Mechanisms are designed to perform functions in various fields. Often, there is no unique mechanism that performs a well-defined function. For example, vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment. This variability in design makes performance comparison difficult. Additionally, the traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analyses to meet target performance. Recently, AI models have been used to reduce the computational cost of FEA. However, there are limitations in data availability and different analysis environments, especially when transitioning from low-fidelity to high-fidelity analysis. In this paper, we propose a multi-fidelity design framework aimed at recommending optimal types and designs of mechanical mechanisms. As an application, vehicle suspension systems were selected, and several types were defined. For each type, mechanism parameters were generated and converted into 3D CAD models, followed by low-fidelity rigid body dynamic analysis under driving conditions. To effectively build a deep learning-based multi-fidelity surrogate model, the results of the low-fidelity analysis were analyzed using DBSCAN and sampled at 5% for high-cost flexible body dynamic analysis. After training the multi-fidelity model, a multi-objective optimization problem was formulated for the performance metrics of each suspension type. Finally, we recommend the optimal type and design based on the input to optimize ride comfort-related performance metrics. To validate the proposed methodology, we extracted basic design rules of Pareto solutions using data mining techniques. We also verified the effectiveness and applicability by comparing the results with those obtained from a conventional deep learning-based design process.

In this paper, we study the \textit{graph condensation} problem by compressing the large, complex graph into a concise, synthetic representation that preserves the most essential and discriminative information of structure and features. We seminally propose the concept of Shock Absorber (a type of perturbation) that enhances the robustness and stability of the original graphs against changes in an adversarial training fashion. Concretely, (I) we forcibly match the gradients between pre-selected graph neural networks (GNNs) trained on a synthetic, simplified graph and the original training graph at regularly spaced intervals. (II) Before each update synthetic graph point, a Shock Absorber serves as a gradient attacker to maximize the distance between the synthetic dataset and the original graph by selectively perturbing the parts that are underrepresented or insufficiently informative. We iteratively repeat the above two processes (I and II) in an adversarial training fashion to maintain the highly-informative context without losing correlation with the original dataset. More importantly, our shock absorber and the synthesized graph parallelly share the backward process in a free training manner. Compared to the original adversarial training, it introduces almost no additional time overhead. We validate our framework across 8 datasets (3 graph and 5 node classification datasets) and achieve prominent results: for example, on Cora, Citeseer and Ogbn-Arxiv, we can gain nearly 1.13% to 5.03% improvements compare with SOTA models. Moreover, our algorithm adds only about 0.2% to 2.2% additional time overhead over Flicker, Citeseer and Ogbn-Arxiv. Compared to the general adversarial training, our approach improves time efficiency by nearly 4-fold.

The exploration of the lunar poles and the collection of samples from the martian surface are characterized by shorter time windows demanding increased autonomy and speeds. Autonomous mobile robots must intrinsically cope with a wider range of disturbances. Faster off-road navigation has been explored for terrestrial applications but the combined effects of increased speeds and reduced gravity fields are yet to be fully studied. In this paper, we design and demonstrate a novel fully passive suspension design for wheeled planetary robots, which couples for the first time a high-range passive rocker with elastic in-wheel coil-over shock absorbers. The design was initially conceived and verified in a reduced-gravity (1.625 m/s${^2}$) simulated environment, where three different passive suspension configurations were evaluated against steep slopes and unexpected obstacles, and later prototyped and validated in a series of field tests. The proposed mechanically-hybrid suspension proves to mitigate more effectively the negative effects (high-frequency/high-amplitude vibrations and impact loads) of faster locomotion (~1\,m/s) over unstructured terrains under varied gravity fields.

A permanently increasing number of on-board automotive control systems requires new approaches to their digital mapping that improves functionality in terms of adaptability and robustness as well as enables their easier on-line software update. As it can be concluded from many recent studies, various methods applying neural networks (NN) can be good candidates for relevant digital twin (DT) tools in automotive control system design, for example, for controller parameterization and condition monitoring. However, the NN-based DT has strong requirements to an adequate amount of data to be used in training and design. In this regard, the paper presents an approach, which demonstrates how the regression tasks can be efficiently handled by the modeling of a semi-active shock absorber within the DT framework. The approach is based on the adaptation of time series augmentation techniques to the stationary data that increases the variance of the latter. Such a solution gives a background to elaborate further data engineering methods for the data preparation of sophisticated databases.

If you happen to fall outside that specified range, navigating the internet, your community, even your own home, can become exponentially more difficult. But it doesn't have to be this way, argues artist, writer and design researcher Sara Hendren. In her new book, What Can a Body Do, Hendren examines the challenges that people with disabilities face on a daily basis in a world that often doesn't take their needs into account and shows that more inclusive design -- from cybernetic prosthetic arms and more accessible city streets to tactile doorbells for the deaf -- isn't just possible, it's already practical. In the excerpt below, Hendren looks at the Jaipur Foot, an unpowered, low-cost prosthetic that has helped nearly two million lower leg amputees in India and other countries regain their ability to walk. From WHAT CAN A BODY DO: How We Meet the Built World by Sara Hendren published on August 18, 2020 by Riverhead, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC.

The obstacle course was a series of speed bumps in a parking lot at the headquarters of ClearMotion, a supplier of high-tech chassis parts for production cars. The challengers were a late-model Mercedes-Benz and a 2016 BMW 535i equipped with the company's technology -- an electrically powered hydraulic device meant to complement the venerable shock absorber and keep the passenger compartment as level as possible. ClearMotion's technology greatly smoothed the way, significantly reducing not just the movement up and down, but also the right-left lurch from bumps on either side. And while the system doesn't make speed bumps obsolete, its goal is to become the kind of system that car owners won't be able to live without once self-driving technology turns them from drivers into passengers. Shakeel Avadhany, the founder and chief executive of ClearMotion, said he had been inspired by the ride in Japanese bullet trains, which can reach 200 m.p.h. with little sensation of movement.

The days of eagerly waiting for the postman to deliver your package could soon be a thing of the past. Amazon has patented a bizarre new system that adds a parachute to a shipping label. The device could help to make sure that packages delivered by drone or other airborne crafts make a soft landing. Amazon has patented a bizarre new system which appears to be shipping label with a built in parachute. Amazon's patent shows a shipping label that conceals a parachute as well as a system of cords and a harness to keep the package in place.

Wearing a headband of replica Tibetan prayer flags, Scott Jurek meets me in a park in Boulder, Colorado to demonstrate proper trail-running technique. This isn't just jogging down a paved suburban street, but racing along forest and mountain trails that would tax even an unhurried hiker. Last year Jurek ran the entire length of the Appalachian Trail--2,189 miles in 46 days, the equivalent of nearly two marathons every day, over rocky terrain, up and down thousands of feet in elevation. We walk along a dusty trail segment--while an avid trail runner, I figured I shouldn't embarrass myself by trying to keep up. What strikes me when I watch him running for demonstration purposes, though, is that Jurek never looks down, no matter how uneven the ground may be. He politely slows pace to stand still and shoot the breeze, waxing rhapsodic about stride rate and length.