Settlement Analysis of Sand Compaction Pile Composite Foundation in Transition Section of Immersed Tube Tunnel

The foundation of the immersed tube transition section of the Hong Kong-Zhu Hai-Macao Bridge Project is distributed with thick silty soil. The method of sand compaction pile + surcharge preloading is used to treat the soft soil ground. In order to determine the foundation consolidation settlement and subsequent residual settlement in the transition section of the immersed tube tunnel, a monitoring system using long wires combined with wireless transmission and long-distance data collection was developed to obtain the measured foundation settlements during the surcharge period. After comparing the measured value with the calculated value, the formula for the composite foundation of the sand compaction pile was revised to obtain a more reasonable residual settlement of the foundation, which could guide the design and construction of the immersed tube tunnel.

Sensor Data Fusion for a Mobile Robot Using Neural Networks

Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

Multiple Linear Regression and Deep Learning in Body Temperature Detection and Mask Detection

In this new normal era, many activities began to operate again, such as offices, malls, etc. This creates a potential mass crowd. The public must follow health protocols as recommended by the government, including wearing masks and checking the temperature to anticipate the spread of the coronavirus. This study tested a tool that included image processing and artificial intelligence to help implement health protocols as recommended by the government. This tool connects Raspberry PI, Thermal Camera (amg8833), Pi Camera, an ultrasonic sensor with Multiple Linear Regression and Deep Learning algorithms. The purpose of this tool is to detect body temperature and detect the use of masks. The system will check on the pi camera frame whether the person is wearing a mask or not. The system is trained using the Deep Learning method to detect the use of masks. The system will check the temperature of the human body and the distance between humans and the tool. Temperature and distance data are entered in multiple linear regression formulas to get more accurate results. The processed results of the system will be displayed on the monitor screen if detected using a mask and the normal temperature will be green and if it is not detected it will be red and give a warning sound. The data is sent to the server and displayed via the web. We found that this tool succeeded in detecting body temperature within a distance of 1 to 3 meters with an accuracy of 99.49%, detecting people using masks with an accuracy of 94.71%, and detecting people not wearing masks with an accuracy of 97.7%.

On Mean Dependency Distance as a Metric of Translation Quality Assessment

This paper has adopted a quantitative approach to carry out a linguistic study, within the theoretical framework of dependency grammar. Translation is a process where source language and target language interact with each other. The present study aims at exploring the feasibility of mean dependency distance as a metric for automated translation quality assessment. The current research hypothesized that different levels of translation are significantly different in the aspect of mean dependency distance. Data of this study were based on the written translation in Parallel Corpus of Chinese EFL Learners which was composed of translations from Chinese EFL learners in various topic. The translations were human-scored to determine the levels of translation, according to which the translations were categorized. Our results indicated that: (1) senior students perform better in translation than junior students, and mean dependency distance of translations from senior group is significantly shorter than the junior; (2) high quality translations yield shorter mean dependency distance than the low quality translations; (3) mean dependency distance of translations is moderately correlated with the human score. The resultant implication suggests the potential for mean dependency distance in differentiating translations of different quality.

Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection

Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle’s external surface.

Boundary rigidity for Randers metrics

If a non-reversible Finsler norm is the sum of a reversible Finsler norm and a closed 1-form, then one can uniquely recover the 1-form up to potential fields from the boundary distance data. We also show a boundary rigidity result for Randers metrics where the reversible Finsler norm is induced by a Riemannian metric which is boundary rigid. Our theorems generalize Riemannian boundary rigidity results to some non-reversible Finsler manifolds. We provide an application to seismology where the seismic wave propagates in a moving medium.

Pengembangan Alat Praktikum Tumbukan Menggunakan Mikrokontroler, Sensor Ultrasonik, dan Lintasan Air Track

Momentum is one of the physics topics that students can better understand with practical activities. The problem found in using the ticker timer as a speed gauge in the collision practicum is that the measurement process interferes with the system being measured. This research develops a collision practicum tool using a microcontroller and ultrasonic sensor as a distance recorder. The glide track used is an air track. Based on the research conducted, it can be concluded that the collision practicum tool with a microcontroller and a glide track in the form of an air track can work well. The distance recorder or DRD is capable of recording distances every 0.2 seconds. The ultrasonic sensor as a gauge has an accuracy of 99.6% and a relative error of 0.56% for sensor 1. Meanwhile, sensor 2 has an accuracy of 99% and a relative error of 0.85%. Through the distance data obtained, it is possible to obtain an increase in momentum before and before the collision.

Method of Assessing the Efficiency of Electrical Power Circuit Separation with the Power Line Communication for Railway Signs Monitoring

The article describes a project to use PLC technology in railway light signs. The proposal presents a solution for the separation of supplying circuits with the use of filters. The necessity of separation is a result of long distance data transmission between sign controllers and LED railway signs using PLC modems which work on the same frequency. Problems in the selection of filter parameters are presented due to the fact that there is no specification for the characteristic of the separation filter, there are only requirements for the reliability communication system. To meet these requirements, the circuits separation efficiency must be assessed. The paper presents a quick method to evaluate separation efficiency. Structure of the network, as well as the set of devices which realize this specific kind of wired sensor network for supervising railway LED sign network and maintenance parameters are also presented in this article.