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2017 4th ICMIBI international conference on Training, Education, and Management (ICMIBI-TEM 2017)

作者： Hongqiang Wan Quanli Han Liping Lei School of Mechanical Engineering Xi'an Technological University

In order to cultivate students' mechanical manufacturing practical ability and innovation ability, combined with the course content, the three-dimensional design of parts, numerical control simulation, combination fixture design and assembly, numerical control processing practice and other experimental items are set. this paper discusses the meaning, content, purpose and characteristics of the comprehensive open experiment of machinery manufacturing. the main contents of the experimental project are given. these experimental projects include parts of the understanding and innovative design, parts of the CAM process planning and processing simulation, parts of a process fixture design, three-dimensional simulation and assembly, CNC programming and CNC machine tool practice and other experimental content. these experiments are designed for students to master the mechanical manufacturing equipment and processing technology theoretical knowledge, provides a good practice environment, to cultivate with independent learning and design ability of talent is of great significance. the experiment of this paper provides a new idea for the cultivation of high-quality talents.

关键词： Mechanical manufacturing NC machining Simulation Modular fixture

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Certified monotonic neural networks 20

Certified monotonic neural networks

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Proceedings of the 34th international conference on Neural Information Processing Systems

作者： Xingchao Liu Xing Han Na Zhang Qiang Liu Department of Computer Science University of Texas at Austin Austin TX Department of Electrical and Computer Engineering University of Texas at Austin Austin TX Tsinghua University

ISBN: (纸本)9781713829546

Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice. Existing methods for learning monotonic neural networks either require specifically designed model structures to ensure monotonicity, which can be too restrictive/complicated, or enforce monotonicity by adjusting the learning process, which cannot provably guarantee the learned model is monotonic on selected features. In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem. this provides a new general approach for learning monotonic neural networks with arbitrary model structures. Our method allows us to train neural networks with heuristic monotonicity regularizations, and we can gradually increase the regularization magnitude until the learned network is certified monotonic. Compared to prior works, our method does not require human-designed constraints on the weight space and also yields more accurate approximation. Empirical studies on various datasets demonstrate the efficiency of our approach over the state-of-the-art methods, such as Deep Lattice Networks [34].

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Design of A Virtual Reality Scenario and Scent Generator for Sensory Training 2020

Design of A Virtual Reality Scenario and Scent Generator for...

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Proceedings of the 2020 4th international Symposium on Computer science and Intelligent Control

作者： Jorge Ortega-Moody Yosselin Castro Islas Levi Howell Kouroush Jenab Victoria Russ Department of Engineering and Technology Management Morehead State University Morehead KY USA

ISBN: (纸本)9781450388894

In the food industry, what makes the difference between a company and its competitors is the quality of its food product, and one of the components that makes quality is the flavor of a product. As trends shift to meet consumer's health and wellness desires formulas are changed with companies trying to retain the same flavoring. this shifting has led to the increased demand of sensory analysis tests. Some of the limitations for sensory analysis is the required space to have the individual booths, time consuming preparation, and material costs. But even with the previous limitations, one of the most important is the training of new users and calibration of existing users. With the development of virtual reality this problem can be more easily rectified by creating a virtual scenario that utilizes all senses and reduces cost of training. the main objective of this research is the development of a virtual scenario for sensory training. this is achieved by recreating a traditional testing environment and training program to analyze scents provided by a scent generator prototype. the methodology will include the design of the training, recreating the environment, programming the interaction with the user and finally the development of a scent generator to release scents.

关键词： Mechatronics Sensory Training Virtual Reality

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LabVIEW Integrated with Python Machine Learning for DC Motor Drive

LabVIEW Integrated with Python Machine Learning for DC Motor...

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science and Information Technology in Smart Administration (ICSINTESA), international conference of

作者： Mana Poyai Dechrit Maneetham Petrus Sutyasadi Department of Mechatronics Engineering Faculty of Technical Education Rajamangala University of Technology Thanyaburi Pathumthani Thailand Department of Mechatronics Vocational Faculty Sanata Dharma University Yogyakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

this study presents a new technique that integrates LabVIEW and Python to enhance the control of DC motor drives through the utilization of machine learning methods. the objective of our research is to leverage LabVIEW’s graphical programming interface in combination with the Scikit-learn Python package to train a Polynomial Regression model. the goal is to forecast Pulse Width Modulation (PWM) values at desired Rotations per Minute (RPM) of a DC motor. the accuracy and reliability of the trained model are evaluated using performance assessment metrics such as the R-squared ($R^{\mathbfth}$) coefficient of determination and the Root Mean Square Error (RMSE). the $\mathbf{R}^th$ score assesses the precision of the model’s fit, whereas the RMSE quantifies the model’s predictive capability by comparing observed and predicted data. this research also includes a comparison between the machine learning technique for DC motor drive and conventional PID control. the findings illustrate the successful integration of LabVIEW and Python in managing DC motor drives, achieving low overshooting and faster attainment of the set RPM point compared to classical PID control. this emphasizes machine learning’s capacity to enhance control strategies; the application of Polynomial Regression yields promising results, demonstrating its effectiveness in predicting PWM and RPM values with an $R^th$ value ranging from 96% to 98% and RMSE within 19.

关键词： PI control Machine learning Pulse width modulation Predictive models DC motors Polynomials Data models PD control Root mean square Python

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Design and development of a small-scale experimental device of refrigeration system based on real faults simulation

Design and development of a small-scale experimental device ...

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2018 4th international conference on Innovative Development of E-commerce and Logistics(ICIDEL 2018)

作者： Qunsheng Liu Lin Wang Huarui Cheng School of Energy and Power Engineering Henan University of Animal Husbandry and Economy

Although significant progress has been made about fault detection and diagnosis(FDD) in the field of refrigeration and air conditioning, the faults that can be effectively diagnosed are still very limited. the routine maintenance and troubleshooting of refrigeration system are normally fulfilled by professional refrigeration technicians or engineers. A small-scale refrigeration system experimental device is designed and developed, as a powerful tool for teaching general theories and training practical skills for the novices of refrigeration system, this device can measure and display temperature and pressure in typical positions of a refrigeration system. In addition, it can generate real common faults for the purpose of experiments and practical training, including filth blockage, excessively high pressure, excessively low pressure, refrigerant undercharge and refrigerant overcharge. With the switching function between normal operation mode and fault operation mode, this device has been successfully applied to the college curriculums of PLC programming, refrigerating equipment maintenance and repair, etc. It is showed that the device facilitates the novices of refrigeration system to understand the changes of temperature and pressure in typical positions of a normal refrigeration system, the reasons of common faults, the methods of fault diagnosis and troubleshooting.

关键词： refrigeration system fault simulation real faults filth blockage excessively high pressure excessively low pressure refrigerant undercharge refrigerant overcharge

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying

Talk to your data: Enhancing Business Intelligence and Inven...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Jerry Zhu Saad Ahmed Bazaz Srimonti Dutta Bhavaraju Anuraag Imran Haider Srijita Bandopadhyay Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Electronics and Communication Engineering Vellore Institute of Technology India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering University of Engineering and Management Kolkata India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

this paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. the first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. the second analysis enhances GPT’s precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. this research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. this study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. these insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving artificial intelligence landscape.

关键词： Industries Structured Query Language Technological innovation Costs Large language models Semantics Inventory management

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2022 the 4th international conference on Modeling, Simulation, Optimization and Algorithm[ICMSOA 2022], 12 November, 2022

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Journal of Physics: conference Series 2023年第1期2508卷 011001页

作者： Zhao, Yichuan Shang, Yilun Xu, Jinyang Luo, Zongwei Ma, Ying Sun, Hui Lu, Peng Ma, Shouchen Liu, Bin Zhu, Quanxin Wang, Zhixiao Zhao, Yingxue Wang, Lulu De Moura Ramos, José Joaquim Dararutana, Pisutti Onat, Altan Ismail, Zul-Atfi Yenilmez, Ismail Ye, Kyaw Zaw Vladimir, Gorbunov Xue, Yu Razmiki, Goharik Petrosyan Chen, Ning Popva, Nina Doulamis, Anastasios Hu, Jie Lo, Man-Fung Metrolho, José Carlos Jiskani, Izhar Mithal Ivancu, Larisa Ma, Quanjin Chubato, Wondaferaw Yohannese Ghani, Noraida Abdul Memon, Imran Das, Pratibhamoy Banerjee, Subhabrata Wang, Haijing Zeng, Weili Georgia State University United States Northumbria University United Kingdom Shanghai Jiao Tong University China China Xiamen University of Technology China Academy of OPTO-Electronics Chinese Academy of Science China Central South University China Henan Polytechnic University China Nanjing Medical University China Hunan Normal University China Xi'an University of Technology China University of International Business and Economics China Shenzhen Technology University China Department of Computer Science University of A Corua Spain Royal Thai Army Chemical Department Thailand Eskisehir Technical University Turkey School of Environmental Engineering Universiti Malaysia Perlis Malaysia Department of Statistics Science Faculty Eskisehir Technical University Eskisehir Turkey Ministry of Defense Myanmar National Research University of Electronic Technology Russia Nanjing University of Information Science and Technology China Armenian State Pedagogical University Armenia Xian Polytechnic University China St.Petersburg Polytechnic University Russia Greece University of Massachuttes United States The Hong Kong Polytechnic University Hong Kong Polytechnic Institute of Castelo Branco Portugal China University of Mining and Technology China Politehnica University of Timisoara Romania Universiti Malaysia Pahang Malaysia Southwest JiaoTong University China Universiti Sains Malaysia Malaysia Zhejiang University China Department of Mathematics Indian Institute of Technology Patna India Department of Electronics and Communication Engineering Institute of Engineering and Management Kolkata India School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China

ABOUT ICMSOA2022Organized by Yaseen Academy, 2022 the 2nd international conference on Modeling, Simulation, Optimization and Algorithm (ICMSOA 2022), which was planned to be held during 11-13 November, 2022 at Sanya, Hainan Province, China. Due to the travel restrictions caused by covid, the participants joined the conference online via Tencent Meeting at 12 November, 2022. the conference looks for significant contributions to related fields of Modeling, Simulation, Optimization and Algorithm. the aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the *** FOR PAPERSPlease make sure your submission is in related areas of the following general topics. the topics include, but are not limited to:Simulation modeling theory and technology, Computational modeling and simulation, System modeling and simulation, Device/VLSI modeling and simulation, Control theory and applications, Military Technology Simulation, Aerospace technology simulation, Information engineering simulation, Energy engineering Simulation, Manufacturing Simulation, Intelligent engineering simulation, Building engineering simulation, Electromagnetic field simulation, Material engineering simulation, Visual simulation, Fluid mechanics engineering simulation, Manufacturing simulation technology, Simulation architecture, Simulation software platform and Intelligent Optimization Algorithm, Dynamic programming, Ant Colony Optimization, Genetic Algorithm, Simulated Annealing Algorithm, Tabu Search Algorithm, Ant Colony System Algorithm, Hybrid Optimization Algorithm in other related *** conference was begun at 10:00am, ended at 17:30am, 12 November, 2022. there were 77 participants in total, 2 keynote speakers and 17 invited oral speakers, Assoc. Prof. Jinyang Xu from Shanghai Jiaotong Univeristy in China and Dr. Victor Koledov from Innowledgement GmbH in Germany delivered their key

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the impact of emotional valence on students learning performance and evaluation: A text mining of students' opinion data

The impact of emotional valence on students learning perform...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), international conference on

作者： Kingsley Okoye Department of Computer Science Writing Lab Institute for Future of Education School of Engineering and Sciences Tecnologico de Monterrey Nuevo Leon CP Mexico

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Emotions classification or valence extraction in textual datasets, e.g. the students' opinion data, is becoming an emerging topic aimed at understanding the impact or intensities of words (terms) used by the users in the different contexts/applications. the method (emotional valence) for textual data scrutiny and exploration have proved to be central to the human experience analysis. To this effect, this study implements a text mining approach that determines the impact of the emotional valence (textual data quantification) shown by the students in their feedback provided during the semester course (pre and post) to determine its (emotional classification) relation or interconnectedness with the students' learning outcome and performance. the proposed method is designed based on the appraisal theories and component process model (CPM) that studies the degree of pleasantness or goal achievement as an effect of valence judgements. Technically, the study identifies the top terms in the students' data that can be used to draw insights or understand the learning experiences or performance by using Corpus feature selection and Term document matrix word processing libraries in R programming software. Also, it utilized the emotional valence score (quantified data) extracted from the analyzed (textual) data to statistically investigate the correlation and effect of the emotions scores (polarization or intensity of words) expressed by the students with their final grade or learning outcome using Sentiment Analysis package and Statistical analysis methods such as the Spearman's rho (ρ), Kendall's tau (r), and Kruskal-Wallis H-test in R. the results shows that while in overall the emotional valence of the students do not influence or determine the outcome of their study or final average grade (p>0.05). When considering the analyzed comments: Pre- and Post- Course; it found that emotional scores (valence) expressed by the students in the Post-Course are more closely related or l

关键词： Text mining Sentiment analysis Technological innovation Correlation Statistical analysis Feature extraction Software Libraries Appraisal Text processing

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A Comprehensive Analysis of Secure Attack Detection on Virtual Machines in Cloud Computing

A Comprehensive Analysis of Secure Attack Detection on Virtu...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Naveen Kumar Adalagere Nemirajaiah Channa Krishna Raju Department of Computer Science and Engineering Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumakuru India Department of Artificial Intelligence and Machine Learning Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumakuru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Cloud computing provides various computing services including processing power, storage, networking, software, analytics, and databases. A Virtual Machine (VM) is a software-based simulation of a physical computer that runs an operating system and applications. the VM can run multiple functions on a single physical machine. Secure attack detection on VMs utilizes implementation techniques to detect and monitor malicious activities in the cloud environment. this detection involves Intrusion Detection Systems (IDS), anomaly detection methods, and real-time monitoring tools that analyse network traffic, system logs, and user behaviour. Optimization techniques are used to enhance the efficiency of the detection models. Swarm-based algorithms such as Intelligent Water Drop (IWD), Enhanced Shark Smell Optimization Algorithm (ESSOA), Brownian movement-centered gravitation search algorithm (BMGSA), Dual Conditional Moth Flame Algorithm (DC-MFA), Bat Optimization Algorithm (BOA), and Ant Colony Algorithm (ACO) are employed for this purpose. Additionally, Evolution-based algorithms such as Multi-objective Whale Optimization algorithm-based Differential Evolution (M-WODE), Gene Expression programming (GEP), Genetic Algorithm (GA), and DragonFly-based Genetic Deep Belief Network (DGDBN) have also been used for anomaly detection. the performance metrics of makespan, energy consumption, Degree of Imbalance (DOI), and execution cost are used to evaluate these approaches.

关键词： Measurement Cloud computing Telecommunication traffic Virtual machines Software Real-time systems Optimization Anomaly detection Monitoring Genetic algorithms

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Enhancing the performance of a multiplayer game by using a parallelizing compiler

Enhancing the performance of a multiplayer game by using a p...

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international conference on Computer Games (CGAMES)

作者： Yasir I. M. Al-Dosary Keiji Kimura Hironori Kasahara Seinosuke Narita Department of Computer Science and Engineering Waseda University Shinjuku Tokyo Japan

Video Games have been a very popular form of digital entertainment in recent years. they have been delivered in state of the art technologies that include multi-core processors that are known to be the leading contributor in enhancing the performance of computer applications. Since parallel programming is a difficult technology to implement, that field in Video Games is still rich with areas for advancements. this paper investigates performance enhancement in Video Games when using parallelizing compilers and the difficulties involved in achieving that. this experiment conducts several stages in attempting to parallelize a well-renowned sequentially written Video Game called ioquake3. First, the Game is profiled for discovering bottlenecks, then examined by hand on how much parallelism could be extracted from those bottlenecks, and what sort of hazards exist in delivering a parallel-friendly version of ioquake3. then, the Game code is rewritten into a hazard-free version while also modified to comply with the Parallelizable-C rules, which crucially aid parallelizing compilers in extracting parallelism. Next, the program is compiled using a parallelizing compiler called OSCAR (Optimally Scheduled Advanced Multiprocessor) to produce a parallel version of ioquake3. Finally, the performance of the newly produced parallel version of ioquake3 on a Multi-core platform is analyzed. the following is found: (1) the parallelized game by the compiler from the revised sequential program of the game is found to achieve a 5.1 faster performance at 8-threads than original one on an IBM Power 5+ machine that is equipped with 8-cores, and (2) hazards are caused by thread contentions over globally shared data, and as well as thread private data, and (3) AI driven players are represented very similarly to Human players inside ioquake3 engine, which gives an estimation of the costs for parallelizing Human driven sessions, and (4) 70% of the costs of the experiment is spent in analyzing

关键词： Games Parallel processing Engines Program processors Weapons Resource management Hazards

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