PMV model is insufficient to capture subjective thermal response from Indians

A controlled laboratory experiment was carried out on forty Indian male college students for evaluating the effect of indoor thermal environment on occupants' response and thermal comfort. During experiment, indoor temperature varied from 21 degrees C to 33 degrees C, and the variables like relative humidity, airflow, air temperature and radiant temperature were recorded along with subject's physiological parameters (skin (T-sk) and oral temperature (T-c)) and subjective thermal sensation responses (TSV). From T-sk and T-c, body temperature (T-b) was evaluated. Subjective Thermal Sensation Vote (TSV) was recorded using ASHRAE 7-point scale. In PMV model, Fanger's T-sk equation was used to accommodate adaptive response. Step-wise regression analysis result showed T-b was better predictor of TSV than T-sk and T-c. Regional skin temperature response, suppressed sweating without dipping, lower sweating threshold temperature and higher cutaneous threshold for sweating were observed as thermal adaptive responses. These adaptive responses cannot be considered in PMV model. To incorporate subjective adaptive response, mean skin temperature (T-sk) is considered in dry heat loss calculation. Along with these, PMV-model and other two methodologies are adopted to calculate PMV values and results are compared. However, recent literature is limited to measure the sweat rate in Indians and consideration of constant Ersw in PMV model needs to be corrected. Using measured T-sk in PMV model (Method(1)), thermal comfort zone corresponding to 0.5 <= PMV <= 0.5 was evaluated as (22.46-25.41) degrees C with neutral temperature of 23.91 degrees C, similarly while using TSV response, wider comfort zone was estimated as (23.25-26.32) degrees C with neutral temperature at 24.83 degrees C, which was further increased to with TSV-PPDnew, relation. It was observed that PMV-model overestimated the actual thermal response. Interestingly, these subjects were found to be less sensitive to hot but more sensitive to cold. A new TSV-PPD relation (PPDnew) was obtained from the population distribution of TSV response with an asymmetric distribution of hot-cold thermal sensation response from Indians. The calculations of human thermal stress according to steady state energy balance models used on PMV model seem to be inadequate to evaluate human thermal sensation of Indians. Relevance to industry: The purpose of this paper is to estimate thermal comfort zone and optimum temperature for Indians. It also highlights that PMV model seems to be inadequate to evaluate subjective thermal perception in Indians. These results can be used in feedback control of HVAC systems in residential and industrial buildings. (C) 2014 Elsevier B.V. All rights reserved.

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

Timer-Based Distributed Node Selection Scheme Exploiting Power Control and Capture

Opportunistic selection in multi-node wireless systems improves system performance by selecting the ``best'' node and by using it for data transmission. In these systems, each node has a real-valued local metric, which is a measure of its ability to improve system performance. Our goal is to identify the best node, which has the largest metric. We propose, analyze, and optimize a new distributed, yet simple, node selection scheme that combines the timer scheme with power control. In it, each node sets a timer and transmit power level as a function of its metric. The power control is designed such that the best node is captured even if. other nodes simultaneously transmit with it. We develop several structural properties about the optimal metric-to-timer-and-power mapping, which maximizes the probability of selecting the best node. These significantly reduce the computational complexity of finding the optimal mapping and yield valuable insights about it. We show that the proposed scheme is scalable and significantly outperforms the conventional timer scheme. We investigate the effect of. and the number of receive power levels. Furthermore, we find that the practical peak power constraint has a negligible impact on the performance of the scheme.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

No More HF: Teflon-Assisted Ultrafast Removal of Silica to Generate High-Surface-Area Mesostructured Carbon for Enhanced CO2 Capture and Supercapacitor Performance

An innovative technique to obtain high-surface-area mesostructured carbon (2545m(2)g(-1)) with significant microporosity uses Teflon as the silica template removal agent. This method not only shortens synthesis time by combining silica removal and carbonization in a single step, but also assists in ultrafast removal of the template (in 10min) with complete elimination of toxic HF usage. The obtained carbon material (JNC-1) displays excellent CO2 capture ability (ca. 26.2wt% at 0 degrees C under 0.88bar CO2 pressure), which is twice that of CMK-3 obtained by the HF etching method (13.0wt%). JNC-1 demonstrated higher H-2 adsorption capacity (2.8wt%) compared to CMK-3 (1.2wt%) at -196 degrees C under 1.0bar H-2 pressure. The bimodal pore architecture of JNC-1 led to superior supercapacitor performance, with a specific capacitance of 292Fg(-1) and 182Fg(-1) at a drain rate of 1Ag(-1) and 50Ag(-1), respectively, in 1m H2SO4 compared to CMK-3 and activated carbon.

No More HF: Teflon-Assisted Ultrafast Removal of Silica to Generate High-Surface-Area Mesostructured Carbon for Enhanced CO2 Capture and Supercapacitor Performance

An innovative technique to obtain high-surface-area mesostructured carbon (2545m(2)g(-1)) with significant microporosity uses Teflon as the silica template removal agent. This method not only shortens synthesis time by combining silica removal and carbonization in a single step, but also assists in ultrafast removal of the template (in 10min) with complete elimination of toxic HF usage. The obtained carbon material (JNC-1) displays excellent CO2 capture ability (ca. 26.2wt% at 0 degrees C under 0.88bar CO2 pressure), which is twice that of CMK-3 obtained by the HF etching method (13.0wt%). JNC-1 demonstrated higher H-2 adsorption capacity (2.8wt%) compared to CMK-3 (1.2wt%) at -196 degrees C under 1.0bar H-2 pressure. The bimodal pore architecture of JNC-1 led to superior supercapacitor performance, with a specific capacitance of 292Fg(-1) and 182Fg(-1) at a drain rate of 1Ag(-1) and 50Ag(-1), respectively, in 1m H2SO4 compared to CMK-3 and activated carbon.