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![Network architecture of the spectral reconstruction MLP as used in [23], with 10 sensor channel inputs, 4 hidden layers with 25 fully connected neurons each, sigmoid activation functions and 81 output nodes for wavelengths between 380 and 780 nm.](publication/371272764/figure/fig2/AS:11431281330456701@1743194877440/Network-architecture-of-the-spectral-reconstruction-MLP-as-used-in-23-with-10-sensor.jpg)
Network architecture of the spectral reconstruction MLP as used in [23], with 10 sensor channel inputs, 4 hidden layers with 25 fully connected neurons each, sigmoid activation functions and 81 output nodes for wavelengths between 380 and 780 nm.
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Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditio...
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A method for spectral reflectance factor reconstruction based on wideband multi-illuminant imaging was proposed, using a programmable LED lighting system and modified Bare Bones Particle Swarm Optimization algorithms. From a set of 16 LEDs with different spectral power distributions, nine light sources with correlated color temperatures in the rang...
A university library is not only a place where students, professionals and readers can find books and other reading materials but it is also a place where reading and writing are done. In the construction and renovation of the libraries in one of the campuses of ABC University, much attention had been given on the physical size, availability of mat...
Citations
... Today's smartphones have advanced imaging capabilities, highquality sensors, various shutter speeds, and onboard image processing, but their accuracy should be investigated further. More advanced and calibrated imaging techniques could enhance accuracy and performance while reducing costs [38][39][40]. Shading systems with specular characteristics are important for glare analysis. Future research should investigate façade lighting for specular reflections, which cannot be detected without photon mapping techniques. ...
Lighting in the built environment has evolved since the rapid uptake of solid-state lighting (SSL) devices [...]
... Therefore, designing an unclonable physical-layer key generation and distribution system is expected to resist the tapping threat in [23]. Neural networks are widely used in fiber channel damage compensation [24,25], channel modeling [26][27][28][29], and channel monitoring [30] because of their strong learning simulation and prediction ability. Long Short Term Memory neural network (LSTM-NN) shows excellent ability for dynamic modeling of time-varying data [31] and has recently been introduced to solve problems such as detecting, locating, and analyzing optical network faults [32], fiber nonlinear compensation in digital dry systems [33], and simultaneous accurate monitoring of optical signal-to-noise ratio and dispersion [34]. ...
In this paper, a scheme to realize unclonable physical-layer security key generation and distribution (PL-SKGD) based on historical fiber channel state information (HFCSI) is proposed. PL-SKGD schemes based on channel characteristics for enhancing the physical-layer security of optical networks have been proposed in recent years. However, there are potential disadvantages in these schemes, such as 1) low key generation rate (KGR): the slow frequency of the analog waveform change of the channel characteristic leading to low KGR; 2) incompatibility with existing infrastructure: active scrambling to increase the frequency of channel characteristic changes, or tracking changes of channel characteristics requires additional devices; 3) easy to be cloned: all of the optical channel state information is reflected in the signal transmitted inside the fiber, which makes it easy to reproduce by illegal eavesdropper through features analysis and other methods. In order to solve the above problems, a PL-SKGD scheme is designed which uses the chain structure composed of long short-term memory neural network (LSTM-NN) units to learn and store the unique mapping relationship between historical channel time series and provides unclonability based on the fundamental fact that the eavesdropper Eve can never obtain the full HFCSI. The simulation conducted in a quadrature phase shift keying point-to-point optical link system verified successfully that KGR = 0.82 Gbit/s error-free SKGD. The loss function of LSTM-NN drops sharply in the early stages of training and remains a small value. The security of the SKGD system is analyzed, which effectively improves the unclonability of the system. Finally, it is verified that the optimal fiber channel length for error-free SKGD of the proposed scheme is 150 km considering the error correction capability of information reconciliation and weighing key sequence error rate and valid bit generation rate.
Addressing the issue of cold load prediction in building energy systems, a multi-modal fusion deep learning approach is proposed. This method constructs input feature sets of three different modalities: sequence-like, image-like, and video-like, and employs bidirectional gated recurrent units, spatiotemporal neural networks, and 3D convolutional neural networks. Additionally, this paper introduces a multi-modal late fusion strategy based on stacking ensemble learning. Experimental results demonstrate that this method performs exceptionally well in cold load prediction tasks, achieving an MAPE of 5.45%, and R2 of 95.25, which is crucial for the practical implementation of low - carbon building energy management.
This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated ageing experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterisation of the sensor’s spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterisation of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017Δ
u
′
v
′ and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.
