Input Split Research Articles

Aging-aware co-optimization of topology, parameter and control for multi-mode input- and output-split hybrid electric powertrains

Existing research has focused on battery intrinsic optimization, Hybrid Energy Storage System (HESS), and powertrain control perspectives to enhance battery life. However, the impact of powertrain physical architecture -control multi-layer co-optimization on battery life has been neglected. Therefore, this study innovatively proposes a multi-mode dedicated hybrid transmission (DHT)—from the perspective of powertrain architecture—to reduce battery life degradation by switching between input split (IS) and output split (OS) modes. In order to fully optimize the battery degradation potential, a topology-parameter-control multi-layer co-optimization (TPCMC) method is proposed in this paper. In the topology layer, graph theory method is used for automatic generation, screening and modeling. In the parameter and control layer, a novel near-optimal energy management strategy, Rapid Dynamic Programming+ (Rapid-DP+), is proposed by introducing Pareto optimization, slope filtering, and Battery Life Aging Optimization (BLAO) methods, which improves the computational efficiency by 3500 times with 9.09–21.1 % battery life optimization. The results show that, relative to the Toyota Prius, the optimized powertrain improves the acceleration by 41.3 %, fuel economy by 6.08 %–8.1 %, and battery life degradation by 9.9 %–22.5 % under different driving cycles. This paper contributes to battery life degradation in the electrified transportation through an advanced TPCMC approach.

Low cost and high performance double‐node upset resilient latch for low orbit space applications

SummaryThis paper proposes a double‐node upset complete resilient (DNUCR) latch to meet the requirements of low orbit space applications for high robustness, cost‐effectiveness, and high performance. With the help of its interlocked loops with multiple feedbacks consisting of C‐elements (CE), input splitting CEs, 1P‐2N and 2P‐1N elements, and inverters, the proposed DNUCR latch preserves the original data even after the radiation incident. Error injection simulations in Synopsys HSPICE show that the proposed DNUCR latch recovers from all DNUs and can withstand large amount of injected charge. The simulation outcomes further show that the proposed DNUCR latch outperforms existing DNU‐resilient latches in terms of delay, power consumption, and power–delay–area product (PDAP), with an average of 48% reduction in delay, 22% savings in power, and 60% reduction in PDAP. The proposed DNUCR latch is also found to be the best among the existing radiation‐hardened latches in terms of charge‐to‐PDAP ratio.

SpinalNet: Deep Neural Network With Gradual Input

Deep neural networks (DNNs) have achieved the state of the art performance in numerous fields. However, DNNs need high computation times, and people always expect better performance in a lower computation. Therefore, we study the human somatosensory system and design a neural network (SpinalNet) to achieve higher accuracy with fewer computations. Hidden layers in traditional NNs receive inputs in the previous layer, apply activation function, and then transfer the outcomes to the next layer. In the proposed SpinalNet, each layer is split into three splits: 1) input split, 2) intermediate split, and 3) output split. Input split of each layer receives a part of the inputs. The intermediate split of each layer receives outputs of the intermediate split of the previous layer and outputs of the input split of the current layer. The number of incoming weights becomes significantly lower than traditional DNNs. The SpinalNet can also be used as the fully connected or classification layer of DNN and supports both traditional learning and transfer learning. We observe significant error reductions with lower computational costs in most of the DNNs. Traditional learning on the VGG-5 network with SpinalNet classification layers provided the state-of-the-art (SOTA) performance on QMNIST, Kuzushiji-MNIST, and EMNIST (Letters, Digits, and Balanced) datasets. Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets. The scripts of the proposed SpinalNet training are available at the following link: <uri>https://github.com/dipuk0506/SpinalNet</uri> <i>Impact Statement</i>&#x2014;Research in deep neural networks (DNNs) has gained significant attention from industries and academia due to their eye-catching performance. DNNs have enabled machines to perform myriad tasks with high accuracy that once only humans could do. Several researchers have recently proposed different types of NNs and have achieved high accuracy. The recent success of biologically inspired convolutional neural networks and the miraculous spinal architecture of humans has motivated us to develop a neural network with gradual inputs. We have achieved superior performance in several datasets. After the first online appearance of the initial version of this paper, several researchers have applied the proposed neural network in several new datasets and reported promising results. We may observe numerous novel applications of SpinalNet in upcoming years.

Automatic Optimization of Input Split and Bias Voltage in Digitally Controlled Dual-Input Doherty RF PAs

Digitally controlled Dual-Input Doherty Power Amplifiers (DIDPAs) are becoming increasingly popular due to the flexible input signal splitting between the main and auxiliary stages. Nevertheless, the presence of many degrees of freedom, e.g., input amplitude split and phase displacement as well as biasing for multiple stages, often involves inefficient trial-and-error procedures to reach a suitable PA performance. This article presents automated parameter setting based on coordinate descent or Bayesian optimizations, demonstrating an improvement in the performance in terms of RF output power and power-added efficiency (PAE) in the presence of broadband-modulated signals, yet maintaining suitable linear behavior for, e.g., communications applications.

Energy Management Strategy for Hybrid Multimode Powertrains: Influence of Inertial Properties and Road Inclination

Multimode hybrid powertrains have captured the attention of automotive OEMs for their flexible nature and ability to provide better and optimized efficiency levels. However, the presence of multiple actuators, with different efficiency and dynamic characteristics, increases the problem complexity for minimizing the overall power losses in each powertrain operating condition. The paper aims at providing a methodology to select the powertrain mode and set the reference torques and angular speeds for each actuator, based on the power-weighted efficiency concept. The power-weighted efficiency is formulated to normalize the efficiency contribution from each power source and to include the inertial properties of the powertrain components as well as the vehicle motion resistance forces. The approach, valid for a wide category of multimode powertrain architectures, is then applied to the specific case of a two-mode hybrid system where the engagement of one of the two clutches enables an Input Split or Compound Split operative mode. The simulation results obtained with the procedure prove to be promising in avoiding excessive accelerations, drift of powertrain components, and in managing the power flow for uphill and downhill vehicle conditions.

Performance evaluation of Convolutional Neural Network for web security

Due to the daily use of web applications in several critical domains such as banking and online shopping, cybersecurity has become a challenge. Recently, deep learning techniques have achieved promising results and attracted cybersecurity researchers. In this paper, we explore and evaluate deep learning techniques used for the security of web applications. We analyze through experiments the different factors influencing the performance of the Convolutional Neural Network (CNN) technique for web attacks detection. The experiments done in this paper focus on CNN and have three goals. First, we evaluate the performance of different CNN models using two different methods of data input presentation and data input splitting. Second, we study the impact of the different CNN hyper-parameters on the attack detection rate. Third, we select the best deep learning toolbox that will be used in our future proposed detection technique. Through the experiments conducted in this paper, we reveal that an adequate tuning of hyper-parameters and the way of pre-processing data input have a significant impact on the attack detection rate.

Handling Non-Local Executions to Improve MapReduce Performance Using Ant Colony Optimization

Improving the performance of MapReduce scheduler is a primary objective, especially in a heterogeneous virtualized cloud environment. A map task is typically assigned with an input split, which consists of one or more data blocks. When a map task is assigned to more than one data block, non-local execution is performed. In classical MapReduce scheduling schemes, data blocks are copied over the network to a node where the map task is running. This increases job latency and consumes considerable network bandwidth within and between racks in the cloud data centre. Considering this situation, we propose a methodology, “improving data locality using ant colony optimization (IDLACO),” to minimize the number of non-local executions and virtual network bandwidth consumption when input split is assigned to more than one data block. First, IDLACO determines a set of data blocks for each map task of a MapReduce job to perform non-local executions to minimize the job latency and virtual network consumption. Then, the target virtual machine to execute map task is determined based on its heterogeneous performance. Finally, if a set of data blocks is transferred to the same node for repeated job execution, it is decided to temporarily cache them in the target virtual machine. The performance of IDLACO is analysed and compared with fair scheduler and Holistic scheduler based on the parameters, such as the number of non-local executions, average map task latency, job latency, and amount of bandwidth consumed for a MapReduce job. Results show that IDLACO significantly outperformed the classical fair scheduler and Holistic scheduler.

A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench

Big Data analytics for storing, processing, and analyzing large-scale datasets has become an essential tool for the industry. The advent of distributed computing frameworks such as Hadoop and Spark offers efficient solutions to analyze vast amounts of data. Due to the application programming interface (API) availability and its performance, Spark becomes very popular, even more popular than the MapReduce framework. Both these frameworks have more than 150 parameters, and the combination of these parameters has a massive impact on cluster performance. The default system parameters help the system administrator deploy their system applications without much effort, and they can measure their specific cluster performance with factory-set parameters. However, an open question remains: can new parameter selection improve cluster performance for large datasets? In this regard, this study investigates the most impacting parameters, under resource utilization, input splits, and shuffle, to compare the performance between Hadoop and Spark, using an implemented cluster in our laboratory. We used a trial-and-error approach for tuning these parameters based on a large number of experiments. In order to evaluate the frameworks of comparative analysis, we select two workloads: WordCount and TeraSort. The performance metrics are carried out based on three criteria: execution time, throughput, and speedup. Our experimental results revealed that both system performances heavily depends on input data size and correct parameter selection. The analysis of the results shows that Spark has better performance as compared to Hadoop when data sets are small, achieving up to two times speedup in WordCount workloads and up to 14 times in TeraSort workloads when default parameter values are reconfigured.

Improved Hadoop Cluster Performance by Dynamic Load and Resource Aware Speculative Execution and Straggler Node Detection

The big data is one of the fastest growing technologies, which can to handle huge amounts of data from various sources, such as social media, web logs, banking and business sectors etc. In order to pace with the changes in the data patterns and to accommodate the requirements of big data analytics, the platform for storage and processing such as Hadoop, also requires great advancements. Hadoop, an open source project executes the big data processing job in map and reduce phases and follows master-slave architecture. A Hadoop MapReduce job can be delayed if one of its many tasks is being assigned to an unreliable or congested machine. To solve this straggler problem, a novel algorithm design of speculative execution schemes for parallel processing clusters, from an optimization perspective, under different loading conditions is proposed. For the lightly loaded case, a task cloning scheme, namely, the combined file task cloning algorithm, which is based on maximizing the overall system utility, a straggler detection algorithm is proposed based on a workload threshold. The detection and cloning of tasks assigned with the stragglers only will not be enough to enhance the performance unless cloning of tasks is allocated in a resource aware method. So, a method is proposed which identifies and optimizes the resource allocation by considering all possible aspects of cluster performance balancing. One main issue arises due to the pre configuration of distinct map and reduce slots based on the number of files in the input folder. This can cause severe under-utilization of slot as map slots might not be fully utilized with respect to the input splits. To solve this issue, an alternative technique of Hadoop Slot Allocation is introduced in this paper by keeping the efficient management of slots model. The combine file task cloning algorithm combines the files which are less than the size of a single data block and executes them in the highly performing data node. On implementing these efficient cloning and combining techniques on a heavily loaded cluster after detecting the straggler, machine is found to reduce the elapsed time of execution to an average of 40%. The detection algorithm improves the overall performance of the heavily loaded cluster by 20% of the total elapsed time in comparison with the native Hadoop algorithm.

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.

An efficient approach to optimise I/O cost in data-intensive applications using inverted indexes on HDFS splits

Hadoop is prominent for its distributed file system (HDFS) and scalability. Hadoop MapReduce framework is extensively used in big data analytics and business-intelligence applications. The analytic queries executed by these applications often include multiple ad hoc queries and aggregate queries with some selection predicates. The cost of executing these queries grows incredibly as the size of dataset grows. The most effective strategy to improve query performance in such applications is to process only relevant data keeping irrelevant data aside, which can be done using index structures. This paper is an attempt to improve query performance by avoiding full scans on data files. The algorithms used in this paper create inverted indexes on HDFS input splits. We show how query processing in MR jobs can benefit in terms of performance by employing these custom inverted indexes. The experiments demonstrate that queries executed using indexed data execute 1.5x faster than the traditional queries.

Hybrid Analog/Digital Continuous Class B/J Mode for Broadband Doherty Power Amplifiers

In this paper, a new digitally driven two input continuous mode Doherty power amplifier (DPA) architecture is proposed along with an analytical-based generic output combiner network design methodology. The load combiner provides the designer a choice to meet the optimum performance for any arbitrary back-off as well as for saturation. The PA’s performance is further optimized with digital input splitting. To verify the proposed theory, a 20-W symmetrical continuous mode DPA is designed using 10-W GaN HEMTs. The proposed amplifier shows a drain efficiency between 56.0% and 75.4% at 41.4–44.6 dBm saturation power and between 45% and 56.5% at 35.7–38.5 dBm output power corresponding to 6-dB back-off. This performance is achieved over the band from 1.25 to 2.3 GHz that corresponds to 59.15% fractional bandwidth. The proposed hybrid analog/digital continuous mode DPA prototype is implemented using field-programmable gate array (FPGA)/DSP platform and qualifies the spectral mask when excited by a modulated long term evolution signal along with digital predistortion.

An efficient approach to optimise I/O cost in data-intensive applications using inverted indexes on HDFS splits

Hadoop is prominent for its distributed file system (HDFS) and scalability. Hadoop MapReduce framework is extensively used in big data analytics and business-intelligence applications. The analytic queries executed by these applications often include multiple ad hoc queries and aggregate queries with some selection predicates. The cost of executing these queries grows incredibly as the size of dataset grows. The most effective strategy to improve query performance in such applications is to process only relevant data keeping irrelevant data aside, which can be done using index structures. This paper is an attempt to improve query performance by avoiding full scans on data files. The algorithms used in this paper create inverted indexes on HDFS input splits. We show how query processing in MR jobs can benefit in terms of performance by employing these custom inverted indexes. The experiments demonstrate that queries executed using indexed data execute 1.5x faster than the traditional queries.

An Enhanced Data-Locality-Aware Task Scheduling Algorithm for Hadoop Applications

In general, Hadoop improves the task scheduling performance by determining data locality based on the location in which the input splits and MapTask are executed. However, if an input split consists of multiple data blocks that are distributed and stored in different nodes, this data location method fails to cope with the degradation in processing performance due to the increased frequency of data block copying. We propose a task scheduling algorithm that solves this issue by defining a method to classify data locality taking into account the location of all data blocks that comprise an input split, categorizing tasks based on the defined method, and sequentially assigning tasks according to a given priority. This study measures the performance of the proposed algorithm through a comparison of the total processing time, MapTask performance time, and data block copying frequency between the proposed algorithm and Hadoop's default task scheduling algorithm. The test results show that the proposed algorithm improved the total processing time by up to 25% and the data block copying frequency by up to 28%, when compared to the default algorithm.

Comparative Study of Different Drive-train Driving Performances for the Input Split Type Hybrid Electric Vehicle

Comparative Study of Different Drive-train Driving Performances for the Input Split Type Hybrid Electric Vehicle

유성기어 효율을 고려한 입력분기 기반 하이브리드 전기자동차의 동력전달 효율 해석

유성기어 효율을 고려한 입력분기 기반 하이브리드 전기자동차의 동력전달 효율 해석

Study of an Improved Hadoop Speculative Execution Algorithm

The problems of difference of nodes capabilities and the unevenly-distributed bandwidth of the network, widespread exist in the heterogeneous clouding environment. Together with the users randomness of submitting jobs, the problems above lead to server synchronization problems.Under the platform of Hadoop and the situations mentioned above, we come up with a method which is based on the native hadoop speculative algorithm to solve the problems. Through monitoring the load-balance in realtime, dynamically assessing the performance of the node and making the speculative tasks happened in high-performance node which meantime is the nearest node from input split, the algorithm effectively reduces the occupation of the network and accelerates executing speed. The experiment result shows that the method in the execution of which the speculative tasks has a high ratio, significantly improved the efficiency and throughput of the cluster.

Configuration Analysis of Plug-in Hybrid Systems using Global Optimization

The purpose of the study is to analyze the configurations of Plug-in Hybrid Electric Vehicles (PHEV) with respect to fuel economy. Existing studies mostly focus on hybrid systems or few PHEV systems by only considering power split ratio and component efficiency. This paper adds original contribution to these literatures. First of all, this study compares and analyzes “series + α” PHEV – Input split, Series-output split and Series-parallel, which is consisted of a single Planetary gear or spur gear and clutches. Those are currently applied to mass-production vehicles such as Toyota Prius PHEV, Chevrolet Volt and Honda Accord PHEV. On top of that, it examines the impact of the transmission mechanical losses on Dynamic programming (DP) results and especially the planetary gear loss is modelled using power split ratio analysis. Lastly, the effect of Series mode for each PHEV system is examined by analysis of the theoretical system efficiency and DP in a certain driving profile. From this study the strength and weakness of PHEV systems are revealed depending on a driving condition and battery status, e.g. charging depleting (CD) or charging sustaining (CS). The PHEV system analysis in this study can help select proper system for a certain purpose.

Assessment by Simu ation of Benefits of New HEV Powertrain Configurations

During the past couple of years, numerous powertrain configurations for Hybrid Electric Vehicles (HEV) have been introduced into the marketplace. The current dominant architecture is the power-split configuration with the input split (single-mode) from Toyota and Ford. General Motors (GM) recently introduced a two-mode power-split configuration for applications in sport utility vehicles. Also, the first commercially available Plug-In Hybrid Electric Vehicle (PHEV) — the GM Volt — was introduced into the market in 2010. The GM Volt uses a series-split powertrain architecture, which provides benefits over the series architecture, which typically has been considered for Electric-Range Extended Vehicles (E-REV). This paper assesses the benefits of these different powertrain architectures (single-mode versus multi-mode for HEV) (series versus GM Voltec for PHEV) by comparing component sizes, system efficiency and fuel consumption over several drive cycles. On the basis of dynamic models, a detailed component control algorithm was developed for each configuration. The powertrain components were sized to meet all-electric-range, performance and grade-capacity requirements. This paper presents and compares the impact of these different powertrain configurations on component size and fuel consumption.

A Data Locality and Skew Aware Task Scheduler for MapReduce in Cloud Computing

Inspired by the success and the increasing prevalence of MapReduce, this work proposes a novel MapReduce task scheduler. MapReduce is by far one of the most successful realizations of large-scale, data-intensive, cloud computing platforms. As compared to traditional programming models, MapReduce automatically and efficiently parallelizes computation by running multiple Map and/or Reduce tasks over distributed data across multiple machines. Hadoop, an open source implementation of MapReduce, schedules Map tasks in the vicinity of their input splits seeking diminished network traffic. However, when Hadoop schedules Reduce tasks, it neither exploits data locality nor addresses data partitioning skew inherent in many MapReduce applications. Consequently, MapReduce experiences a performance penalty and network congestion as observed in our experimental results. Recently there has been some work concerned with leveraging data locality in Reduce task scheduling. For instance, one study suggests a locality-aware mechanism that inspects Map inputs and predicts corresponding consuming reducers. The input splits are subsequently assigned to Map tasks near the future reducers. While such a scheme addresses the problem, it targets mainly public-resource grids and doesn&apos;t fully substantiate the accuracy of the suggested prediction process. In this study, we propose Locality-Aware Skew-Aware Reduce Task Scheduler (LASAR), a practical strategy for improving MapReduce performance in clouds. LASAR attempts to schedule each reducer at its center-of-gravity node. It controllably avoids scheduling skew, a situation where some nodes receive more reducers than others, and promotes effective pseudo-asynchronous Map and Reduce phases resulting in earlier completion of submitted jobs, diminished network traffic, and better cluster utilization. vWe implemented LASAR in Hadoop-0.20.2 and conducted extensive experimentations to evaluate its potential. We found that it outperforms current Hadoop by 11%, and by up to 26% for the utilized benchmarks. We believe LASAR is applicable to several cloud computing environments and multiple essential applications, including but not limited to shared environments and scientific applications. In fact, a large body of work observed partitioning skew in many of critical scientific applications. LASAR paves the way for these applications, and others, to get effectively ported to various clouds.