Browse Topic: Customization

Items (134)
Mechanical component failure often heralds superficial damage indicators such as color alteration due to overheating, texture degradation like rusting or false brinelling, spalling, and crack propagation. Conventional damage assessment relies heavily on visual inspections performed by technicians, a practice bogged down by time constraints and the subjective nature of human error. This research paper delves into the integration of deep learning methodologies to revolutionize surface damage evaluation, addressing significant bottlenecks in diagnostic precision and processing efficiency. We detail the end-to-end process of developing an intelligent inspection system: selecting appropriate deep learning architectures, annotating datasets, implementing data augmentation, optimizing hyperparameters, and deploying the model for widespread user accessibility. Specifically, the paper highlights the customization and assessment of state-of-the-art models, including EfficientNet B7 for
Cury, RudonielGioria, GustavoChandrasekaran, Balaji
ABSTRACT A customized approach to Pseudo Random Number Generation (PRNG) is developed specifically for the highly parallelizable sensor models in the ground vehicle autonomy application domain. The work considers three desirable attributes (namely quality, efficiency and determinism). Furthermore, the application demands high fanout (1:1Million+) seeding of traditional PRNGs. An approach using hash functions to generate the seeds for the PRNGs, each of which generates a small (i.e. 20) run of numbers, to handle determinism is investigated. Quality and efficiency are evaluated for multiple combinations of hash functions and PRNGs and a pareto front is created. Quality assessments were performed using industry standard testing suites (TestU01 and PractRand) and efficiency of various hash, PRNG, and batch size combinations was benchmarked on Windows/x64, ARM and NVIDIA/CUDA architectures. Citation: J. Kaniarz, M. Brudnak, “Evaluation of Hash-Seeded Pseudo-Random Number Generators in
Kaniarz, JohnBrudnak, Mark
Nanosensors are transforming the field of disease detection by offering unprecedented sensitivity, precision, and speed in identifying biomarkers associated with various health conditions. These tiny sensors, often built at the molecular or atomic scale, can detect minute changes in biological samples, enabling the early diagnosis of diseases such as cancer, infectious diseases, and neurological disorders
Researchers have developed SPINDLE, a pioneering robotic rehabilitation system. Combining virtual reality (VR) with customized resistance training, SPINDLE offers personalized therapy to enhance strength and dexterity for activities of daily living (ADLs). Its adaptability and potential for home use represent a major advancement in tremor rehabilitation, with broader healthcare implications
Imagine a portable 3D printer you could hold in the palm of your hand. The tiny device could enable a user to rapidly create customized, low-cost objects on the go, like a fastener to repair a wobbly bicycle wheel or a component for a critical medical operation
Chocolate-flavored pills for children who hate taking medicine. Several drugs combined into one daily pill for seniors who have trouble remembering to take their medications. Drugs printed at your local pharmacy at personalized dosages that best suit your health needs. These are just a few potential advantages of 3D drug printing, a new system for manufacturing drugs and treatments on-site at pharmacies, healthcare facilities, and other remote locations
North America's first electric, fully integrated custom cab and chassis refuse collection vehicle - slated for initial customer deliveries in mid-2024 - is equipped with a standard advanced driver-assistance system (ADAS). “A typical garbage truck uses commercial off-the-shelf active safety technologies, but the electrified McNeilus Volterra ZSL was purpose-built with active safety technologies to serve our refuse collection customer,” said Brendan Chan, chief engineer for autonomy and active safety at Oshkosh Corporation, McNeilus' parent company. “We wanted to make the safest and best refuse collection truck out there. And by using cloud-based simulation, we could accelerate the development of ADAS and other technologies,” Chan said in an interview with Truck & Off-Highway Engineering during the 2024 dSPACE User Conference in Plymouth, Michigan
Buchholz, Kami
Aerospace is an industry where competition is high and the need to ensure safety and security while managing costs is foremost. Stakeholders, who gain the most by working together, do not necessarily trust each other. Changing backbone technologies that drive enterprise systems and secure historical records does not happen quickly (if at all). At best, businesses adapt incrementally, building customized applications on top of legacy systems. The complexity of these legacy systems leads to duplication of efforts and data storage, making them very inefficient. Technology that augments, rather than replaces, is needed to transform these complex systems into efficient, digital processes. Blockchain technology offers collaborative opportunities for solving some of the data problems that have long challenged the aerospace industry. The industry has been slow to adopt the technology even though experts agree that it has real potential to revolutionize the global supply chain—including
Walthall, RhondaDavid, AharonFarell, JamesHann, RichardJohansen, Tor A.
Advanced Driver Assistance Systems (ADAS) are becoming common on passenger cars and pickup trucks. Accordingly, the manufacturers and installers of aftermarket equipment for these vehicles have an interest in confirming the functionality of ADAS when their equipment is put in place. However, there is very little publicly available information on the effect of aftermarket components on original equipment ADAS. To address this deficiency, a research program was undertaken in which a 2022 Chevrolet Silverado 1500 light truck was tested in four different hardware configurations, including stock as well as three modified conditions. Aftermarket modifications to the vehicle consisted of increased tire diameters, a level kit, and two different lift kits. A series of physical tests were carried out to evaluate the ADAS performance of the vehicle with modifications. Tests were designed to investigate differences in driver alerts including lane departure warnings, forward collision warnings
Bastiaan, JenniferMuller, MikeMorales, Luis
With 40 years of experience to its name, Sunview Patio Doors Ltd. (acquired by Novatech Group in 2021), has solved one of the industry’s top challenges: meeting customers’ increased demands for faster and better services, while providing an option for product customization. Its ability to adopt digital technology allowed the company to satisfy its customers and compete globally in the marketplace
A team has developed medical adhesives that are not only safe for human use but also customizable for different organs. The researchers used mussel-derived adhesive proteins to develop customized underwater bio-adhesive patches (CUBAP
A team of Rice University engineers has launched a first-of-its-kind, open-source software that constructs and uses personalized computer models of how individual patients move to optimize treatments for neurologic and orthopedic mobility impairments
For 2D surface temperature monitoring applications, a variant of Electrical Impedance Tomography (EIT) was evaluated computationally in this study. Literature examples of poor sensor performance in the center of the 2D domains away from the side electrodes motivated these efforts which seek to overcome some of the previously noted shortcomings. In particular, the use of ‘sensing skins’ with novel tailored baseline conductivities was examined using the EIDORS package for EIT. It was found that the best approach for detecting a temperature hot spot depends on several factors such as the current injection (stimulation) patterns, the measurement patterns, and the reconstruction algorithms. For well-performing combinations of these factors, customized baseline conductivities were assessed and compared to the baseline uniform conductivity. It was discovered that for some EIT applications, a tailored distribution needs to be smooth and that sudden changes in the conductivity gradients should
Sjöberg, Magnus
A research team has designed a fall-risk assessment system that enables doctors to create personalized risk-management strategies for patients based on their individual movement patterns at home
The collaborative process can foster the kind of creativity and ingenuity that leads to the most innovative medical technology. In this Expert Insight, Nadia Hajjar, Category Manager for Life Sciences at Porex, provides perspective on the ins and outs of designing custom components, including leveraging customization to decrease device complexity and reduce component costs. Hajjar spoke with Medical Design Briefs about how the company focuses on innovation by identifying new materials even within their traditional material platforms and how deeper relationships with suppliers reduces the complexity and adds speed to the design process
Wearable wireless biosensors are an integral part of digital healthcare and monitoring. Commonly used chipless resonant antenna-based biosensors are simple and affordable but have limited applicability due to their low sensitivity. Now, researchers from Japan have developed a novel, wireless, parity–time symmetry-based bioresonator that can detect minute concentrations of tear glucose and blood lactate. This highly sensitive, tunable, and robust bioresonator has the potential to have a great impact on personalized health monitoring and digitized healthcare systems
Imagine if one test could tell you whether you were protected from the latest COVID-19 variant. A partnership between state-of-the-art robotics and cutting-edge medical research will allow doctors at the University of Texas Medical Branch (UTMB) to give their patients more personalized and useful information
The two-wheel system design is widely used in various mobile tools, such as remote-control vehicles and robots, due to its simplicity and stability. However, the specific wheel and body models in the real world can be complex, and the control accuracy of existing algorithms may not meet practical requirements. To address this issue, we propose a double inverted pendulum on mobile device (DIPM) model to improve control performances and reduce calculations. The model is based on the kinetic and potential energy of the DIPM system, known as the Euler-Lagrange equation, and is composed of three second-order nonlinear differential equations derived by specifying Lagrange. We also propose a stable feedback control method for mobile device drive systems. Our experiments compare several mainstream reinforcement learning (RL) methods, including linear quadratic regulator (LQR) and iterative linear quadratic regulator (ILQR), as well as Q-learning, SARSA, DQN (Deep Q Network), and AC. The
Yu, ZhenghongZhu, Xuebin
Upper-limb forequarter amputations that involve the removal of the entire arm and scapula require highly customized prosthetic devices that are expensive but yet usually underutilized due to their high maintenance and low comfort levels. At the same time, while cosmetic prostheses — artificial limbs that provide patients the appearance of a pre-amputated body part — have a higher rate of continuous use, they have limitations in functional use
Made-to-order footwear, clothing, and jewelry. Patient-specific replacement joints and medication tailored to their age and weight. Bespoke bicycles, and office chairs that fit like the proverbial glove. These are just a few examples of the customized or personalized products that consumers have come to demand in recent years. How are manufacturers able to fulfill these expectations? In addition, how can they keep the final price from being driven upward by the immense complexity and supply chain disruption that product customization should cause
Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) that require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage. To evaluate this platform, we select cooperative on-ramp
Zhao, XuanpengLiao, XishunWang, ZiranWu, GuoyuanBarth, MatthewHan, KyungtaeTiwari, Prashant
The promise of personalized medicine involves a simple device that keeps each person apprised of their level of health, identifies even trace amounts of undesirable biomarkers in blood or saliva, and serves as an early warning system for diseases
The CORA rating metric is frequently used in the field of injury biomechanics to compare the similarity of response time histories. However, subjectivity exists within the CORA metric in the form of user-customizable parameters that give the metric the flexibility to be used for a variety of applications. How these parameters are customized is not always reported in the literature, and it is unknown how these customizations affect the CORA scores. Therefore, the purpose of this study was to evaluate how variations in the CORA parameters affect the resulting similarity scores. A literature review was conducted to determine how the CORA parameters are commonly customized within the literature. Then, CORA scores for two datasets were calculated using the most common parameter customizations and the default parameters. Differences between the CORA scores using customized and default parameters were statistically significant for all customizations. Furthermore, most customizations produced
Albert, Devon L
Upper limb forequarter amputations that involve the removal of the entire arm and scapula require highly customized prosthetic devices that are expensive but yet usually underutilized due to their high maintenance and low comfort levels. At the same time, while cosmetic prostheses — artificial limbs that provide patients the appearance of a pre-amputated body part — have a higher rate of continuous use, they have limitations in functional use
In order to improve the public service attributes and the utilization efficiency of CAV, the paper proposes a customizable and cruising autonomous bus (CCAB) route planning model. This model makes CCAB as customizable and fast as a taxi, and at the same time as a bus to meet multiple demands. Considering that CCAB needs to meet personalized customized riding demand, the paper combines allocation strategy of customized demands to establish a real-time optimization method for customizable and cruising routes based on the elliptical feasible area. This enable CCAB to have the ability of autonomous cruising and autonomous planning of customized routes. In CAV environment, the driver will be replaced by on-board computer of CCAB, the driver’s empirical route selection method will be replaced by the route planning model proposed in this paper. CCAB use historical ride data from each stop to predict future demand. Based on prediction results, the potential passenger rate of route is
Haijian, BaiJun, WangLiyang, Wei
In order to solve the problem that the existing customized bus can only select transfer points on the static line, and cannot dynamically plan the route according to the user's real-time demand, this paper establishes a route planning model of unmanned customized bus with the total cost minimization as the optimization goal. The model comprehensively considers the operating cost, passenger satisfaction and charging demand of driverless public transport to ensure efficient transportation and excellent user experience. Dijkstra algorithm is applied to solve the shortest path selection problem involved in the model, and genetic algorithm is used to solve the model. The route selection schemes of vehicle transportation plan, driving route and charging plan are obtained to achieve the goal of minimizing the total cost. The validity and practicability of the model and method are verified by the example of Sioux Falls network
Zhang, XiaofengZhao, XiaomeiXie, DongfanBi, Jun
Additive manufacturing, or 3D printing, offers a way to craft miniaturized devices that contain multiple materials with new capabilities, and that are designed on demand for more personalized applications
In the aerospace industry, competition is high and the need to ensure safety and security while managing costs is paramount. Furthermore, stakeholders—who gain the most by working together—do not necessarily trust each other. Now, mix that with changing enterprise technologies, management of historical records, and customized legacy systems. This issue touches all aspects of the aerospace industry, from frequent flyer miles to aircraft maintenance and drives tremendous inefficiency and cost. Technology that augments, rather than replaces, is needed to transform these complex systems into efficient, digital processes. Blockchain technology offers collaborative opportunities for solving some of the data problems that have long challenged the industry. This SAE EDGE™ Research Report by Rhonda D. Walthall examines how blockchain technology could impact the aerospace industry and addresses some of the unsettled concerns surrounding its implementation. Click here to access the full SAE
Walthall, Rhonda
Additive manufacturing can reduce the time and material costs in a design cycle and enable on-demand printing of customized parts. New multi-material 3D printers that can print both metal and dielectric materials enable the additive manufacturing of antennas and RF components. Developments in software are critical to leveraging this capability; good tools allow more effort to go towards creation than implementation. Three devices are described in detail in this article to demonstrate the 3D printing of RF components. First, a Marchand balun is presented, demonstrating rapid prototyping of a complex, multilayer RF circuit. Next, a monopole array is shown with an integrated beam-steering network and radome to show rapid prototyping of a complete antenna system. Finally, a bowtie antenna with rounded corners is presented, showing good performance in the Kuband
Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM). The GMM generates a specific driving style for each vehicle based on the speed, acceleration, jerk, time, and space headway features of the leading vehicle
Xing, YangHuang, ChaoLv, ChenLiu, YahuiWang, HongCao, Dongpu
Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper. The analysis not only focuses on the routes chosen by the
Jin, XinTakayama, ToshinoriYashiro, AiNakamura, Taiki
In the current scenario, vehicles are majorly owned by individuals where they have their own personal settings or accessories as per their individual preferences. In Shared mobility all features/controls are not personalized to everyone who shares the vehicle, which hinders the usage of shared vehicle. For shared mobility/Autonomous vehicle to be successful, it must play a significant role in customer engagement. To enhance the customer engagement, we need to satisfy individual customer by customizing the vehicle for their needs. This will give a cognitive feel of personal vehicle in a shared environment. We need technologies and design in improving vehicle interior and exterior systems to address personalization. We will involve Design Thinking approach by customer interactions in each zone of vehicle both interior and exterior to identify personalization needs. The zones of study include Frunk & Trunk compartment zone, Interaction zones, interior & exterior zone. We will rank the
Dayakar, SureshSubramanian, VijayasarathyReddy, KeshavaShiramgond, Vijaykumar
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