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Unsupervised domain adaptation methods have been proposed to tackle the problem of covariate shift by minimizing the distribution discrepancy between the feature embeddings of source domain and target domain. However, the standard evaluation protocols assume that the conditional label distributions of the two domains are invariant, which is usually not consistent with the real-world scenarios such as long-tailed distribution of visual categories. In this article, the imbalanced domain adaptation (IDA) is formulated for a more realistic scenario where both label shift and covariate shift occur between the two domains. Theoretically, when label shift exists, aligning the marginal distributions may result in negative transfer. Therefore, a novel cluster-level discrepancy minimization (CDM) is developed. CDM proposes cross-domain similarity learning to learn tight and discriminative clusters, which are utilized for both feature-level and distribution-level discrepancy minimization, palliating the negative effect of label shift during domain transfer. Theoretical justifications further demonstrate that CDM minimizes the target risk in a progressive manner. To corroborate the effectiveness of CDM, we propose two evaluation protocols according to the real-world situation and benchmark existing domain adaptation approaches. Extensive experiments demonstrate that negative transfer does occur due to label shift, while our approach achieves significant improvement on imbalanced datasets, including Office-31, Image-CLEF, and Office-Home.

The scarcity of labels is common and great challenge in traditional supervised learning. Semi-supervised learning (SSL) leverages unlabeled samples to alleviate the absence of label information. Similar with annotation, label proportion is another type of prior information and plays a significant role in classification tasks. Compared with the acquisition of labels, label proportion can be obtained more easily. For example, only a small number of patients have been diagnosed with or not with cancers in hospital database, while the proportion with cancer can be generally estimated by historical records. How to incorporate such prior information of label proportion is crucial but rarely studied in literature. Traditional SSL methods often ignore this prior information and will lead to performance degradation inevitably. To solve this problem, we propose a novel SSL with Label Proportion (SSLLP). Our approach encourages to preserve label consistency and label proportion by imposing the cardinality bound constraints. Our formulated problem equals to a mixed-integer constrained submodular minimization and it is difficult to be solved directly. Therefore, we transformed the original problem into a convex one by Lov $\acute{\text{a}}$ sz extension and designed an efficient solving algorithm. Extensive experimental results present the improved performance of our method over several state-of-the-art methods.

Spectrum map is an essential tool for a range of emerging applications of 5G and 6G networks. Despite the great efforts that have been put on the construction of spectrum maps, access to accurate and valid spectrum data in dynamically changing environments emphasizes the need for more advanced solutions. To this end, the idea of spectrum map prediction is introduced. In this paper, we address the problem of spectrum map prediction from incomplete and corrupted historical spectrum observations. We propose three techniques to solve the problem. First, we combine the spectrum map with prediction functionalities so as to offer a huge potential in dynamically changing environments. Second, by fully exploiting the inherent spatial-temporal-spectral structures of the spectrum data and the sparsity of anomalies and missing data, we model the spectrum map as a 3rd-order spectrum tensor and formulate the spectrum map prediction problem as a low-rank tensor completion problem. Third, we design a robust online spectrum map prediction (ROSMP) algorithm based on the alternating direction minimization method. Finally, extensive numerical evaluations using a real spectrum measurement dataset confirm the efficacy and efficiency of ROSMP and show the superiority of ROSMP over the baselines.

The general tensor-based methods can recover missing values of multidimensional images by exploiting the low-rankness on the pixel level. However, especially when considerable pixels of an image are missing, the low-rankness is not reliable on the pixel level, resulting in some details losing in their results, which hinders the performance of subsequent image applications (e.g., image recognition and segmentation). In this article, we suggest a novel multiscale feature (MSF) tensorization by exploiting the MSFs of multidimensional images, which not only helps to recover the missing values on a higher level, that is, the feature level but also benefits subsequent image applications. By exploiting the low-rankness of the resulting MSF tensor constructed by the new tensorization, we propose the convex and nonconvex MSF tensor train rank minimization (MSF-TT) to conjointly recover the MSF tensor and the corresponding original tensor in a unified framework. We develop the alternating directional method of multipliers (ADMMs) to solve the convex MSF-TT and the proximal alternating minimization (PAM) to solve the nonconvex MSF-TT. Moreover, we establish the theoretical guarantee of convergence for the PAM algorithm. Numerical examples of real-world multidimensional images show that the proposed MSF-TT outperforms other compared approaches in image recovery and the recovered MSF tensor can benefit the subsequent image recognition.

Space vector modulation (SVM) is a mature modulation scheme, which is widely used for voltage sourced inverters. In this paper, a flexible online SVM scheme is proposed for a cascaded H-bridge multilevel inverter (CHMI) under short-circuit and open-circuit switch as well as cell failure conditions. The proposed scheme directly calculates all the active redundant switching states and the dwelling duty cycles of the three space vectors enclosing the reference vector for a CHMI such that balanced line-to-line voltages and currents under failure conditions are generated. Accordingly, the resultant switching sequences are determined to meet any specific requirement, e.g., even-order harmonics elimination and minimization of switching losses. Furthermore, in order to reduce voltage stress on the semiconductors under short- and open-circuit switch conditions, an equal power distribution criterion is introduced. The proposed SVM scheme is fast and independent of the number of levels. Experimental results are presented to demonstrate performance and effectiveness of the proposed scheme.

In this article, an adaptive phase compensator and a high-gain optimizer are proposed for the fast extremum-seeking (ES) scheme acting on a Hammerstein plant. Unlike the widely applied three-time scale tuning, a time scale-independent tuning is achieved by restricting the plant to have a Hammerstein structure and elevating the adaptation of the control input which makes it as fast as the dither signal. Albeit the existed fast ES schemes utilize a high-frequency sinusoidal dither in order to achieve fast minimization of the plant's static nonlinearity, the phase shift of the plant could not be neglected as the frequency is high and the ES may fail to reach the extreme values if the shift is large enough. Thus, an effective adaptive frequency varying phase compensation method is introduced to deal with the phase shift under the assumptions in this article. Moreover, a high-gain optimizer is presented to remove the restriction that the adaptation gain has to be small and could still guarantee the stability properties, which makes the proposed scheme more practical. The effectiveness of the proposed scheme is illustrated by numerical simulations.

Lisätään OA-artikkeli, kun julkaistu We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The aim is to improve the tracking and estimation accuracy with respect to the classical Bayesian filters and smoothers. We formulate the estimation problem as a dynamic generalized group Lasso problem and develop a class of smoothing-and-splitting methods to solve it. The Levenberg-Marquardt iterated extended Kalman smoother-based multiblock alternating direction method of multipliers (LM-IEKS-mADMMs) algorithms are based on the alternating direction method of multipliers (ADMMs) framework. This leads to minimization subproblems with an inherent structure to which three new augmented recursive smoothers are applied. Our methods can deal with large-scale problems without preprocessing for dimensionality reduction. Moreover, the methods allow one to solve nonsmooth nonconvex optimization problems. We then prove that under mild conditions, the proposed methods converge to a stationary point of the optimization problem. By simulated and real-data experiments, including multisensor range measurement problems, marine vessel tracking, autonomous vehicle tracking, and audio signal restoration, we show the practical effectiveness of the proposed methods.

O objetivo deste trabalho é apresentar uma metodologia para fornecer uma solução para o Problema de Roteamento de Veículos Green Bi-objetivo (Bi-objective Green Vehicle Routing Problem, BGVRP). A metodologia, ilustrada por meio de um estudo de caso (problema de distribuição de jornais) e instâncias da literatura, foi dividida em três etapas: Etapa 1, tratamento dos dados; Etapa 2, abordagens meta-heurísticas (híbridas ou não híbridas), utilizadas comparativamente; Etapa 3, análise dos resultados, com comparação dos algoritmos. Uma otimização de 19,9% foi alcançada para a Função Objetivo 1 (FO1; minimização das emissões de CO2) e, consequentemente, o mesmo percentual para a minimização da distância total, e 87,5% para a Função Objetivo 2 (FO2; minimização da diferença na demanda). Abordagens meta-heurísticas híbridas alcançaram resultados superiores para o estudo de caso e as instâncias. Desta forma, o procedimento aqui apresentado poderá trazer benefícios para a sociedade já que considera questões ambientais, além do balanceamento do trabalho entre os roteiros, garantindo economia e satisfação para possíveis usuários.

This article proposes a multiobjective mathematical model to optimize the multiperiod aggregate production planning (APP) of multiproduct companies. Although there are many studies of lean manufacturing (LM), its integration with APP has not been studied. The present article is intended to integrate APP and LM, including an analysis of market winners, market qualifiers, and waste. The model's objective functions include the cost, lead time, and waste minimization in addition to maximizing the product quality. A solution procedure is suggested to solve the model using IBM CPLEX 12.4 software. The model is investigated in three different case studies to check its applicability and generalizability. According to the obtained results, the proposed model provides an optimized APP with regard to the major concerns of LM, including waste, overproduction, time, and sourcing. In addition, according to the sensitivity analysis, lean weighting of the objective functions provides a better output than using equal weighting.

Mobile edge computing and nonorthogonal multiple access (NOMA) have been considered as promising technologies that can satisfy rigorous requirements of industrial internet of things systems. However, system dynamics, including channel states and computation task requests, may continuously change NOMA decoding order and computation uploading time, making it difficult to reduce latency using conventional highly complex optimization methods. In this study, we investigate a novel scheme that effectively reduces the average task delay to improve the quality of service for all users by jointly optimizing subchannel assignment (SA), offloading decision (OD), and computation resource allocation (CRA). To deal with the high complexity, the original multi-server problem is first decomposed into multiple single-server problems. Subsequently, each single-server problem is decoupled into CRA and SA/OD subproblems. Using convex optimization, a closed-form solution is derived for the optimal CRA action. Concurrently, the optimal SA/OD action is obtained using a distributed multi-agent deep reinforcement learning algorithm. Simulation results reveal that the proposed scheme significantly outperforms the state-of-the-art schemes. In particular, it reduces the action decision duration by 30 times while achieving a near-optimal performance of up to 97% of the optimum under the exhaustive search scheme.

Magnesium (Mg) alloys are extensively used in the automotive and aircraft industries due to their prominent properties. The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement. This decision entails the selection of suitable process parameters concerning various conflicting factors, so it has to be addressed with the Multiple Criteria Decision Making (MCDM) method. Therefore, this work addresses the MCDM problem through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and COPRAS (COmplex PRoportional ASsessment) methods. The assessment carried out in the material Mg AZ91 with the Solid Carbide (SC) drill bit. The dependent parameters like drilling time, burr height, burr thickness, and roughness are considered with the independent parameters like spindle speed and feed rate. Drilling alternatives are ranked using the above said two methods and the results are evaluated. The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev. The identical sequencing order was observed in TOPSIS and COPRAS method. The empirical model was developed through Box-Behnken design for each response. Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation. The trends of various dependents based on the heterogeneity of various independents are not identical, these complex mechanisms are identified and reported. The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank. The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

We study decentralized non-convex finite-sum minimization problems described over a network of nodes, where each node possesses a local batch of data samples. In this context, we analyze a single-timescale randomized incremental gradient method, called GT-SAGA. GT-SAGA is computationally efficient as it evaluates one component gradient per node per iteration and achieves provably fast and robust performance by leveraging node-level variance reduction and network-level gradient tracking. For general smooth non-convex problems, we show the almost sure and mean-squared convergence of GT-SAGA to a first-order stationary point and further describe regimes of practical significance where it outperforms the existing approaches and achieves a network topology-independent iteration complexity respectively. When the global function satisfies the Polyak-Lojaciewisz condition, we show that GT-SAGA exhibits linear convergence to an optimal solution in expectation and describe regimes of practical interest where the performance is network topology-independent and improves upon the existing methods. Numerical experiments are included to highlight the main convergence aspects of GT-SAGA in non-convex settings.

This article emphases an advanced method for controlling the pollution of vehicles through sensor integration process. All sensor nodes will be connected to a control centre and it will be monitored using Online Monitoring System (OMS) with the help of an Improved Smoke Detection Algorithm (ISDA). The purpose of using ISDA is to monitor the vehicles which are located in different areas under different temperatures where, the ISDA divides the regions into circles. The data obtained in OMS will be aggregated and it will be plotted in MATLAB for better understanding. To test the efficiency of the projected method three test cases which include control of energy, minimization of range and cot have been performed. It is observed that in all three cases ISDA proves to be more efficient in terms of energy consumption, cost and distance when compared with existing methods.

This article presents a dual-stage coordinated control approach for voltage regulation and congestion management of active distribution networks (ADN) in the presence of photovoltaic (PV) generators and electric vehicle stations (EVS). The proposed scheme operates on rule-based model predictive control (RBMPC) to optimally manage the settings of the regulating devices, i.e., on-load tap changer (OLTC), distribution static synchronous compensator (DSTATCOM), PV generators, and EV inverters that possess different temporal characteristics. The first (hourly time-scale) and the second (one-minute time-scale) stages of the dual-stage coordinated control mechanism are designed to correct the long-term and short-term voltage fluctuations, respectively. The objectives of the proposed approach are to minimize the number of OLTC operations (first stage) and changes in set-points of PV and EV inverters, and DSTATCOM (second stage) in addition to minimization of slack variables, energy loss, and voltage error at EV stations. In both the stages, predefined rules are set for MPC to optimize different objectives on the basis of the magnitudes of bus voltages. Simulations are performed on 33-bus and 38-bus distribution networks to test the efficacy of the control approach. It is observed that the proposed approach could mitigate the voltage variations as well as line congestion for different scenarios.

This paper presents a rolling optimization algorithm for minimizing total vehicle delay for real-time traffic signal control in urban road networks, where the vehicle delay minimization problem is formulated as a quadratically-constrained quadratic programming problem, which is NP-hard. The programming problem is relaxed and resolved using the rolling optimization algorithm in a distributed manner. In particular, we introduce network partition to a large-scale urban road network and then present a regional rolling optimization algorithm to eliminate the low efficiency caused by the large number of decision variables. Numerical experiments are performed on one of Beijing's district road networks to validate the efficiency of the proposed method in benchmark against several typical techniques.

This paper studies an optimal formation control problem of general integrator chain multi-agent systems with strongly convex local cost functions. The control goal is to design appropriate controllers for the agents such that they reach a desired formation shape and the global cost function is minimized. To achieve the goal, a modular control scheme with two modules is proposed. First, in Module I, an optimal formation signal generator is designed, whose outputs asymptotically converge to the minimizer of the global cost function. Second, in Module II, by employing the outputs into the feedback loop as the agents reference trajectories, some linear tracking controllers are designed for the agents to track the references asymptotically. With both modules, the proposed control scheme realizes the optimal formation control goal asymptotically. Moreover, the proposed control scheme is also applied to solving an optimal formation control problem of multiple mobile robots, where the robots total relative weighted squared moving distance requires minimization. Both simulations and experiments validate the effectiveness of the proposed control scheme.

It is necessary for cost consideration to minimize the number of the used layers in a PCB design. Traditionally, the number of the used layers outside components in bus assignment depends on the locations of the bus pins inside components in escape routing in a PCB design. In this paper, the concept of introducing the flexible escape directions in escape routing is considered in the bus assignment outside components in a PCB design. Clearly, the flexible consideration of the escape directions inside components can lead to the reduction on the number of the used layers in the bus assignment outside components. Given a set of buses on a set of components in a PCB design, based on the introduction of the flexible escape directions in escape routing and the construction of the possible bus connections in bus assignment, the two upper bounds of the layer numbers inside and outside components can be firstly computed. By eliminating the redundant bus connections for the given buses, an integrated algorithm can be further proposed to minimize the number of the used layers in a PCB design. Based on the assignment constraints from the intersection relations inside components, the physical connections of the given buses can be assigned onto a minimal set of used layers. Compared with the two-phase algorithm using Yan’s routing algorithm in direction-constrained rectangle escape routing and Yan’s assignment algorithm in bus assignment, the experimental results show that our proposed integrated algorithm use reasonable CPU time to reduce 32.1% of the layer number for 10 tested examples in a PCB design on the average.

The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently. However, minimizing the t-TNN faces some drawbacks. For example, the obtained solution could be suboptimal to the original problem due to its loose approximation. In this article, we extract a unified nonconvex surrogate of the tensor tubal rank as a tighter regularizer, which involves many popular nonconvex penalty functions. An iterative reweighted t-TNN algorithm is proposed to solve the resulting generalized nonconvex tubal rank minimization for tensor recovery. It converges to a critical point globally with rigorous proofs based on the Kurdyka-Łojasiwicz property. Furthermore, we provide the theoretical guarantees for exact and robust recovery by developing the tensor null space property. Extensive experiments demonstrate that our approach markedly enhances recovery performance compared with several state-of-the-art convex and nonconvex methods.

Minimization of negative environmental impact of transport cannot be pursued through mobility limiting, but rather through efficient utilization of natural resources. Some of the ways to reduce harmful pollution and noise include increasing the use of electric energy for transportation and developing electromobility. However, municipalities face difficult decisions connected with the siting of charging stations, for example, budget limitations are an important factor. The presented method allows for selecting a subset of existing parking lots where the charging devices will be sited. As the inputs, only easily accessible data is required. Applying a genetic algorithm combined with fuzzy logic and the Pareto front analysis, one could establish a set of optimal solutions for multiple pre-defined restrictive and partially contradictory criteria. The method has been discussed using a real example of a medium-sized city in southern Poland. Its results have also made it possible to verify whether a budget required for the planned investment is substantiated.

This paper considers scheduling in a two-machine flow shop to maximize the total early work subject to a common due date. The early work of a job is a parameter defined as the total amount of the job that is processed before the specified due date. We mainly focus on the unweighted model in this paper, and propose a dynamic programming approach running in O ( n 2 d 2 ) time (compared with the previous result in O ( n 2 d 4 ) time for the weighted case discussed in the literature). Then we analyse the problem from an approximation point of view, in which we first show that Johnson’s algorithm, one of the most classical ones in flow shop scheduling, can only achieve the worst performance ratio for the considered problem (although it is an optimal one for makespan minimization). With the motivation of proposing better approximation algorithms, we further design a fully polynomial time approximation scheme (FPTAS). Finally, we point out that the approximation results also work for the weighted model - if a specific constraint is satisfied.

In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumptions during machine processing and crane transportation. Different from the methods in the literature, crane lift operations have been investigated for the first time to consider the processing time and energy consumptions involved during the crane lift process. The IGSA algorithm is then developed to solve the CFJSPs considered. In the proposed IGSA algorithm, first, each solution is represented by a 2-D vector, where one vector represents the scheduling sequence and the other vector shows the assignment of machines. Subsequently, an improved construction heuristic considering the problem features is proposed, which can decrease the number of replicated insertion positions for the destruction operations. Furthermore, to balance the exploration abilities and time complexity of the proposed algorithm, a problem-specific exploration heuristic is developed. Finally, a set of randomly generated instances based on realistic industrial processes is tested. Through comprehensive computational comparisons and statistical analyses, the highly effective performance of the proposed algorithm is favorably compared against several efficient algorithms.

In this paper, we investigate multiple unmanned aerial vehicles (UAVs) enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer non-convex problem, we decompose the problem into two subproblems: UAV trajectory and hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed MACO-based algorithm, is given by leveraging MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

Capacitor banks, when optimally placed in an electrical network improve power quality and reliability. For determining the capacitors optimal location, the prevailing index methods mainly focus on single criterion, e.g., either of voltage stability, loss reduction, system loadability, and neglect some of the equally critical quantities while proving the solution. For obtaining a pragmatically feasible techno-commercial solution, it is advantageous to consider multiple criteria. This article proposes a novel approach for optimal capacitor allocation, which allows to choose as many objective functions (criteria) as desired. In the proposed approach, initially, the load flow study is executed to obtain the networks quantities for an uncompensated network. Subsequently, optimal locations of capacitor banks are identified through the proposed indexing method based on the application of Shannons Entropy. The capacitor banks optimal sizes are determined using the particle swarm optimization algorithm. The cost function is formulated as the minimization of sum of active power loss cost and the incurred annual capacitors cost subject to different operational constraints. The proposed approach is compared with that of the previously published methods by applying on radial distribution networks of different sizes (IEEE-12 bus, 34-bus and 108-bus) and the proposed approach is found to be superior.

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of non-cooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This paper proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). Firstly, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e. to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the non-adjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly non-ideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multi-variate input conditions.

In this paper, a varying-gain neural bi-criterion velocity minimization self-motion (VGN-BCVM-SM) approach is proposed to solve the self-motion problem for a redundant robot manipulator. Firstly, based on quadratic programming method and neural dynamic method, the proposed approach is derived in detail. For comparisons, a traditional fixed-parameter neural bi-criterion velocity minimization self-motion (FPN-BCVM-SM) approach is also presented. Then, the convergence and robustness of the proposed method is analyzed theoretically. Theoretical analysis shows that the proposed approach has global convergence and can overcome the errors of kinematics measurements. Computer simulations based on a six degrees-of-freedom manipulator demonstrate that the proposed approach can effectively avoid the robot manipulator exceeding the physical limits of joints. Meanwhile, the proposed VGN-BCVM-SM has higher efficiency and accuracy than fixed-parameter approach for solving self-motion problem of a redundant robot manipulator.

The presence of array back lobe is a major source of interference, power loss, and degrades the signal-to-interference ratio (SIR) in wireless communication systems. In this paper, new distributed beamforming techniques based on an optimized elliptical arc geometry (EAG) are proposed for back lobe cancellation of linear antenna arrays (LAA). The optimized EAG allows for flexible control of the elements' locations and initial transmission phases of their radiations, allowing them to add constructively towards the main beam direction and add destructively towards the back lobe direction. Firstly, two EAG based beamforming techniques are introduced using either a single ellipse-EAG (SE-EAG) or double ellipses-EAGs (DE-EAG). In these techniques, the back lobes are minimized by optimizing both the minor axes of the elliptical arcs and the elements' angular distributions using the particle swarm optimization (PSO) technique. While the elements' transmission energies are kept the same as that of the original LAA. For further back lobe minimization, the double ellipses-EAG with optimized energies (DE-EAG-WOE) beamforming technique is introduced, where both elements' transmission energies and positions are optimized. To verify the superiority and the possibility of practical validation of the proposed techniques, the proposed distributed array configurations based on the DE-EAG-WOE are realized using the CST Microwave Studio software package using dipole elements.

by Suresh Velamuri, S. Hari Charan Cherukuri, Suresh Kumar Sudabattula, Natarajan Prabaharan and Eklas Hossain

This paper develops a distributed cooperative energy management system with two distributed control layers for speed-coupling plug-in hybrid electric vehicles. By introducing personalized non-stationary inference, this system can fuse driving behavior and vehicle state information to adaptively adjust power-split control parameters for the improvement of vehicle energy economy. In the on-board control layer, five sets of personalized control parameters are optimized offline by using chaos-enhanced accelerated particle swarm optimization. In the distributed control layer, interval type-2 fuzzy sets are applied to develop a real-time driving style recognition function. Driving behavior is detected remotely, via the vehicle to everything network, and downloaded to adaptively adjust power-split control parameters in the on-board vehicle controller. Hardware-in-the-loop testing is carried out based on the four laboratory driving cycles and four personal driving cycles. The proposed system has been demonstrated with strong robustness that saves energy by up to 5.25% over the equivalent consumption minimization strategy (ECMS), especially for gentle drivers. Even under harsh communication conditions (with signal loss 80+%), it still performs better than the ECMS (by 0.57%) and the series-parallel control strategy (by 2.66%).

As the power density of modern CPUs is gradually increasing, thermal management has become one of the primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing the system temperature. In this paper, we propose CARTAD, a new reinforcement learning (RL) based task scheduling and DVFS method for temperature minimization and latency guarantee on multicore systems. The novelty of CARTAD framework is that we exploit machine learning technique to analyze the applications’ intermediate representations (IRs) generated by compiler and identify an important feature which is critical for predicting the application’s performance. With the newly explored feature, we construct a RL-based scheduler with the more effective state representation and reward function such that the system temperature can be minimized while guaranteeing applications’ latency. We implement and evaluate CARTAD on real platforms in comparison with the stateof-the-art approaches. Experimental results show CARTAD can reduce the maximum temperature by up to 16∘C and the average temperature by up to 10∘C.

In this paper, we study the problem of identification for the one-dimensional Burgers equation. This problem consists in identifying the set of initial data evolving to a given target at a final time. Due to the property of non-backward uniqueness of Burgers equation, there may exist multiple initial data leading to the same given target. We fully characterize the set of initial data leading to a given target along forward entropic evolution of Burgers equation. The proof is based on a backward-forward method and generalized backward characteristics. Simulations to sonic boom minimization are given using a wave-front tacking algorithm.

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

Enormous amounts of raw materials are consumed through various industrial processes worldwide. Vast amounts of raw materials utilized by the chemical industry typically go to waste. Mass integration is a distinctive method of streamlining the consumption of raw materials in chemical industries. Mass integration was developed by employing principles of pinch analysis in order to optimize mass exchange networks. To apply the concepts of mass integration, this work introduces a Matrix Laboratory (MATLAB) code designed on the bases of the composition driving force graphical design approach. This MATLAB code was devised to analyze details of mass transfer operations with the lowest number of calculations possible. The algorithm’s detailed coding allows for designing mass exchange networks above and below the pinch. For maximum mass recovery, pinch principles were employed, and were delivered in an educational manner to students and young researchers in the field. The MATLAB code was designed to develop a useful toolbox for process integration as well as determine the number of plates required for each mass exchanger. The model was tested at pyrolysis plant with the aim of achieving mass targets and optimum matches for maximum recovery at the lowest costs possible.

This study proposes an exact model for timetable recovery after disturbances in the context of high-frequency public transport services. The objective of our model is the minimization of the deviation between the actual headway and the respective planned value. The resulting mathematical program for the rescheduling problem is nonlinear and non-smooth; thus, it cannot be solved to optimality. To rectify this, we reformulate the model using slack variables. The reformulated model can be solved to global optimality in real-time with quadratic programming. We apply the model to real data from the red metro line in Washington D.C. in a series of experiments. In our experiments, we investigate how many upstream trips should be rescheduled to respond to a service disturbance. Our findings demonstrate an improvement potential of service regularity of up to 30% if we reschedule the five upstream trips of a disturbed train.

This paper proposes the optimal window-symbolic time series analysis (OW-STSA) methodology to optimize parameters of feature extraction and pattern classification in industrial processes. The underlying theory is built upon minimization of an empirical risk function (ERF) to discriminate between nominal and anomalous operations of the physical process under consideration. In particular, the proposed methodology produces: i) optimized windows of the time series used for pattern classification and anomaly detection, and ii) optimized identification of feature extractors and classifier parameters. The algorithm is realized by segmenting a given time series into windows of equal size. Then, the stationary state probability (SSP) vector is computed for each window in the sense of OW-STSA for anomaly prediction with locally optimal accuracy of detection performance. The proposed methodology has been experimentally validated in laboratory environment with different classifiers for two distinct industrial processes. The first experiment addresses detection of fatigue failure in polycrystalline-alloy structures using time series of ultrasonic signals. The second experiment investigates detection of thermoacoustic instability (TAI) in an emulated combustion system using time series of pressure-wave signals. In both experiments, the proposed OW-STSA methodology yielded excellent detection performance of anomalous behavior with multiple classification techniques.

Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which may fail in challenging cases when initial error is large and overlapping area is low. In this paper, a novel approach named collaborative semantic map matching (COSEM) is proposed to estimate the relative transformation between robots. COSEM jointly performs multimodal information fusion, semantic data association, and optimization in a unified framework. Firstly, each robot applies a multimodal information fusion model to generate local semantic maps. Since the correspondences between local maps are latent variables, a flexible semantic data association strategy is proposed using Expectation-Maximization. Instead of assigning hard geometry data association, semantic association and geometry association are jointly estimated. Then, the minimization of the expected cost results in a rigid transformation matrix between two semantic maps. Evaluations on Semantic KITTI benchmarks and real world experiments show the improved accuracy, convergence, and robustness.

Due to the existence of multiple magnetization states (MSs) of the low coercive force (LCF) PMs, the losses minimization of variable flux memory machine (VFMM) under different MSs unable obtained simultaneously. This paper proposes a loss-reduction-oriented (LRO) optimization methodology for overall efficiency improvement in VFMM. Firstly, a recently developed hybrid-magnetic-circuit (HMC) VFMM is selected as a design example to investigate the proposed optimization methodology. Subsequently, the architecture and overall flowchart of the proposed LRO optimization methodology are introduced and given, which includes two rounds of optimization, namely dual-operating-mode rough optimization (DOM-RO) and multiple-operating-mode elaborate optimization (MOM-EO). Besides, a computationally efficient multi-mode field-circuit coupled (MMFC) model is established in finite element (FE) software to account for multiple operating modes within two simple case studies. On this basis, the proposed optimization methodology is applied to the HMC-VFMM, in which the LRO objective function is defined in terms of the time proportions of various MSs in an overall driving cycle. According to the optimization results, the electromagnetic characteristics of the machine are investigated, especially the machine loss and its efficiency maps under different MSs are analyzed. Finally, a prototype is manufactured and tested to verify the validity of the proposed optimization strategy.

Affected by high switching speed and parasitic parameters, crosstalk problem of eGaN HEMT in a phase-leg configuration cannot be ignored. By decreasing gate driver turn-off voltage, the false turn-on phenomenon of device caused by crosstalk can be avoided, but it is hard to realize the minimization of total power loss. Thus, we analyze the influence of gate driver turn-off voltage on eGaN HEMT reverse conduction loss, switching loss and crosstalk voltage, and propose an optimized design rules for determining turn-off voltage value in a phase-leg configuration. By reasonably choosing gate driver turn-off voltage, a balance between optimal loss and reliable operation is achieved, thereby effectively alleviating the conflict between efficiency and reliability in the power electronic system.

The advent of vehicular edge computing (VEC) has generated enormous attention in recent years. It pushes the computational resources in close proximity to the data sources and thus caters for the explosive growth of vehicular applications. Owing to the high mobility of vehicles, these applications are of latency-sensitive requirements in most cases. Accordingly, such requirements still pose a great challenge to the computing capabilities of VEC, when these applications are outsourced and executed in VEC. Against this backdrop, we propose a new mathematical model which respectively generalizes the computation and communication models, and applies application oriented caching into VEC in this paper. Based on this model, a new strategy is further proposed to optimize the average response time of applications over an infinite time-slotted horizon for VEC. A long-term energy consumption constraint is imposed to guarantee the stability of VEC system, and the Lyapunov optimization technology is adopted to tackle this constraint issue. Two greedy heuristics are put forward to help find the approximate optimal solution in the drift-plus-penalty based algorithm. Extensive experiments have been conducted to evaluate the response time and energy consumption in the caching assisted VEC. The simulation results have shown that the proposed strategy can dramatically optimize the average response time while satisfying the long-term energy consumption constraint.

This paper discusses the makespan minimization of a production batch within a specific concurrent system, seru production system. A seru production system consists of multiple independent serus. A seru is a compact assembly origination in which products are assembled from-the-beginning-to-the-end without disruptions. One capability of a seru production system is its responsiveness. A performance measure used to evaluate a seru system’s responsiveness is the makespan of production batches assembled within the seru system. This study addresses the makespan minimization problem through an optimal seru loading policy. The problem is formulated as a min-max integer optimization model. An exact dimension-reduction Algorithm is developed to obtain the optimal allocation that minimizes the makespan. We show that the solution space increases very quickly. In contrast, our algorithm is efficient with a polynomial computational complexity of O ( n 2 ) , where n is the total number of serus in a seru system. To verify the usefulness of the developed exact dimension-reduction algorithm, we compare it with a widely practiced greedy algorithm through experiments. We find that our optimal algorithm is robust in most cases and the greedy algorithm is efficient when variability in production efficiencies is high. This result can guide us to adopt different algorithms under different business environments. If the variability in production efficiencies is high, e.g., new employees and/or new products assembly, the greedy algorithm is efficient. For other cases, our optimal algorithm should be adopted to obtain the minimum makespan. We also extend the method to the application of a rotating seru.

Autodriver algorithm aims to develop a path-following algorithm for autonomous vehicles using road geometry data and vehicle dynamics. In this study, a novel smart Autodriver algorithm is developed according to practical implications utilizing a more realistic vehicle model and consideration of real-time applicability. A ghost-car path-following approach is introduced to define the desired location of the vehicle at every instance during various maneuvers. Key steady-state characteristics of turning vehicles, namely the curvature, yaw rate, and side-slip responses are discussed and used to construct a path-following controller based on the Autodriver algorithm. A feedback control based on Sliding Mode Control (SMC) is also designed and applied to minimize transient errors between the road and the vehicle positions. Finally, simulations are performed to analyze the path-following performance of the proposed scheme compared to a Model Predictive Controller (MPC) as a widely accepted popular method for autonomous vehicles. Hardware-in-the-loop (HIL) tests are also performed to investigate real-time applicability of the controllers. The results show promising controller performance in terms of error minimization, passenger comfort, and low computational cost for the proposed method.

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a sophisticated three-dimensional (3D) environment, where the UAV’s trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified two-dimensional scenario and the availability of perfect channel state information (CSI), this paper considers a practical 3D urban environment with imperfect CSI, where the UAV’s trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired by the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV’s trajectory and we present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of the UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of the IoT nodes, the UAV’s position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV’s movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.