Evaluation and prioritization of rice production practices and constraints under temperate climatic conditions using Fuzzy Analytical Hierarchy Process (FAHP)

Due to overwhelming complex and vague nature of interactions between multiple factors describing agriculture, Multi-Criteria Decision Making (MCDM) methods are widely used from farm to fork to facilitate systematic and transparent decision support, figure out multiple decision outcomes and equip decision maker with confident decision choices in order to choose best alternative. This research proposes a Fuzzy Analytical Hierarchy Process (FAHP) based decision support to evaluate and prioritize important factors of rice production practices and constraints under temperate climatic conditions and provides estimate of weightings, which measure relative importance of critical factors of the crop under biotic, abiotic, socio-economic and technological settings. The results envisage that flood, drought, water logging, late sali, temperature and rainfall are important constraints. However, regulating transplantation time; maintaining planting density; providing training to the educated farmers; introducing high productive varieties like Shalimar Rice-1 and Jhelum; better management of nutrients, weeds and diseases are most important opportunities to enhance rice production in the region. Therefore, the proposed system supplements farmers with precise decision information about important rice production practices, opportunities and constraints

FEM Analysis of Squirrel Cage Induction Motor Fed with Raised Sine Wave Supply

AC motors are used frequently for many industrial applications such as material handling, traction, electric vehicles etc. A novel non-sinusoidal modulation technique employing Raised Sine Wave (RSW) for the PWM inverter is proposed in this paper. Squared Sine Wave has a distinct advantage of reduced rate of change at zero crossing of each half cycle, and eliminates the need for dead band. An Finite Element Analysis (FEM) is carried out to study its suitability for AC Induction Motor. The results show that the operation has a constant startup torque for all load conditions, thus providing a smooth start from zero speed to full rated speed. This feature makes it most suitable for applications requiring frequent startup such as traction. The operation of the conventional Variable Frequency Drives using Conventional Sine Wave (CSW) is compared with the results obtained with RSW supply.DOI:http://dx.doi.org/10.11591/ijece.v3i2.170

Robust Passenger Vehicle Classification Using Physical Measurements From Rear View

Vehicle classification has become a very important subject of study because of its importance in autonomous navigation, surveillance and traffic analysis. Classification of vehicles from the rear view is challenging because all the vehicles have subtle appearance differences from the rear view, changing illumination conditions, presence of shadows and real-time considerations. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear view where the camera’s field of view is directly behind the vehicle. In this paper we present a multi-class vehicle classification system which classifies a vehicle into one of four possible classes Sedan, Minivan, SUV and Pickup truck when seen from its rear view. For a given geometric setup of the camera we use a feature set of Visual Rear Ground Clearance, height of the vehicle and perpendicular distance between the bottom of the license plate and bottom of the rear bumper for classifying the vehicle. Results are shown on large data-sets of freeway videos

Increasing the Robustness of Deep Learning Models Using Generative Networks

Over the past few years deep learning has demonstrated impressive performance on many important practical problems such as image, video, and audio classification. This work develops three novel applications for automated stem cell classification, automated sports analytics, and a novel framework for defending deep learning models from white and black box adversarial attacks. In the field of stem cell classification, it is very expensive and time consuming to generate data which is very intrusive and not easy to obtain. This work leverages an ensemble of generative networks to create a large dataset of synthetic human embryonic stem cell (hESC) images which are exclusively used for training deep learning classifiers. In order to verify that the data distribution of the synthetic images is similar to that of the real-world images, the quality of the synthetic images are validated at a pixel level and high dimensional feature level with respect to the real-world data. Experimental results show that the classifiers trained on the synthetic dataset are able to achieve high performance when evaluated on real-world data and can be used as a tool for annotating more data saving hours of manual labor.In the field of automated sports analytics, it is very important to analyze every minute detail in order to generate reliable statistics for every individual player. This work develops a novel framework for automatically generating the tactical statistics of soccer players directly from a video. The proposed approach empirically shows that high-level features learned from specific soccer matches do not necessarily generalize across all soccer matches and it is not feasible to obtain datasets for every single match. To solve this, the proposed approach develops a match-specific application that uses previously recorded videos of teams to learn fine-grained features that can generalize across other matches played by the same respective teams. Although generative networks have had huge success in augmenting existing datasets which improve the performance of deep learning classifiers, this work shows that they often overlook minute details when generating new data which is very important in sports analytics and can cause the performance of the classifiers to drop. This work proposes a novel generative architecture that learns to generate synthetic images with fine-grained structures which further improves our system to generate accurate tactical statistics for the players. Various ablation studies are performed to show the improvement in performance and significance of the results across different soccer matches.Despite their outstanding performance, these models are vulnerable to adversarial manipulation of their input which could lead to poor performance. These adversarial manipulations are carefully crafted perturbations that are so subtle that a human observer does not even notice the modification at all, but can cause deep learning models to predict incorrect results. In order to address this vulnerability, this work proposes a novel white box defense algorithm that uses generative networks with Probabilistic Adversarial Robustness to neutralize adversarial examples by concentrating the sample probability to adversarial-free zones. Although, our proposed defense achieves state-of-the-art classification accuracy, this is not a reliable metric to determine if an image is ``adversarial-free''. This is a foundational problem for online image verification applications where the ground-truth of the input image is not known and hence we cannot validate the performance of the classifier or know if the image is ''adversarial-free'' or not. To address this problem, this work proposes a novel framework that uses an ensemble of individual defenses whose performance is continuously validated in a loop using Bayesian uncertainties and does not require any information about the black box classifier such as its architecture, parameters, or training dataset. Unlike existing defense mechanisms that requires knowing the ground-truth of the input data and modifying/re-training the black box classifier which is not feasible in online applications, our defense is designed in the first place to provide proactive protection to any existing deep learning based model. Evaluation on various public benchmark datasets including autonomous driving and face biometrics datasets shows that our defense can consistently detect adversarial examples and purify them against a variety of attacks with different ranges of perturbations

Increasing the Robustness of Deep Learning Models Using Generative Networks

Over the past few years deep learning has demonstrated impressive performance on many important practical problems such as image, video, and audio classification. This work develops three novel applications for automated stem cell classification, automated sports analytics, and a novel framework for defending deep learning models from white and black box adversarial attacks. In the field of stem cell classification, it is very expensive and time consuming to generate data which is very intrusive and not easy to obtain. This work leverages an ensemble of generative networks to create a large dataset of synthetic human embryonic stem cell (hESC) images which are exclusively used for training deep learning classifiers. In order to verify that the data distribution of the synthetic images is similar to that of the real-world images, the quality of the synthetic images are validated at a pixel level and high dimensional feature level with respect to the real-world data. Experimental results show that the classifiers trained on the synthetic dataset are able to achieve high performance when evaluated on real-world data and can be used as a tool for annotating more data saving hours of manual labor.In the field of automated sports analytics, it is very important to analyze every minute detail in order to generate reliable statistics for every individual player. This work develops a novel framework for automatically generating the tactical statistics of soccer players directly from a video. The proposed approach empirically shows that high-level features learned from specific soccer matches do not necessarily generalize across all soccer matches and it is not feasible to obtain datasets for every single match. To solve this, the proposed approach develops a match-specific application that uses previously recorded videos of teams to learn fine-grained features that can generalize across other matches played by the same respective teams. Although generative networks have had huge success in augmenting existing datasets which improve the performance of deep learning classifiers, this work shows that they often overlook minute details when generating new data which is very important in sports analytics and can cause the performance of the classifiers to drop. This work proposes a novel generative architecture that learns to generate synthetic images with fine-grained structures which further improves our system to generate accurate tactical statistics for the players. Various ablation studies are performed to show the improvement in performance and significance of the results across different soccer matches.Despite their outstanding performance, these models are vulnerable to adversarial manipulation of their input which could lead to poor performance. These adversarial manipulations are carefully crafted perturbations that are so subtle that a human observer does not even notice the modification at all, but can cause deep learning models to predict incorrect results. In order to address this vulnerability, this work proposes a novel white box defense algorithm that uses generative networks with Probabilistic Adversarial Robustness to neutralize adversarial examples by concentrating the sample probability to adversarial-free zones. Although, our proposed defense achieves state-of-the-art classification accuracy, this is not a reliable metric to determine if an image is ``adversarial-free''. This is a foundational problem for online image verification applications where the ground-truth of the input image is not known and hence we cannot validate the performance of the classifier or know if the image is ''adversarial-free'' or not. To address this problem, this work proposes a novel framework that uses an ensemble of individual defenses whose performance is continuously validated in a loop using Bayesian uncertainties and does not require any information about the black box classifier such as its architecture, parameters, or training dataset. Unlike existing defense mechanisms that requires knowing the ground-truth of the input data and modifying/re-training the black box classifier which is not feasible in online applications, our defense is designed in the first place to provide proactive protection to any existing deep learning based model. Evaluation on various public benchmark datasets including autonomous driving and face biometrics datasets shows that our defense can consistently detect adversarial examples and purify them against a variety of attacks with different ranges of perturbations

DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC.

Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson's, Huntington's, diabetes mellitus, etc. hESC are a reliable developmental model for early embryonic growth because of their ability to divide indefinitely (pluripotency), and differentiate, or functionally change, into any adult cell type. Their adaptation to toxicological studies is particularly attractive as pluripotent stem cells can be used to model various stages of prenatal development. Automated detection and classification of human embryonic stem cell in videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. Currently video annotation is done by hand, a process which is very time consuming and exhaustive. To solve this problem, this paper introduces DeephESC 2.0 an automated machine learning approach consisting of two parts: (a) Generative Multi Adversarial Networks (GMAN) for generating synthetic images of hESC, (b) a hierarchical classification system consisting of Convolution Neural Networks (CNN) and Triplet CNNs to classify phase contrast hESC images into six different classes namely: Cell clusters, Debris, Unattached cells, Attached cells, Dynamically Blebbing cells and Apoptically Blebbing cells. The approach is totally non-invasive and does not require any chemical or staining of hESC. DeephESC 2.0 is able to classify hESC images with an accuracy of 93.23% out performing state-of-the-art approaches by at least 20%. Furthermore, DeephESC 2.0 is able to generate large number of synthetic images which can be used for augmenting the dataset. Experimental results show that training DeephESC 2.0 exclusively on a large amount of synthetic images helps to improve the performance of the classifier on original images from 93.23% to 94.46%. This paper also evaluates the quality of the generated synthetic images using the Structural SIMilarity (SSIM) index, Peak Signal to Noise ratio (PSNR) and statistical p-value metrics and compares them with state-of-the-art approaches for generating synthetic images. DeephESC 2.0 saves hundreds of hours of manual labor which would otherwise be spent on manually/semi-manually annotating more and more videos