Joint Algorithms Research Articles

Joint modelling of the order-dependent parts supply strategies sequencing, kitting and batch supply for assembly lines: insights from industrial practice

ABSTRACT Parts can be supplied from warehouses to assembly lines via several production-order-independent and -dependent parts supply strategies. Order-dependent parts supply strategies sequencing, kitting and batch supply share enough similarities that allow joint modelling in picking order planning and execution, whereas line stocking, just-in-time, just-in-sequence and just-in-sequence kit supply require separate modelling. Joint modelling is the precondition for setting up a software system in industrial practice, covering multiple parts supply strategies efficiently. With similar input–output relations and process steps, joint algorithms can be used. This work presents insights from a software system for sequencing, kitting and batch supply implemented in the automotive industry. The main process steps modelled are order-dependent part requests determination, bundling of part requests to picking orders, scheduling and release, and picking and transportation execution. Given the prevalence of assembly lines, sharing knowledge about successful modelling of parts supply strategies is crucial both for practitioners and researchers.

An Axiomatic Perspective on the Performance Effects of End-Host Path Selection

In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection. In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestioncontrol dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the "axioms") such as efficiency, fairness, and loss avoidance. We analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantititative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.

A robust approach for the decomposition of high-energy-consuming industrial loads with deep learning

The knowledge of the users’ electricity consumption pattern is an important coordinating mechanism between the utility company and the electricity consumers in terms of key decision makings. The load decomposition is therefore crucial to reveal the underlying relationship between the load consumption and its characteristics. However, load decomposition is conventionally performed on the residential and commercial loads, and adequate consideration has not been given to the high-energy-consuming industrial loads leading to inefficient results. This paper thus focuses on the load decomposition of the industrial park loads (IPL). The commonly used parameters in a conventional method are however inapplicable in high-energy-consuming industrial loads. Therefore, a more robust approach is developed comprising a three-algorithm model to achieve this goal on the IPL. First, the improved variational mode decomposition (IVMD) algorithm is introduced to denoise the training data of the IPL and improve its stability. Secondly, the convolutional neural network (CNN) and simple recurrent units (SRU) joint algorithms are used to achieve a non-intrusive and non-invasive decomposition process of the IPL using a double-layer deep learning network based on the IPL characteristics. Specifically, CNN is used to extract the IPL data characteristics while the improved long and short-term memory (LSTM) network, SRU, is adopted to develop the decomposition model and further train the load data. Through the robust decomposition process, the underlying relationship in the load consumption is extracted. The results obtained from the numerical examples show that this approach outperforms the state-of-the-art in the conventional decomposition process.

Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System.

Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intelligent human resource management system and uses backpropagation learning to adjust training errors and determine connection weights. Moreover, this paper estimates the probability of a certain event through regression analysis, predicts and analyzes the human resource management process, and builds an intelligent human resource management system with the support of joint algorithms. In order to explore the reliability of the joint algorithm proposed in this paper, the effectiveness of the algorithm proposed in this paper is verified through simulation tests. The experimental research results show that the human resource management system based on BP neural network and logistic regression proposed in this paper has good practical effects.

Algorithms for Picking and Distribution of Online Orders in New Retail Enterprises

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed.

An axiomatic perspective on the performance effects of end-host path selection

In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection.In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestion-control dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the “axioms”) such as efficiency, fairness, and loss avoidance. Moreover, we analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantitative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.

AGNEP: An Agglomerative Nesting Clustering Algorithm for Phenotypic Dimension Reduction in Joint Analysis of Multiple Phenotypes.

Genome-wide association study (GWAS) has identified thousands of genetic variants associated with complex traits and diseases. Compared with analyzing a single phenotype at a time, the joint analysis of multiple phenotypes can improve statistical power by taking into account the information from phenotypes. However, most established joint algorithms ignore the different level of correlations between multiple phenotypes; instead of that, they simultaneously analyze all phenotypes in a genetic model. Thus, they may fail to capture the genetic structure of phenotypes and consequently reduce the statistical power. In this study, we develop a novel method agglomerative nesting clustering algorithm for phenotypic dimension reduction analysis (AGNEP) to jointly analyze multiple phenotypes for GWAS. First, AGNEP uses an agglomerative nesting clustering algorithm to group correlated phenotypes and then applies principal component analysis (PCA) to generate representative phenotypes for each group. Finally, multivariate analysis is employed to test associations between genetic variants and the representative phenotypes rather than all phenotypes. We perform three simulation experiments with various genetic structures and a real dataset analysis for 19 Arabidopsis phenotypes. Compared to established methods, AGNEP is more powerful in terms of statistical power, computing time, and the number of quantitative trait nucleotides (QTNs). The analysis of the Arabidopsis real dataset further illustrates the efficiency of AGNEP for detecting QTNs, which are confirmed by The Arabidopsis Information Resource gene bank.

Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

Time-sensitive networks (TSNs) support not only traditional best-effort communications but also deterministic communications, which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints. No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications. However, due to inappropriate message fragmentation, the realtime performance of no-wait scheduling algorithms is reduced. Therefore, in this paper, joint algorithms of message fragmentation and no-wait scheduling are proposed. First, a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions. Second, to improve the scalability of our algorithm, the worst-case delay of messages is analyzed, and then, based on the analysis, a heuristic algorithm is proposed to construct low-delay schedules. Finally, we conduct extensive test cases to evaluate our proposed algorithms. The evaluation results indicate that, compared to existing algorithms, the proposed joint algorithm improves schedulability by up to 50%.

Speeding Up VM Startup by Cooperative VM Image Caching

Virtual machine (VM) management is at the core of virtualized cloud data centers. Among others, how to reduce the startup delay of VMs is a key issue for improving user experience and resource utility. In this paper, we study this issue by jointly considering VM placement and VM image caching. We formulate the joint placement problem and design several joint algorithms, including both online and offline algorithms, to speed up VM startup. In our design, we adopt the cooperative caching approach, where image cache copies are shared among physical machines (PMs) so as to reduce image retrieval time. The key point of our algorithms lies in how to appropriately place VM image cache among PMs so as to speed up VM startup as much as possible. The proposed algorithms are evaluated by extensive simulations via SimGrid. The results show that our algorithms can achieve shorter startup delay in most cases, compared with existing ones.

Coded Caching for Smart Grid Enabled HetNets With Resource Allocation and Energy Cooperation

With the development of millions more small-cell base stations (SBSs) and billions of connected devices in the future wireless networks, the need for energy-efficient system design and operation will be even more compelling. This paper investigates the content caching, resource allocation and energy cooperation in coded cache enabled heterogeneous networks (HetNets), where SBSs with cache ability are powered by both conventional grids and renewable energy sources, and energy can be shared among SBSs via the smart grid. We formulate the joint optimization problem, aims at minimizing the conventional grid energy consumption while satisfying quality of service (QoS) requirements of users. To deal with the mixed integer non-linear optimization problem, the original problem is decomposed into three lower level problems, i.e., content caching, content delivery and energy cooperation. We first propose an alternative iterative algorithm to optimize content caching and content delivery by jointly applying convex optimization techniques and matching theory. Then, by discussing two representative energy cooperative scenarios of smart grid supported networks, we propose efficient energy cooperation algorithms for the two scenarios, respectively. Furthermore, the joint content caching, content delivery and energy cooperation optimization algorithms are developed to minimize the conventional grid energy consumption in the considered HetNets. Simulation results demonstrate that the joint algorithms can significantly reduce the conventional grid energy consumption compared with other benchmarks.

SOC Estimation of Lithium Battery Based on Dual Adaptive Extended Kalman Filter

The estimation accuracy of single extended Kalman filter is not high, also it is affected by the initial value of state of charge (SOC). The second-order RC equivalent circuit model of lithium battery is established, and a joint algorithm, dual extended Kalman filter (DEKF) is proposed. Besides, the covariance matching theory is introduced for DEKF under the complex condition of uncertain noise statistical characteristics to improve the estimation accuracy. The improved DEKF is compared with another joint algorithms, i.e. recursive least squares and extended Kalman filter (RLS-EKF). Through the validation of battery test data, the modified dual extended Kalman filter based on covariance adaptive algorithm can realize real-time online estimation of battery SOC and time-varying parameters, and the estimation accuracy of lithium battery SOC and battery time-varying parameters is higher.

Estimation of the stepwise random disturbance parameters with the unknown moment of appearance

We have carried out the synthesis of the joint algorithms for the estimation of the parameters of Gaussian random disturbance by the maximum likelihood method. Application of the generalization of local Markov approximation method and small parameter method makes it possible for us to find the asymptotically exact expressions for the characteristics of the specified estimates. We also establish the error range of the obtained theoretical formulas experimentally, by statistical computer simulation.

Joint FDE and MLSD Algorithm for 56-Gbit/s Optical FTN-PAM4 System Using 10G-Class Optics

In this paper, we propose a joint frequency-domain equalizer (FDE) and maximum likelihood sequence detection (MLSD) algorithm to eliminate the serious inter-symbol interference for 56-Gbit/s optical faster-than-Nyquist 4-level pulse-amplitude modulation (FTN-PAM4) system using 10 G-class optics. The simplified FDE structure is first designed to replace the feed-forward equalization (FFE) and post filter structure in conventional joint FFE, post filter and MLSD algorithm. In the joint algorithms, the simplified FDE structure has approximately $13\%$ and $1\%$ computational complexity compared to FFE with least-mean-square (FFE-LMS) adaptive algorithm and FFE with recursive-least-squares (FFE-RLS) adaptive algorithm, respectively. Therefore, the proposed joint FDE and MLSD algorithm has much lower computational complexity than the conventional joint FFE, post filter and MLSD algorithm. Meanwhile, the experimental results show that the performance of joint FDE and MLSD algorithm is better than that of joint FFE-LMS, post filter and MLSD algorithm, and is similar with that of joint FFE-RLS, post filter and MLSD algorithm. In conclusion, the proposed joint FDE and MLSD algorithm has both low computational complexity and excellent performance for optical FTN-PAM4 systems, which shows its potential for applications in low-cost and low-power short-reach optical interconnects.

Stackelberg Game for User Clustering and Power Allocation in Millimeter Wave-NOMA Systems

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access-based millimeter wave (mm-wave-NOMA) system. To reduce the system overhead, hybrid precoding techniques are adopted at the base station by using the channel state information of cluster heads (CHs) only. In order to maximize the sum rate of the system, Stackelberg game-based optimization problems are formulated for two cases with different quality of some targets: Case 1 focuses on improving the data rates of CHs; and Case 2 aims to increase the rates of cluster members when the CHs’ data rates are caped. With the aid of coalitional game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. Then, the optimal power allocation coefficients of the users in each cluster are obtained by the derived closed-form expressions. The properties of the proposed joint algorithms are analyzed in terms of Stackelberg equilibrium, the complexity, the convergence and the stability. The simulation results demonstrate that: 1) the proposed algorithms can significantly improve the sum rate and reduce the outage probability of the mm-wave-NOMA system and 2) the application of NOMA in mm-wave systems is capable of achieving promising gains over conventional orthogonal multiple access-based frameworks in both cases.

Improved joint algorithms for reliable wireless transmission of 3D color-plus-depth multi-view video

Error control techniques like Error Resilience (ER) and Error Concealment (EC) are preferred techniques to ameliorate the lost Macro-Blocks (MBs) in the 3D Video (3DV) communication systems. In this paper, we present different enhanced ER-EC algorithms for intra-frame images for 3DV and Depth (3DV + D) communication through wireless networks. At the encoder, the slice structured coding, explicit flexible macro-block ordering, and context adaptive variable length coding are utilized. At the decoder, a hybrid approach comprising spatial circular scan order interpolation algorithm and temporal partitioning motion compensation algorithm is suggested to reconstruct the Disparity Vectors (DVs) and Motion Vectors (MVs) of the erroneous color images. For the corrupted depth images, a depth-assisted EC algorithm is proposed. Then, the optimum concealment MVs and DVs are chosen by employing the weighted overlapping block motion and disparity compensation algorithm. Furthermore, the Bayesian Kalman Filter (BKF) is utilized as an amelioration tool due to its efficiency to smooth the remnant inherent corruptions in the formerly optimally chosen color and depth DVs and MVs to obtain a good video quality. Simulation results on several 3DV streams show that the suggested algorithms have extremely adequate subjective and objective video quality performance compared to the traditional methods, particularly at high Packet Loss Rates (PLRs).

Design of Wireless Blood Pressure Monitor and Its Data Encryption Method

A wireless blood pressure measurement system was designed to facilitate the measurement of the patient's blood pressure and to transmit the measured data safely and reliably. Through PDA, radio frequency identification technology (RFID) and bluetooth technology, the function of reading patients' information and statistics and analysis of blood pressure, heart rate data was realized. The IDEA and RSA joint algorithms were used to encrypt the patients' data and the key of the IDEA algorithm to ensure the security of the patient' data. The test results showed that the system had high accuracy of measurement data, safe and reliable transmission, and improved the nurses' work efficiency.

Flow distribution-aware load balancing for the datacenter

Leveraging centralized and distributed load balancing algorithm to jointly schedule long and short flows attracts much attention since by classifying flows, they could reduce the completion times of flows without losing the scalability. However, existing centralized-distributed joint algorithms generally classify flows according to a static threshold and are unable to adapt to traffic dynamics. More importantly, through over investigation, the overheads of the controller caused by classifying flows and handling flows can be further reduced.In this paper, we present a Flow Distribution-Aware Load Balancing (FDALB) mechanism to reduce flow completion times and achieve high scalability. In FDALB, flows are split into short flows and long flows according to a threshold. The traffic of short flows and long flows are balanced by distributed and centralized algorithms respectively. To adapt to traffic dynamics, we propose a simple yet effective scheme adaptively adjusting the splitting threshold. To reduce the overheads of classifying flows, FDALB leverages end-hosts to tag long flows, which requires no changes in networking hardware. To further reduce the overheads of handling flows, we proposed a new centralized algorithm without requiring flow rates information. Using realistic datacenter workloads, we show that FDALB reduces the average FCT of flows by up to 47% over ECMP while achieves higher scalability than the state-of-art load balancing mechanism Mahout.

Accelerated join evaluation in Semantic Web databases by using FPGAs

SummaryWhile the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and cannot meet these performance requirements anymore. In recent years, systems with many processing cores are considered to satisfy these demands. Furthermore, these systems include more and more heterogeneous cores tailor‐made to solve one specific task in an efficient manner. However, dedicated hardware accelerators are inflexible and cannot be adapted to the requirements of a dedicated query. Thus, the challenge is orchestrating the diversity of the functionality of all the cores to be optimized for performance/energy efficiency. In this paper, a concept is introduced on how to develop a flexible Field‐Programmable Gate Arrays (FPGA)‐based hardware accelerator to improve the performance of query evaluation in a Semantic Web database. As a first step to the hardware/software system, several joint algorithms are implemented on an FPGA and evaluated against a well‐developed software solution (implemented in C). The comparison shows a significant speedup of up to 10 times. Because of the complexity of the join operator, it is promising that the overall performance of query evaluation can be further enhanced by processing whole queries on an FPGA. Copyright © 2015 John Wiley & Sons, Ltd.

Modal Parameters Identification of Civil Engineering Structures Based on the Random Decrement Technique

Modal identification technique is an effective measure for building materials health diagnosis, structural optimization design and safety assessment in structural engineering field. The time-domain method can only use the response signals to identify the modal parameters under ambient excitation, which became a wide concern topic in recent years. According to the points above, the theories of random decrement technique, complex exponential method and time-series method are presented. The random decrement technique is combined with the other two methods to become joint algorithms. A numerical simulation model is constructed to verify the feasibility and validity of the two joint algorithms.

Top Down Implementation of Joint MODEM and CODEC Transceiver Design for DSRC Coded-FSK Systems Over High Mobility Fading Channels

The joint detection and verification of frequency shift keying (FSK) modulation and demodulation (MODEM), Manchester coding and decoding (CODEC) schemes are proposed for dedicated short range communication (DSRC) systems over high mobility fading channels. The proposed joint coded-FSK detection scheme with low complexity advantage can outperform the conventional separated coded-FSK detection scheme. It is due to the joint scheme with time diversity gain to enhance the detection performance. Moreover, the proposed joint algorithms with floating-point and fixed-point designs are realized in the software-defined-ratio (SDR) platform. Based on the measurement results via SDR equipments, it is confirmed that the implementation of VHDL hardware circuit design of the proposed joint detection scheme can provide robust performance over high mobility Rician multipath fading channel environment.