Spreadsheet Data Transformation for Ontology Engineering in Petrochemical Equipment Inspection Tasks

2021 ◽  
pp. 562-571
Author(s):  
Nikita O. Dorodnykh ◽  
Aleksandr Yu. Yurin
Keyword(s):  
Data Transformation ◽  
Ontology Engineering ◽  
Petrochemical Equipment ◽  
Inspection Tasks

Jaya Ant Lion Optimization-Driven Deep Recurrent Neural Network for Cancer Classification Using Gene Expression Data (Preprint)

2020 ◽  
Author(s):  
Ramachandro Majji
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Early Stage ◽  
Data Transformation ◽  
Cancer Classification ◽  
Data Normalization ◽  
Ant Lion Optimization ◽  
Deep Recurrent Neural Network ◽  
Novel Strategy ◽  
Feature Dimension

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.

scholarly journals bSSA: Binary Salp Swarm Algorithm with Hybrid Data Transformation for Feature Selection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sayar Singh Shekhawat ◽  
Harish Sharma ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Basit Qureshi
Keyword(s):  
Feature Selection ◽  
Data Transformation ◽  
Salp Swarm Algorithm ◽  
Hybrid Data ◽  
Swarm Algorithm

scholarly journals Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 642
Author(s):  
Luis Miguel González de Santos ◽  
Ernesto Frías Nores ◽  
Joaquín Martínez Sánchez ◽  
Higinio González Jorge
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Execution Time ◽  
Point Cloud ◽  
Indoor Environments ◽  
Calculation Algorithm ◽  
Time Restrictions ◽  
Planning Algorithm ◽  
Inspection Tasks ◽  
Path Planning Algorithm

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

scholarly journals Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1511
Author(s):  
Taylor Simons ◽  
Dah-Jye Lee
Keyword(s):  
Neural Networks ◽  
Visual Inspection ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Quality Inspection ◽  
Agricultural Produce ◽  
Resource Limited ◽  
Inspection Tasks ◽  
Computational Resources ◽  
Small Models

There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models.

scholarly journals Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1385
Author(s):  
Yurong Feng ◽  
Kwaiwa Tse ◽  
Shengyang Chen ◽  
Chih-Yung Wen ◽  
Boyang Li
Keyword(s):  
System Design ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Visual Inspection ◽  
Spatial Information ◽  
Free Flight ◽  
Object State ◽  
Aerial Vehicles ◽  
Inspection Tasks ◽  
Autonomous Inspection

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

scholarly journals Characterizing the Effects of Explosive Ordnance Disposal Operations on the Human Body While Wearing Heavy Personal Protective Equipment

2021 ◽  
pp. 001872082199262
Author(s):  
Yi-Ning Wu ◽  
Adam Norton ◽  
Michael R. Zielinski ◽  
Pei-Chun Kao ◽  
Andrew Stanwicks ◽  
...  
Keyword(s):  
Human Body ◽  
Personal Protective Equipment ◽  
Evaluation Methodology ◽  
Protective Equipment ◽  
Physiological Measurement ◽  
Cognitive Stress ◽  
Explosive Ordnance Disposal ◽  
And Performance ◽  
Inspection Tasks ◽  
Muscle Activations

Objective To provide a comprehensive characterization of explosive ordnance disposal (EOD) personal protective equipment (PPE) by evaluating its effects on the human body, specifically the poses, tasks, and conditions under which EOD operations are performed. Background EOD PPE is designed to protect technicians from a blast. The required features of protection make EOD PPE heavy, bulky, poorly ventilated, and difficult to maneuver in. It is not clear how the EOD PPE wearer physiologically adapts to maintain physical and cognitive performance during EOD operations. Method Fourteen participants performed EOD operations including mobility and inspection tasks with and without EOD PPE. Physiological measurement and kinematic data recording were used to record human physiological responses and performance. Results All physiological measures were significantly higher during the mobility and the inspection tasks when EOD PPE was worn. Participants spent significantly more time to complete the mobility tasks, whereas mixed results were found in the inspection tasks. Higher back muscle activations were seen in participants who performed object manipulation while wearing EOD PPE. Conclusion EOD operations while wearing EOD PPE pose significant physical stress on the human body. The wearer’s mobility is impacted by EOD PPE, resulting in decreased speed and higher muscle activations. Application The testing and evaluation methodology in this study can be used to benchmark future EOD PPE designs. Identifying hazards posed by EOD PPE lays the groundwork for developing mitigation plans, such as exoskeletons, to reduce physical and cognitive stress caused by EOD PPE on the wearers without compromising their operational performance.

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