Browse Topic: People and personalities

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Image dehazing techniques can play a vital role in object detection, surveillance, and accident prevention, especially in scenarios where visibility is compromised because of light scattering by atmospheric particles. To obtain a high-quality image or as an initial step in processing, it’s crucial to restore the scene’s information from a single image, given that this is an ill-posed inverse problem. The present approach utilized an unsupervised learning approach to predict the transmission map from a hazy image and used YOLOv8n to detect the car from a clear recovered image. The dehazing model utilized a lightweight parallel channel architecture to extract features from the input image and estimate the transmission map. The clear image is recovered using an atmospheric scattering model and given to the YOLOv8n for car detection. By incorporating dark channel prior loss during training, the model eliminates the need for a paired dataset. The proposed dehazing model with fewer
Dave, ChintanPatel, HetalKumar, Ahlad
This SAE Recommended Practice is intended to establish a procedure to certify the fundamental driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. The associated family of documents listed below establish driving skill criteria for various specific categories. SAE J3300: Driving level SAE J3300/1: Low mu/winter driving SAE J3300/2: Trailer towing SAE J3300/3: Automated driving Additional certifications to be added as appropriate. This main document provides: (1) common definitions and general guidance for using this family of documents, (2) directions for obtaining certification through Probitas Authentication®1, and (3) driving level examination requirements.
Driving Skills Standards Committee
A good Noise, Vibration, and Harshness (NVH) environment in a vehicle plays an important role in attracting a large customer base in the automotive market. Hence, NVH has been given significant priority while considering automotive design. NVH performance is monitored using simulations early during the design phase and testing in later prototype stages in the automotive industry. Meeting NVH performance targets possesses a greater risk related to design modifications in addition to the cost and time associated with the development process. Hence, a more enhanced and matured design process involves Design Point Analysis (DPA), which is essentially a decision-making process in which analytical tools derived from basic sciences, mathematics, statistics, and engineering fundamentals are used to develop a product model that better fulfills the predefined requirement. This paper shows the systematic approach of conducting a Design Point Analysis-level NVH study to evaluate the acoustic
Ranade, Amod A.Shirode, Satish V.Miskin, AtulMahamuni, Ketan J.Shinde, RahulChowdhury, AshokGhan, Pravin
The acceleration vibe of a car's engine can be enhanced and a brand-specific auditory identity can be created via active sound design. Currently, experienced engineers are desperately required when the active sound design for car acceleration roar was processing, which consumed substantial time and human resources. Therefore, it is critical to conduct a research on the evaluation model for estimating car acceleration sound quality to improve sound design efficiency and reducing costs. 1,003 acceleration roars samples of common cars were collected in this paper, all of which could be commonly heard by the road. Nine psychoacoustic objective parameters, such as loudness, sharpness, and roughness, were calculated through Artemis Suite software, establishing a database for the sound quality of car acceleration sounds.Moreover, subjective evaluations of sound playback and objective data analysis were conducted to obtain the ratings of acceleration sounds. Firstly, five objective
Xiong, ChenggangXie, LipingZhang, ZheweiShi, WeijieQian, YushuLiu, Zhien
Bearings are fundamental components in automotive systems, ensuring smooth operation, efficiency, and longevity. They are widely used in various automotive systems such as wheel hubs, transmissions, engines, steering systems etc. Early detection of bearing defects during End-of-Line (EOL) testing and operational phases is crucial for preventive maintenance, thereby preventing system malfunctions. In the era of Industry 4.0, vibrational, accelerometer, and other IoT sensors are actively engaged in capturing performance data and identifying defects. These sensors generate vast amounts of data, enabling the development of advanced data-driven applications and leveraging deep learning models. While deep learning approaches have shown promising results in bearing fault diagnosis, they often require extensive data, complex model architectures, and specialized hardware. This study proposes a novel method leveraging the capabilities of Vision Language Models (VLMs) and Large Language Models
Chandrasekaran, BalajiCury, Rudoniel
To optimize vehicle chassis handling stability and ride safety, a layered joint control algorithm based on phase plane stability domain is proposed to promote chassis performance under complicated driving conditions. First, combining two degrees-of-freedom vehicle dynamics model considering tire nonlinearity with phase plane theory, a yaw rate and side slip angle phase plane stability domain boundary is drew in real time. Then based on the real-time stability domain and hierarchical control theory, an integrated control system with active front steering (AFS) and direct yaw moment control (DYC) is designed, and the stability of the controller is validated by Lyapunov theory. Finally, the lateral stability of the vehicle is validated by Simulink and CarSim simulations, real car data, and driving simulators under moose test and pylon course slalom test. The experimental results confirm that the algorithm can enhance the maneuverability and ride safety for intelligent vehicles.
Liao, YinshengZhang, ZhijieSu, AilinZhao, BinggenWang, Zhenfeng
In addition to providing safety advantages, sound and vibration are being utilized to enhance the driver experience in Battery Electric Vehicles (BEVs). There's growing interest and investment in using both interior and exterior sounds for pedestrian safety, driver awareness, and unique brand recognition. Several automakers are also using audio to simulate virtual gear shifting of automatic and manual transmissions in BEVs. According to several automotive industry articles and market research, the audio enhancements alone, without the vibration that drivers are accustomed to when operating combustion engine vehicles, are not sufficient to meet the engagement, excitement, and emotion that driving enthusiasts expect. In this paper, we introduce the use of new automotive, high-force, compact, light-weight circular force generators for providing the vibration element that is lacking in BEVs. The technology was developed originally for vibration reduction/control in aerospace applications
Norris, Mark A.Orzechowski, JeffreySanderson, BradSwanson, DouglasVantimmeren, Andrew
For mature virtual development, enlarging coverage of performances and driving conditions comparable with physical prototype is important. The subjective evaluation on various driving conditions to find abnormal or nonlinear phenomena as well as objective evaluation becomes indispensable even in virtual development stage. From the previous research, the road noise had been successfully predicted and replayed from the synthesis of system models. In this study, model based NVH simulator dedicated to virtual development have been implemented. At first, in addition to road noise, motor noise was predicted from experimental models such as blocked force and transfer function of motor, mount and body according to various vehicle conditions such as speed and torque. Next, to convert driver’s inputs such as acceleration and brake pedal, mode selection button and steering wheel to vehicle’s driving conditions, 1-D performance model was generated and calibrated. Finally, the audio and visual
Park, SangyoungDirickx, TomKang, Yeon JuneNam, Jeong MinGonçalves, Vinícius Valencia
As India’s economy expands and road infrastructure improves, the number of car owners is expected to grow substantially in the coming years. This market potential has intensified competition among original equipment manufacturers (OEMs) to position their products with a focus on cost efficiency while delivering a premium user experience. Noise and Vibration (NV) performance is a critical differentiator in conveying a vehicle's premiumness, and as such, NV engineers must strategically balance the achievement of optimal acoustic performance with constraints on cost, mass, and development timelines. Traditionally, NV package optimization occurs at the prototype or advanced prototype stage, relying heavily on physical testing, which increases both cost and time to market. Furthermore, late-stage design changes amplify these challenges. To address these issues, this paper proposes the integration of Hybrid Statistical Energy Analysis (HSEA) into the early stages of vehicle development
Rai, NiteshMehta, MakrandRavindran, Mugundaram
There are some paradoxical keys to NVH engineering success that are not taught in engineering schools. This paper will describe these in detail and provide examples to add context. The first unexpected key is that a good generalist makes a better expert. The more you understand the complete product development process, and the better contacts you have throughout the product development organization, the easier it will be for you to find cost effective solutions to your specific issues. Next, you need to know your customers, and that means both internal and external customers. If you work for a supplier, it means knowing original equipment manufacturer (OEM) and end user customers. The more you understand the customers’ needs, the better you can address them and make your product stand out. Another key is to try to turn a crazy idea into something practical. Sometimes you might find a completely insane solution to your problem, such as making a major component out of solid gold. If you
Reinhart, Thomas
A test and signal processing strategy was developed to allow a tire manufacturer to predict vehicle-level interior response based on component-level testing of a single tire. The approach leveraged time-domain Source-Path-Contribution (SPC) techniques to build an experimental model of an existing single tire tested on a dynamometer and substitute into a simulator vehicle to predict vehicle-level performance. The component-level single tire was characterized by its acoustic source strength and structural forces estimated by means of virtual point transformation and a matrix inversion approach. These source strengths and forces were then inserted into a simulator vehicle model to predict the acoustic signature, in time-domain, at the passenger’s ears. This approach was validated by comparing the vehicle-level prediction to vehicle-level measured response. The experimental model building procedure can then be adopted as a standard procedure to aid in vehicle development programs.
Nashio, HiroshiKajiwara, KoheiRinaldi, GiovanniSakamoto, Yumiko
The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results
Hank, StefanKamp, FabianGomes Lobato, Thiago Henrique
The process of producing aircraft parts involves the drilling of aluminum alloys. This creates a large amount of chips, which are removed using air, but sometimes they still remain within the holes. This is checked by inspectors through visual inspection. However, the quality of human inspection varies based on skill level and fatigue. Thus, image-based inspection should be used to stabilize and further improve inspection quality. This study aims to build a framework for chip detection based on image processing. Taking into account on-site implementation, the system must have low installation and running costs and be standalone. Therefore, we adopt the KIZKI algorithm, which satisfies these conditions. KIZKI means awareness in Japanese. This is a model of human peripheral vision and saccades. It does not require training like AI and can achieve high-speed and high-performance detection using a low-performance computer. In other words, there is no need for a computer with an expensive
Iinuma, MarinSato, JunyaTsuji, Masahiko
Machining metal has its challenges as many shops will attest, but machining glass is another matter – one that Dan Bukaty Jr., President of Precision Glass & Optics (PG&O) is well schooled in. Mr. Bukaty and his 35-person shop manufacture high-end precision glass optics for customers such as IMAX, Intuitive Surgical, Boeing and NASA, to name a few. The products PG&O make can range from the ordinary to the extraterrestrial, such as mirrors that it fabricated for the Hobby–Eberly Telescope to measure dark energy in outer space.
In February, the Joint Interagency Field Experimentation (JIFX) team at the Naval Postgraduate School (NPS) executed another highly collaborative week of rapid prototyping and defense demonstrations with dozens of emerging technology companies. Conducted alongside NPS’ operationally experienced warfighter-students, the event is a win-win providing insight to accelerate potential dual-use applications.
Researchers at the DoE’s SLAC National Accelerator Laboratory and Stanford University with collaborators at the University of Oregon and Manchester Metropolitan University have found a way to tease hydrogen out of the ocean by funneling seawater through a double-membrane system and electricity. The design successfully generated hydrogen gas without producing large amounts of harmful byproducts. The results, published in Joule, could help advance efforts to produce low-carbon fuels.
A Northwestern University-led team of researchers has developed a new fuel cell that harvests energy from microbes living in dirt. About the size of a standard paperback book, the completely soil-powered technology could fuel underground sensors used in precision agriculture and green infrastructure. This potentially could offer a sustainable, renewable alternative to batteries, which hold toxic, flammable chemicals that leach into the ground, are fraught with conflict-filled supply chains and contribute to the ever-growing problem of electronic waste.
Scientists from Tomsk Polytechnic University and Saratov State University teamed up with colleagues from Taiwan and proposed to make a laser “blade” for a medical scalpel with a specified curved shape using a photonic “hook.” Currently there are laser scalpels only with an axisymmetric focus area, i.e., with a cylindrical blade. According to scientists, changing the shape of the blade will expand the possibilities of using the laser in medicine, while it is about two times thinner than the cylindrical option. The concept and its rationale are published in the Journal of Biophotonics.
Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, in collaboration with Temasek Life Sciences Laboratory (TLL) and MIT, have developed a groundbreaking near-infrared (NIR) fluorescent nanosensor capable of simultaneously detecting and differentiating between iron forms — Fe(II) and Fe(III) — in living plants.
The race is on for leadership in cislunar space, considered a gateway to the future of space exploration. Yet operating in this domain introduces unique challenges for propulsion systems. In contrast to low-Earth orbit (LEO), the cislunar environment requires higher precision propulsion solutions; these are necessary to enable rapid and accurate maneuvering of spacecraft and long-term sustainability. Propellants like hydrazine and nitrogen tetroxide offer the high energy density required for cislunar missions, but they must be handled very differently from the inert, non-reactive gases at play in LEO systems.
A major challenge in self-powered wearable sensors for health care monitoring is distinguishing different signals when they occur at the same time. Researchers from Penn State and China’s Hebei University of Technology addressed this issue by uncovering a new property of a sensor material, enabling the team to develop a new type of flexible sensor that can accurately measure both temperature and physical strain simultaneously but separately to more precisely pinpoint various signals.
A team of researchers has developed self-powered, wearable, triboelectric nanogenerators (TENGs) with polyvinyl alcohol (PVA)-based contact layers for monitoring cardiovascular health. TENGs help conserve mechanical energy and turn it into power.
With the advancement of control technology in the automotive field, there is a possibility of cross-system redundant control between various actuators. As for the braking system, current brake-by-wire system often uses mechanical backup braking methods to give the vehicle a certain braking capacity after failure. However, in the mechanical backup braking mode, the brake master cylinder is connected to the supporting wheel cylinder, and the brake assist is lost, which leads to an increase in brake pressure and makes it difficult for the driver to step on the brake pedal. Meanwhile, due to the limitation of the brake master cylinder stroke, the maximum braking deceleration of the vehicle is only 3 m/s2 after the driver fully presses the brake pedal. The above two defects greatly affect the safety of the vehicle during backup braking. To solve the above problems, this article takes electric vehicles as the research object, designs a new type of hydraulic circuit for the braking system
Tian, BoshiLi, LiangLiao, YinshengLv, HaijunHu, ZhimingSun, YueQu, Wenying
In this study, an initial approach using deep reinforcement learning to replicate the complex behaviors of motorcycle riders was presented. Three learning examples were demonstrated: following a target velocity, maintaining stability at low speeds, and following a target trajectory. These examples serve as a starting point for further research. Additionally, the proficiency of the constructed models was examined using rider proficiency evaluation methods developed in previous studies. Initial results indicated that the models have the potential to mimic real rider behaviors; however, challenges such as differences between the model’s output and what humans can produce were also identified for future work.
Mitsuhashi, YasuhiroMomiyama, YoshitakaYabe, Noboru
Fuel cell vehicles (FCVs) offer a promising solution for achieving environmentally friendly transportation and improving fuel economy. The energy management strategy (EMS), as a critical technology for FCVs, faces significant challenges of achieving a balanced coordination among the fuel economy, power battery life, and durability of fuel cell across diverse environments. To address these challenges, a learning-based EMS for fuel cell city buses considering power source degradation is proposed. First, a fuel cell degradation model and a power battery aging model from the literature are presented. Then, based on the deep Q-network (DQN), four factors are incorporated into the reward function, including comprehensive hydrogen consumption, fuel cell performance degradation, power battery life degradation, and battery state of charge deviation. The simulation results show that compared to the dynamic programming–based EMS (DP-EMS), the proposed EMS improves the fuel cell durability while
Song, DafengYan, JinxingZeng, XiaohuaZhang, Yunhe
The research team led by Dr. Daeho Kim and Dr. Jong Hwan Park at the Nano Hybrid Technology Research Center of the Korea Electrotechnology Research Institute (KERI) has developed a groundbreaking process technology that enables ultrafast, 30-second preparation of hard carbon anodes for sodium-ion batteries, using microwave induction heating.
Driving automation systems rely heavily on sophisticated electronics to function effectively, and economic pressure poses new challenges in manufacturing. Tightly integrated sensors, processors, and communication modules monitor and control the vehicle's operation at any time. Size, weight, power, and cost constraints put pressure on manufactures to reduce stack electronics, miniaturize boards, and innovate over the traditional sequential assemble/test cycle. Consequently, designers and manufacturers reduce access to boards, remove test points, co-locate RF with other components, and break the sequential SMT line. Radio-frequency (RF) reflectometry is a mature and reliable technology essential for characterizing materials, components, and analog circuits. It provides precise insights into electromagnetic properties like impedance and permittivity, crucial for optimizing RF and microwave designs. Widely used in fields from material science to quantum computing, RF reflectometry is key
Moreno, CarlosSharma, RakshitPabbi, SrijanFischmeister, Sebastian
Los Angeles-based plastics contract manufacturer Kal Plastics deployed UR10e trimming cobot for a fraction of the cost and lead time of a CNC machine, cut trimming time nearly in half, and reduced late shipments to under one percent — all while improving employee safety and growth opportunities.
A team at the Johns Hopkins Applied Physics Laboratory (APL) is creating an artificial intelligence-driven capability that automates much of the work that goes into designing, setting up, developing and running wargames. The effort holds promise to dramatically amplify the impact and value of wargames and similar exercises for the military and other government agencies.
The authors will present findings from their cradle-to-cradle Product Carbon Footprint (PCF) study which captures an objective and comprehensive system level evaluation of the greenhouse gas (GHG) footprint of four different material types used in the same automotive application: Unsaturated Polyester Resin (UPR) SMC, steel, aluminum and glass fiber reinforced polypropylene (PP-GF). This study includes the simulation driven design of four mid-sized pickup boxes which were designed according to automotive requirements and relevant design guidelines for each material. OEM experts were consulted to validate the relevant specifications and boundary conditions. The technical paper includes details on the geometric design, simulation, production processes, life cycle and environmental impact assessment all in compliance with ISO standards (14040/14044) for the Cradle-to-Cradle PCF. This paper provides guidance and insights to help engineers develop effective strategies for material selection
Halsband, AdamLeinemann, TomkeBeer, MarkusHaiss, Eric
Under extreme driving conditions, such as emergency braking, rapid acceleration, and high-speed cornering, the tire, as the vehicle’s only direct connection to the road, plays a critical role in influencing dynamic performance and driving stability. Accurately predicting and tire longitudinal force under such combined slip conditions is key to improving vehicle control precision and ensuring driving safety. This study proposes a tire longitudinal force estimation strategy based on an intelligent tire system. The core of this system consists of three integrated PVDF (Polyvinylidene Fluoride) sensors embedded in the tire, which, due to their exceptional sensitivity, can precisely capture dynamic deformation information of the tire under varying conditions. This provides real-time, detailed data to better understand the complex interaction forces between the tire and the road. To study and validate the longitudinal force estimation model, the research team employed a high-precision indoor
Zhang, ZipengXu, NanTang, ZepengChen, Hong
In numerous automotive and industrial applications, efficient heat extraction is crucial to prevent system inefficiencies or catastrophic failures. The design of heat exchangers is inherently complex, involving multiple stages defined by the depth of analysis, number of design variables, and the accuracy of physical models. Designers must navigate the trade-offs between highly accurate yet computationally expensive models and less accurate but computationally cheaper alternatives. Multi-fidelity modeling offers a solution by integrating different fidelity models to deliver precise results at a reduced computational cost. In addition to managing these trade-offs, designers often face multi-objective challenges, where optimizing one aspect may lead to compromises in others. Multi-objective optimization, therefore, becomes essential in balancing these competing objectives to achieve the best overall design. In this context, Gaussian Process-based methods have gained prominence as
Chaudhari, PrathameshTovar, Andres
To provide an affordable and practical platform for evaluating driving safety, this project developed and assessed 2 enhancements to an Unreal-based driving simulator to improve realism. The current setup uses a 6x6 military truck from the Epic Games store, driving through a pre-designed virtual world. To improve auditory realism, sound cues such as engine RPM, braking, and collision sounds were implemented through Unreal Engine's Blueprint system. Engine sounds were dynamically created by blending 3 distinct RPM-based sound clips, which increased in volume and complexity as vehicle speed rose. For haptic feedback, the road surface beneath each tire was detected, and Unreal Engine Blueprints generated steering wheel feedback signals proportional to road roughness. These modifications were straightforward to implement. They are described in detail so that others can implement them readily. A pilot study was conducted with 3 subjects, each driving a specific route composed of a straight
Duan, LingboXu, BoyuGreen, Paul
In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals
Miller, BrandonSun, RuixiaoSujan, Vivek
Topology reasoning plays a crucial role in understanding complex driving scenarios and facilitating downstream planning, yet the process of perception is inevitably affected by weather, traffic obstacles and worn lane markings on road surface. Combine pre-produced High-definition maps (HDMaps), and other type of map information to the perception network can effectively enhance perception robustness, but this on-line fused information often requires a real-time connection to website servers. We are exploring the possibility to compress the information of offline maps into a network model and integrate it with the existing perception model. We designed a topology prediction module based on graph attention neural network and an information fusion module based on ensemble learning. The module, which was pre-trained on offline high-precision map data, when used online, inputs the structured road element information output by the existing perception module to output the road topology, and
Kuang, QuanyuRui, ZhangZhang, SongYixuan, Gao
Automotive industry is growing rapidly with innovations leading to increase in new features and improving the Quality of vehicles. These new components are developed with the available design standards across global OEMs. This Quality research paper aims to address the need of revision of design standards due to environmental factors prevailing in India. With the increase towards autonomous mobility, the number of electronics is also increasing, and this involves hardware & software evaluation. The hardware testing is a point of concern due to increase in the failure rate from the markets. Environment changes are very much evident with the growing economies and OEMs are developing the components with innovation, but if the basic design standards are not revised in parallel with the changing environment, the issues will continue to trouble the end customers. The failed cases data received from across the country was analyzed and observed that the cases are majorly reported from urban
Marwah, RamnikPyasi, PraveenBindra, RiteshGarg, Vipin
In this paper, an incremental coordinated control method through anti-squat/lift/dive suspension is proposed based on and suited to a distributed drive electric vehicle with front and rear dual motors. The precise relationship between the suspension reaction force and the driving force of the wheel is derived as the control model through an in-depth analysis of the wheel motion and force. Through imposing the first-order dynamics, the proposed method not only provides the longitudinal speed control of the vehicle but also suppresses the longitudinal, vertical and pitch vibration of the vehicle. Simulation results show that the suspension reaction force formula derived in this paper is more suitable for dynamic conditions, and compared with the control method based on the simplified suspension anti-squat/lift/dive control model, the proposed method using the accurate control model has superior comprehensive control performance.
Feng, CongWu, GuangqiangYang, Yuchen
Automated driving is an important development direction of the current automotive industry. Level 3 automated driving allows the driver to perform non-driving related tasks (NDRTs) during automated driving, however, once the operating conditions exceed the designed operating domain, the driver is still required to take over. Therefore, it is important to rationally design takeover requests (TORs) in Level 3 conditional automated driving. This paper investigates the effect of directional tactile guidance on driver takeover performance in emergency obstacle avoidance scenarios during the transfer of control from automated driving mode to manual driving. 18 participants drove a Level 3 conditional automated driving vehicle in a driving simulator on a two-way four-lane urban road, performed a takeover, and avoided obstacles while performing non-driving related tasks. The driver's takeover performance during the takeover process was measured and subjective driver evaluation data was
Liang, XinyingLiang, YunhanMa, XiaoyuanWang, LuyaoChen, GuoyingHu, Hongyu
Mesekon Oy, a Finnish welding manufacturer that produces complex welded steel structures for the marine, energy, and paper industries, needed a flexible and collaborative solution to improve efficiency, reduce defects, and enhance workplace ergonomics by automating repetitive and physically demanding welding operations.
Researchers at Rice University have found a new way to improve a key element of thermophotovoltaic (TPV) systems, which convert heat into electricity via light. Using an unconventional approach inspired by quantum physics, Rice engineer Gururaj Naik and his team designed a thermal emitter that can deliver high efficiencies within practical design parameters.
Drone show accidents highlight the challenges of maintaining safety in what engineers call “multiagent systems” — systems of multiple coordinated, collaborative, and computer-programmed agents, such as robots, drones, and self-driving cars.
Researchers have developed a pacifier designed to monitor a baby’s electrolyte levels in real time, potentially eliminating the need for repeated invasive blood draws. The team constructed a tiny tunnel, or microfluidic channel, into the body of the pacifier.
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