Military Academy

About: Military Academy is a based out in . It is known for research contribution in the topics: Population & Context (language use). The organization has 2478 authors who have published 3003 publications receiving 33188 citations.

Ultra-precision machining by the cylindrical polishing process

Abstract: A process planning method for removing an arbitrary and axially symmetric error profile by the cylindrical polishing process (abbreviated as the CPP process) is proposed in this study. This method is to plan the dwelling-time of the polishing tool so that the error profile can be accurately removed. The tool dwelling-time distribution is solved by a non-negative least square method. By using this method, the residual error between actual and desired removal depths may be induced. It is shown that the residual error is related to the width of the machining zone, the wavelength of the error profile, and the tool’s resolution. The computer simulations indicate that the residual error is always negligible when the wavelength of the error profile is larger than the width of the machining zone. If the wavelength of the error profile is large, a small size of the tool’s resolution is found effectively to reduce the residual error. The experimental study confirms that an arbitrary error profile can be accurately removed on the basis of the proposed method.

Drivers’ Drowsiness Detection and Warning Systems for Critical Infrastructures

Abstract: Road traffic accidents, due to driver fatigue, tend to inflict high mortality rates comparing with accidents involving rested drivers. Currently there is an emerging automotive industry trend towards equipping vehicles with various driver-assistance technologies. Third parties also started producing complementary systems, including ones that can detect the driver's degree of fatigue, but this growing field requires further research and development.The main purpose of this paper is the development and implementation of a system capable to detecting and alert, in real-time, the driver's level of fatigue. A system like this is expected to make the driver aware of the assumed danger when his level of driving and taking decisions are reduced and is indicating a sleep break as the necessary approach. By monitoring the state of the human eyes, it is assumed that the signs of driver fatigue can be detected early enough to prevent a possible road accident, which could result in severe injuries or ultimately, in fatalities. Hence, in this work the authors are focused on the video monitoring of the driver face, especially on his eyes position in time, when open or closed, using a machine learning object detection algorithm, the Haar Cascade. Two pretrained Haar classifiers, a face cascade, and an eye cascade were imported from the OpenCV GitHub repository. The OpenCV library, as well as other required packages, were installed on a BeagleBone Black Wireless development board. The software implementation, in order to achieve the driver's drowsiness detection, was made through the Python software program. The proposed system manages to alert if the eyes of the driver are being kept closed for more than a certain amount of time by triggering a set of warning lights and sounds. The large-scale implementation of this type of system will drop the number of road accidents caused by the drivers’ fatigue, thus saving countless lives and bringing a reduction of the socio-economic costs associated with these tragic events.

The Study of Fatigue Behaviors and Dislocation Structures in Interstitial-Free Steel

Abstract: There are three types of cyclic hardening for cyclically deformed interstitial-free (IF) steels. The magnitude of cyclic hardening was unobvious and dislocation cells smaller than 2 μm were very hard to find when total strain amplitude (Δe/2) was controlled to within 0.1 pct. When Δe/2 is increased to 0.125 to 0.3 pct, secondary cyclic hardening takes place prior to fatigue failure. Δe/2 = 0.6 pct, following an initial rapid-hardening stage. Dislocation cells smaller than 2 μm tend to develop near grain boundaries and triple junction of the grains while cycling just above Δe/2 = 0.125 pct. Such dislocation development results in secondary hardening. However, no failure occurs if cycling just below Δe/2 = 0.1 pct; hence, the fatigue limit for IF steel should be very close to Δe/2 = 0.1 pct.

Sonoco Green Decoration of Clinoptilolite with MgO Nanoparticles as a Potential Carrier for 5-Fluorouracil Drug: Loading Behavior, Release Profile, and Cytotoxicity

Abstract: Natural clinoptilolite was decorated by green MgO nanoparticles (G. MgO/Clino) and characterized as a potential drug delivery system for 5-Fluorouracil drug. The synthetic G. MgO/Clino as carrier exhibits an enhanced loading capacity of 244.5 mg/g which is higher than clinoptilolite (163 mg/g). The loading reaction of 5-FC follows the equilibrium and kinetic properties of Langmuir and Pseudo-first order models, respectively. Therefore, the loaded 5-FC molecules are of homogenous and monolayer forms with Gaussian energy (8.51 kJ/mol) related to physisorption and zeolitic ion exchange processes. Also, the thermodynamic study declared feasible, endothermic, and spontaneous loading reactions of the 5-FC drug. The release study emphasizes a long and continuous profile for 150 h either in gastric buffer (pH 1.2) or intestinal buffer (pH 7.4). The release kinetics follow Higuchi and Hixson-Crowell model in addition to Korsmeyer–Peppas model with diffusion exponent (0.50 at pH 1.2 and 0.47 at pH 7.4). This suggests non-Fickian transport behavior for the released 5-FC molecules by both erosion and diffusion mechanism. The cytotoxicity study on the colorectal normal cell (CCD-18Co) demonstrates significant safety and biocompatibility properties of G. MgO/Clino as a drug carrier. Moreover, the G. MgO/Clino carrier induced the cytotoxic effect on the colon cancer cells (HCT-116) by controlling the diffusion rate and the oxidative effect of the green MgO nanoparticles.

A Smart Flow Sensor for Flow Direction Measurement

Abstract: The purpose of this paper is to apply MEMS techniques to manufacture a gas flow sensor that consists with an airflow rate and airflow direction sensing units for detection of airflow states. This study uses a silicon wafer as a substrate which is deposited silicon nitride layers. To form the airflow rate sensing unit, a micro heater and a sensing resistor are manufactured over a membrane that released by a back-etching process. The airflow direction sensing unit is made of four cantilever beams that perpendicular to each other and integrated with piezoresistive structure on each micro-cantilever, respectively. As the cantilever beams are formed after etching the silicon wafer, it bends up a little due to the released residual stress induced in the previous fabrication process. As air flows through the airflow rate sensor, the temperature of the sensing resistor decreases and the evaluation of the local temperature changes determines the airflow rate. On the proposed sensor, the airflow direction can be determined through comparing the resistance variation caused by different deformation of cantilever beams at different directions.