sciences
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• v.30 no.3
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• pp.283-292
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• 2004

Valid evaluation of community reaction to road-traffic noise exposure is important for the policy making and quality of life. The purpose of this study is to evaluate the relationship between community reaction and nighttime road-traffic noise. The study measured nighttime roadside noise caused by high traffic which is greater than LAeq(22:00-07:00) 65dB(A) and lower than 65dB(A) at Chonju city, from March to April, 2003. Three hundred sixty seven subjects, aged from 20 to 65, were selected from those who are residing close to the major roads. They were asked to answer the questions regarding noise source, stress(PWI-SF), annoyance, disturbance of specific activity, disturbance of sleep, somatic symptom, and four suggested confounding factors. The relationship between annoyance, somatic symptom and noise exposure was not significant. Adjusted odds ratio(95% C.I.) for disturbance of communication　 and disturbance of attention and rest　 were 1.59(1.03-2.71), 1.64(1.06-2.81), respectively. On the other hand sleep disturbance　 was indicated as 1.34(0.77-2.32). The results suggested that federal policy-making about road-traffic noise should consider community reaction evaluated by various perspective including annoyance, disturbance of specific activity, disturbance of sleep, and confounding factors.

• Acta Via Serica
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• v.2 no.1
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• pp.109-120
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• 2017

The city of Basra, established on the shore of Basra Bay in the south of modern Iraq, played an important role in agriculture and trade for centuries, with its geography and its position where two great rivers of Mesopotamia flow. Before being established with its current name by the Muslim Arabs, the city was known as Teredon in the Chaldean period and VehiŞtebad ErdeŞir in the Sasanid period. It was reestablished with the name Basra in the early period of Islam by Arabs between Hijri 14-16 (635-637 CE). Afterward, the city became one of the most important centers of trade, science and thought; had a perfect cultural diversity; and hosted important schools of Arabic language and thought for centuries. Besides the commercial effects of its being a transfer point on the axis of Europe, Mesopotamia, Iran, and India, the schools of thought which emerged here were affected by this mobility. In this paper, we try to reveal the philosophical-religious approach which the Ikhwan al-Safa school of thought in Basra, one of the most important cities of the Silk Road, created in parallel with the characteristics of this city. Shiite Ismaili beliefs and thoughts in the region and its characteristics which feed different religions and traditions emerging from Egypt and with the scientific approach of Greek thought; with Indian-Iranian teachings that merge Greek thought and Neoplatonic philosophy, give us the summary of Silk Road civilizations.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.85-89
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• 2012

This paper presents a study on the system identification of the in-situ output shaft torque measurement system using a non-contacting magneto-elastic torque transducer installed in a vehicle drivline. The frequency response (transfer) function (FRF) analysis is conducted to interpret the dynamic interaction between the output shaft torque and road side excitation due to the road roughness. In order to identify the frequency response function of vehicle driveline system, two power spectral density (PSD) functions of two random signals: the road roughness profile synthesized from the road roughness index equation and the stationary noise torque extracted from the original torque signal, are first estimated. System identification results show that the output torque signal can be affected by the dynamic characteristics of vehicle driveline systems, as well as the road roughness.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.979-989
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• 2016

Traversable region detection is an essential step for advanced driver assistance systems and self-driving car systems, and it has been conducted by detecting lanes from input images. The performance can be unreliable, however, when the light condition is poor or there exist no lanes on the roads. To solve this problem, this paper proposes an algorithm which utilizes the information about the number of lanes and texture analysis. The proposed algorithm first specifies road region candidates by utilizing the number of lanes information. Among road region candidates, the road region is determined as the region in which texture is homogeneous and texture discontinuities occur around its boundaries. Traversable region is finally detected by dividing the estimated road region with the number of lanes information. This paper combines the proposed algorithm with a lane detection-based method to construct a system, and simulation results show that the system detects traversable region even on the road with poor light conditions or no lanes.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• Korean Journal of Agricultural Science
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• v.40 no.3
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• pp.209-214
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• 2013

This study examined the work time, work posture, and work intensity for the actual measurement step in forest road the design work that was being carried out. The measurement of the forest road was being carried by a team of three workers and a team of four workers. The examination of work time found that the measurement of 1km took about 8 hours for the four-worker team and 12 hours for the three-worker team. The examination of work intensity found that the energy metabolic rates of the three-worker team were lower than four-worker team. Because their energy consumption per minute decreased as their work time and rest time increased. Furthermore, when appropriate rest time was applied according to work time, the energy metabolic rate decreased and the work intensity became lower. The four-worker team was more advantageous from the time and cost aspects of the forest road measurement work. Furthermore, as the rest time was very low compared to the work time, more efficient forest road measurement work would be possible if the work intensity was lowered by considering the rest time when calculating the standard work time.

• Coupled systems mechanics
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• v.7 no.6
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• pp.707-729
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• 2018

The semi-active optimal vibration control of nonlinear torsion-bar suspension vehicle systems under random road excitations is an important research subject, and the boundedness of MR dampers and the uncertainty of vehicle systems are necessary to consider. In this paper, the differential equations of motion of the coupling torsion-bar suspension vehicle system with MR damper under random road excitation are derived and then transformed into strongly nonlinear stochastic coupling vibration equations. The dynamical programming equation is derived based on the stochastic dynamical programming principle firstly for the nonlinear stochastic system. The semi-active bounded parametric optimal control law is determined by the programming equation and MR damper dynamics. Then for the uncertain nonlinear stochastic system, the minimax dynamical programming equation is derived based on the minimax stochastic dynamical programming principle. The worst-case disturbances and corresponding semi-active bounded parametric optimal control are obtained from the programming equation under the bounded disturbance constraints and MR damper dynamics. The control strategy for the nonlinear stochastic vibration of the uncertain torsion-bar suspension vehicle system is developed. The good effectiveness of the proposed control is illustrated with numerical results. The control performances for the vehicle system with different bounds of MR damper under different vehicle speeds and random road excitations are discussed.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.79-89
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• 2019

While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4419-4441
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• 2016

A cause of vehicle accidents is the reduced visibility due to bad weather conditions such as fog. Therefore, an onboard vision system should take visibility detection into account. In this paper, we propose a simple and effective approach for measuring the visibility distance using a single camera placed onboard a moving vehicle. The proposed algorithm is controlled by a few parameters and mainly includes camera parameter estimation, region of interest (ROI) estimation and visibility computation. Thanks to the ROI extraction, the position of the inflection point may be measured in practice. Thus, combined with the estimated camera parameters, the visibility distance of the input foggy image can be computed with a single camera and just the presence of road and sky in the scene. To assess the accuracy of the proposed approach, a reference target based visibility detection method is also introduced. The comparative study and quantitative evaluation show that the proposed method can obtain good visibility detection results with relatively fast speed.