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Stress evaluation of a submerged floating tunnel (SFT) is an issue important for determining the section dimensions required to resist environmental loads. However, the complex interaction between an SFT and surrounding fluid has confined most research on SFTs to longitudinal global time-history analyses based on Morison's equation [1]. Even though these analyses give sufficient information in the longitudinal direction, too little information about the circumferential direction compels an SFT section to be designed conservatively. This means that SFT design requires additional information on the structural behavior of the tunnel in the circumferential direction for efficient design. Accordingly, a supplementary approach by which to obtain structural responses in the circumferential direction is introduced in this paper. Upon consideration of the static responses equivalent to the dynamic behavior, three-dimensional (3-D) finite-element analyses of an SFT were performed by application of static loads corresponding to dynamic loads equivalent to those of wave, current and earthquake. The validation of each equivalent static load was supported by the results from comparison of the tension forces in mooring lines obtained using OrcaFlex [2] and ABAQUS [3]. These were used mainly for longitudinal dynamic analysis and 3-D stress evaluation, respectively, of an SFT. Based on the stresses obtained in the longitudinal and circumferential directions, the selection of suitable section dimensions for an SFT is considered.

In this paper, an experimental study was conducted to investigate the effect of varying prestressing force on the nonlinearity of concrete. Prestressed concrete beams, which have been rarely studied in terms of ultrasonic nonlinearity, were prepared. When the prestressing force changes, the stress condition of the concrete inside the beam is affected. This leads to the generation of micro-cracks and a change of the mechanical properties, and as such it is necessary to introduce a nonlinear ultrasonic technique that sensitively reflects microstructural change. In the experiment, a repetitive prestressing load history, which includes maximum levels of 45%, 60%, and 75% based on the compressive strength, was designed to evaluate the effects of repetitive loading and different levels of loading on the nonlinearity. A comparison with the measured results of the linear ultrasonic method was also carried out to verify the stress-dependent change of nonlinearity induced by external loads. With the experimental results, the adopted technique is found to be more sensitive to damage then conventionally used ultrasonic method. As the investigation of the methodology is effective in prestressed concrete structures, the possibility of ultrasonic nonlinearity as an indicator of prestressing force is evaluated.

In this work, an experimental study was carried out to investigate the stress-dependent characteristics of concrete under cyclic loading, based on a nonlinear resonant ultrasonic method. Since concrete subjected to cyclic loading accompanies variations in the elastic and plastic characteristics of concrete in relation to microstructural changes, the adoption of a nonlinear ultrasonic approach is required, which has advantages for the evaluation of micro-cracks in concrete. In this experimental study, two types of loading were considered, namely, continuously increased and cyclic repeated, to identify their effects on the ultrasonic nonlinearity of concrete and the load-induced damage. An additional experiment on the exposure of concrete samples to high temperature was conducted to further investigate the effects of cyclic loading accompanied by an increased occurrence of micro-cracking. A comparison analysis was also performed on the experimental results, and the potential to monitor the stress history of concrete by using the nonlinear resonant ultrasonic method was evaluated.

As the lifespan of concrete structures increases, their load carrying capacity decreases owing to cyclic loads and long-term effects such as creep and shrinkage. For these reasons, there is a necessity for stress state monitoring of concrete members. Particularly, it is necessary to evaluate the concrete structures for behavioral changes by using a technique that can overcome the measuring limitations of usual ultrasonic nondestructive evaluation methods. This paper proposes the use of a nonlinear ultrasonic method, namely, nonlinear resonant ultrasonic spectroscopy (NRUS) for the measurement of nonlinearity parameters for stress monitoring. An experiment compared the use of NRUS method and a linear ultrasonic method, namely, ultrasonic pulse velocity (UPV) to study the effects of continuously increasing loads and cyclic loads on the nonlinearity parameter. Both NRUS and UPV methods found a similar direct relationship between load level and that parameter. The NRUS method showed a higher sensitivity to micro-structural changes of concrete than UPV method. Thus, the experiment confirms the possibility of using the nonlinear ultrasonic method for stress state monitoring of concrete members.

Detection-based pedestrian counting methods produce results of considerable accuracy in non-crowded scenes. However, the detection-based approach is dependent on the camera viewpoint. On the other hand, map-based pedestrian counting methods are performed by measuring features that do not require separate detection of each pedestrian in the scene. Thus, these methods are more effective especially in high crowd density. In this paper, we propose a hybrid map-based model that is a new directional pedestrian counting model. Our proposed model is composed of direction estimation module with classified foreground motion vectors, and pedestrian counting module with principal component analysis. Our contributions in this paper have two aspects. First, we present a directional moving pedestrian counting system that does not depend on object detection or tracking. Second, the number and major directions of pedestrian movements can be detected, by classifying foreground motion vectors. This representation is more powerful than simple features in terms of handling noise, and can count the moving pedestrians in images more accurately.

In this paper, we presented an efficient pedestrian counting method for estimating the total number of crowed pedestrians in the video. For this, it detect interest points of the foreground area and the principal components of features - the number of foreground pixels, the number of edge pixels, and textures. These interest points and the principal components of features as an input value of multi-layer neural network that has been presented in this paper, it was learned in the back propagation algorithm utilizing sigmoid function.

Thermomechanically treated Fe3Al–(0, 2, 4, 6at.%)Cr alloys were isothermally oxidized at 1000 °C in air, and their oxidation characteristics were studied using thermogravimetric analyzer, X-ray diffractometer, scanning electron microscope, electron probe microanalyzer, and TEM/EDS. It was found that Cr decreased the oxidation resistance of Fe3Al alloys to a certain extent. The oxide scales that formed on the unalloyed Fe3Al alloys consisted primarily of α-Al2O3 containing a small percentage of dissolved iron ions. Less than 1% of dissolved chromium ions was additionally present in the oxide scale formed on the Fe3Al–Cr alloys. An Al-free, Fe-enriched zone was formed beneath the oxide scale, owing to Al consumption to form the oxide scale. The oxide scale on all alloys had poor adherence.

Surveillance system to improve safety and security is a major demand for the management and control of public area. Crowd management and control system requires a situation recognition technique which can predict accidents and provide alarms to the monitoring personnel. In this paper, we propose an abnormal behavior detection technique by using trajectory extraction of moving objects in video. Abnormal behavior includes running persons. The proposed abnormal behavior detection system separates background and foreground using Gaussian mixture model. And then, foreground image is used to generate the trajectories of moving objects using a Kanade-Lucas-Tomasi algorithm of the optical flow method. In addition, noise removal step is added to improve the accuracy of the created trajectory. From the trajectory of moving objects information, such as length, pixel, coordinate and moving degree is extracted. As the result of the estimation of abnormal behavior, objects' behavior is configured and analyzed based on a priori specified scenarios, such as running persons. In the results, proposed system is able to detect the abnormal behavior in public area.

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.

In metropolitan area, the use of analog CCTV surveillance system has steadily increased to detect and prevent crimes, terror, big persons and cars crowded in road that CCTV surveillance systems have long been employed in metropolitan area. It is necessary to equip intelligent surveillance systems, which provide an efficient solution for the problems because the cities become bigger than now in population. Therefore, a method that can efficiently measure crowd density is needed. We present an efficient method that estimates information of crowd movement by using the optical flow and the numbers of moving objects by using the numbers of edge pixels. Finally, we will classify the crowd density using the two estimated information according to five levels of crowd density.

In this paper we present the improved Fast Time Series Evaluation(IFTSE) algorithm that can fast and efficiently track the objects in bad environment conditions. Object tracking in intelligent surveillance system is an important part to identify suspicious objects' behavior. But object tracking is exhaustive and time-consuming process and we cannot also efficiently search the trajectory of detected objects due to bad conditions (e.g. bad camera capacity, dust particles in the air, lighting changes). To demonstrate the performance of the proposed IFTSE algorithm for tracking the objects, we introduce evaluation metrics. A prototype tracking system that IFTSE algorithm is employed is implemented using Visual C++. We increase true positive rate by approximately 6% and reduce false alarm rate by approximately 2% and reduce id change by approximately 30%.