Multi Stream Research Articles

Verification of the self-starting problem of a Vertical Axis Wind Turbine with Inclined Blades

In this paper, a theoretical investigation of the impact of varying the blade inclination angle b on the power coefficient of vertical-axis wind turbines equipped with straight blades has been demonstration, where the inclination angle was modified from twenty-five degrees to eighty-five degrees. Within the framework of this study, the term SS-VAWT was assigned to refer to the rotor which is the subject of the research. The "Multi Stream Tube-MST" mathematical model was used after some of its equations were modified to be consistent with the geometrical shape of the SS-VAWT rotor. A computer program was designed using the "Microsoft Visual Basic" programming language as an application of the mathematical model used, which led to the effectiveness of this program and compared its final results with previous experimental results from other studies, this work was carried out on twelve obligatory SS-VAWT, which are classified into four namely H group which is characterized by rotor high change effect, R group which is characterized by diameter effect, N group which is characterized by number of blades effect and C group influenced by the length of blade chord line. The results are represented by power curves as a function of tip speed ratio. The behavior of the curves for each case was verified and compared to determine the optimum case. All results were taken in the small range of tip speed ratio λ0 extending from 0 to 2.8. Since the Cp coefficient and the λ0 coefficient are dimensionless values, the results can be applied to large-sized air rotors, provided that there is geometric similarity. Because they found that the feather's 45-degree tilt and location reduce the problem of starting self-motion.

Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.

HEALTH POLICY EVALUATION OF TAMIL NADU DOORSTEP HEALTHCARE SCHEME (MAKKALAI THEDI MARUTHUVAM) BY KINGDON'S MULTIPLE STREAMS FRAMEWORK

The rising burden of non-communicable diseases and the number of people with non-Communicable diseases (NCDs) missing out on regular care due to the fear of contracting COVID-19 paved way for a new health policy called Tamil Nadu Doorstep Healthcare Scheme (Makkalai Thedi Maruthuvam) in 2021. Though the process of policy making has been described through various frameworks, Kingdon's multi stream policy is a dynamic model that explains political agenda setting for specic health issues. Kingdon suggests that public policy comprises of three independent streams namely problem stream, policy stream and political stream.

Multi streams with dynamic balancing-based Conditional Generative Adversarial Network for paired image generation

Computer vision society experienced the birth of new CNN architecture known as Generative Adversarial Networks (GANs), which can generate fake images similar to real ones. The widespread use of GANs leads the image-to-image translation strategy dealing with more diverse tasks that were treated using traditional CNNs, such as medical analysis and semantic segmentation. In this paper, we propose a generic GAN referred to as Multi Streams with Dynamic Balancing-based Conditional Generative Adversarial Network (MSDB-CGAN). The MSDB-CGAN serves more challenging applications, that require multi input images such as binocular depth estimation, efficiently through its dedicated input streams and automatic skip connections. Moreover, the proposed GAN analyzes the inputs according to the target image, then assigns dynamic weights to the input streams. To validate the proposed MSDB-CGAN, we targeted four challenging tasks: binocular depth estimation, human-pose translation, middle frame interpolation, and future frame prediction. These applications present different inputs requirements and configurations. The reported quantitative and qualitative comparisons prove that the MSDB-CGAN significantly outperforms the existing GANs as well as traditional CNN-based architectures.

Waste Characterization Study of Pre-Covid-19 Commercial Office Buildings

This study examines the average commercial office waste profile by region and by program type. The regions included in this study, including New York, the Mid Atlantic, California and others, had different recycling/composting program mandates. Within regions, building waste diversion programs differed as well. The results of the review reflect some differences in the success of the diversion programs utilized: single stream recycling, multi stream recycling and with or without organics programs. Over 100 waste audit results were compiled representing commercial office waste prior to the changes in the commercial real estate and waste industries during the COVID 19 pandemic.

Temperature Distribution Analysis along the Length of Floating Head Multi Stream Heat Exchanger

Heat transfer between two streams is common and simple and well established and perfectly commercialized. Normally, the exchanger that is used for this purpose is shell and tube heat exchanger but in some industrial production unit where more than one reactant is to be preheated or pre-cooled for chemical reaction and same as post heating and post cooling required of multiple streams at same or different temperatures, Problem that is associated with such type shell and tube heat exchanger is that it can’t handle the multiple stream and for handling multiple streams we required more number of exchangers due to which capital cost increases and required more care of handling because the number of units increases. To overcome this problem, we need more than one heat sinks with one or more than one heat source that will minimize the covered volume per unit heat transfer area, the number of unit operation, operation time, man power and the capital cost with increasing thermal efficiency and heat utilization so to overcome this problem we need to move towards multi stream heat exchanger for handling multiple streams at once for heat exchange. Multi stream heat exchanger is opening of a new class of heat transfer equipment which deals more than two different streams for heat exchange. Such a way number of units can be reduced, which minimize time and space. With a little bit increase in complexity the operational cost will decrease and improve the thermal efficiency of heat transfer equipment, which minimize thermal losses and maximize the heat utilization which directly decrease the equipment size and capital cost. In the previous study we have discuss our research on the fabrication and Comparative Study of Floating Head (Triple pipe) Multi Stream Heat Exchanger with Shell & Tube This work is about the investigation involves the tentative examination of the heat exchange through the Floating Head Multi-Stream Heat Exchanger to evaluate the temperature distribution along the length, in which cool liquids are flowing through the inner and external pipe and hot liquid is moving through the central pipe of the exchanger.

Public Participation in the Policy Making Process: An Evaluation of Makueni County, Kenya

The purpose of the study was to evaluate public participation in the policy making process in Makueni County. This was so because, despite the Makueni County Government generally accepted framework for public participation, there is limited information on how that framework has impacted or been used in the policy making process. Specifically, the study sought to assess the extent of awareness by the residents of Makueni of their democratic right and opportunities to participate in the policy making process; analyzed the levels of citizens involvement in policy making processes in Makueni County; established the perception towards public participation in policy making process by residents and management in Makueni County; and determined the barriers to public participation in policy making process by residents and management in Makueni County. The study was anchored on Stakeholder Theory, Arnstein’s Ladder and the Multi Streams Model. The study was hinged on descriptive survey research design and targeted the 884,527 residents of Makueni and the 217 County Public service Departmental staff in Makueni County. Stratified sampling was used to delineate 117 staff in terms of their positions, whether management or operations excluding the subordinate staff. Simple random sampling was used to get 396 residents. Focus Group Discussions and questionnaires were the primary data collection instruments. Quantitative data was analyzed using descriptive statistics in form of percentages and frequencies. Linear regression model analysis was also used to analyze data. The results established that the residents were unaware of their democratic right and opportunities for participation in policy making process in Makueni County. There were also low levels of citizens’ involvement in policy making processes in Makueni County. Further, perception about public participation by residents was both negative for those who felt let down by the county management and positive for those who mainly felt hopeful that despite those challenges, things improve for the better in terms of public participation in policy making process in Makueni County. Additionally, financial, physical resource, human resource and technological barriers were identified as having considerable negative impact on citizen participation in policy making process. The study thus recommends that Makueni County management should embark on renewed promotional strategies to create awareness among citizens of their democratic right and opportunities for participation in policy making process in Makueni County. Further, the citizens themselves should be proactive and employ necessary mechanisms of obtaining information from the county government and demand participation. The Makueni County management should on the other hand, formulate a framework that stipulates the measures to be applied towards fully involving the citizens. The Makueni County management should also be more transparent and accountable in order to help build a positive perception about public participation amongst the residents. Moreover, the Makueni County management should engage in robust resource mobilization of finances, physical, human and technological resources that advance public participation in policy making process. On a policy level, the County Assembly should amend existing public participation legislation to make it more responsive to the needs of citizens. The executive should on their part robustly implement public participation processes going forward.

Multi-view motion modelled deep attention networks (M2DA-Net) for video based sign language recognition

Currently, video-based Sign language recognition (SLR) has been extensively studied using deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In addition, using multi view attention mechanism along with CNNs could be an appealing solution that can be considered in order to make the machine interpretation process immune to finger self-occlusions. The proposed multi stream CNN mixes spatial and motion modelled video sequences to create a low dimensional feature vector at multiple stages in the CNN pipeline. Hence, we solve the view invariance problem into a video classification problem using attention model CNNs. For superior network performance during training, the signs are learned through a motion attention network thus focusing on the parts that play a major role in generating a view based paired pooling using a trainable view pair pooling network (VPPN). The VPPN, pairs views to produce a maximally distributed discriminating features from all the views for an improved sign recognition. The results showed an increase in recognition accuracies on 2D video sign language datasets. Similar results were obtained on benchmark action datasets such as NTU RGB D, MuHAVi, WEIZMANN and NUMA as there is no multi view sign language dataset except ours.

MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

Tracking objects across multiple video frames is a challenging task due to several difficult issues such as occlusions, background clutter, lighting as well as object and camera view-point variations, which directly affect the object detection. These aspects are even more emphasized when analyzing unmanned aerial vehicles (UAV) based images, where the vehicle movement can also impact the image quality. A common strategy employed to address these issues is to analyze the input images at different scales to obtain as much information as possible to correctly detect and track the objects across video sequences. Following this rationale, in this paper, we introduce a simple yet effective novel multi-stream (MS) architecture, where different kernel sizes are applied to each stream to simulate a multi-scale image analysis. The proposed architecture is then used as backbone for the well-known Faster-R-CNN pipeline, defining a MS-Faster R-CNN object detector that consistently detects objects in video sequences. Subsequently, this detector is jointly used with the Simple Online and Real-time Tracking with a Deep Association Metric (Deep SORT) algorithm to achieve real-time tracking capabilities on UAV images. To assess the presented architecture, extensive experiments were performed on the UMCD, UAVDT, UAV20L, and UAV123 datasets. The presented pipeline achieved state-of-the-art performance, confirming that the proposed multi-stream method can correctly emulate the robust multi-scale image analysis paradigm.

Learning to See Through a Few Pixels: Multi Streams Network for Extreme Low-Resolution Action Recognition

Human action recognition is one of the most pressing questions in societal emergencies of any kind. Technology is helping to solve such problems at the cost of stealing human privacy. Several approaches have considered the relevance of privacy in the pervasive process of observing people. New algorithms have been proposed to deal with low-resolution images hiding people identity. However, many of these methods do not consider that social security asks for real-time solutions: active cameras require flexible distributed systems in sensible areas as airports, hospitals, stations, squares and roads. To conjugate both human privacy and real-time supervision, we propose a novel deep architecture, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi Streams Network</i> . This model works in real-time and performs action recognition on extremely low-resolution videos, exploiting three sources of information: RGB images, optical flow and slack mask data. Experiments on two datasets show that our architecture improves the recognition accuracy compared to the two-streams approach and ensure real-time execution on Edge TPU (Tensor Processing Unit).

Pinch and sensitivity analyses of hydrogen liquefaction process in a hybridized system of biomass gasification plant, and cryogenic air separation cycle

A novel integrated system for liquid hydrogen production using biomass gasification is presented in this study, so that an integrated structure of the cryogenic air separation unit, acid gas removal unit, hydrogen liquefaction cycle and power plant using biomass gasification has been developed. This integrated structure produces 8.097 MW power, 0.1660 kg/s liquid hydrogen and 5.81 kg/s compressed carbon dioxide. In this regard, pure hydrogen with the rate of 0.5540 kg/s is produced by gasification of 4.480 kg/s rice husk (as fuel) where about 30% of which 0.1660 kg/s entered liquefaction cycle as feed and then is introduced as final product of the system. Rest of pure hydrogen produced in gasification process (0.3880 kg/s) is burned to generate required power for hydrogen liquefaction cycle and other parts of system including air separation cycle. Combined heat and power efficiency of integrated gasification combined cycle and coefficient of performance of hydrogen liquefaction cycle are 62.10% and 4.360, respectively. By applying pinch method on each multi stream heat exchanger of hydrogen liquefaction system, the related heat exchanger networks are extracted. Sensitivity analysis is performed over the integrated system main parameters and the results show that by increasing the rate of liquefied hydrogen to total produced hydrogen to 54.00%, the combined heat and power efficiency will rise up to 83.40%.

Multi stream 3D hyper-densely connected network for multi modality isointense infant brain MRI segmentation

Automatic accurate segmentation of medical images has significant role in computer-aided diagnosis and disease treatment. The segmentation of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) tissues plays an important role in infant brain structure for studying early brain development. However, this task is very challenging due to low contrast between GM and WM in isointense phase (approximately 6-8 months of age). In this study, we develop a hyper-densely connected convolutional neural network (CNN) for segmentation of volumetric infant brain. The proposed model provides dense connection between layers to improve the performance of flow information in the network. It also allows the multiscale contextual information by concatenating the feature maps of early, intermediate, and later layers. This architecture employs MR-T1 and T2 as input, which are processed in two separate independent paths, and then their low, intermediate, and high layer features are fused for final segmentation. An important change relative to earlier densely connected networks is the application of direct layer connections from the same and different paths. In this scenario, each modality is processed in an independent path, and dense connections occur not only between layers within the same path, but also between layers in different paths. Adopting such dense connectivity leads to benefits of deep supervision and improved gradient flow. Furthermore, by combining the feature maps of early, intermediate, and late convolutional layers, our architecture injects multiscale information into the final segmentation. This suggested approach is examined in the MICCAI Grand Challenge iSEG and obtains significant advantages over existing approaches in terms of parameter efficiency and segmentation accuracy on 6-month infant brain MRI segmentation.

Algorithm Based Multistage and Multi Stream Optimization of Buffer Capacity

Algorithm Based Multistage and Multi Stream Optimization of Buffer Capacity

Multi modal spatio temporal co-trained CNNs with single modal testing on RGB–D based sign language gesture recognition

Abstract Extracting hand movements using single RGB video camera for sign language recognition is a necessary attribute in developing an automated sign language recognition system. Local spatio temporal methods has shown encouraging outcomes for hand extraction using color cues. However, the color intensities does not behave as an independent entity during video capture in real environments. This has become a roadblock in the development of sign language machine translator for processing video data in real world environments. Not surprisingly, the result is more accurate when additional information is provided in the form of depth for sign language recognition in real environments. In this paper, we make use of a multi modal feature sharing mechanism with a four-stream convolutional neural network (CNNs) for RGB – D based sign language recognition. Unlike the multi stream CNNs, where output class prediction is based on independently operated two or three modal streams due to scale variations, we propose a feature sharing multi stream CNN on multi modal data for sign language recognition. The proposed 4 – stream CNN divides into two input data groupings under the training and testing spaces. The training space uses four inputs: RGB spatial in main stream and depth spatial, RGB and depth temporal on Region of interest mapping (ROIM) stream. The testing space uses only RGB and RGB temporal data for prediction from the trained model. The ROIM stream shares the multi modal data to generate ROI maps of the human subject, which are used to regulate the feature maps in RGB stream. The scale variations in the three streams is managed by translating the depth map to fit the RGB data. Sharing of multi modal features with RGB spatial features during training has circumvented overfitting on RGB video data. To validate the proposed CNN architecture, the accuracy of the classifier is investigated with RGB-D sign language data and three benchmark action datasets. The results show a remarkable behaviour of the classifier in handling missing depth modal during testing. The robustness of the system against state – of – the – art action recognition methods is studied using contrasting datasets.

POLICY CHANGE – THE ROLE OF IDEAS AND POLICY ENTREPRENEURS

ABSTRACT&#x0D; Understanding policy process involves many distinctive approaches. The most common are institutional, groups or networks, exogenous factors, rational actors, and idea-based approach. This paper discussed the idea-based approach to explain policy process, in this case policy change. It aims to analyse how ideas could assist people to understand policy change. What role do they play and why are they considered as fundamental element? It considers that ideas are belong to every policy actor, whether it is individual or institution. In order to answer these questions, this paper adopts Kingdon’s multi streams approach to analyse academic literatures. Through this approach, the relationship between ideas and policy change can be seen clearer. Ideas only can affect in policy change if it is agreed and accepted by policy makers. Therefore the receptivity of ideas plays significant role and it emerges policy entrepreneurs. They promote ideas (through problem framing, timing, and narrative construction) and manipulate in order to ensure the receptivity of ideas. Although policy entrepreneurs play significant role, political aspects remains the most important element in the policy process.&#x0D; Keywords: policy change, ideas, idea-based approach, Kingdon’s multiple streams, policy entrepreneurs.

다중흐름모형을 통한 장애등급제 폐지의 정책결정과정 분석

다중흐름모형을 통한 장애등급제 폐지의 정책결정과정 분석

Process development and sensitivity analysis of novel integrated helium recovery from natural gas processes

Two novel integrated cryogenic helium extraction from natural gas processes are introduced and analyzed. Helium recovery efficiency, energy consumption per extracted helium, energy and exergy analyzes are studied for the proposed processes. Multi streams heat exchangers composite curves show that thermal design of the processes has been done with good efficiency. The results show that efficiency of the integrated processes is higher than the flash method. Efficiency of the modified Linde and Exxon mobile integrated processes are 96% and 95% respectively. Based on the results modified Linde process performance is better than the modified Exxon mobile process. Exergy analysis results show that valves have more exergy destruction comparing to the other equipment. Also air coolers have low exergy loss and high exergy efficiency.

Self-Organizing Peer-To-Peer Middleware for Healthcare Monitoring in Real-Time.

As the number of elderly persons with chronic illnesses increases, a new public infrastructure for their care is becoming increasingly necessary. In particular, technologies that can monitoring bio-signals in real-time have been receiving significant attention. Currently, most healthcare monitoring services are implemented by wireless carrier through centralized servers. These services are vulnerable to data concentration because all data are sent to a remote server. To solve these problems, we propose self-organizing P2P middleware for healthcare monitoring that enables a real-time multi bio-signal streaming without any central server by connecting the caregiver and care recipient. To verify the performance of the proposed middleware, we evaluated the monitoring service matching time based on a monitoring request. We also confirmed that it is possible to provide an effective monitoring service by evaluating the connectivity between Peer-to-Peer and average jitter.

Superstructure based techno-economic optimization of the organic rankine cycle using LNG cryogenic energy

A process design of the organic Rankine cycle utilizing LNG cryogenic exergy is proposed using superstructure optimization. The superstructure imbeds about 1024 possible process alternatives, and the most profitable process configuration and the operating condition are decided simultaneously using a stochastic optimization solver and Aspen Plus-MATLAB interface. The optimum process configuration includes a multi stream cryogenic heat exchanger, a five-stage turbine with reheaters, three stage vapor re-condensation processes and direct contact heaters. In addition, the exergy transfer from the LNG to the working fluid is maximized by using a multi component mixture as working fluid. The 1st law efficiency of the proposed process reaches about 26.2% with 85 °C of waste heat source and it is about 42% higher than that of the conventional ORC. The annual profit of the optimum process is about 39 M$ and it can be interpreted as 24$ of profit per kg LNG evaporation. Sensitivity analysis is also presented to show the reliability of the stochastic solution found in this study.

Hybrid Low Complex near Optimal Detector for Spatial Modulation

In our previous work maximum throughput in multi stream MIMO is analyzed by overcoming the inter antenna interference. To mitigate the Inter antenna interference spatial modulation can be used. Spatial Modulation(SM) aided MIMO systems are the emerging MIMO systems which are low complex and energy efficient. These systems additionally use spatial dimensions for transmitting information. In this paper a low complex detector based on matched filter is proposed for spatial modulation to achieve near maximum likelihood performance while avoiding exhaustive ML search since MF based detector exhibits a considerable reduced complexity since activated transmitting antenna and modulated amplitude phase modulation constellation are estimated separately. Simulation results show the performance of the proposed method with optimal ML detector, MRC and conventional matched filter methods.