China
Africa
Explore chapters and articles related to this topic
Remote Sensing Technique
Published in Ajai, Rimjhim Bhatnagar, Desertification and Land Degradation, 2022
Starting from the first principle, we know that all matter above absolute zero (i.e. −273.15 oC) emit EM energy, for example, all the objects around us emit EM radiation in thermal infrared. In the case of a black body, it absorbs all the radiation incident on it and re-emits all the energy it receives at all wavelengths. Black body, an ideal body having emissivity 1, emits radiation as per Planck's law and the magnitude of emitted radiation at different wavelengths depends on its physical temperature. Figure 8.3 shows the typical black-body radiation curve which shows the variation in the intensity of radiation with wavelength, it also shows how the peak of the radiation curve shifts towards a smaller wavelength when the temperature of the source is increased. At room temperature, the black-body emission is mainly in the IR region. After 1,000°C, it starts emitting light in colours of red, orange, yellow, white, blue and then increasing amounts of UV. The emission of the Earth becomes the major source of energy for passive remote sensing when observation is made at wavelengths beyond a few micrometres.
Two-Photon Interferometry II: Two-Photon Interference of Thermal Field
Published in Yanhua Shih, An Introduction to Quantum Optics, 2020
It may not be uneasy to accept the quantum concept of two-photon interference for “classical” thermal radiation. However, this is not the first time in the history of physics we apply quantum mechanical concepts to “classical” light. We should not forget that it was Planck’s theory of blackbody radiation originated the theory of quantum physics. Indeed, the radiation Planck dealt with was “classical” thermal light.
Temperature Measurements
Published in Douglas O. J. deSá, Instrumentation Fundamentals for Process Control, 2019
The Stefan Boltzmann law for blackbody radiation states that the total energy radiated per second per square centimeter is proportional to the absolute temperature raised to the fourth power, or: E = σ T4
Conceptualizing paradigms: on reading Kuhn’s history of the quantum
Published in Annals of Science, 2022
16 years after The Structure of Scientific Revolutions1 (third edition used here),2 Thomas Kuhn published Black-Body Theory and the Quantum Discontinuity, 1894–19123 (second edition used here),4 a historical study of how the quantum entered physics. When the book appeared, the standard view was that in 1901, Max Planck introduced the quantum by conceptualizing black-body radiation in terms of ‘linear electrical oscillator[s] with energy restricted to integral multiples of the energy quantum hν, ν being the oscillator frequency and h the universal constant later known by Planck’s name’.5 According to Kuhn, however, ‘the concept of restricted resonator energy played no role in [Planck’s] thought’.6
An infrared energy harvesting device using planar cross bowtie nanoantenna arrays and diode-less rectification based on electron field emission
Published in Journal of Modern Optics, 2020
A. Chekini, S. Sheikhaei, M. Neshat
One solution is to harvest energy from the infrared band. The energy radiated from the Sun has a large amount of infrared radiation. However, a major source of infrared radiation is the Earth itself. All objects, based on the black body radiation phenomenon, radiates energy into its environment. The spectrum of its radiation depends on the temperature. Around 300°K, the radiation has mostly spread in the mid-infrared band, with a peak at around 10 µm wavelength. Therefore, a mid-infrared energy harvesting device can harvest this energy and convert it into electricity. An advantage of this type of energy harvesting device over solar panels is that they can operate round the clock in a day, during the nights or cloudy days, and in countries and places with a low amount of solar radiation. Those infrared energy harvesters can also be used in conjunction with solar panels to harvest energy from the mid-infrared band of solar radiation.
Fault classification using deep learning based model and impact of dust accumulation on solar photovoltaic modules
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
Rahma Aman, M. Rizwan, Astitva Kumar
The present study is specifically concerned with the comprehensive analysis of hotspots, which are regarded as the most prominent factor influencing the overall operational efficiency of photovoltaic (PV) panels. One of the key outcomes of this research indicates that the identification of hotspots is of critical significance in accurately assessing their detrimental impact on the performance of PV cells. Therefore, there is a need for a fault detection model that can accurately preprocess data and classify faulty and healthy panels to improve solar panel performance. The faults are classified by pretrained deep learning CNN image classifiers, i.e. AlexNet and SqueezeNet. For fault detection and classification hotspots are created first the hotspots are created by the accumulation of dust on the solar panels and the hotspot images are taken in the real-time environment and then these images are captured by thermal imagers. Infrared (IR) thermography uses midwave (MWIR, 3–5 μm) or long-wave (LWIR, 7–14 μm) infrared sensors to create thermal images or thermograms of things under inspection. Planck’s black body radiation law says all objects produce infrared radiation proportional to their temperatures. IR thermography can measure the surface temperature and temperature trend of a body or solar PV module under inspection. The proposed method is shown in Figure 4. The thermal imager used for capturing the images is shown in Figure 2. The hotspot is created by the effect of dust, here dust is spread on modules in four parts in the first two parts a thin layer of dust is spread over 50% and 100% of a module and in the next parts thick layer of dust is spread over 50% and 100% of the module and hotspots are formed due to the effect of dust these hotspot images are captured by the thermal camera and the power drop is recorded by the solar analyzer as shown in Figure 3. Spread. Below are the steps that use thermal imagers to further obtain the fault classification.