Taiwan Association of Engineering and Technology Innovation: E-Journals
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Nonlinear Dynamic Analysis of Direct Acting Tensioner of an Offshore Floating Platform
Get PDFThe offshore floating platform is the key equipment in offshore and gas development. The significant heave motions occur with the excitation of wind and waves, which will affect the safety of a riser system. A direct acting tensioner can be applied to reduce the effects on the riser system and be widely used on different kinds of offshore platforms. Based on the analysis of the structure and working principle of a direct acting tensioner (DAT), the nonlinear dynamic performance of DAT riser system was studied. Additionally, the dynamic model of the DAT riser system is established and the dynamic response was gained by the numerical integration method. The differences of dynamic responses were compared between a linear model and a nonlinear model. The response on different side of the equilibrium position is asymmetric because of the nonlinear stiffness of DAT. The results can be helpful for the design of DAT
Optimizing Engine Start Systems with Application to Sailing/Coasting and Mild Hybridization
Get PDFEngine start systems are key to providing a good customer experience for today’s drivers. Considerable effort goes into ensuring a smooth and quiet engine start, especially in vehicles equipped with start/stop systems. We present two novel mechatronic starters that are designed to improve start quality by enabling faster and quieter engine starts. In the first proposed concept, the traditional alternator is replaced with a motor/generator unit that is capable of exerting positive torque on the engine as needed, in addition to the conventional power generation function. The motor/generator is selectively connected to the crankshaft via a selectable geared or belted connection to enable different operating modes. This starter executes a 400 ms faster start for a typical engine when compared to a conventional 12V starter. We also present a second starter that uses an integrated two-speed gear train to crank the engine. The cranking gear ratio is changed from the initial high ratio to a lower ratio once the engine starts to spin. This ratio change allows the starter motor to continue to operate in a favorable torque-speed zone and push the engine to a higher pre-ignition rpm than a conventional starter, resulting in a quieter, smoother start.
We also present results from incorporating the belted/geared starter concept in vehicles with sailing/coasting mode as well as in mild hybrid propulsion systems. Sailing/coasting mode of operation is enabled by the quick engine re-start capability of this starter allowing seamless switching between fuelled and unfuelled engine operation. Such an operation could reduce fuel consumption by about 3-6% on the NEDC driving cycle, without regenerative braking. One may further hybridize the propulsion system by adding a battery for storing regenerative braking energy. Using such an architecture, a 6-8% fuel economy improvement on the WLTP certification driving cycle may be achieved, depending on voltage and power levels implemented, as well as energy storage systems included
On the Position Determination of Docking Station for AUVs Using Optical Sensor and Neural Network
Get PDFDetecting the relative position of the docking station is a very important issue for the homing of AUVs (Autonomous Unmanned Vehicles). To detect the position of the light source, a pinhole camera model structure was proposed like the camera model. However, due to the sensor resolution and the distortion errors of the pinhole camera system, the application of the camera of docking the under turbid sea environments is almost impossible.
In this paper, a new method detecting the position of the docking station using a light source is presented. Also, a newly developed optical sensor which makes it much easier to sense the light source than the camera system for homing of the AUV under the water is performed. In addition, to improve the system, a neural network (NN) algorithm constructing a model relating the light inputs and optical sensor which are developed in this study is proposed.
To evaluate the performance of the NN algorithm, the experiments were performed in the air beforehand. The result shows that the NN algorithm with AUV docking system using the NN model is better than the pinhole camera model
Engineering Properties of Ternary Cementless Blended Materials
Get PDFA new non-cement blended materials is developed as a full replacement of cement without alkali activator. This study was conducted to explore a suitable method for activating new ternary green materials with desulfurization gypsum, water-quenched blast-furnace slag and co-fired fly ash from circulating fluidized bed combustion as non-cement inorganic binder. Test subject was included flowability, compressive strength, absorption, total charge-passed from rapid chloride permeability test, chloride diffusion coefficient from accelerated chloride migration test and SEM observation. Test results indicate that a ternary mixture containing 1% desulfurization gypsum, 60% water-quenched blast-furnace slag and 39% co-fired fly ash was a suitable development in compressive strength. The new non-cement blended materials were performed a well compressive strength, lower absorption, and lower chloride diffusion coefficient. In addition, the compressive strength decreased as the inclusion of desulfurization gypsum increased. It was concluded that using desulfurization gypsum alone decreased the setting time and compressive strength. SEM micrographs were verified the development in compressive strength originated from the C-S-H and C-A-S-H gel produced by Ca(OH)2, SiO2, and Al2O3
A Learning-Based EM Clustering for Circular Data with Unknown Number of Clusters
Get PDFClustering is a method for analyzing grouped data. Circular data were well used in various applications, such as wind directions, departure directions of migrating birds or animals, etc. The expectation & maximization (EM) algorithm on mixtures of von Mises distributions is popularly used for clustering circular data. In general, the EM algorithm is sensitive to initials and not robust to outliers in which it is also necessary to give a number of clusters a priori. In this paper, we consider a learning-based schema for EM, and then propose a learning-based EM algorithm on mixtures of von Mises distributions for clustering grouped circular data. The proposed clustering method is without any initial and robust to outliers with automatically finding the number of clusters. Some numerical and real data sets are used to compare the proposed algorithm with existing methods. Experimental results and comparisons actually demonstrate these good aspects of effectiveness and superiority of the proposed learning-based EM algorithm
Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks
Get PDFThe traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models
Real Time Scanning-Modeling System for Architecture Design and Construction
Get PDFThe disconnection between architectural form and materiality has become an important issue in recent years. Architectural form is mainly decided by the designer, while material data is often treated as an afterthought which doesn’t factor in decision-making directly. This study proposes a new, real-time scanning-modeling system for computational design and autonomous robotic construction. By using cameras to scan the raw materials, this system would get related data and build 3D models in real time. These data would be used by a computer to calculate rational outcomes and help a robot make decisions about its construction paths and methods. The result of an application pavilion shows that data of raw materials, architectural design, and robotic construction can be integrated into a digital chain. The method and gain of the material-oriented design approach are discussed and future research on using different source materials is laid out
Innovative Configuration Design of Two-Wire Tip Mechanisms for a Tipping-Bucket Rain Gauge
Get PDFThis paper evaluates the measurement accuracy of the three designs of an innovative Tipping-Bucket Rain Gauge (TBRG) tip mechanism. A water-level detecting circuit is used to replace the conventional reed switch sensor, which addresses the disadvantages of the magnetic sensing method that became a factor in quantification uncertainties. The TBRG configuration designs were the bucket-feed, which detects the presence of water inside the bucket, and the bottom-feed and the center-feed, which use the tip-impact method in measuring rainfall. The constant flow method is used in the experimentation. The bucket-feed shows potential in precision rainfall measurement for having -3.84% and -2.68% accuracy errors at 6 mL/min and 11 mL/min respectively, without correction algorithm applied. The tip-impact application for the bottom-feed and the center-feed resulted in a higher error percentage from the volumetric flow samples. The result indicates that actual detection in the bucket brings more measurement accuracy than the tip-counting technique
Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems
Get PDFWhale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions
Machine Vision and Deep Learning Based Rubber Gasket Defect Detection
Get PDFThis study develops an automated optical inspection system for silicone rubber gaskets using traditional rule-based and deep learning detection techniques. The specific object of interest is a 5 mm × 10 mm × 5 mm mobile device power supply connector gasket that provides protection against foreign body inclusion and water ingression. The proposed system can detect a total of five characteristic defects introduced during the mold-based manufacture process, which range from 10-100 μm. The deep learning detection strategies in this system employ convolutional neural networks (CNN) developed using the TensorFlow open-source library. Through both high dynamic range image capture and image generation techniques, accuracies of 100% and 97% are achieved for notch and residual glue defect predictions, respectively