Fairness analysis of TCP variants in asymmetric network

Background/Objectives: Safety matters are being discussed and countermeasures to prevent various breakdowns/accidents are urgently required. Therefore, we propose a safety management solution for ESS fire monitoring. Methods/Statistical analysis: BLE 5.0-based ultra-compact wireless sensor-based edge that can detect off-gas early, as well as measure the temperature, humidity, vibration, and smoke in the golden time of approximately 10 minutes from the occurrence of thermal runaway inside the ESS to a fire. This paper proposes a safety system and an ESS-integrated safety management solution. Findings: It is known that the ESS currently in operation is installed without an integrated control method and a systematic protection system between facilities in order to connect new and renewable energy and to quickly supply the site, so there are many problems. For this reason, various accidents such as reactor failure and fire are occurring in the ESS installed at the actual site. Recently, 22 fires occurred in ESS facilities installed nationwide in 2019, and the government has urgently stopped ESS operation and formed a joint public-private investigation committee to clarify the cause. Among the various causes of ESS fires that have been raised, the ESS design should be considered. This paper reported an edge safety-based on BLE 5.0 that is capable of detecting off-gas and detecting temperature, humidity, vibration, and smoke early in the golden time of approximately 10 minutes from the occurrence of thermal runaway inside the ESS to a fire. A system and ESS integrated safety management solution were proposed. In this study, internal monitoring of an ESS was possible by integrating directly with existing and new ESS. The administrator can observe all conditions on the web through the cloud-based ESS integrated safety management system. Improvements/Applications: This paper analyzed and developed a cloud-based ESS integrated safety management system. It will be helpful in ESS fire management in the future and will be useful in designing artificial intelligence-based safety management solutions.

Section: Introductionmentioning

confidence: 99%

A Study on Cloud-based Safety ESS Watchdog System Using Ultra-Compact Wireless Sensor

et.al¹

2021

Deep Packet Filtering Mechanism for Secure Internetworks

et.al¹

2021

In this paper, we propose a Deep Packet Filtering Mechanism (DPFM) to analyze and filter malicious data packets moving between network environments. DPFM analyzes the behavior of malicious packets on the network and extracts information about the network as a sequence. After performing the word embedding process on the extracted sequence data using the word2vec technique, it detects malicious packets on the network by learning the LSTM model. In the past, research on filters to prevent malicious packets from entering the network by converting packets into data at the sending and receiving destinations and analyzing their purpose and maliciousness is insufficient. Since DPFM proceeds at the network boundary to analyze and extract malicious packets, primary detection is possible. In this paper, more accurate identification is possible by deep learning of network packets as well as OPcode and system calls, which are static analysis data.

Analysis Of Eddy Current Loss Of IPMSM According To The Material Of Permanent Magnet

et.al¹

2021

In order to reach the performance of the permanent magnet embedded rotor, the choice of magnet is very important. It must be thermally stabilized, and at this point, discussion of eddy current losses is necessary.To proceed with this study, a permanent magnet embedded synchronous motor used in the compressor currently being designed was selected. To derive the eddy current losses in the neodymium-magnets, current density was calculated through the equation. The eddy current loss was mathematically derived using the magnetic conductivity and residual magnetic flux density. Finally, comparative verification was performed through finite element analysis simulation. In this paper, eddy current losses in a N series magnet are mathematically analyzed and we perform comparative verification through simulation using finite element analysis. The Br value indicating the residual magnetic flux density is the lowest in N30 series and the largestin the N48 series. In the case of using the N30 series, the amount of magnetic flux that can be generated is low, so in order to increase the same output, the electric field must be increased by drawing more current from the stator winding. That is, the torque can be further increased. However, since the magnetic flux density experienced by the permanent magnet also increases, eddy current loss that may occur in the magnet eventually increases. There are also a method of using a split magnet to reduce eddy current losses. Inthe case of a permanent magnet holding a large residual magnetic flux density, the magnets loss is reduced, but there is a disadvantage that the price may be expensive. The losses in the permanent magnet are dissipated as heat. If the eddy current loss increases, the magnet demagnetizes, which in turn leads to a decrease in performance. In the selection of magnets, analysis of losses is essential.