Ultimate Strength of RC Wall Panels in One-Way In-Plane Action

Test results of 24 reinforced concrete wall panels in one-way in-plane action are presented. The panels were loaded at a small eccentricity to reflect possible eccentric loading in practice. Influences of slenderness ratio, aspect ratio, vertical steel, and horizontal steel on the ultimate load are studied. An empirical equation modifying two existing methods is proposed for the prediction of ultimate load. The modified equation includes the effects of slenderness ratio, amount of vertical steel, and aspect ratio. The results predicted by the proposed modified method and five other available equations are compared with 48 test data. The proposed modified equation is found to be satisfactory and, additionally, includes the effect of aspect ratio which is not present in other methods.

Computer modelling studies on the mechanism of action of ribonuclease T1

The mechanism of action of ribonuclease (RNase) T1 is still a matter of considerable debate as the results of x-ray, 2-D nmr and site-directed mutagenesis studies disagree regarding the role of the catalytically important residues. Hence computer modelling studies were carried out by energy minimisation of the complexes of RNase T1 and some of its mutants (His40Ala, His40Lys, and Glu58Ala) with the substrate guanyl cytosine (GpC), and of native RNase T1 with the reaction intermediate guanosine 2',3'-cyclic phosphate (G greater than p). The puckering of the guanosine ribose moiety in the minimum energy conformer of the RNase T1-GpC (substrate) complex was found to be O4'-endo and not C3'-endo as in the RNase T1-3'-guanylic acid (inhibitor/product) complex. A possible scheme for the mechanism of action of RNase T1 has been proposed on the basis of the arrangement of the catalytically important amino acid residues His40, Glu58, Arg77, and His92 around the guanosine ribose and the phosphate moiety in the RNase T1-GpC and RNase T1-G greater than p complexes. In this scheme, Glu58 serves as the general base group and His92 as the general acid group in the transphosphorylation step. His40 may be essential for stabilising the negatively charged phosphate moiety in the enzyme-transition state complex.

Ultimate Strength of R.C. Wall Panels in Two-Way In-Plane Action

Test results of 24 reinforced concrete wall panels in two-way action (i.e., supported on all the four sides) and subjected to in-plane vertical load are presented. The load is applied at an eccentricity to represent possible accidental eccentricity that occurs in practice due to constructional imperfections. Influences of aspect ratio, thinness ratio, slendemess ratio, vertical steel, and horizontal steel on the ultimate load are studied. Two equations are proposed to predict the ultimate load carried by the panels. The first equation is empirical and is arrived at from trial and error fitting with test data. The second equation is semi-empirical and is developed from a modification of the buckling strength of thin rectangular plates. Both the equations are formulated so as to give a safe prediction of a large portion of ultimate strength test results. Also, ultimate load cracking load and lateral deflections of identical panels in two-way action (all four sides supported) and oneway action (top and bottom sides only supported) are compared.

Effective action and adiabatic expansions for the 1-D and 2-D hubbard models at half-filling

We analyze here the occurrence of antiferromagnetic (AFM) correlations in the half-filled Hubbard model in one and two space dimensions using a natural fermionic representation of the model and a newly proposed way of implementing the half-filling constraint. We find that our way of implementing the constraint is capable of enforcing it exactly already at the lowest levels of approximation. We discuss how to develop a systematic adiabatic expansion for the model and how Berry's phase contributions arise quite naturally from the adiabatic expansion. At low temperatures and in the continuum limit the model gets mapped onto an O(3) nonlinear sigma model (NLsigma). A topological, Wess-Zumino term is present in the effective action of the ID NLsigma as expected, while no topological terms are present in 2D. Some specific difficulties that arise in connection with the implementation of an adiabatic expansion scheme within a thermodynamic context are also discussed, and we hint at possible solutions.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

RAPID HUMAN ACTION RECOGNITION IN H.264/AVC COMPRESSED DOMAIN FOR VIDEO SURVEILLANCE

This paper discusses a novel high-speed approach for human action recognition in H. 264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of our work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can effect in reduced hardware utilization and fast recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust in outdoor as well as indoor testing scenarios. We have tested our method on two benchmark action datasets and achieved more than 85% accuracy. The proposed algorithm classifies actions with speed (>2000 fps) approximately 100 times more than existing state-of-the-art pixel-domain algorithms.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Salmon action plan: River Kent, July 2001

The report presents the Environment Agency's 5 year Salmon Action Plan for the River Kent for the period from January 2001, providing the background to the plan, responses to the public consultation given, and proposed actions.

The proposed Lune net limitation order 1999 and associated byelaws

The River Lune rises in the hills around Tebay in thje North of England and runs through rural farming country to Morecambe bay. It is generally considered as a river of high purity and unspoiled nature. The salmon fishery was at one time considered amongst the best in England and Wales, with very high catches to both rod and net fishermen. During the late 1960's the disease UDN decimated the stock. Since then there has been a recovery of the stock, but this is considered by most anglers and netsmen to be a partial recovery of some of the previous stock components. In recent years anglers and netsmen have voiced their concerns over the Lune stock and have lobbied for action to improve the Lune fishery. This net limitation order (NLO) and the separate byelaw in conjunction with habitat improvement are proposed as part of the strategy for future conservation and management of this salmon fishery. The fishery is currently exploited by 37 licensed netsmen, the highest number of any single estuary in England and Wales. There are 26 haaf, 10 drift and 1 seine nets available. Current estimates of the rod fishery are that 1100 to 1400 anglers fish 14 000 days per year. The River Luhe is one of the few rivers within England and Wales that has the benefit of an accurate fish counter. The counter is at Forge Weir approximately 4 km upstream of the tidal limit. The counts, together with records of the catches from the rod and net fishery, enable a reasonably accurate assessment of both rod and net exploitation. Extensive surveys of the juvenile population, carried out in 1991 and 1997, provide additional information. The purpose of this document is to describe and explain the current state of the salmon population in the River Lune and in doing so, demonstrate the current need for stock conservation. A second purpose is to demonstrate that the proposed NLO and byelaw package should allow the salmon population to reach its conservation target (spawning escapement target).

Vision, Instruction, and Action

This thesis describes Sonja, a system which uses instructions in the course of visually-guided activity. The thesis explores an integration of research in vision, activity, and natural language pragmatics. Sonja's visual system demonstrates the use of several intermediate visual processes, particularly visual search and routines, previously proposed on psychophysical grounds. The computations Sonja performs are compatible with the constraints imposed by neuroscientifically plausible hardware. Although Sonja can operate autonomously, it can also make flexible use of instructions provided by a human advisor. The system grounds its understanding of these instructions in perception and action.

A Model for Deliberation, Action, and Introspection

This thesis investigates the problem of controlling or directing the reasoning and actions of a computer program. The basic approach explored is to view reasoning as a species of action, so that a program might apply its reasoning powers to the task of deciding what inferences to make as well as deciding what other actions to take. A design for the architecture of reasoning programs is proposed. This architecture involves self-consciousness, intentional actions, deliberate adaptations, and a form of decision-making based on dialectical argumentation. A program based on this architecture inspects itself, describes aspects of itself, and uses this self-reference and these self-descriptions in making decisions and taking actions. The program's mental life includes awareness of its own concepts, beliefs, desires, intentions, inferences, actions, and skills. All of these are represented by self-descriptions in a single sort of language, so that the program has access to all of these aspects of itself, and can reason about them in the same terms.

Action design research: a comparison with canonical action research and design science

This research in progress paper addresses the IS issue in relation to conducting relevant research while keeping academic rigor. In particular, it contributes to the ongoing academic conversation around the investigation on how to incor-porate action in design science research. In this document the philosophical underpinnings of the recently proposed methodology called Action Design Re-search [1] are derived, outlined and integrated into Burrel and Morgan’s Par-adigmatic Framework (1979)[6]. The results so far show how Action Design Research can be considered as a particular case of Design Science Research (rather than a methodology closely related to Action Research) although they can assume two different epistemological positions. From these philosophical perspectives, future works will involve the inclusion of actual research projects using the three different methodologies. The final goal is to outline and structure the divergences and similarities of Action Design Research with Design Science Research and Canonical Action Research.