Pellet size dependent steam reforming of polyethylene terephthalate waste for hydrogen production over Ni/La promoted Al2O3 catalyst

The effect of different pellet sizes of nickel (Ni) and lanthanum (La) promoted Al₂O₃ support on the catalytic performance for selective hydrogen production from polyethylene terephthalate (PET) plastic waste via steam reforming process has been investigated. The catalysts were prepared by impregnation method and were characterized using XRD, BET, TPD-CO₂, TPR, SEM, EDX, TEM and TGA. The results showed that NiLa-co-impregnated Al₂O₃ catalyst has excellent activity for the production of hydrogen. Feed conversion of 88.53% was achieved over 10% Ni/Al₂O₃ catalyst which increased to 95.83% in the case of 10% Ni-5% La/Al₂O₃ catalysts with a H₂ selectivity of 70.44%. The catalyst performance in term of gas production and feed conversion was further investigated under various operating parameters, e.g., feed flow-rate, and catalyst pellet size. It was found that at 0.4 ml/min feed flow rate, highest feed conversion and H₂ selectivity were achieved. The Ni particles, which are the noble-based active species are highly effective, thus offered good hydrogen production in the phenol-PET steam reforming process. Incorporation of La as a promoter in Ni/Al₂O₃ catalyst has significantly increased the catalyst reusability with prolonged stability. The NiLa/Al₂O₃ catalyst with larger size showed remarkable activity due to the presence of significant temperature gradients inside the pellet compared to smaller size. Additionally, the catalyst showed only slight decrease in H₂ selectivity and feed conversion even after 24 h, although production of carbon nanotubes was evidenced on its surface.     

Exploring video content structure for hierarchical summarization

In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot- segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units. A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.Published online: 15 September 2004 Corespondence to: Xingquan ZhuThis research has been supported by the NSF under grants 9972883-EIA, 9974255-IIS, 9983248-EIA, and 0209120-IIS, a grant from the state of Indiana 21^th Century Fund, and by the U.S. Army Research Laboratory and the U.S. Army Research Office under grant DAAD19-02-1-0178.     

Humanoids skill learning based on real-time human motion imitation using Kinect

In this paper, a novel framework which enables humanoid robots to learn new skills from demonstration is proposed. The proposed framework makes use of real-time human motion imitation module as a demonstration interface for providing the desired motion to the learning module in an efficient and user-friendly way. This interface overcomes many problems of the currently used interfaces like direct motion recording, kinesthetic teaching, and immersive teleoperation. This method gives the human demonstrator the ability to control almost all body parts of the humanoid robot in real time (including hand shape and orientation which are essential to perform object grasping). The humanoid robot is controlled remotely and without using any sophisticated haptic devices, where it depends only on an inexpensive Kinect sensor and two additional force sensors. To the best of our knowledge, this is the first time for Kinect sensor to be used in estimating hand shape and orientation for object grasping within the field of real-time human motion imitation. Then, the observed motions are projected onto a latent space using Gaussian process latent variable model to extract the relevant features. These relevant features are then used to train regression models through the variational heteroscedastic Gaussian process regression algorithm which is proved to be a very accurate and very fast regression algorithm. Our proposed framework is validated using different activities concerned with both human upper and lower body parts and object grasping also.     

Gate and common pathway detection in crowd scenes and anomaly detection using motion units and LSTM predictive models

In this paper, we propose two approaches to analyze the crowd scenes. The first one is motion units and meta-tracking based approach (MUDAM Approach). In this approach, the scene is divided into a number of dynamic divisions with coherent motion dynamics called the motion units (MUs). By analyzing the relationships between these MUs using a proposed continuation likelihood, the scene entrance and exit gates are retrieved. A meta-tracking procedure is then applied and the scene dominant motion pathways are retrieved. To overcome the limitations of the MUDAM approach, and detect some of the anomalies, that may happen in these scenes, we proposed another new LSTM based approach. In this approach, the scene is divided into a number of static overlapped spatial regions named super regions (SRs), which cover the whole scene. Long Short Term Memory (LSTM) is used in defining a predictive model for each of the scene SRs. Each LSTM predictive model uses its SR tracklets in the training, such that, it can capture the whole motion dynamics of that SR. Using apriori known scene entrance segments, the proposed LSTM predictive models are applied and the scene dominant motion pathways are retrieved. an anomaly metric is formulated to be used with the LSTM predictive models to detect the scene anomalies. Prototypes of our proposed approaches were developed and evaluated on the challenging New York Grand Central station scene, in addition to four other crowded scenes. Four types of anomalies that may happen in the crowded scenes were defined in the context, and our proposed LSTM based approach was used in detecting these anomalies. Experimental results on anomalies detection were applied too on a number of data sets. Ov erall, the proposed approaches managed to outperform the state of the art methods in retrieving the scene gates and common pathways, in addition to detecting motion anomalies.

     

[NH3(CH2)2NH3][Co(SO4)2(H2O)4]: Chemical preparation, crystal structure, thermal decomposition and magnetic properties

Cobalt ethylenediammonium bis(sulfate) tetrahydrate, [NH₃(CH₂)₂NH₃][Co(SO₄)₂(H₂O)₄], has been synthesised by slow evaporation at room temperature. It crystallises in the triclinic system, space group , with the unit cell parameters: a = 6.8033(2), b = 7.0705(2), c = 7.2192(3) Å, α = 74.909(2)°, β = 72.291(2)°, γ = 79.167(2)°, Z = 1 and V = 317.16(2) Å³. The Co(II) atom is octahedrally coordinated by four water molecules and two sulfate tetrahedra leading to trimeric units [Co(SO₄)₂(H₂O)₄]. These units are linked to each other and to the ethylenediammonium cations through OW-H…O and N-H…O hydrogen bonds, respectively. The zero-dimensional structure is described as an alternation between cationic and anionic layers along the crystallographic b-axis. The dehydration of the precursor proceeds through three stages leading to crystalline intermediary hydrate phases and an anhydrous compound. The magnetic measurements show that the title compound is predominantly paramagnetic with weak antiferromagnetic interactions.     

ENHANCING KALMAN FILTERING–BASED TIGHTLY COUPLED NAVIGATION SOLUTION THROUGH REMEDIAL ESTIMATES FOR PSEUDORANGE MEASUREMENTS USING PARALLEL CASCADE IDENTIFICATION

Integrated global positioning system (GPS) solutions that utilize micro-electro-mechanical systems (MEMS)-based inertial sensors provide a more accurate navigation solution than stand-alone GPS in challenging scenarios. To keep the integrated solution less affected by sensor errors and to decrease the cost, a reduced inertial sensor system (RISS), which consists of only one gyroscope and two accelerometers, together with an odometer and integrated with GPS, is proposed. Tightly coupled integration is a better choice in demanding scenarios, as it can provide GPS aiding even when the number of visible satellites is three or less. However, inaccuracies of pseudoranges measured by the GPS receiver and used as aiding in the RISS/odometer/GPS integration solution will affect the overall positioning accuracy. This article explores the benefits of using parallel cascade identification (PCI), a nonlinear system identification technique that improves the overall navigation solution by modeling residual pseudorange correlated errors to be used by a Kalman filter (KF)–based tightly coupled RISS/odometer/GPS navigational solution. When less than four satellites are visible, the identified parallel cascade model for the still visible satellites is used to predict the residual pseudorange errors for these respective satellites, and the corrected pseudorange value is provided to KF. The performance of PCI for correcting the pseudoranges is examined and verified using road test trajectories and compared to a traditional tightly coupled RISS/odometer/GPS KF solution. The results demonstrate the advantages of this technique in correcting the pseudoranges and enhancing the positional solution.