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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Using Box-Behnken Design for Optimizing the Processing Parameters for the Injection Molding Process 7th

Using Box-Behnken Design for Optimizing the Processing Param...

引用

7th International Conference on Green Technology and Sustainable Development

作者： Trinh, V. L. Nguyen, X. C. Nguyen, T. S. Nguyen, H. K. Nguyen, H. H. Hoang, X. K. Nguyen, H. S. Tran, V. H. Nguyen, T. P. Hanoi Univ Ind Sch Mech & Automot Engn 298 Caudien St Hanoi 10000 Vietnam Hanoi Univ Ind Vietnam Japan Ctr 298 Caudien St Hanoi 10000 Vietnam Hanoi Univ Ind Dept Sci & Technol 298 Caudien St Hanoi 10000 Vietnam Hungvuong Univ Fac Nat Sci Nongtrang Area Viettri Phutho Vietnam Hanoi Univ Ind Dept External Affairs 298 Caudien St Hanoi 10000 Vietnam Ho Chi Minh City Univ Technol & Educ HCMUTE Fac Vehicle & Energy Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Injection molding technology produces mass plastic products. Polyethylene terephthalate glycol (PETG) is the thermoplastic material used to make various plastic products for industrial equipment, electrical components, consumer goods, car industry, and aerospace applications. The product quality is really important and mainly depends on the processing parameters during injection molding process. This paper demonstrates a method of using Box-Behnken design for optimizing the processing parameters to reduce the defects for injection molding process. The processing parameters include the melt temperature, injection pressure, packing time, and filling time that is introduced to investigate the effect of injection molding parameter on the product quality problems such as weldline, short shot, shrinkage, and warpage. The results show that Box-Behnken is effective method in design of experiment procedure for optimizing the technical parameters of the manufacturing process to improve product quality.

关键词： Injection molding PETG Box-Behnken method processing parameters optimization warpage

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Target Acquisition by Reinforcement Learning-Based Bin Tilting with a Robotic Arm 18th

Target Acquisition by Reinforcement Learning-Based Bin Tilti...

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Li, Qiuyang Gao, Ziyan Elibol, Armagan Chong, Nak Young Japan Adv Inst Sci & Technol Sch Informat Sci Nomi 9231292 Japan

ISBN: (纸本)9783031449802;9783031449819

Target acquisition is one hot topic in the field of robotic manipulation. This task becomes more difficult especially when the target is fully non-visible that the target may be fully-occluded by other objects. How to acquire the target efficiently is one of the key issues. The existing methods leverage a series of pushing and grasping to acquire the target, the time cost, however, may have large variations that is influenced by the number of objects and the clutterness around the target. In this work, we propose a novel method which is not to re-arrange the objects surrounding the target but to tilt the container, and a deep reinforcement learning model to learn the tilt policies based on image data. Compared with other methods, the proposed method makes a significant change in the distribution of locations of the objects inside the bin after each tilting action so that to achieve efficient target acquisition. The final simulation experiments demonstrate the effectiveness and efficiency of our proposed method with a success rate of up to 92%.

关键词： Target acquisition Deep q-learning Bin tilting

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Automatic Functional Tests of Cash Registers 28th

Automatic Functional Tests of Cash Registers

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2024 International Conference Automation

作者： Hys, Agnieszka Drapikowski, Pawel Cent Off Measures Ul Elektoralna 2 PL-00139 Warsaw Poland Poznan Univ Tech Pl Marii Sklodowskiej Curie 5 PL-60965 Poznan Poland

ISBN: (纸本)9783031782657;9783031782664

The paper presents formal requirements and a prototype of a stand for automatic functional tests of cash registers. The authors are unaware of any automatic cash register test stands offered on the market and operating. An OCR system was used to detect digits and inscriptions on keys, and their spatial coordinates are determined using stereo vision. Deep neural networks were used to detect pictograms from outside the OCR database. Due to the different size of the cash registers, the stereo vision system must be reconfigured in terms of the height of the cameras and their base. Fast stereo calibration based on only a pair of images was used, which provides sufficient measurement properties. The calculated key coordinates are sent to an industrial robot equipped with a flexible pointer adapted to pressing the keys. The key sequence enabling individual tests is entered by the operator based on the cash register documentation. The advantage of the proposed stand is its versatility, which allows for testing of cash registers of different manufacturer and other devices with similar functionality. The stand is intended for implementation at Central Office of Measures.

关键词： Cach register Functional tests Stereovision Convolutional neural network

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Improving Replenishment for Retail: Utilizing Planogram Information

Improving Replenishment for Retail: Utilizing Planogram Info...

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Intelligent systems Conference

作者： Topcu, Berkay Goksu, Dogukan Askin, Nur Yildirim, Mehmet Can Aktas, Tunahan Mentes, Berat Utkan Yildiz Tech Adv Analyt Dept Istanbul Turkiye

ISBN: (纸本)9783031663284;9783031663291

Replenishment is the process of restocking inventory to maintain sufficient stock levels of products for sale. This involves analyzing sales data, monitoring stock levels and determining the optimal quantity of products to order from suppliers in order to meet customer demand. Each retail store needs to determine the ideal replenishment amount for each product, which can be challenging as it requires continuous manual effort and strategic decision-making expertise. In this paper, we present an end-to-end replenishment recommendation system specifically designed for frozen products in a retail company. Our approach combines historical sales data with computer vision based real-time product recognition on store shelves to recommend the optimal replenishment amount for a specific product at a specific store. We begin by forecasting the demand for the upcoming period using time-series forecasting methods. Then, we determine the initial replenishment quantity for each product in every store using a stochastic optimization model based on expected values. This model is designed to maximize profitability. This initial quantity is further adjusted by comparing planogram information with real-time product recognition from freezer images. Thanks to the high accuracy of over 90% in product recognition, our replenishment results have demonstrated an 18.77% improvement in total profitability for the selected products. Our end-to-end replenishment recommendation system's outcomes are provided to sales representatives as suggestions in the order-taking phase and more than 70% of our automatic replenishment values are used without any adjustments.

关键词： Retail analytics Replenishment Planogram Computer vision Deep learning

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Deep Learning Approach for Cancer Detection Through Gene Selection 4th

Deep Learning Approach for Cancer Detection Through Gene Sel...

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4th Congress on Intelligent systems (CIS)

作者： Famitha, S. Moorthi, M. Prathyusha Engn Coll Comp Sci & Engn Thiruvallur India Saveetha Engn Coll Biomed Engn & Med Elect Kanchipuram India

ISBN: (纸本)9789819990399;9789819990405

Uncontrolled cell division results in a tumor that spreads to certain other body cells which leads to a serious disorder known as a tumor. It is necessary to encourage the creation of novel medications and therapeutic techniques. The categorization of microarray information is essential and the selection of the appropriate genes is a critical step in the categorization of microarray information. In this research, unsupervised deep learning was employed to uncover the hierarchical organization of the information on the tumor genes. Deep learning uses many layers of hidden units to collect alternate, hierarchical structures connected to representations of the input information. Researchers predict that the deep learning-based hierarchical structure would be connected to the biological signaling system. The ability to use information from various tumor kinds to automatically generate characteristics that aid in the identification and subsequent diagnosis of a particular cancer is the key advantage of the proposed protocol above earlier tumor detection methods. Here, the method is used to identify and characterize different cancerous tumors, according to data about gene expression.

关键词： Tumor detection Unsupervised deep learning Classification Gene selection

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Federated Learning for Enhanced Malware Threat Detection to Secure Smart Power Grids 16th

Federated Learning for Enhanced Malware Threat Detection to ...

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Shafi, Saira Tariq, Noshina Khan, Farrukh Aslam Ali, Aftab Air Univ Dept Avion Engn Islamabad Pakistan King Saud Univ Ctr Excellence Informat Assurance Riyadh 11653 Saudi Arabia Ulster Univ Sch Comp Belfast BT15 1ED Antrim North Ireland Natl Univ Comp & Emerging Sci Dept Artificial Intelligence & Data Sci Islamabad Pakistan

ISBN: (纸本)9783031775703;9783031775710

Cyber-Physical systems (CPS) and the Internet of Everything (IoE) are vital in improving the efficiency of contemporary structures like Smart Grids by supporting the interconnection of people, processes, data, and things. Due to the convergence of devices and systems required by the IOE, smart grid has to be equipped with highly effective and reliable methods of attack detection. This research introduces a novel federated learning approach that utilizes the "CIC-MalMem-2022" dataset to enhance malware detection through a two-layered framework. The CPS layer focuses on local model training, while the Fog layer integrates thesemodels into a global framework. The results demonstrate that the global model achieves superior performance with an accuracy of 99% and a precision of 99% on the training dataset, compared to the local model's 85% accuracy and 75% precision on the test dataset. Additionally, the global model maintains 88% accuracy and 78% precision on the test set, underscoring the efficiency of the federated learning approach.

关键词： Smart Grids Cyber-Physical systems Federated Learning Malware Machine Learning

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Models for Real-Time Emotion Classification: FER-2013 Dataset

Models for Real-Time Emotion Classification: FER-2013 Datase...

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Intelligent systems Conference

作者： Ortiz Diaz, Agustin Alejandro Tamayo, Sergio Cleger De Oliveira, Delrick Nunes Abensur, Geovana Amorim Sidia Inst Sci & Technol Ave Darcy Vargas 654 BR-69055035 Manaus Amazonas Brazil

ISBN: (纸本)9783031664304;9783031664311

The recognition of emotions and facial expressions is a topic of study that is intertwined with several fields of research: for example, artificial intelligence, digital games, and human-computer interaction (HCI). Head-mounted displays (HMD) have revolutionized virtual worlds. These devices have allowed people to interact in environments with great realism. Recognizing emotions and facial expressions within an HMD has been a great challenge because they cause optical occlusions. The same HMDs occlude important areas of the face, such as the eyes. However, as of 2021, some of the latest HMDs manufactured have incorporated cameras and/or sensors to control what is happening behind these devices. Our working group is developing a project whose objective is to develop and validate a set of techniques that allow us to recognize emotions and facial expressions so that they can be projected onto an avatar in real-time within these HMDs. This work, which is part of the first stage of the project, documents the results of three CNN models designed to recognize emotions in real-time. We adapted three CNN structures for training on the well-known FER-2013 dataset. Our three models are made up of one, two, and three convolutional channels respectively and work by dividing the input image for analysis without any other preprocessing. According to our objectives, the proposed models achieved promising results in terms of accuracy values and training time. One of the models reaches 63.0% accuracy, with a standard deviation of +/- 0.005, considering that no preprocessing is performed on the input images.

关键词： Recognition of emotions Facial expressions CNN HMD FER-2013

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Lightweight Human-Like Robotic Leg with Four-Bar Mechanism Joints 27th

Lightweight Human-Like Robotic Leg with Four-Bar Mechanism J...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Sivak, Oleksandr Mianowski, Krzysztof Vonwirth, Patrick Berns, Karsten RPTU Kaiserslautern Landau Dept Comp Sci D-67663 Kaiserslautern Germany Warsaw Univ Technol Fac Power & Aeronaut Engn Warsaw Poland

ISBN: (纸本)9783031707216;9783031707223

This paper presents the design and kinematic modeling of a lightweight 2-D robot leg prototype equipped with four-bar mechanism joints. Leveraging principles from human biomechanics and robotic engineering, the leg design aims to mimic natural human movement patterns while maintaining structural integrity and efficiency. Through a comprehensive kinematic model, we analyze joint angles, velocities, and torques to ensure the leg capability to execute walking and squatting movements. The model parameters are adjusted based on biomechanical considerations and human locomotion data.

关键词： Bipedal Human-like locomotion Kinematics Lightweight robot Mechanical design

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Framing and BERTology: A Data-Centric Approach to Integration of Linguistic Features into Transformer-Based Pre-trained Language Models 1

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Intelligent systems Conference (IntelliSys)

作者： Avetisyan, Hayastan Safikhani, Parisa Broneske, David German Ctr Higher Educ Res & Sci Studies DZHW Lange Laube 12 D-30176 Hannover Germany

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

Our study tackles the concept of framing in political news media, its linguistic realization, and the integration of the latter into BERT. To this end, we utilized a human-labeled dataset of news articles annotated with the issue and game frame along with deep linguistic features capturing this frame. We fine-tuned BERT on a binary classification task adding the features to the input. We demonstrate which specific linguistic categories seem to be comprehended by BERT and should be paid more attention to in the further development of BERT-like language models.

关键词： BERT Linguistic features Framing

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How Starting Points and Representations Affect Software Modularisation: An Empirical Analysis 1

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Intelligent systems Conference (IntelliSys)

作者： Maramazi, Faisal Odebode, Afees Mann, Ashley Swift, Stephen Arzoky, Mahir Brunel Univ London Uxbridge UB8 3PH England

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Exploring the software system via different clustering representations and starting points is not an easy task. Calculating the search space's fitness with the existence of various representations and starting points would be challenging since every representation's results vary due to several factors. This paper aims to exploit different automated software modularisation clustering representations by deploying Random Mutation Hill Climbing (search-based) algorithm and five starting points. Fitness functions, including EVM and EVMD, were tested on fifty open-source datasets ranging in different sizes and run for a million iterations to find the best software clustering combination of representation and starting point. The paper presents empirical results that compare the robustness of representations that led to yield some interesting insights related to the List of Lists.

关键词： Software modularisation Clustering Representations SBSE

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