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Maintenance is one of the most critical and expensive operations during the life cycle of metallic structures, in particular in the aeronautic industry. However, early detection of fatigue cracks is one of the most demanding operations in global maintenance procedures. In this context, non-destructive testing using image techniques may represent one of the best solutions in such situations, especially thermal stress analyses (TSA) using infrared thermography. The purpose of this paper is to access and characterize the main stress profile calculated through temperature variation, for different load frequencies.

In this paper, a cyclic load is applied to an aluminum sample component while infrared thermal image is being acquired. According to the literature and experiments, a cyclic load applied to a material results in cyclic temperature variation.

Frequency has been shown to be an important parameter in TSA evaluations, increasing the measured stress profile amplitude. The loading stimulation frequency and the maximum stress recorded show a good correlation (R² higher than 0.995). It was verified that further tests and modeling should be performed to fully comprehend the influence of load frequency and to create a standard to conduct thermal stress tests.

This work revealed that the current infrared technology is capable of reaching far more detailed thermal and spatial resolution than the one used in the development of TSA models. Thus, for the first time the influence of mechanical load frequency in the thermal profiles of TSA is visible and consequentially the measured mechanical stress.

The purpose of this paper is to develop an inverse approach for 3D thermal sources determination.

The developed approach is based on the Green's function for Poison's equation. Forward and inverse couple electromagnetic‐thermal field problems are formulated. Finite elements models are built and applied. Thermal field data are acquired by thermo vision camera. The thermal field sources are determined inside of the investigated inaccessible volume object using modeled and measured data with the developed approach.

The presented method and implemented examples demonstrate the possibilities of the developed approach for inverse source problem solution and determination of thermal field distributions of electrical devices.

The proposed inverse method uses the Green's function for Poison's equation for solution of thermal field problem taking into account the couple electromagnetic‐thermal problems. Proposed inverse method is very fast, accurate and can be used in many practical activities for electrical current determination and visualization in inaccessible regions only by measured external thermal field. Thermal field data needed for the method are easily acquired by thermo vision camera.

Reviews the use of three defect diagnosis options available to surveyors and engineers, with particular regard to high‐rise buildings in Hong Kong. Draws comparisons between the various defect diagnosis techniques , such as traditional manual hammer tapping, impact‐echo system and thermography.

As real‐time, high‐fidelity visual scene simulation has become ubiquitous in the training, modeling and simulation community, a growing need for more than “out‐the‐window” scene simulation has developed. A strong requirement has developed for the ability to simulate the output of different types of sensors, especially electro‐optical (EO), infrared (IR), night vision goggle (NVG) and radar systems. To satisfy the need for advanced sensor simulation, Evans & Sutherland (E&S) has developed a physics‐based, dynamic, real‐time sensor simulation which allows users to model advanced EO, IR and NVG devices that are fully correlated with the “out‐the‐window” visual view. In this paper, the unique sensor simulation capabilities of E&S will be described. A brief description of the physics employed, input and output are presented along with example images.

Schools should be adequately built and operated to protect students' health. Green building rating systems, including Leadership in Energy and Environmental Design (LEED), assist the construction industry in improving both the resource efficiency and indoor environmental quality of its buildings. Construction professionals may waive some green modifications and available optional credits due to their high costs or construction complexities. This study investigates whether cost-effective green modifications can adequately address the student health.

In an effort to identify how school projects in Dubai, UAE prioritized LEED credits related to occupant well-being, the study identified eight LEED credits (called “Health and wellbeing” credits). Cost data from a sample of nine Dubai schools were used to develop an indicator, named the Feasibility Index Score (FIS), to quantify the attractiveness of LEED credits based on their cost and implementation complexity. Physical measurements taken from the sample schools give a window into current indoor environmental quality (IEQ) conditions of schools in the local region, while FIS provides insight into potential financial barriers towards improving these conditions.

The authors identified eight “Health and wellbeing” credits, which may net up to 14 points or 13% of all possible LEED points. Despite this, assessments of the sample schools revealed that six of the “Health and wellbeing” credits exhibited relatively low FIS values. This may cause these credits to be waived when lower tiers of LEED certifications are desired.

A sample of nine schools was chosen for this research; further investigation using a greater sample size is recommended.

The paper's IEQ assessment indicates the importance of health-related credits and suggests implementing them regardless of their FIS.

This paper recognizes the importance of providing more weight to credits that directly impact the health of occupants, particularly when upgrading existing structures.

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.

The purpose of this study is to provide an in-depth understanding about the indoor-orbital electrical inspection robot, which is useful for motivating the further investigation on the inspection of electrical equipment. Currently, electric energy has a strong correlation with the economic development of the country. Intelligent substations play an important role in the transmission and distribution of the electricity; the maintenance of the substation has attracted intensive attention due to the requirement of reliability and safety. The indoor-orbital electrical inspection robot has increasingly become the main tool to realize the unmanned. Hence, a systematic review is conducted systematically reviewing the current technical status of the indoor-orbital electrical inspection robot and discuss the existed problems.

In this paper, the most essential achievements in the field of indoor-orbital electrical inspection robots were reported to present the current status, and the mechanical structures and key inspective technologies were also discussed.

Four recommendations are provided from the analyzed review, which have made constructive comments on the overall structural design, functionality, intelligence and future development direction of the indoor-orbital electrical inspection robot, respectively.

To the best of the authors’ knowledge, this is the first systematic review study on indoor-orbital electrical inspection robots; it fills the theoretical gap and proffers design ideas and directions for the development of the indoor-orbital electrical inspection robot.

There is an increasing requirement for inspection of electronic components, assemblies and interconnections, and to meet this demand there are new developments in inspection techniques. None of the techniques is universally applicable, but many are capable of consistent and reliable results. This paper outlines the major techniques which are available and summarises their capabilities. The limitations on types of component and boards which may be examined are listed, and the difficulties of detecting some flaws with some techniques are highlighted and the reasons considered.