Comparisons of GM (1,1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan

AbstractThis paper represents the first study to compare seven types of first–order and one–variable grey differential equation model [abbreviated as GM (1, 1)] and back-propagation artificial neural network (BPNN) for predicting hourly particulate matter (PM) including PMio and PM2.5 concentrations in Dali area of Taichung City, Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) was 16.76%, 132.95, and 11.53, respectively for PM10 prediction. For PM2.5 prediction, the minimum MAPE, MSE, and RMSE value of 21.64%, 40.41, and 6.36, respectively could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) could predict the hourly PM variation precisely even comparing with BPNN

Reconfigurable Architecture and Dataflow for Memory Traffic Minimization of CNNs Computation

Computation of convolutional neural network (CNN) requires a significant amount of memory access, which leads to lots of energy consumption. As the increase of neural network scale, this phenomenon is further obvious, the energy consumption of memory access and data migration between on-chip buffer and off-chip DRAM is even much more than the computation energy on processing element array (PE array). In order to reduce the energy consumption of memory access, a better dataflow to maximize data reuse and minimize data migration between on-chip buffer and external DRAM is important. Especially, the dimension of input feature map (ifmap) and filter weight are much different for each layer of the neural network. Hardware resources may not be effectively utilized if the array architecture and dataflow cannot be reconfigured layer by layer according to their ifmap dimension and filter dimension, and result in a large quantity of data migration on certain layers. However, a thorough exploration of all possible configurations is time consuming and meaningless. In this paper, we propose a quick and efficient methodology to adapt the configuration of PE array architecture, buffer assignment, dataflow and reuse methodology layer by layer with the given CNN architecture and hardware resource. In addition, we make an exploration on the different combinations of configuration issues to investigate their effectiveness and can be used as a guide to speed up the thorough exploration process

Reconfigurable Architecture and Dataflow for Memory Traffic Minimization of CNNs Computation

Computation of convolutional neural network (CNN) requires a significant amount of memory access, which leads to lots of energy consumption. As the increase of neural network scale, this phenomenon is further obvious, the energy consumption of memory access and data migration between on-chip buffer and off-chip DRAM is even much more than the computation energy on processing element array (PE array). In order to reduce the energy consumption of memory access, a better dataflow to maximize data reuse and minimize data migration between on-chip buffer and external DRAM is important. Especially, the dimension of input feature map (ifmap) and filter weight are much different for each layer of the neural network. Hardware resources may not be effectively utilized if the array architecture and dataflow cannot be reconfigured layer by layer according to their ifmap dimension and filter dimension, and result in a large quantity of data migration on certain layers. However, a thorough exploration of all possible configurations is time consuming and meaningless. In this paper, we propose a quick and efficient methodology to adapt the configuration of PE array architecture, buffer assignment, dataflow and reuse methodology layer by layer with the given CNN architecture and hardware resource. In addition, we make an exploration on the different combinations of configuration issues to investigate their effectiveness and can be used as a guide to speed up the thorough exploration process

Delusions in Patients with Dementia with Lewy Bodies and the Associated Factors

Objective. Delusions are common neuropsychiatric symptoms in patients with dementia with Lewy bodies (DLB). The aim of this study was to investigate the associated factors of delusions in patients with DLB. Method. A retrospective study of outpatients with DLB registered in a regional hospital’s database was performed. The associated factors including cognitive performance, clinical features, vascular risk factors, and neuropsychiatric symptoms between delusional and nondelusional patients with DLB were compared. Results. Among 207 patients with DLB, 106 (51.2%) were delusional and 101 (48.8%) were not. Delusion of other persons are stealing was the most common symptom (35.3%). The delusional group had a significantly higher diagnostic rate of probable than possible DLB, higher disease severity, poorer cognitive performance, more severe neuropsychiatric symptoms, and higher caregiver burden (all p<0.05). In addition, the delusional group had a significantly lower frequency of diabetes compared to the nondelusional group (odds ratio=0.28, p<0.001). Conclusion. Delusion of other persons are stealing was the most common delusional symptom. The patients with DLB who presented with delusions had poorer cognitive function and more severe neuropsychiatric symptoms. A novel finding is that the DLB patients with diabetes had a lower frequency of delusions

Recent Advances in Micro-LEDs Having Yellow–Green to Red Emission Wavelengths for Visible Light Communications

Visible light communication (VLC), which will primarily support high-speed internet connectivity in the contemporary world, has progressively come to be recognized as a significant alternative and reinforcement in the wireless communication area. VLC has become more popular recently because of its many advantages over conventional radio frequencies, including a higher transmission rate, high bandwidth, low power consumption, fewer health risks, and reduced interference. Due to its high-bandwidth characteristics and potential to be used for both illumination and communications, micro-light-emitting diodes (micro-LEDs) have drawn a lot of attention for their use in VLC applications. In this review, a detailed overview of micro-LEDs that have long emission wavelengths for VLC is presented, along with their related challenges and future prospects. The VLC performance of micro-LEDs is influenced by a number of factors, including the quantum-confined Stark effect (QCSE), size-dependent effect, and droop effect, which are discussed in the following sections. When these elements are combined, it has a major impact on the performance of micro-LEDs in terms of their modulation bandwidth, wavelength shift, full-width at half maximum (FWHM), light output power, and efficiency. The possible challenges faced in the use of micro-LEDs were analyzed through a simulation conducted using Crosslight Apsys software and the results were compared with the previous reported results. We also provide a brief overview of the phenomena, underlying theories, and potential possible solutions to these issues. Furthermore, we provide a brief discussion regarding micro-LEDs that have emission wavelengths ranging from yellow–green to red colors. We highlight the notable bandwidth enhancement for this paradigm and anticipate some exciting new research directions. Overall, this review paper provides a brief overview of the performance of VLC-based systems based on micro-LEDs and some of their possible applications