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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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A Convolutional Neural Network for skin lesion classification in Dermoscopic Images using discrete wavelet transform

A Convolutional Neural Network for skin lesion classificatio...

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European Control Conference (ECC)

作者： Miron Mihaela Anisia Luiza Culea-Florescu Simona Moldovanu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Dunarea de Jos University of Galati 47 Domneasca Str. Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Galați Dunarea de Jos University of Galati 47 Domneasca Str. Galati Romania

The objective of this paper is to investigate a new technique for skin lesion classification based on discrete wavelet transforms and convolutional networks. More precisely, for preprocessing the images, two 2D family wavelets and Low-Low (LL), High-High (HH) bands were used to generate subimages for each skin lesion image as melanoma and normal nevi. On each subimage datasets of nevi and melanoma, five convolutional neural networks (CNNs) were tested. The experiments were conducted on the public Mednode dataset which contains 170 images (70 melanoma and 100 nevi cases). The accuracy, precision, recall, and F1-score metrics were extracted from confusion matrix for each test performed on the proposed CNN architecture. All metrics show that LL subimages are recommended to be used in classifying melanoma vs. nevi.

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Dynamic behaviors and digital circuit implementation of a Rulkov neuron with a non-polynomial memristor synaptic weight

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The European Physical Journal Special Topics 2025年 1-18页

作者： Tamba, Victor Kamdoum Tofou, Junior Tchiaze Pham, Viet-Thanh Grassi, Giuseppe Department of Telecommunication and Network Engineering IUT-Fotso Victor of Bandjoun University of Dschang Bandjoun Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam Department of Engineering for Innovation University of Salento Lecce Italy

Memristors have proven since their discovery to play a very important role as artificial synapses in the implementation of artificial neural networks. Just as biological synapses, memristors can significantly influence the behaviors of the neural network. A non-polynomial memristor is used in this work to investigate the memristive synaptic weight effect on the dynamics of a simplified Rulkov neuron. The model is first introduced from where theoretical analyses show the system’s ability to develop regular and irregular behaviors. Analysis methods such as two parameter charts, bifurcation diagrams and maximum Lyapunov exponents (MLE) are then used to study the dynamics of the proposed Rulkov model. The numerical results show that the memristive synaptic weight Rulkov neuron model can exhibit self-excited and hidden firings. Furthermore, the system can undergo interesting offset boosting dynamics and antimonotonicity features (i.e., bubble bifurcations) for some system parameters. To corroborate the computational findings, the proposed model is implemented on an Arduino Due microcontroller board, from where the experimental results are seen to be in agreement with the obtained numerical results.

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Optimizing the energy cost function for a wastewater treatment plant model by employing dynamic generation of a two-dimensional reference point 28

Optimizing the energy cost function for a wastewater treatme...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Roşu, Bogdan Murariu, Gabriel Roşu, Adrian Iticescu, Cǎtalina Mocanu, George Tupu, Eliza Arseni, Maxim Dunǎrea de Jos University of Galaţi Faculty of Automation Computer Sciences Electronics and Electrical Engineering Galati Romania Dunǎrea de Jos University of Galaţi REXDAN Research Infrastructure Faculty of Sciences and Environment Galati Romania Dunǎrea de Jos University of Galaţi Faculty of Physical Education and Sport Galati Romania Rǎsvan Angheluţǎ Natural Sciences Museum Complex Galati Romania SPINOFF GEOMAX S.R.L. Galati Romania

ISBN: (纸本)9798350339918

This article presents a study on determining the optimal reference values for two proportional-integral (PI) type controllers taking also into account the impact on a simulated wastewater treatment plant (WWTP). In this regard, we considered a predictive control algorithm for a distributed model, which includes correlation between subsystems. Additionally, we have taken into consideration the comparative testing of two interpolation methods for the purpose of dynamically generating reference points, applied on Benchmark Simulation Model No.2 (BSM2) with an implemented control strategy. This allowed us to determine the global minimum points and propose a method for aligning the global minimum points obtained for both cost functions. © 2023 IEEE.

关键词： Cost functions

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Development of a UAV-based System for Technical Diagnostics of Overhead Power Lines 24

Development of a UAV-based System for Technical Diagnostics ...

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24th International Conference on Computational Problems of Electrical engineering, CPEE 2023

作者： Kozlovskyi, Oleksandr Trushakov, Dmitro Rendzinyak, Serhiy Korud, Vasyl Central Ukrainian National Technical University Department of Electrical Systems and Energy Management Kropyvnytskyi Ukraine Central Ukrainian National Technical University Department of Automation and Control in Technical Systems Kropyvnytskyi Ukraine Ivan Franko National University of Lviv Faculty of Electronics and Computer Technologies Lviv Ukraine Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350330342

The problems associated with the operation of overhead power lines and ways of improving control over their condition with the help of UAVs are considered. A structural diagram of the system of technical diagnostics of overhead lines based on UAVs was developed, for which the necessary diagnostic parameters were selected according to informative criteria. An analysis of types of UAVs was carried out in order to determine their suitability and efficiency of use for diagnosing the condition of overhead lines. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Enhancing Control of a 21-Level Modular Multilevel Converter through Grid-Based Model Predictive Current Control

Enhancing Control of a 21-Level Modular Multilevel Converter...

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2023 IEEE Asia Meeting on Environment and Electrical engineering, EEE-AM 2023

作者： Tran Hung, Cuong Nguyen Nhat, Tung Pham Viet, Phuong Nguyen Xuan, Phuc Nguyen Quang, Thuan Nhat Hai, Nguyen Thuyloi University Faculty of Electrical & Electronics Engineering Ha Noi Viet Nam Ha Noi University of Science and Technology Department of Industrial Automation Ha Noi Viet Nam Key Laboratory for High Voltage Techniques Institute of Energy Ha Noi Viet Nam Hanoi University of Science and Technology Computer Science Department School of Information and Communications Technology Hanoi Viet Nam

ISBN: (纸本)9798350381061

The Modular Multilevel Converter (MMC) has a flexible structure, extending the number of voltage levels on the AC side. This structure has many advantages such as low switching frequency, and a smaller number of transistor valves. Therefore, the MMC converter makes it easy to apply control algorithms and is widely applied to high-voltage power grids. This paper presents the control method of an MMC converter on the AC side, with 10 Sub-modules (SM) in each phase to generate 21 voltage levels. The process is performed by the current control method based on Finite Control Set - Model Predictive Control (FCS-MPC). With the advantage of fast response, optimizing the converter's working process by predicting the working states of the converter, according to the model, the FCS-MPC control algorithm has been used in this model. The results of the proposed method are proven by using Matlab/Simulink software. The results have shown the requirements of the DC/AC converter such as sinusoidal current, and voltage with 21 voltages. © 2023 IEEE.

关键词： Model predictive control

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Deep learning-based segmentation of breast masses using convolutional neural networks 12

Deep learning-based segmentation of breast masses using conv...

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12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Anghelache Nastase, I.-N. Moldovanu, S. Moraru, L. The Modelling & Simulation Laboratory Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Emil Racovita Theoretical Highschool 12-14 Regiment 11 Siret Street Galati800332 Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Department of Chemistry Physics & Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.61

关键词： Graphic methods

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Traffic Accident Severity Prediction with Ensemble Learning Methods

SSRN

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SSRN 2023年

作者： Çeven, Süleyman Albayrak, Ahmetahmet Department of Electronics and Automation Technology Düzce Vocational School Düzce University Düzce Turkey Department of Computer Engineering Faculty of Technology Düzce University Düzce Turkey

In this study, decision tree-based models are proposed for classification of traffic accident severity. Traffic accident severity is classified into three categories. The data set used in the study belongs to the province of Kayseri, Turkey. The data consists of urban traffic accident reports (23074 accidents) between 2013 and 2021. There are 39 variables in the data set. As a result of data preprocessing, 15 variables that are meaningful and can be used for the model in the data set were determined. Since the input variables of the model mainly contain categorical data, they were coded with pseudo-coding and a total of 93 input variables were obtained. In the studies, ensemble learning methods such as Random Forest, AdaBoost and MLP methods were used. F1 scores of these methods were found to be 91.72%, 91.27% and 88.95%, respectively. Feature importance levels were calculated for 15 variables used in the model. Gini index and decision trees were used while calculating the importance of the features. Driver fault (0.64) was found to have the most effect on traffic accident severity. This study focuses especially on urban traffic accidents. Urban traffic is crowded in terms of both vehicles and pedestrians. As a result of this, according to the findings obtained in this study, traffic accidents occurred mostly at the intersections with crowded urban areas. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Reversing BLE Remote Control Protocols of Dual Channel LED Lamps with CCT and Illumination Control

Reversing BLE Remote Control Protocols of Dual Channel LED L...

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Automatics and Informatics (ICAI), International Conference

作者： Rostislav Kandilarov Yordan Yordanov Department of Machine Science Machine Elements Engineering Graphics and Physics Faculty of Transport University of Ruse “Angel Kanchev” Ruse Bulgaria Computer Systems and Technologies Faculty of Electrical Engeneering Electronics and Automation University of Ruse “Angel Kanchev” Ruse Bulgaria

In this paper various BLE protocols for communication between remote or Android applications and intelligent dual channel (cool and warm white) LED lamps were reverse engineered. Three protocols were fully decoded and several partially. Replay attack is tested and confirmed working. Demo C++ library was developed and a proof-of-concept firmware for esp32 based development board was used to verify the decoding and the attacks applicability.

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Melanoma Detection using a Random Forest Algorithm 10

Melanoma Detection using a Random Forest Algorithm

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Damian, Felicia-Anisoara Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method;image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue);23 colour features extraction using the minimum and maximum RGB intensity and Hu moments;Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features;98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features;and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features. © 2022 IEEE.

关键词： Image segmentation

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