Analyzing the Resilience of Convolutional Neural Networks Implemented on GPUs: Alexnet as a Case Study

There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN algorithms to improve their performance as well as the Latency. However, GPUs are prone to soft errors. These errors can impact the behaviors of the GPU dramatically. Thus, the generated fault may corrupt data values or logic operations and cause errors, such as Silent Data Corruption. unfortunately, soft errors propagate from the physical level (microarchitecture) to the application level (CNN model). This paper analyzes the reliability of the AlexNet model based on two metrics: (1) critical kernel vulnerability (CKV) used to identify the malfunction and light- malfunction errors in each kernel, and (2) critical layer vulnerability (CLV) used to track the malfunction and light-malfunction errors through layers. To achieve this, we injected the AlexNet which was popularly used in healthcare applications on NVIDIA’s GPU, using the SASSIFI fault injector as the major evaluator tool. The experiments demonstrate through the average error percentage that caused malfunction of the models has been reduced from 3.7% to 0.383% by hardening only the vulnerable part with the overhead only 0.2923%. This is a high improvement in the model reliability for healthcare applications

A novel prediction of radiation interference impact in single wind turbine on microwave link

This A wind turbine produces renewable energy that supplies with power, especially in countries and states such as Qatar, which is one of the largest producers of natural gas globally. However, there are numerous challenges in utilizing renewable energy sources in generating power, particularly the degradation effect on telecommunication services due to radiation interference caused by the electromagnetic field induced by the dynamic movement of such devices or systems. Similarly, undesirable signals could interfere with communication systems, reducing both quality and performance, while wildlife such as birds, collide with the turbine rotor cutting edges. This paper undertakes an extensive examination into using a single wind turbine and its impact as a sustainable power source in providing renewable energy in reducing the level of pollution in Qatar while simultaneously considering the need to safeguard the surrounding wildlife, such as birds. The study proposes to use an advanced sensor ACS712 Hall Effect to convert the electromagnetic field to electrical flow, gauge the wind speed and direction and repulse birdlife. The proposal considers the environmental characteristics of the Al-Karaana village as the projected installation site to maximize the possibility of presenting a well-investigated solution in addressing the challenges and obstacles that could occur due to the application of wind energy supported by telecommunications infrastructure such as microwave systems. The scientific approach of this study assessed the possible solutions to mitigate the difficulties related to the use of wind turbines in the wireless communications field, wildlife, and natural environment

Modeling over the sea surface within elevated duct

Modeling marine environment is a rapidly evolving field expected to gain more concentration in wide aspects. Sea environment duct accounted as analytical comparative study utilizing super high frequencies SHF to investigate different parameters affect the signal propagation, which consider nonuniform nature in term of variation of electromagnetic prosperities in the duct causes different phenomena such as refraction and bending. This paper takes into account the matter via focus in elevated duct to compare with sea surface and model the refractivity profiles utilizing parabolic equation. The key goal of the work is to present fading phenomenon affect, which is caused by various atmospheric parameters and proof it does not exclusively influence the path loss, but also the sea specification itself could play a vital role in term of propagation factor such as electric field which is different from sea to another. So, this proofing would helps researchers which are working in the area to design and select appropriate model and give a reliable wireless communication when consider the coastal link budget. This work result emphasized the versa correlation between the coverage area and frequency in sea environment, and makes sure that the reduction due to several common parameters becomes clear at higher gigahertz frequencies. Overall the proposal considers the coastal ducts from refractive profiles viewpoint to ensure reliable link over the sea

The Impact of Noise Label on Beampattern and SINR of MVDR Beamformer

Minimum Variance Distortionless Response (MVDR) is basically a unity gain adaptive beamformer which is suffered from performance degradation due to the presence of interference and noise. Also, MVDR is sensitive to errors such as the steering vector errors, and the nulling level. MVDR combined with a linear antenna array (LAA) is used to acquire desired signals and suppress the interference and noise. This paper examines the impact of the noise variance label (σn2) and the number of interference sources by using Signal to Interference plus Noise Ratio (SINR) and array beampattern as two different Figure-of-Merits to measure the performance of the MVDR beamformer with a fixed number of array elements (L). The findings of this study indicate that the MVDR have successfully placed nulls in the nonlook angle with average SINR of 99.6, 49.6, 24.9 dB dB for σn2.of -50, 0, 50 dB, respectively. Also, the MVDR provides accurate majorlobe to the real user direction, even the σn2 are bigger than desired signal power. The proposed method was found to perform better than some existing techniques. Based on this analysis, the beampattern is not heavily relies on the σn2. Moreover, the SINR strongly depends on the σn2 and the number of SNOIs

A review of Convolutional Neural Networks in Remote Sensing Image

Effectively analysis of remote-sensing images is very important in many practical applications, such as urban planning, geospatial object detection, military monitoring, vegetation mapping and precision agriculture. Recently, convolutional neural network based deep learning algorithm has achieved a series of breakthrough research results in the fields of objective detection, image semantic segmentation and image classification, etc. Their powerful feature learning capabilities have attracted more attention and have important research value. In this article, firstly we have summarized the basic structure and several classical convolutional neural network architectures. Secondly, the recent research problems on convolutional neural network are discussed. Later, we summarized the latest research results in convolutional neural network based remote sensing fields. Finally, the conclusion has made on the basis of current issue on convolutional neural networks and the future development direction

The impact of partial discharge on the dielectric properties of refined bleached and deodorized palm oil (RBDPO)

Several years ago, insulation oil played an important role in electrical power equipment. Mineral oil extracted from petroleum resources (a non-renewable source) has been widely used as an insulating material in the transformers. Therefore, there is a need to find renewable alternatives to the petroleum-based mineral oil. Refined bleached and deodorized palm oil (RBDPO) is a palm-based oil which has undergone several modifications. In this paper, the dielectric properties of RBDPO and the conventional transformer oil were investigated, followed by an investigation of its partial discharge (PD) impact on the dielectric properties. The breakdown voltage was measured according to the international electrotechnical commission (IEC 6056) standard and the viscosity test was done according to BS EN ISO 3104:1996. The experiments have proved that RBDPO can be used as an insulating oil due to the closeness of its dielectric properties compared to the conventional oil

Shunt Hybrid Active Power Filter Based on Two Compensation Strategies with PI and Fuzzy Logic Controllers

Industrial designs have tremendously changed within the last decade, with its time and nonlinear variation loads in power frameworks spectrum expanding widely. This revolution has resulted in increased quality control problems such as current unbalance, current and voltage harmonics, flicker and poor power factor in control frameworks. The aim of this paper is to address this problem through the development of Synchronous Reference Frame and Reactive Power (SRF and P-Q) control methods. The DC voltage was regulated to its set reference for providing the current reference using proportional-Integral (PI) and fuzzy logic controllers. From the results, Fuzzy logic control was shown to achieve an adequate DC capacitor energy storage optimization, the sinusoidal type of the current and the change of the power factor. A low Total Harmonic Distortion (THD) that met the suggestions of IEEE- 519 standard on symphonious levels was achieved with the proposed method

Development and validation of scale using rasch analysis to measure students’ entrepreneurship readiness to learn embedded system design course

Embedded systems are growing rapidly as the technology paves the way for the rise of future of smart manufacturing through a wide range of industries. The intensity demands of innovation required a steady supply of innovative and entrepreneurship engineers to ensures the industry players have a sustainable supply of talent to fuel their growth and investments. The university acknowledge the current and future demand of the labour market by offering embedded system course that are developed to equipped the next generation engineers with innovation and entrepreneurship skills to enable them to turn their ideas into reality. This paper developed and validated a scale to measure the student entrepreneurship skills readiness for embedded systems design course using the Rasch analysis.The content validity results show that CVR is 0.92 and CVI is 0.96 indicating an excellent content validity. The pilot test result show that the scale Cronbach alpha is 0.80 indicating excellent scale reliability. The construct validity of the scale was evaluated using WINSTEPS version 3.92.1, with results indicated that all the items of the scale fit the Rasch model with satisfactoryfit index and showedexcellent consistency, with reliability alpha of 0.99 foe items and 0.75 for persons. The findings depicted that most of the students have poor business and entrepreneurship skills, such as marketing and negotiation abilities. Therefore, higher learning institutions need to embed acquirable entrepreneurial skills in the prerequisites courses to provide adequate training to the students, increasing their creativity and maximizing their potential to be successful entrepreneurs

Flotation separation of poly (ethylene terephthalate and vinyl chloride) mixtures based on clean corona modification: Optimization using response surface methodology

Postconsumer polyethylene terephthalate (PET) has potential applications in many areas of manufacturing, but contamination by hazardous polyvinyl chloride (PVC) in common waste streams can reduce its recyclable value. Separating collected PET-PVC mixtures before recycling remains very challenging because of the similar physicochemical properties of PET and PVC. Herein, we describe a novel flotation process with corona modification pretreatment to facilitate the separation of PET-PVC mixtures. Through water contact angle, surface free energy, X-ray photoelectron and FT-IR characterization, we found that polar hydroxyl groups can be more easily introduced on the PVC surface than on the PET surface induced by corona modification. This selective wetting can suppress the floatability of PVC, leading to the separation of PET as floating product. A reliable mechanism including two different hydrogen-abstraction pathways was established. Response surface methodology consisting of Plackett-Burman and Box-Behnken designs was adopted for optimization of the combined process, and control parameters were solved based on high-quality prediction models, with fitting from significant variables and interactions. For physical or chemical circulation strategies with PET purity prioritization, the validated purity of the product reached 96.05% at a 626 W corona power, 5.42 m/min passing speed, 24.78 mg/L frother concentration and 286 L/h air flow rate. For the energy recuperation strategy with PET recovery prioritization, the factual recovery reached 98.08% under a 601 W corona power, 6.04 m/min passing speed, 27.55 mg/L frother concentration and 184 L/h air flow rate. The current work provides technological insights into the cleaner disposal of waste plastics

Enhancing the performance of cascaded three-level VSC STATCOM by ANN controller with SVPWM integegration

This article presents a cascaded three-level voltage source converter (VSC) based STATCOM employing an artificial neuron network (ANN) controller with a new simple circuit of space vector pulse width modulation (SVPWM) technique. The main aim of utilizing ANN controller and SVPWM technique is to minimize response time (RT) of STATCOM and improve its performance regard to PF amplitude, and total harmonic distortion (THD) of VSC output current during the period of lagging/leading PF loads (inductive/capacitive loads). The performance of STATCOM is tested using MATLAB/SIMULINK in IEEE 3-bus system. The simulation results clearly proved that the STATCOM with intelligent controller is more efficient compared to a conventional controller (PI controller), where ANN enables the voltage and current to be in the same phase rapidly (during 1.5 cycles) with THD less than 5%