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Study Case on Implementation Elevator 2 Floor System on PLC OMRON CP1E N30 9

Study Case on Implementation Elevator 2 Floor System on PLC ...

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9th International Conference on Education and Technology, ICET 2023

作者： Habibi, Muhammad Afnan Wibawa, Arif Fadlika, Irham Irfani, Mochamad Farhan Ali Firmansyah, Mohamad Farel Baskoro, Angger Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Dept. of Electrical Engineering Malang Indonesia National Yang Ming Chiao Tung University Dept. of Electrical Engineering and Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350358292

The swift progression of industrial technology underscores the need for adept.professionals capable of skillfully implementing and managing intricate systems. Vocational education assumes a pivotal role in cultivating competent intermediaries proficient in translating theoretical knowledge into practical applications. At the core of industrial automation lies the programmable Logic Controller (PLC), a specialized digital computer that interfaces with sensors, switches, and actuators to execute real-time control programs, thus facilitating meticulous industrial processes. This study concentrates on the practical deployment of a two-floor elevator system utilizing the OMRON CP1E N30 PLC. Elevators, integral to contemporary structures, mandate precision for secure vertical conveyance. Leveraging input signals from elevator buttons, sensors, and switches, the PLC orchestrates efficient motor control and dependable door operations. This hands-on application underscores the PLC's significance within real-time control contexts. Nonetheless, Vocational Higher Education graduates frequently grapple with the challenge of transitioning to industry settings due to limited practical exposure. This partnership harmonizes with the center's objective of enriching vocational education by integrating sophisticated technologies like PLCs, equipping the workforce with the acumen to bridge the gap between theoretical foundations and the pragmatic demands of industry. © 2023 IEEE.

关键词： Elevators

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Decoding Asymmetric Neural Dynamics in Visual Processing via TMS-induced Cortical Effective Connectivity

Decoding Asymmetric Neural Dynamics in Visual Processing via...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Edoardo Paolini Ilaria Siviero Davide Bonfanti Gloria Menegaz Silvia Savazzi Chiara Mazzi Silvia F. Storti Dept. of Computer Science University of Verona Verona Italy Dept. of Neuroscience Biomedicine and Movement Science University of Verona Verona Italy Dept. of Engineering for Innovation Medicine University of Verona Verona Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Exploring brain connectivity is made possible through the combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) signals. While previous research has investigated hemispheric asymmetries in signal propagation at the electrode space following occipital TMS, this work extends this exploration at the source level. The sources of the neural activity were reconstructed using EEG data and mapped into regions of interest. The TMS evoked potential (TEP) was estimated using a Bayesian approach, which provides better results when the number of trials is limited. Subsequently, time-varying effective connectivity was calculated for uncovering hemispheric asymmetries in the dynamics of signal propagation. A distinct contralateral influence emerged following stimulation of the left occipital lobe, especially toward the parietal and frontal lobes, aligning with the observations on the scalp. In conclusion, our study advances the understanding of brain connectivity dynamics, particularly in hemispheric interactions, and paves the way for further insights into the implications of TMS on neural networks.

关键词： Electrodes Visualization Electric potential Frontal lobe Transcranial magnetic stimulation Scalp Neural activity Electroencephalography Occipital Lobe Engineering in medicine and biology

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Course Recommendation System for Company Job Placement Using Collaborative Filtering and Hybrid Model 13

Course Recommendation System for Company Job Placement Using...

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13th International Conference on Data science, Technology and Applications, DATA 2024

作者： Park, Jaeheon Lee, Suan Lee, Woncheol Kim, Jinho Kangwon Joint Program of Software Convergence Course Kangwon National University Chuncheon Korea Republic of School of Computer Science Semyung University Jecheon Korea Republic of SeedsSoft Co. Ltd. Chuncheon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9789897587078

This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students' interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system's recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Collaborative learning

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Adapting Large Language Models to Narrative Content 3

Adapting Large Language Models to Narrative Content

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3rd Workshop on Artificial Intelligence and Creativity, CREAI 2024

作者： Musacchio, Elio Siciliani, Lucia Basile, Pierpaolo Semeraro, Giovanni Italian National PhD Program in Artificial Intelligence University of Bari Aldo Moro Bari Italy Dept. of Computer Science University of Bari Aldo Moro Via E. Orabona 4 Bari70125 Italy

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, adapting them to narrative content remains challenging. This paper explores the opportunities in adapting open-source LLMs to narrative contexts, where coherence, plot development, and character consistency are paramount. We investigate existing techniques for adapting and then fine-tuning LLMs on narrative data and propose a solution tailored to the specific demands of narrative generation. Furthermore, we analyze the performance of the proposed approach on the standard dataset WritingPrompts by exploring several corpora for the adaptation step. Moreover, we propose a qualitative evaluation involving human feedback. Results show that the adaption helps the model improve the generation and accuracy of prompts ranking. © 2024 Copyright for this paper by its authors.

关键词： Artificial Intelligence Generative AI Large Language Models Narrative Content Generation Narrative Content Understanding

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LLaVA-NDiNO: Empowering LLMs with Multimodality for the Italian Language 8

LLaVA-NDiNO: Empowering LLMs with Multimodality for the Ital...

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8th Workshop on Natural Language for Artificial Intelligence, NL4AI 2024

Since their initial inception, large language models have undergone many innovations. One of these innovations concerns multimodality. Several adaptation strategies have been developed to expand LLMs to process multimodal signals. However, the training procedure for these multimodal models is performed on English-only vision-language datasets in the current literature, limiting their capabilities for other languages. This work proposes the first family of LMMs for the Italian language. We trained them using state-of-the-art backbone models and datasets, translated into Italian using the most up-to-date machine translation model available. In support of open science, we publicly release the data, models, and code used to develop these models. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visual languages

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A Hybrid BCI for Robotic Device Navigation

A Hybrid BCI for Robotic Device Navigation

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Annual Conference on Information sciences and Systems (CISS)

作者： Yih-Choung Yu Hayden Fisher Angela Busheska Lily Thompson Lisa Gabel Dept. of Electrical and Computer Engineering Lafayette College Easton PA USA Children’s Hospital of Philadelphia Philadelphia PA USA Neuroscience Program Lafayette College Easton PA USA Dept. of Psychology & Neuroscience Program Lafayette College Easton PA USA

Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities in navigating our increasingly technologically dependent society. With applications such as electric wheelchairs and advanced prosthetics in mind, the goal of this research is to develop a system that enables the use of electroencephalographic (EEG) and electromyographic (EMG) signals to control the movement of a robot. An EEG cap was used to obtain occipital alpha power density, frontal muscular artifacts, and sensorimotor mu rhythms, which were then sent back to a PC via Bluetooth for further processing. Signal-processing algorithms and models were developed and implemented to determine the user’s mental activity and send signals to the external physical device. The preliminary results from the pilot experiments were very promising. The algorithms will be implemented for real-time signal processing and tested with a BCI-controlled robotic device.

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Scalability in Autoencoder-based OFDM Communication System 2

Scalability in Autoencoder-based OFDM Communication System

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Tsugawa, Seizan Toma, Takao Oshiro, Shiho Wada, Tomohisa University of the Ryukyus Computer Science and Intelligent Systems Program Okinawa Japan Magna Design Net Inc Okinawa Japan University of the Ryukyus Information Technology Center Okinawa Japan University of the Ryukyus Area of Computer Science and Intelligent Systems Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 , but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0. As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Intelligent Constellation Generation based on Autoencoder Communication System 2

Intelligent Constellation Generation based on Autoencoder Co...

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Matsumoto, Kaisei Toma, Takao Oshiro, Shiho Wada, Tomohisa Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Area of Computer Science and Intelligent Systems University of the Ryukyus Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

关键词： Quadrature phase shift keying

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Feeling the Groove: Identification of Bass patterns from polyphonic audio 22

Feeling the Groove: Identification of Bass patterns from pol...

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22nd IEEE International Conference on Communication Technology, ICCT 2022

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Roy, Kaushik West Bengal State University Dept. of Computer Science India Music Program New York University Abu Dhabi Gazelien Records Lab Dept. of Arts and Humanities United Arab Emirates Brainware University Dept. of Computational Science India

ISBN: (纸本)9781665470674

Technology has found its way into disparate spheres of music, from production to performance. Researchers have attempted to automate multitudinous aspects in this domain. One of the interests has been towards the automated generation of music pieces itself. Bass grooves are an integral part of most music pieces. It makes a piece sound complete and bridges the gap between the percussion and melody sections. Thus, it is essential for machines to understand bass grooves for automated music analysis and production. Automatically distinguishing bass grooves is difficult and it aggravates even more for polyphonic music. In polyphonic music, the bass grooves tend to be at a lower volume and its frequency range has profound overlap with the percussion section which contributes to the complexity of identification. In this paper, a system is presented to distinguish bass grooves in the presence of drums. Experiments were performed with 7 grooves totaling 4473 clips which were modeled using MFCC-based features. The highest accuracy of 97.38% was obtained using multi-layer perceptron (MLP)-based classification. © 2022 IEEE.

关键词： Music

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