Hasil untuk "Machine design and drawing"

Julian Borowski, Stephan Rudolph

The development of modern vehicles faces an increase in complexity, as well as a need for shorter development cycles and a seamless cross-domain integration. In order to meet these challenges, a graph-based design language which formalizes and automates engineering workflows is presented and applied in a design case study to a Formula Student race car suspension system. The proposed method uses an ontology-based vocabulary definition and executable model transformations to compile design knowledge into a central and consistent design graph. This graph enables the automatic generation of consistent 3D CAD models, domain-specific simulations and suspension kinematic analyses, replacing manual and error-prone tool and data handover processes. The design language captures both the structural and dynamic behavior of the suspension, supports variant exploration and allows for integrated validation, such as 3D collision detection. The study illustrates how graph-based design languages can serve as ‘digital DNA’ for knowledge-based product development, offering a scalable, reusable platform for engineering automation. This approach enhances the digital consistency of data, the digital continuity of processes and the digital interoperability of tools across all relevant engineering disciplines in order to support the validation of early-stage designs and the optimization of complex systems.

Arthur Appel

Janssen Arend, Stamm Marcel, Spangemacher Lars et al.

The objective to increase the transferred power (“heat”) of heat exchangers, here finned heat exchangers, leads to an increase in the heat transfer surfaces through thinner wall thicknesses and thus higher packing densities. This transfer can be further optimized by choosing materials with high heat transfer coefficients like copper. Various joining processes, such as welding or brazing can be used to complete the heat exchanger components. Laser beam brazing has an outstanding advantage: heat is applied only where it is needed to create bonds. The base material does not melt and the heat-affected zone becomes smaller, reducing the risk of mechanical distortion. Laser beam brazing has a disadvantage in terms of accessibility. In dense situations, the laser beam may fail to reach its target because the brazed joints may be hidden. Such “shadowing” means that the process cannot be carried out in ideal conditions, i.e., in the case of a circumferential seam, a full, uninterrupted circular movement may not be possible at an angle of incidence of 45 degrees. If steeper or flatter angles are chosen to achieve accessibility, this will result in uneven heating of the parts being joined, in this case the fin and the tube. The work presented shows the development of such brazing process. Firstly, it has to be established whether interruption and resumption of the laser brazing process will produce a usable result and further studies on heat distortion and process stability will be carried out to ensure the process is suitable for batch production.

Jiahao Chen, Xuan Xu, Jiafu Yang

In order to address the significant nonlinear dynamic characteristics and limited trajectory tracking accuracy of unmanned vehicles under cornering conditions, this paper proposes a trajectory tracking control strategy based on Adaptive Model Predictive Control (AMPC). First, to enhance the accuracy of the vehicle model, an 11-degree-of-freedom vehicle dynamics model is established, incorporating pitch, roll, yaw, rotation around the Z-axis, and wheel-axis rotation. The vehicle motion equations are derived using Lagrangian analytical mechanics. Meanwhile, the tire model is optimized by accounting for the influence of vehicle attitude changes on tire mechanical properties. Based on the principles of nonlinear model predictive control (NMPC) and adaptive control, the AMPC algorithm is developed, its prediction model is constructed, and appropriate control constraints are defined to ensure improved accuracy and stability in trajectory tracking. Finally, simulations under double-lane-change and serpentine driving conditions are conducted using a co-simulation platform involving Carsim and Matlab/Simulink. The results demonstrate that the proposed controller achieves high trajectory tracking accuracy, effectively suppresses vehicle yaw, pitch, and roll motions, and enhances both the smoothness of trajectory tracking and the overall dynamic stability of the vehicle.

Stevie Chancellor, M. Birnbaum, E. Caine et al.

Powered by machine learning techniques, social media provides an unobtrusive lens into individual behaviors, emotions, and psychological states. Recent research has successfully employed social media data to predict mental health states of individuals, ranging from the presence and severity of mental disorders like depression to the risk of suicide. These algorithmic inferences hold great potential in supporting early detection and treatment of mental disorders and in the design of interventions. At the same time, the outcomes of this research can pose great risks to individuals, such as issues of incorrect, opaque algorithmic predictions, involvement of bad or unaccountable actors, and potential biases from intentional or inadvertent misuse of insights. Amplifying these tensions, there are also divergent and sometimes inconsistent methodological gaps and under-explored ethics and privacy dimensions. This paper presents a taxonomy of these concerns and ethical challenges, drawing from existing literature, and poses questions to be resolved as this research gains traction. We identify three areas of tension: ethics committees and the gap of social media research; questions of validity, data, and machine learning; and implications of this research for key stakeholders. We conclude with calls to action to begin resolving these interdisciplinary dilemmas.

Gang Ge, Qian Wang, Yizhou Zhang et al.

In the booming development of flexible electronics represented by electronic skins, soft robots, and human–machine interfaces, 3D printing of hydrogels, an approach used by the biofabrication community, is drawing attention from researchers working on hydrogel‐based stretchable ionotronic devices. Such devices can greatly benefit from the excellent patterning capability of 3D printing in three dimensions, as well as the free design complexity and easy upscale potential. Compared to the advanced stage of 3D bioprinting, 3D printing of hydrogel ionotronic devices is in its infancy due to the difficulty in balancing printability, ionic conductivity, shape fidelity, stretchability, and other functionalities. In this review, a guideline is provided on how to utilize the power of 3D printing in building high‐performance hydrogel‐based stretchable ionotronic devices mainly from a materials’ point of view, highlighting the systematic approach to balancing the printability, printing quality, and performance of printed devices. Various 3D printing methods for hydrogels are introduced, and then the ink design principles, balancing printing quality, printed functions, such as elastic conductivity, self‐healing ability, and device (e.g., flexible sensors, shape‐morphing actuators, soft robots, electroluminescent devices, and electrochemical biosensors) performances are discussed. In conclusion, perspectives on the future directions of this exciting field are presented.

K.M. Kostick-Quenet, S. Gerke

As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML’s human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.

Antonio A. Ginart, Naveen Kodali, Jason Lee et al.

While frontier large language models (LLMs) are capable tool-using agents, current AI systems still operate in a strict turn-based fashion, oblivious to passage of time. This synchronous design forces user queries and tool-use to occur sequentially, preventing the systems from multitasking and reducing interactivity. To address this limitation, we introduce asynchronous AI agents capable of parallel processing and real-time tool-use. Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting, integrated with automatic speech recognition and text-to-speech. Drawing inspiration from the concepts originally developed for real-time operating systems, this work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.

E. Herrewijnen, Meagan B. Loerakker, Marloes Vredenborg et al.

Decision-making aided by Artificial Intelligence in high-stakes domains such as law enforcement must be informed and accountable. Thus, designing explainable artificial intelligence (XAI) for such settings is a key social concern. Yet, explanations are often misunderstood by end-users due to being overly technical or abstract. To address this, our study engaged with police employees in the Netherlands, who are users of a text classifier. We found that for them, usability and usefulness are of great importance in explanation design, whereas interpretability and understandability are less valued. Further, our work reports on how design elements included in machine learning model explanations are interpreted. Drawing from these insights, we contribute recommendations that guide XAI system designers to cater to the specific needs of specialized users in high-stakes domains and suggest design considerations for machine learning model explanations aimed at domain experts.

Brett A. Halperin, Gary Hsieh, E. McElroy et al.

Despite the central role that stories play in social movement-building, they are difficult to sustainably document for many reasons. To explore this challenge, this paper describes the design of a community-based conversational storytelling agent (CSA) to document digital stories of housing insecurity. Building on insights from an ongoing grassroots project, the Anti-Eviction Mapping Project, we share how a study initially focused on CSA-support opened an investigation of the role that artificial intelligence may play in housing justice movements. Drawing from 17 interviews with narrators of housing insecurity experiences and collectors of such stories, we find that collectors perceive opportunities to expand means of documentation with multimedia and multi-language support. Meanwhile, some narrators perceive potential for a CSA to offer therapeutic storytelling experiences and document otherwise unrecorded stories. Yet, CSA encounters also surface perils of machine bias, as well as reduced possibilities of human connections and relations.

Biadacz Renata

The research problem revolves around an attempt to answer the questions: “Are enterprises from the SME sector interested in implementing strategic management accounting instruments, including Kaizen Costing? Is Kaizen Costing more widely used in SMEs operating in Poland?" The aim of the article is therefore to highlight the importance of Kaizen and Kaizen Costing and to draw attention to how much support these solutions can be for SMEs in the current social, economic, and environmental conditions. The article presents the results of surveys conducted in this regard.

Hosik Lee, Sangyoon Oh, Kyung-Soo Kim et al.

Considering the nonlinear dynamics, this paper devises an advanced position trajectory tracking controller with a model-free filter for two-wheeled vehicle (TWV) applications. The proposed technique preserves a simple structure in the form of the proportional–integral (PI) controller involving the model-free filter and nonlinearly structured feedback gains, which make the following contributions: (a) the proposed filter smooths the position and yaw angle measurements according to the first-order convergence rate without any model information; and (b) the PI control with the nonlinearly structured feedback gains robustly stabilizes the position and yaw angle errors along the desired first-order system to accomplish the trajectory tracking mission, which is obtained by the pole-zero cancellation (PZC) in the presence of modeling errors. MATLAB/Simulink was used to emulate the resulting feedback system and validate the effectiveness of the proposed technique.

El Marzougui Mustapha, Messaoudi Najat, Dachry Wafaa et al.

Demand-Driven Material Requirements Planning (DDMRP) is an emerging inventory management approach that has garnered significant attention from academia and industry. Numerous recent studies have highlighted the advantages of DDMRP compared to traditional methods such as material requirement planning (MRP), Theory of constraint (TOC), and Kanban. However, the performance of DDMRP relies on several parameters that affect its effectiveness. Parameterization models and the optimization of control variables have significantly contributed to the field of inventory management and have proven to be effective and practical in addressing challenges by providing a structured approach to handling complex variables and constraints. This paper introduces an innovative parameterization model that leverages deep reinforcement learning (DRL) to parameterize a DDMRP system in the face of uncertain demand. The main objective is to dynamically determine the optimal values for the variability and lead time factors within the DDMRP framework, to maximize customer service levels and optimize inventory efficiency. The results of this study emphasize the effectiveness of DRL as an automated decision-making approach for controlling DDMRP parameters. Additionally, the findings highlight the potential for enhancing the performance of the DDMRP approach, particularly in terms of on-time delivery (OTD) and average on-hand inventory (AOHI) by adjusting the variability and lead-time factors.

Sergii Panchenko, Juraj Gerlici, Alyona Lovska et al.

This research highlights the results of a comprehensive study of the efficiency of modernized brake systems operation of freight wagons. The inspection of the modernized elements of the lever brake system of bogies and the measurement of the wear parameters of composite brake pads during each cycle of the experimental wagons in the interval of mileage from 2.1 to 197.8 thousand km were carried out. A statistical approach was used to study the wear parameters of brake pads of modernized bogies brake systems determined during operational studies. This allowed appropriate dependencies of brake pad wear to be obtained. Based on the research results, a regression model was developed. This makes it possible to predict the residual wear resource of composite brake pads with modernized braking systems of bogies for the entire inter-repair period of operation of freight wagons guaranteed by the wagon repair company. The peculiarity of the model is that it considers the total and additional mileage of the freight wagon. This makes it possible to more accurately predict the residual lifetime of composite brake pads. It was established that, under the condition of uniform wear of brake pads, the average mileage of a freight wagon during the use of modernized brake systems of bogies can reach up to 284.57 thousand km, which increases the resource of composite pads’ wear approximately by 2.59 times. The generated model was verified by the F-criterion. Approbation of experimental devices for uniform wear of composite pads in operation established that measures to modernize brake systems of freight wagons ensure the reliable and efficient operation of the brake lever system as a whole.

Ivan S. Maksymov

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.

Krishna Pillutla, Galen Andrew, P. Kairouz et al.

We present a rigorous methodology for auditing differentially private machine learning algorithms by adding multiple carefully designed examples called canaries. We take a first principles approach based on three key components. First, we introduce Lifted Differential Privacy (LiDP) that expands the definition of differential privacy to handle randomized datasets. This gives us the freedom to design randomized canaries. Second, we audit LiDP by trying to distinguish between the model trained with $K$ canaries versus $K - 1$ canaries in the dataset, leaving one canary out. By drawing the canaries i.i.d., LiDP can leverage the symmetry in the design and reuse each privately trained model to run multiple statistical tests, one for each canary. Third, we introduce novel confidence intervals that take advantage of the multiple test statistics by adapting to the empirical higher-order correlations. Together, this new recipe demonstrates significant improvements in sample complexity, both theoretically and empirically, using synthetic and real data. Further, recent advances in designing stronger canaries can be readily incorporated into the new framework.

Sejin Paik

Abstract In an era characterized by the widespread use of algorithmic systems and platforms in news production and distribution practices, the ethical practices of journalists face significant challenges. Drawing on Floridi’s onlife framework, this study aims to shed light on journalist-machine interactions and explores new ways to rearchitect journalism ethical standards through an integrative, object-oriented approach. In-depth interviews with local news workers throughout the U.S. reveal a range of issues related to decontextualization in algorithmic platform design, the hidden price of platform partnerships, and the growing reliance on automated tools that foreshadow ethical issues to come. These algorithmically-induced challenges appear to be particularly pronounced in local newsrooms, highlighting the disproportionate impact of algorithmic systems on under-served media sectors. Discussions are made around the constant push-and-pull over editorial power dynamics apparent in local news workers’ use of algorithmic systems. A distributed responsibility model is proposed as a practical way to hold multiple actors, including both humans and algorithms, accountable for journalism’s ethical standards in the algorithmic era.

Satoko Honda, Ryuki Tanaka, Guren Matsumura et al.

Flexible sensors that can be attached to the body to collect vital data wirelessly enable real‐time, early‐stage diagnosis for human health management. Wearable sweat sensors have received considerable attention for real‐time physiological monitoring. Unlike conventional methods that require blood‐drawing in a clinic, sweat analyses may enable noninvasive tracking of health conditions for early‐stage diagnosis. Even though a variety of studies to monitor metabolites and other substances have been conducted, automatic, continuous, long‐term, simultaneous monitoring of perspiration rate and electrolytes, which are important parameters in dehydration, has yet to be achieved because of challenges related to sensor design. Here a wireless, wearable, integrated, microfluidic sensor system that can continuously measure these parameters in real‐time for prolonged periods are presented. The proposed sensors are systematically characterized, and machine learning is used to predict device tilt angle to calibrate sensor output signals. Using the sensor design to form a water droplet in a fluidic channel, high‐volume perspiration rate is continuously monitored for more than 7000 s (total sweat volume >170 µL). By testing 10 subjects, physiological responses to ingestion of a sports drink are confirmed by measuring perspiration rhythm changes extracted from real‐time, continuous sweat impedance and rate.

Yangyang Pei, Jianing An, Ke Wang et al.

Flexible electronics have gained great attention in recent years owing to their promising applications in biomedicine, sustainable energy, human-machine interaction, and toys for children. Paper mainly produced from cellulose fibers is attractive substrate for flexible electronics because it is biodegradable, foldable, tailorable, and light-weight. Inspired by daily handwriting, the rapid prototyping of sensing devices with arbitrary patterns can be achieved by directly drawing conductive inks on flat or curved paper surfaces; this provides huge freedom for children to design and integrate "do-it-yourself (DIY)" electronic toys. Herein, viscous and additive-free ink made from Ti3 C2 TX MXene sediment is employed to prepare disposable paper electronics through a simple ball pen drawing. The as-drawn paper sensors possess hierarchical microstructures with interweaving nanosheets, nanoflakes, and nanoparticles, therefore exhibiting superior mechanosensing performances to those based on single/fewer-layer MXene nanosheets. As proof-of-concept applications, several popular children's games are implemented by the MXene-based paper sensors, including "You say, I guess," "Emotional expression," "Rock-Paper-Scissors," "Arm wrestling," "Throwing game," "Carrot squat," and "Grab the cup," as well as a DIY smart whisker for a cartoon mouse. Moreover, MXene-based paper sensors are safe and disposable, free from producing any e-waste and hazard to the environment.

Renkai Ma, Yubo Kou

To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.