Hasil untuk "Machine design and drawing"

Sara Atef

Accurate short-term traffic delay prediction is essential for effective intersection management and real-time traffic control. Although deep learning models have shown strong predictive capabilities in traffic forecasting, the influence of input feature configuration on prediction performance remains insufficiently understood. This study investigates how different feature groups affect short-term delay prediction at an urban roundabout using high-resolution, approach-level traffic data collected at one-minute intervals. Five feature scenarios are evaluated, ranging from temporal indicators only (S0) to a comprehensive feature set combining historical delay, operational traffic indicators, demand measurements, and temporal context (S4). Two recurrent neural network architectures, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are examined under two forecasting horizons (1-min and 5-min ahead). To ensure robustness, each configuration is trained through repeated runs and evaluated using statistical significance analysis. Results show that the temporal-only baseline produces the largest prediction errors (MAE ≈ 22.5 s), while scenarios incorporating operational traffic indicators significantly improve prediction accuracy. The full feature configuration (S4) achieves the best performance for the 1-min horizon, reaching MAE values of 17.24 s and 17.22 s for GRU and LSTM, respectively. For the 5-min horizon, prediction errors increase and performance differences between feature scenarios become smaller. Additional experiments across multiple approaches confirm the general consistency of the proposed framework, while hyperparameter sensitivity analysis indicates limited dependence on model capacity. Overall, the findings highlight the importance of operational traffic indicators—particularly queue dynamics and stop patterns—for reliable short-term delay forecasting and provide practical guidance for designing efficient real-time traffic prediction systems.

Hiromu Mori, Takayuki Tanaka, Akihiko Murai et al.

Abstract We are currently developing a suit that assists human running motion based on the Smart Suit (SS). The SS is a wearable assistive device that intervenes in trunk kinetic chain movements by linking trunk rotation to hip flexion and extension through elastic belts. By intervening in the trunk kinetic chain, our goal is to enhance stiffness, thereby improving running economy and speed. The purpose of this study is to acquire fundamental insights into the changes in leg and trunk rotational stiffness induced by the SS, as well as the underlying mechanisms. We defined the SS torque intervention rate $$p_{ss}$$ p ss , which focuses on the magnitude of force, and the peak time difference index, $$e_{lag}$$ e lag , between SS and humans, which focuses on the timing of force, and analyzed the relationship between the stiffness change rate due to SS wearing. The results of the analysis suggest that SS affects human running sensitively rather than mechanically. We also confirmed that the degree of adaptation to SS, assessed by the timing gap between SS exertion and human exertion, contributes significantly to changes in trunk rotation stiffness.

Keina Kitaura, Takashi Kusaka, Takumi Honda et al.

Abstract A model-based approach using tile-based models was developed to estimate the depth and size of voids within exterior walls. However, during field applications with portable devices, antenna misalignment has been identified as a key factor reducing the accuracy of these estimations. To address this issue, an index was proposed to evaluate measurement uncertainty through model-based analysis, enhancing data accuracy. Additionally, data accuracy was improved by incorporating labeling from electromagnetic radar and hammering inspection results. The efficacy of the proposed method was validated through data analysis from building exterior wall inspections at construction sites, followed by comparisons of the estimated results. Notably, when classifying tiles as healthy or defective, linear separations in void depth and size increased by 0.549, while separation in void size increased by 0.522.

Md Ibrahim Ibne Alam, Ankur Senapati, Anindo Mahmood et al.

Internet service providers (ISPs) need to connect with other ISPs to provide global connectivity services to their users. To ensure global connectivity, ISPs can either use transit service(s) or establish direct peering relationships between themselves via Internet exchange points (IXPs). Peering offers more room for ISP-specific optimizations and is preferred, but it often involves a lengthy and complex process. Automating peering partner selection can enhance efficiency in the global Internet ecosystem. We explore the use of publicly available data on ISPs to develop a machine learning (ML) model that can predict whether an ISP pair should peer or not. At first, we explore public databases, e.g., PeeringDB, CAIDA, etc., to gather data on ISPs. Then, we evaluate the performance of three broad types of ML models for predicting peering relationships: tree-based, neural network-based, and transformer-based. Among these, we observe that tree-based models achieve the highest accuracy and efficiency in our experiments. The XGBoost model trained with publicly available data showed promising performance, with a 98% accuracy rate in predicting peering partners. In addition, the model demonstrated great resilience to variations in time, space, and missing data. We envision that ISPs can adopt our method to fully automate the peering partner selection process, thus transitioning to a more efficient and optimized Internet ecosystem.

Soaad Hossain, James Rasalingam, Arhum Waheed et al.

With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry.

Rimma Dzhusupova, Richa Banotra, Jan Bosch et al.

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule‐based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI‐based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost‐benefit analysis and potential scale‐up of the developed software. Our goal is to share the successful experience of AI‐based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry.

N. Lettieri, Alfonso Guarino, Rocco Zaccagnino et al.

Kasemset Chompoonoot, Phuruan Kanokrot, Opassuwan Takron

Shallot is one of several horticultural products exported from Thailand to various countries. Despite an increase in shallot prices over the years, farmers face challenges in price forecasting due to fluctuations and other relevant factors. While different forecasting techniques exist in the literature, there is no universal approach due to varying problems and datasets. This study focuses on predicting shallot prices in Northern Thailand from January 2014 to December 2020. Traditional and machine learning models, including ARIMA, Holt-Winters, LSTM, and ARIMA-LSTM hybrids, are proposed. The LSTM model considers temperature and rainfall as influencing factors. Evaluation metrics include RMSE, MAE, and MAPE. Results indicate that the ARIMA-LSTM hybrid model performs best, with RMSE, MAE, and MAPE values of 10.275 Baht, 8.512 Baht, and 13.618%, respectively. Implementing this hybrid model can provide shallot farmers with advanced price information for informed decision-making regarding cultivation expansion and production management.

Muneer Roaa Mohammed, Idzikowski Adam, Al-Zubiedy Ali

This work is a Copper oxide (CuO) thin films were effectively produced using cold spray technique. The process take place in an inert gas (helium) without using catalyst. Nano CuO was deposited on a glass slide, using helium as carrier gas heated to 100, 200, 300, and 400 °C, respectively on heated glass substrates at 300°C. The effect of structural and electrical properties was examined at each temperature for each film. AFM images show that the CuO thin film have different diameters ranging from 80 to 600 nm, and low surface roughness about 20.9 nm. The measured value of copper oxide resistivity was found to be decrease very much with the increasing temperature. All the result showed that copper oxide is suitable material for photovoltaic applications. This research is part of a larger work for the solar cells industry. Therefore, the aim of this research is to study the electrical properties of solar cells in the primary stages of manufacturing from available materials at low costs.

Kalchenko Volodymyr, Kolohoida Antonina, Pasov Gennadiy et al.

Problem. The development of automotive technology for various purposes and the improvement of existing car models, requires a fast and flexible design process. Spatial models of details and nodes are the starting points for design, and the necessary design documentation is performed on their basis. When manufacturing parts on CNC machines, it is necessary to quickly obtain a control program that will increase the accuracy of the product by ensuring the optimal trajectory of the mutual movement of the part and the tool. Therefore, the creation of a comprehensive methodology for the design of car parts, their basic inspection and obtaining design documentation is an important technical task. Goal. The main goal of the work is to determine the rational sequence and basic principles of computer-aided design, research and manufacturing of car and engine parts, and the development of mathematical spatial models of surfaces in order to optimize control programs for CNC machines. Methodology. The approaches adopted in the work for solving the set goal are based on general design principles. The methods of conducting static and dynamic calculations, as well as spatial mathematical modeling of the processes of manufacturing parts were also used. Results. A comprehensive methodology for designing parts of engines and cars has been developed. The main principles of creating spatial models have been determined in order to achieve their flexibility and simple editing. Methods of automating the development of drawings and design documentation are described. The software options for the primary verification of static strength and frequency analysis are considered. A calculation program has been developed for constructing transient and amplitude-phase-frequency characteristics. The mathematical spatial modeling of typical surfaces of the camshaft is provided with the aim of further research and optimization of the development of a control program for processing on a CNC machine tool. Originality. The defined basic principles of designing parts of engines and cars provide an opportunity to create a flexible spatial model and more fully automate the process of drawing up technical documentation. The developed mathematical spatial models of the supporting and working surfaces of the camshaft make it possible to write a control program with the determination of the optimal trajectory of the mutual movement of the part and the tool. Practical value. The obtained results can be recommended when designing car parts and assemblies.

Hao Chen, Thomas Barthel

Tensor networks developed in the context of condensed matter physics try to approximate order-$N$ tensors with a reduced number of degrees of freedom that is only polynomial in $N$ and arranged as a network of partially contracted smaller tensors. As we have recently demonstrated in the context of quantum many-body physics, computation costs can be further substantially reduced by imposing constraints on the canonical polyadic (CP) rank of the tensors in such networks [arXiv:2205.15296]. Here, we demonstrate how tree tensor networks (TTN) with CP rank constraints and tensor dropout can be used in machine learning. The approach is found to outperform other tensor-network-based methods in Fashion-MNIST image classification. A low-rank TTN classifier with branching ratio $b=4$ reaches a test set accuracy of 90.3\% with low computation costs. Consisting of mostly linear elements, tensor network classifiers avoid the vanishing gradient problem of deep neural networks. The CP rank constraints have additional advantages: The number of parameters can be decreased and tuned more freely to control overfitting, improve generalization properties, and reduce computation costs. They allow us to employ trees with large branching ratios, substantially improving the representation power.

Sam van der Poel, Dakotah Lambert, Kalina Kostyszyn et al.

Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification. This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular languages. Different kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or monomial expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. Finally, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that performance depends significantly on the kind of test set, the class of language, and the neural network architecture.

Kevin Ma, Daniele Grandi, Christopher McComb et al.

Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty. We experiment with prompt engineering and find that leveraging few-shot learning can lead to the generation of solutions that are more similar to the crowdsourced solutions. These findings provide insight into the quality of design solutions generated with LLMs and begins to evaluate prompt engineering techniques that could be leveraged by practitioners to generate higher-quality design solutions synergistically with LLMs.

Julian Togelius, Ahmed Khalifa, Sam Earle et al.

Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing games; it is also about generating game content, modeling players, and many other applications. Many of these applications pose interesting problems for EML. We will structure this chapter on EML for games based on whether evolution is used to augment machine learning (ML) or ML is used to augment evolution. For completeness, we also briefly discuss the usage of ML and evolution separately in games.

Anjali Singh

Abstract: Lately, the business and dress have shown quick improvement in design. An online business website with a wide scope of choices requires a decent method for sending, sort, and successfully convey items and data to clients. Online display is a program that is utilized to feature and sell clothing at closeout, paying little heed to identity, orientation, or different qualities. Individuals can join online to become individuals, and everybody can send an advanced duplicate of their work by classification. They can organize their drawings at sell off or at assigned costs. Every client can set up their own store to get their number one plans and access them with next to no issues. Picture based directing frameworks (FRSs) draw in an ever-increasing number of individuals to quick design retailers since it permits shoppers to shop. With the progression of innovation, the best-in-class innovation offers numerous amazing open doors for picture handling, investigation, sequencing, and detachment. In spite of the numerous open doors, the quantity of investigations on this theme is little. Potential investigations won't think about the plan of the innovation and the separating strategies. As the creators know, this is the primary point in the exploration of the new plan of the drawing framework and the strategy for separating. Moreover, this audit inspects the different potential ways that can be executed in the future to give a model. This article will help specialists, researchers, and professionals intrigued by AI, PC vision, and dress deals to figure out the idea of various advising frameworks.

V. Popov, G. Muller-Kamskii, A. Kovalevsky et al.

Mr. Zaki Shaikh, Mr. Viraj Tilekar, Mr. Atharva Pawar et al.

Research in biometrics has grown substantially in recent years with an increasing number of applications. One of the most important applications is healthcare. Identification of the appropriate biomarkers with respect to particular fitness problems and detection of the same is of paramount significance for the improvement of medical decision assistance systems. For the sufferers laid low with Parkinson's Disease (PD), it's been duly found that impairment in the handwriting is directly proportional to the severity of the sickness. Also, the velocity and pressure implemented to the pen while sketching or writing something also are much lower in sufferers affected by Parkinson's disorder. Therefore, successfully figuring out such biomarkers accurately and precisely at the onset of the disorder will result in a better medical diagnosis. Therefore, a system is designed for studying Spiral drawing patterns and wave drawing patterns in sufferers affected by Parkinson's disease. With the help of various Machine Learning Algorithms, we will be able to analyse the spiral pattern and wave pattern and check whether the person is suffering from Parkinson’s Disease or not.

Casey J. Den Ouden, Paul S. Wills, Lucas Lopes et al.

While robotics have been widely used in many agricultural practices such as harvesting, seeding, cattle monitoring, etc., aquaculture farming is an important, fast-growing sector of agriculture that has not seen significant adoption of advanced technologies such as robotics and the Internet of Things (IoT). In particular, dissolved oxygen (DO) monitoring, a practice in pond aquaculture essential to the health of the fish crops, remains labor-intensive and time-consuming. The Hybrid Aerial Underwater robotiCs System (HAUCS) is an IoT framework that aims to bring transformative changes to pond aquaculture. This paper focuses on the latest development in the HAUCS mobile sensing platform and field deployment. To address some shortcomings with the current implementation, the development of a novel rigid Kirigami-based robotic extension subsystem that can expand the functionality of the HAUCS platform is also being discussed.

Raphael Mieth, Frank Gauterin, Felix Pauli et al.

Vehicle drive systems are often oversized for common customer operation in order to cover the high demands of rare driving events such as towing a trailer, high acceleration or steep inclines. This high torque and power requirement affects the efficiency map and the highest efficiency is around the area of increased torque and speed. However, in everyday use, drive systems are mostly driven by customers at low speed and load, and therefore are not operating in the most efficient area. Designing a drive system that only covers the area of highest customer operation can increase efficiency by moving the sweet spot of efficiency to the relevant area, and thus reduce energy consumption. Therefore, customer data need to be analyzed in order to identify customer requirements and to localize the area of greatest operation. The method presented in this paper analyzes customer data in order to identify design-relevant parameters for a customer-specific drive system design. The available customer data results from event-based counts and are submitted as a statistical frequency distribution. These statistics are compared with discrete time series recorded during test drives in order to derive representative time series that correspond to customer behavior. By applying the time frame-based load analysis to these relevant time series, the desired design-relevant parameters are pointed out.

Christian Rudolf Albrecht, Jenny Behre, Eva Herrmann et al.

Vehicle self-localization is one of the most important capabilities for automated driving. Current localization methods already provide accuracy in the centimeter range, so robustness becomes a key factor, especially in urban environments. There is no commonly used standard metric for the robustness of localization systems, but a set of different approaches. Here, we show a novel robustness score that combines different aspects of robustness and evaluate a graph-based localization method with the help of fault injections. In addition, we investigate the influence of semantic class information on robustness with a layered landmark model. By using the perturbation injections and our novel robustness score for test drives, system vulnerabilities or possible improvements are identified. Furthermore, we demonstrate that semantic class information allows early discarding of misclassified dynamic objects such as pedestrians, thus improving false-positive rates. This work provides a method for the robustness evaluation of landmark-based localization systems that are also capable of measuring the impact of semantic class information for vehicle self-localization.