Development of representative section selection method for large-scale concrete pavement remodeling project based on trial and error correction approach

2019 ◽  
Vol 12 (1) ◽  
pp. 17-25
Author(s):  
Jun-Hyeok Lee ◽  
Dong-Hyuk Kim ◽  
Ki-Hoon Moon ◽  
Augusto Cannone Falchetto ◽  
Jin-Chul Kim ◽  
...  
Keyword(s):  
Error Correction ◽  
Large Scale ◽  
Selection Method ◽  
Concrete Pavement ◽  
Trial And Error

LARGE SCALE EXPERIMENTS AND ANALYSES ON RUNOFF PROCESS FROM POROUS CONCRETE PAVEMENT

2018 ◽  
Vol 74 (4) ◽  
pp. I_967-I_972
Author(s):  
Morihiro HARADA ◽  
Shigemitsu HATANAKA ◽  
Naoki MISHIMA ◽  
Shohei IIO
Keyword(s):  
Large Scale ◽  
Concrete Pavement ◽  
Porous Concrete

scholarly journals Identification of The Concept Selection Method for Product Design and Development in The Manufacturing Industry

2018 ◽  
Vol 7 (2.29) ◽  
pp. 352 ◽  
Author(s):  
Shiau Wei Chan ◽  
Izzuddin Zaman ◽  
Md Fauzi Ahmad ◽  
Check Yee Liew
Keyword(s):  
Product Design ◽  
Manufacturing Industry ◽  
Selection Method ◽  
Product Development Process ◽  
Market Demand ◽  
Trial And Error ◽  
Concept Selection ◽  
Design And Development ◽  
Essential Knowledge ◽  
Production Areas

Concept selection is the most critical aspect of the entire product development process. However, many industries are not aware of this, or they might not possess essential knowledge about concept selection. Thus, this study aims to identify the concept selection method used by a series of particular areas within the manufacturing industry. In this study, the researcher conducted interviews with six managers from various production areas in the manufacturing industry. Then, the obtained data were analyzed qualitatively. The concept selection methods used for product design and development in the manufacturing industry were found to be based on various factors, including orders received, building and evaluating prototypes, discussion among executives, market demand, trial and error and the market’s standard deviation. This study serves as a guideline to help managers to evaluate concepts in a more practical way.

scholarly journals A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features

2020 ◽  
Vol 12 (23) ◽  
pp. 3978
Author(s):  
Tianyou Chu ◽  
Yumin Chen ◽  
Liheng Huang ◽  
Zhiqiang Xu ◽  
Huangyuan Tan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Local Features ◽  
Selection Method ◽  
Image Features ◽  
Feature Point ◽  
Feature Points ◽  
Point Selection ◽  
Retrieval Task ◽  
Street View

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.

Product Development Task Planning Using Worth Flow Analysis of Interacting Components

2007 ◽  
Author(s):  
Toshiharu Miwa ◽  
Kosuke Ishii
Keyword(s):  
Product Development ◽  
Large Scale ◽  
Early Stage ◽  
Flow Analysis ◽  
Manufacturing Firms ◽  
Trial And Error ◽  
Task Planning ◽  
Feedback Information ◽  
Development Cycle ◽  
Planning Methods

The acceleration of product development cycle continues to be a significant challenge for manufacturing firms around the world. This paper describes a task planning method for minimizing trial and error to reduce the development time in large-scale and complicated product development at the early stage of product development. The proposed method matches the group of product components according to geometry and determines the development sequence of each component to minimize the amount of feedback information across task groups. The method applies as evaluation index for task prioritization the product-sum of engineering interaction among components and worth of each component, the “worth flow.” The paper shows with an example of the generic hair drier with simple mechanical structure that this method contributes to the reduction of the size of task group by 22% and amount of information required for setting the interface links by 65% compared to the conventional planning methods.

scholarly journals Observations and numerical simulations of the braking effect of forests on large-scale avalanches

2018 ◽  
Vol 59 (77) ◽  
pp. 50-58 ◽  
Author(s):  
Yukari Takeuchi ◽  
Koichi Nishimura ◽  
Abani Patra
Keyword(s):  
Numerical Simulations ◽  
Large Scale ◽  
Friction Angle ◽  
Subalpine Forest ◽  
Trial And Error ◽  
Disaster Reduction ◽  
Forested Area ◽  
Avalanche Dynamics ◽  
Bed Friction ◽  
Good Agreement

ABSTRACTAlthough the disaster reduction effects of forest braking have long been known empirically, they have not been known in detail down to recent. In this study, we ascertained forest braking effect by numerical simulations using the avalanche dynamics program, TITAN2D, to model large-scale avalanches. One of these avalanches occurred in the Makunosawa valley, Myoko, and damaged a cedar forest; the others occurred on Mt. Iwate and damaged a subalpine forest. All avalanches damaged many trees and terminated within the forests. In our simulations, the resistance of the forests to avalanches is simulated using a larger bed friction angle. Fitting the observations from the Makunosawa avalanche by trial and error, a bed friction angle of 13–14° in the non-forested area and of 25° in the forested area is obtained. We conducted simulations of the Mt. Iwate avalanches using the same method as for the Makunosawa valley avalanche, and obtained good agreement between observations and simulations. Simulations reveal that without the forest, the avalanche would have traveled at least 200 m farther than the forest's actual end in the Makunosawa valley, and at least 200 m and possibly up to 600 m farther on Mt. Iwate. This study therefore clearly shows that forests provide a braking effect for avalanches.

scholarly journals IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6336 ◽  
Author(s):  
Mnahi Alqahtani ◽  
Hassan Mathkour ◽  
Mohamed Maher Ben Ismail
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Feature Selection Method ◽  
Selection Method ◽  
Attack Detection ◽  
Gradient Boosting ◽  
Fisher Score ◽  
Detection Approach ◽  
Extreme Gradient Boosting ◽  
Iot Devices

Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.

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