Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Machines01:19

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The gut microbiota alleviates depression by remodeling gut-brain energy metabolism.

Cell metabolism·2026
Same author

The Cervical Lymph Node Positive Metastatic Probability Is a Significant Predictor of Survival for Oral Squamous Cell Carcinoma-A Nationwide Study.

Cancers·2025
Same author

Astrocytic glucocorticoid receptors in the ventral hippocampus modulate anxiety-like behaviors.

Brain research bulletin·2025
Same author

Gut microbiota regulates innate anxiety through neural activity of medial prefrontal cortex in male mice.

Frontiers in neuroscience·2025
Same author

Brain-Wide Spatiotemporally Distinct Traveling Waves Drive Anxiety-Like Behaviors in Mice.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Lysosomal TFEB-TRPML1 Axis in Astrocytes Modulates Depressive-like Behaviors.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Multi-Hardware Benchmarking of Open-Source Large Language Models with Retrieval-Augmented Generation for Mitsubishi

Ming-Feng Yeh1, Ching-Chuan Luo2, Cheng-Lin Lu1

  • 1Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, Taiwan.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) with Retrieval-Augmented Generation (RAG) significantly improve programming for legacy industrial controllers. A curated dataset and open-source LLMs offer effective, on-premise code generation for smart manufacturing.

Keywords:
Instruction ListMitsubishi FX-seriesindustrial monitoringlarge language modelon-premise AI for industrial automationprogrammable logic controllerretrieval-augmented generationsmart manufacturingstatic syntax verificationsustainable manufacturing

Related Experiment Videos

Last Updated: Jun 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Industrial Automation and Control Systems
  • Artificial Intelligence and Machine Learning
  • Software Engineering for Embedded Systems

Background:

  • Smart manufacturing relies on Programmable Logic Controllers (PLCs) for automation.
  • Legacy PLC programming languages, like Mitsubishi FX-series Instruction List (IL), are complex and under-represented in LLM training data.
  • IEC 61131-3 Edition 3.0 deprecates IL, necessitating new approaches for code generation.

Purpose of the Study:

  • To benchmark ten open-source Large Language Models (LLMs) for generating PLC programs in a deprecated language.
  • To evaluate the effectiveness of Retrieval-Augmented Generation (RAG) compared to LLM-only configurations.
  • To introduce a static syntax checker for evaluating the quality of generated PLC code.

Main Methods:

  • Benchmarking ten open-source LLMs (7B-122B parameters) using a 285-question dataset.
  • Implementing a Retrieval-Augmented Generation (RAG) pipeline with ChromaDB, all-MiniLM-L6-v2 embeddings, and Maximal Marginal Relevance (MMR) retrieval.
  • Developing and applying a three-tier static syntax checker (Lexical/Syntactic/Semantic) to assess code quality.

Main Results:

  • RAG significantly improved the syntactic pass rate by +6.7 to +61.1 percentage points across all tested LLMs.
  • The best configuration (qwen3.5:122b with RAG) achieved a 95.8% syntactic pass rate, surpassing the ground-truth baseline.
  • A curated dialect corpus with a locally-hosted open-source LLM proved more effective than simply scaling model size.

Conclusions:

  • Retrieval-Augmented Generation (RAG) enhances LLM performance for generating code in legacy industrial languages.
  • Open-source LLMs paired with curated datasets and RAG provide a viable solution for on-premise industrial code generation.
  • This approach supports reproducible, sustainable smart manufacturing by enabling effective tooling for deprecated but deployed systems.