Model predictive control for a thermostatic controlled system

Abstract — This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic con-trolled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperatures are estimated by reduced order observers and evaporation temperature is regulated by an algorithmic suction pressure control scheme. The method is applied to a validated simulation benchmark. The results show that even with the thermostatic control valves, there exists significant potential to reduce the operating cost. I

Natural Fractures Characterization and In Situ Stresses Inference in a Carbonate Reservoir—An Integrated Approach

In this paper, we characterized the natural fracture systems and inferred the state of in situ stress field through an integrated study in a very complex and heterogeneous fractured carbonate heavy oil reservoir. Relative magnitudes and orientations of the in-situ principal stresses in a naturally fractured carbonate heavy oil field were estimated with a combination of available data (World Stress Map, geological and geotectonic evidence, outcrop studies) and techniques (core analysis, borehole image logs and Side View Seismic Location). The estimates made here using various tools and data including routine core analysis and image logs are confirmatory to estimates made by theWorld Stress Map and geotectonic facts. NE-SW and NW-SE found to be the dominant orientations for maximum and minimum horizontal stresses in the study area. In addition, three dominant orientations were identified for vertical and sub-vertical fractures atop the crestal region of the anticlinal structure. Image logs found useful in recognition and delineation of natural fractures. The results implemented in a real field development and proved practical in optimal well placement, drilling and production practices. Such integrated studies can be instrumental in any E&P projects and related projects such as geological CO2 sequestration site characterization

Management of Oral Lichen Planus by 980 nm Diode Laser

Introduction: Oral lichen planus (OLP) is a mucocutaneous disease with uncertain etiology. As the etiology is unknown standard treatment modalities are not available. The traditional and common treatment relies on corticosteroids whether topical or systemic. In recent years, development of lasers made a proper path to use this instrument for treatment of the diseases which are refractory to conventional treatments. Previous studies in this field used CO2, ND:YAG, Excimer and some wavelength of diode lasers for the treatment of different types of lichen planus.Case Report: In this study, we present an OLP case which is treated using 980 nm diode laser. The result was measured by visual analogue scale (VAS) and clinical assessment; as a result, symptoms including pain and soreness started to decrease within a week, and by the end of a month completely subsided; the lesion disappeared totally as well. No recurrence was observed after a month and no side-effect was reported.Conclusion: 980 nm diode laser can be successfully used for treatment of patients with OLP

Radar Voxel Fusion for 3D Object Detection

Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and weather conditions that need to be handled. In contrast to more constrained environments, such as automated underground trains, automotive perception systems cannot be tailored to a narrow field of specific tasks but must handle an ever-changing environment with unforeseen events. As currently no single sensor is able to reliably perceive all relevant activity in the surroundings, sensor data fusion is applied to perceive as much information as possible. Data fusion of different sensors and sensor modalities on a low abstraction level enables the compensation of sensor weaknesses and misdetections among the sensors before the information-rich sensor data are compressed and thereby information is lost after a sensor-individual object detection. This paper develops a low-level sensor fusion network for 3D object detection, which fuses lidar, camera, and radar data. The fusion network is trained and evaluated on the nuScenes data set. On the test set, fusion of radar data increases the resulting AP (Average Precision) detection score by about 5.1% in comparison to the baseline lidar network. The radar sensor fusion proves especially beneficial in inclement conditions such as rain and night scenes. Fusing additional camera data contributes positively only in conjunction with the radar fusion, which shows that interdependencies of the sensors are important for the detection result. Additionally, the paper proposes a novel loss to handle the discontinuity of a simple yaw representation for object detection. Our updated loss increases the detection and orientation estimation performance for all sensor input configurations. The code for this research has been made available on GitHub