Research on Electrostatic Monitoring of Tribo-Contacts with Dynamic Adaptive Fusion Method

Electrostatic monitoring is a unique and rapid developing technique applied in the prognostics and health management of the tribological system based on electrostatic charging and sensing phenomenon. It has considerable advantages in condition monitoring of tribo-contacts with high sensitivity and resolution. Unfortunately, the monitoring result can be affected due to the switch of operating conditions that reduces its accuracy. This paper presents a dynamic adaptive fusion approach, moving window local outlier factor based on electrostatic features to overcome the influence. Life cycle experiments of rolling bearings and railcar gearbox were carried out on an electrostatic monitoring platform. The MWLOF method was used to extract and analyze the experimental data, combined with the Pauta criterion to judge wear faults quantitatively, and compare with other feature extraction results. It is verified that the proposed method can overcome the influence of changes in working conditions on the monitoring results, improve the monitoring sensitivity, and provide an accurate reference for friction and wear faults.

Analysis of Vegetative Cover Vulnerability in Rohingya Refugee Camps of Bangladesh Utilizing Landsat and Per Capita Greening Area (PCGA) Datasets

The vegetative cover in and surrounding the Rohingya refugee camps in Ukhiya-Teknaf is highly vulnerable since millions of refugees moved into the area, which led to severe environmental degradation. In this research, we used a supervised image classification technique to quantify the vegetative cover changes both in Ukhiya-Teknaf and thirty-four refugee camps in three time-steps: one pre-refugee crisis (January 2017), and two post-refugee crisis (March 2018, and February 2019), in order to identify the factors behind the decline in vegetative cover. The vegetative cover vulnerability of the thirty-four refugee camps was assessed using the Per Capita Greening Area (PCGA) datasets and K-means classification techniques. The satellite-based monitoring result affirms a massive loss of vegetative cover, approximately 5482.2 hectares (14%), in Ukhiya-Teknaf and 1502.56 hectares (79.57%) among the thirty-four refugee camps, between 2017 and 2019. K-means classification revealed that the vegetative cover in about 82% of the refugee camps is highly vulnerable. In the end, a recommendation as to establishing the studied region as an ecological park is proposed and some guidelines discussed. This could protect and reserve forests from further deforestation in the area, and foster future discussion among policymakers and researchers.

Application to a Case Study

We present an application of the operational monitoring tool SIPRIUS+ to an ongoing case study representative of urban brownfield regeneration projects: the Pôle Viotte neighbourhood, located in Besançon (France). We start with a description of the brownfield site, followed by the regeneration project. Then, we evaluate 52 indicators linked to the context, project, and governance. Each monitoring result is illustrated by a graph showing the evolution of the performances. Then, we analyse the overall results, which allows us to assess the sustainability strategy of the regeneration project. Through this analysis, we identify four actions to improve sustainability objectives. Finally, we conclude that SIPRIUS+ has the potential to contribute to the integration of sustainability issues into the dynamics of neighbourhoods in transition. The operational monitoring tool is expected to contribute to decision-making in a multi-disciplinary manner, without giving ready-made solutions. Interactions with project stakeholders reveal that, while the use of such a tool would require a change in project management, the evolutions to adopt to include this practice appear not only feasible but realistic and desired.

Performance Evaluation of Soil Cell

Soil Cell is a novel Low-Impact Development practice that is suitable for densely developed urban environment. This thesis seeks to gain further understanding of soil cell’s performance in terms of water quality and quality control; and to assess the exfiltration performance of perforated pipe in soil cells by monitoring an active cell. The monitoring result showed that the cell was very effective in treating particulate (90% concentration reduction), metal (91% lead concentration reduction to 53% copper concentration reduction) and phosphorus (74% concentration reduction). The cell was ineffective in treating chloride (1% concentration increase, no statistical significance) and even caused nitrate leaching (65% concentration increase). Field performance of the soil cell indicated that the inflow perforated backed up frequently, restricting the flow into the cell but laboratory testing of the perforated pipes’ hydraulic performance confirms that the pipe has a sufficient capacity to convey the flows of up to 10 L/s.

Performance Evaluation of Soil Cell

Soil Cell is a novel Low-Impact Development practice that is suitable for densely developed urban environment. This thesis seeks to gain further understanding of soil cell’s performance in terms of water quality and quality control; and to assess the exfiltration performance of perforated pipe in soil cells by monitoring an active cell. The monitoring result showed that the cell was very effective in treating particulate (90% concentration reduction), metal (91% lead concentration reduction to 53% copper concentration reduction) and phosphorus (74% concentration reduction). The cell was ineffective in treating chloride (1% concentration increase, no statistical significance) and even caused nitrate leaching (65% concentration increase). Field performance of the soil cell indicated that the inflow perforated backed up frequently, restricting the flow into the cell but laboratory testing of the perforated pipes’ hydraulic performance confirms that the pipe has a sufficient capacity to convey the flows of up to 10 L/s.

Evaluation of distributions on radioactive materials around the port of Fukushima Daiichi Nuclear Power Plant through both monitoring and numerical simulations on seawater flow

<p>A large amount of radionuclides has been released into the environment by the accident of Fukushima Daiichi Nuclear Power Plant (1F). About 10 years have passed since the accident, and the effects have significantly decreased. However, it should be noted that they still remain in spite of their tiny contributions. On the other hand, there is now an important demand that the radioactive materials should be monitored continuously due to the decommissioning work at 1F being carried. Thus, we evaluate the behavior of the radioactive materials in seawater around the port of 1F using both the monitoring result measured by TEPCO (Tokyo Electric Power Company Holdings) and the simulation results on seawater flow. In this presentation, we introduce the simulation methods and report the insights through the monitoring results. Finally, we reveal the mechanism of spreading the materials in the seawater around 1F port by evaluating with both the monitoring data and the simulation results.</p><p> </p>

An intelligent diagnosis model of accidents in nuclear power plant

This essay focus on the situation when the operator of the main control room can not handle the accident in time or make wrong judgment under accident condition in nuclear power plant, which may lead to the occurrence of major accidents or even more accidents concurrently. An intelligent fuzzy diagnosis model based on Grey Relational degree is constructed in this paper, by establishing accident-state correlation matrix and accident-state probability matrix, defining the vector of information weights for accident characteristic vector and monitoring result vector with linear transform, using Grey Relation to measure the relevance degree between monitoring result vector and accident characteristic vector, Searching for suspected accidents approaching to real solutions in iterative recursive algorithms, realizing automatic fuzzy diagnosis of multiple accidents. The results of simulation experiments of LOCA accidents indicate that the model and algorithm can diagnose various common accidents accurately and rapidly in complex nuclear power systems, providing strategy for operator’s diagnosis decision.

Implementasi Multiple Honeypot pada Raspberry Pi dan Visualisasi Log Honeypot Menggunakan ELK Stack

ABSTRAKPerlindungan terhadap keamanan jaringan merupakan hal yang sangat penting untuk dilakukan. Mengingat kemudahan dalam mengakses jaringan memungkinkan adanya gangguan dari pihak yang ingin menyerang, merusak, bahkan mengambil data penting. Honeypot memang tidak menyelesaikan masalah pada keamanan jaringan, namun honeypot membuat penelitian tentang serangan menjadi lebih sederhana dengan konsep yang mudah untuk dimengerti dan dimplementasikan. Penelitian ini menerapkan beberapa honeypot menggunakan Raspberry pi dan ELK stack untuk monitoring hasil yang didapatkan oleh honeypot. Tujuan dari penelitian ini untuk merancang sistem yang mampu mendeteksi serangan pada jaringan menggunakan honeypot. Raspberry pi digunakan sebagai sensor honeypot untuk pemantauan ancaman keamanan terbukti hemat biaya dan efektif menggantikan komputer desktop. ELK stack memudahkan pemusatan data dari berbagai sumber dan membuat analisis log yang awalnya rumit untuk dianalisis menjadi lebih menarik.ABSTRACTProtection of network security is very important to do. Given the ease in accessing the network allows for interference from parties who want to attack, destroy, and even retrieve important data. Honeypot does not solve the problem on network security, but the honeypot makes research about attacks become simpler with concepts that are easy to understand and implement. This research applies some honeypot using Raspberry pi and ELK stack for monitoring result obtained by honeypot. The purpose of this research is to design a system capable of detecting attacks on a network using a honeypot. Raspberry pi is used as a honeypot sensor for monitoring proven cost-effective and cost-effective security threats to replace desktop computers. The ELK stack facilitates the convergence of data from multiple sources and makes log analysis initially complex for analysis to be more interesting.

Development of real time internet of things (IoT) based air quality monitoring system

The atmospheric air pollution is a major concern in modern cities, especially in developing countries like Malaysia. In this paper, we have reported an effective implementation for Internet of Things used for monitoring the level of air pollution based on Malaysia Air Pollution Index (API). The low-cost and real time system would be able to monitor regular air quality pollutants including Particulate Matter (PM) of PM2.5, PM10 and Carbon Monoxide (CO) gas as well as the temperatures and humidity of the surroundings. The system has capability to detect Good, Moderate, Unhealthy, Very Unhealthy and Hazardous API status. Based on 5 weeks of experimental API monitoring result on specified test location, the system was able to demonstrate promising results in providing a reliable real time monitoring of the air quality condition.