Reliability

The vulnerability of prominent silicon-based U-shaped Metal-Oxide-Semiconductor Field
Effect Transistors (UMOSFET) to destructive radiation effects when operating in terrestrial atmospheric
environments is addressed. It is known that secondary particles from nuclear reactions between atmospheric
neutrons and the constituent materials of electronic devices can trigger Single-Event Burnout (SEB)
destructive failure in power MOSFETs. The susceptibility of UMOSFETs to SEBs induced by atmospheric

This script implements and analyzes various algorithms for graph processing, focusing on domination problems, including double domination and certified dominating sets.

Script Overview

The script generates tree graphs, calculates certified dominating sets using ApproxCert and other algorithms, and evaluates their performance in terms of execution time and results. Results are saved in a CSV file for further analysis.

The dataset file contains data of different reliability demonstration tests (RDTs). For this dataset, an RDT is a failure-based test for a product with Weibull distributed failure times (due to a single failure mechanism), which are load-dependent according to the inverse power law, and which are evaluated using maximum likelihood estimation. Each RDT is capable of demonstrating a particular lifetime t_RCS for a given required reliability and confidence, and with probability of test success of 80 %.

The trench gate or U-groove MOSFET (UMOSFET) has become widely adopted as a semiconductor device globally, gradually replacing the traditional double-diffused MOSFET (DMOSFET) in many applications. Evaluating the reliability of UMOSFETs regarding neutron-induced radiation effects is crucial for understanding their response to ubiquitous atmospheric neutrons. This study presents comparative experimental and computational results of Single-Event Effects induced by monoenergetic fast neutrons in UMOS and DMOS power transistors.

MOM-OnSem (Ontology Semantics for MOM Standards) defines the formal semantics of object models within MOM standards IEC 62264 as a reusable ontology theory using an Event-B-based framework. This formalized and reusable ontology semantics serves as a foundation for designing MOM systems. 

We develope a novel TCM hallucination detection dataset, Hallu-TCM, sine no prior work has attempted this task in TM. We selected 1,260 TCM exam questions including 16 TCM subjects, input them into GPT-4, and collected their feedback. In the first level, we utilize Qwen-Max interface to annotate feedback multiple times with the binary label. If Qwen-Max consistently provided the same label across annotations, we adopted that label. For contentious cases, we recruited higher-degree research students who can understand and solve complex questions, including three Ph.D.

Experiment Details:

We organized and collected two years' worth of complete fault work orders from a wind farm, and structured these work orders into a fault diagnosis event knowledge graph using the proposed algorithm. This graph includes fault modes, fault impacts, fault symptoms, inspection schemes, root cause identification, and maintenance strategies, covering all potential fault information and handling methods for wind turbines. This dataset records the head entity-relation-tail entity information in the form of triples using JSON format.

This dataset contains a list of Australian government domains (dedicated to the general population and indigenous people of Australia) collected through our research. We queried these domains to retrieve their NS records (labeled as NS_LVL1) and identified the DNS provider companies using Whois. Subsequently, we queried the NS_LVL1 domains to obtain their name servers (labeled as NS_LVL2) and determined their provider names. Using the list of DNS providers in Australia, we then used IP location to find the geolocation of these DNS providers.

This study is utilized for submodule open-circuit fault detection uncertainty analysis of modular multilevel converters. The dataset consists of 8 uncertainty factors and 15 system variables under four operation scenarios. The 1000 sets of uncertainty factor samples are generated randomly as initial configuration of the system. The 15 system variables are obtained by 1000 Monte Carlo simulations. We found that there are 153 residual samples exceeded the threshold of 0.8, which indicated a high false alarm rate.