Reliability
Solar insecticidal lamps (SIL) are commonly used agricultural pest control devices that attract pests through a lure lamp and eliminate them using a high-voltage metal mesh. When integrated with Internet of Things (IoT) technology, SIL systems can collect various types of data, e.g., pest kill counts, meteorological conditions, soil moisture levels, and equipment status. However, the proper functioning of SIL-IoT is a prerequisite for enabling these capabilities. Therefore, this paper introduces the component composition and fault analysis of SIL-IoT.
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This study investigates the worst-case Single-Event Effect (SEE) response in semiconductor
power devices induced by energetic ions. An existing predictive model is refined and extended to potentially
consider various semiconductor materials, other geometries and technologies. Comprehensive expressions
for predicting critical ion energies that lead to worst-case SEE response in semiconductor power devices are
obtained through computational simulations. A dedicated experiment is conducted to investigate the model
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This dataset pertains to degradation testing utilizing tightened threshold censoring. It comprises various forms of data, including simulation data that provide insights into theoretical models and expected outcomes. Additionally, it includes test result data, which document the actual performance and behavior of the products under testing conditions. The repeatability test data are also part of this dataset, which ensures that the results are consistent and reproducible across multiple tests.
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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
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Two Approaches to Constructing Certified Dominating Sets in Social Networks: R Script Description
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.
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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 %.
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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.
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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.
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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.
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176 Views
Experiment Details:
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