Reducing Waste in 3D Printing Using a Neural Network Based on an Own Elbow Exoskeleton

Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted).

SimpliFI: Hardware Simulation of Embedded Software Fault Attacks

Fault injection simulation on embedded software is typically captured using a high-level fault model that expresses fault behavior in terms of programmer-observable quantities. These fault models hide the true sensitivity of the underlying processor hardware to fault injection, and they are unable to correctly capture fault effects in the programmer-invisible part of the processor microarchitecture. We present SimpliFI, a simulation methodology to test fault attacks on embedded software using a hardware simulation of the processor running the software. We explain the purpose and advantage of SimpliFI, describe automation of the simulation framework, and apply SimpliFI on a BRISC-V embedded processor running an AES application.

Waterflood in a Tight, Heterogeneous, Water-Sensitive, and Massively Fractured Reservoir

The T-K Formation is a tight, heterogeneous, and water-sensitive sandstone formation. To enable production, most wells in this reservoir are hydraulically fractured. After a successful massive fracturing campaign, which boosted the production up to 7,500 BOPD, the exploitation led to a dramatic pressure decline. The reservoir pressure had declined from initial value of 1,230 psi down to 450 psi in only six years. A waterflood project was then prepared and implemented in 2009, by converting watered-out producers into injectors, to increase the reservoir pressure as well as to improve recovery factor. Comprehensive studies and thorough surveillance activities were carried out to ensure the success of the project. As results of the on-going operation, the reservoir pressure is increased, enabling more infill drillings and hydraulic fracturing programs to be performed. Field annual decline rate is decreased from 63% to 32%. In addition, some wells that were dead due to insufficient reservoir pressure are brought back to production after the threshold bottomhole pressure is exceeded. To date, recovery factor is 14% and has surpassed the predicted ultimate waterflood recovery factor of 13.4% by the pre-injection simulation study; the pre-injection simulation study indicated a depletion RF of 9%. In terms of cumulative oil production, the waterflood is expected to contribute around 11.4 MMstb of oil. This paper summarizes the journey of T-K waterflood development. This includes pre-injection reservoir characterizations, formation damage mechanism analyses, and practical surveillance activities performed. The workflow and analyses presented in this paper can be used as a benchmark for managing a waterflood project, especially in reservoirs with similar challenges.