Application of palm shell activated carbon filter as a medium of indoor air contaminant adsorbent for indoor air quality improvement

For decades, the inclusion of activated carbon (AC) adsorption technique through filtration has gained significant interest on improvement of indoor air quality (IAQ) by reducing level of pollutant. The interest of reseachers in palm shell AC (PSAC) keep increase owing to the fact that this material has superior characteristic as compared to commercial AC. However, the investigation of PSAC performance for air filtration are still limited and no research could be found on relating the effect of burner for carbonization on PSAC properties. Therefore, the current research was focused on producing PSAC by using new fabricated burner, exploring the effect of combination of physical and chemical activation towards PSAC properties and investigating of PSAC air filter performance used in Mechanical Ventilation Air Conditioning (MVAC) system. Preliminary studies began with IAQ monitoring in different building condition. The present data revealed that at certain situation, the buildings environment was below than satisfactory level and required mitigation plan by introducing new air filtration media in MVAC system. The best quality of charcoal was obtained by Horizontal burner with less fume formation during carbonization process compare to other design. The physical properties analysis of palm shell charcoal showed the carbonization time (CT) 2 hours gained better charcoal properties and highly recommended to continue into the activation process. After the activation process, PSAC physical+chemical shows significantly higher pore development, surface area and adsorption capacity compare to the other process. The lowest density and the highest porosity up to 0.4632 g/cm and 7.11% was calculated while the highest Iodine number of 1091.05 mg/g and BET surface area of 713.7 m 3 /g was obtained respectively in PSAC physical+chemical. Meanwhile, microstructure and composition analysis shows that, PSAC physical+chemical fully produced honeycomb form of porosity and comprised of C, O, K and Ca contents for high adsorption capacity. The improvement of IAQ in the buildings was achieved with the application of PSAC air filter which shows low concentration of CO2 with 302 ppm, CO with 0.4 ppm , TVOC with 0.1 ppm and PM10 with 0.02mg/m 2 respectively compare to the commercial filter

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

Using acoustic sensor technologies to create a more terrain capable unmanned ground vehicle

Unmanned Ground Vehicle’s (UGV) have to cope with the most complex range of dynamic and variable obstacles and therefore need to be highly intelligent in order to cope with navigating in such a cluttered environment. When traversing over different terrains (whether it is a UGV or a commercial manned vehicle) different drive styles and configuration settings need to be selected in order to travel successfully over each terrain type. These settings are usually selected by a human operator in manned systems on what they assume the ground conditions to be, but how can an autonomous UGV ‘sense’ these changes in terrain or ground conditions? This paper will investigate noncontact acoustic sensor technologies and how they can be used to detect different terrain types by listening to the interaction between the wheel and the terrain. The results can then be used to create a terrain classification list for the system so in future missions it can use the sensor technology to identify the terrain type it is trying to traverse, which creating a more autonomous and terrain capable vehicle. The technology would also benefit commercial driver assistive technologie